2070 citations as recorded by crossref.

1. Modelling soil erosion in a Mediterranean watershed: Comparison between SWAT and AnnAGNPS models O. Abdelwahab et al. https://doi.org/10.1016/j.envres.2018.06.029
2. Application of the SWAT model to assess climate and land use/cover change impacts on water balance components of the Kabul River Basin, Afghanistan J. Ougahi et al. https://doi.org/10.2166/wcc.2022.261
3. Impact of Climate Change on Daily Streamflow and Its Extreme Values in Pacific Island Watersheds O. Leta et al. https://doi.org/10.3390/su10062057
4. The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models Y. Kim et al. https://doi.org/10.1007/s00477-022-02366-0
5. Can We Calibrate a Daily Time-Step Hydrological Model Using Monthly Time-Step Discharge Data? S. Adla et al. https://doi.org/10.3390/w11091750
6. Integrated Water Quality Management Model for the Rural Transboundary River Basin—A Case Study of the Sutla/Sotla River G. Ćosić-Flajsig et al. https://doi.org/10.3390/w13182569
7. Meteorological and Hydrological Drought Assessment for Dong Nai River Basin, Vietnam under Climate Change V. Linh et al. https://doi.org/10.1007/s11036-021-01757-x
8. Revisiting linear regression to test agreement in continuous predicted-observed datasets A. Correndo et al. https://doi.org/10.1016/j.agsy.2021.103194
9. Effects of initial abstraction ratios in SCS-CN method on runoff prediction of green roofs in a semi-arid region W. Liu et al. https://doi.org/10.1016/j.ufug.2021.127331
10. Implementation of robust statistics in the calibration, verification and validation step of model evaluation to better reflect processes concerning total phosphorus load occurring in the catchment M. Ostojski et al. https://doi.org/10.1016/j.ecolmodel.2016.04.004
11. Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments A. Jodar-Abellan et al. https://doi.org/10.1007/s11356-024-34404-5
12. Estimation of Nonpoint Source Nitrate Concentrations in Indiana Rivers Based on Agricultural Drainage in the Watershed Y. Jiang et al. https://doi.org/10.1111/jawr.12216
13. Parsimonious modeling of hydrologic responses in engineered watersheds: Structural heterogeneity versus functional homogeneity N. Basu et al. https://doi.org/10.1029/2009WR007803
14. Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia R. Deo & M. Şahin https://doi.org/10.1016/j.atmosres.2015.03.018
15. Effects of LID Implementation on Hydrological Processes in an Urban Catchment under Consolidation in Brazil F. Zanandrea & A. Silveira https://doi.org/10.1061/(ASCE)EE.1943-7870.0001417
16. Application of Different Mapping Techniques for Precipitation and Temperature-Based Meteorological Drought Methods in the Black Sea Region of Türkiye A. Keskiner et al. https://doi.org/10.1007/s00024-026-04059-z
17. Signatures of regular black holes from the quasar continuum spectrum I. Banerjee https://doi.org/10.1140/epjc/s10052-023-11320-5
18. Simulation of Past Changes in the Austrian Snow Cover 1948–2009 T. Marke et al. https://doi.org/10.1175/JHM-D-17-0245.1
19. A refined regional modeling approach for the Corn Belt – Experiences and recommendations for large-scale integrated modeling Y. Panagopoulos et al. https://doi.org/10.1016/j.jhydrol.2015.02.039
20. Calibração do Storm Water Management Model (SWMM) utilizando algoritmos evolucionários multiobjetivo K. Formiga et al. https://doi.org/10.1590/s1413-41522016131862
21. Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling J. Mtamba et al. https://doi.org/10.3390/rs70100836
22. Determining an effective short term COVID-19 prediction model in ASEAN countries O. Sharif et al. https://doi.org/10.1038/s41598-022-08486-5
23. Modeling blue and green water availability in Africa J. Schuol et al. https://doi.org/10.1029/2007WR006609
24. The distributed model intercomparison project – Phase 2: Experiment design and summary results of the western basin experiments M. Smith et al. https://doi.org/10.1016/j.jhydrol.2013.08.040
25. Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach S. Monhart et al. https://doi.org/10.5194/hess-23-493-2019
26. Enhanced SCS-CN models for long-duration hydrologic simulations across various time scales H. Aragaw https://doi.org/10.1007/s40808-025-02374-3
27. Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. https://doi.org/10.5194/hess-16-3049-2012
28. Modeling rainfall interception components of forests: Extending drip equations N. José https://doi.org/10.1016/j.agrformet.2019.107704
29. Possibilities of sewer model simplifications A. Fischer et al. https://doi.org/10.1080/15730620903038453
30. Assessment of evaluation methods using infiltration data measured in heterogeneous mountain soils L. Jačka et al. https://doi.org/10.1016/j.geoderma.2016.04.023
31. Comparison between the USLE, the USLE-M and replicate plots to model rainfall erosion on bare fallow areas P. Kinnell https://doi.org/10.1016/j.catena.2016.05.017
32. Real-time prediction of river chloride concentration using ensemble learning Q. Zhang et al. https://doi.org/10.1016/j.envpol.2021.118116
33. Using JABOWA-3 for forest growth and yield predictions under diverse forest conditions of Nova Scotia, Canada M. Ashraf et al. https://doi.org/10.5558/tfc2012-137
34. Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand W. Me et al. https://doi.org/10.5194/hess-19-4127-2015
35. Investigating human-induced threat to hydrological regime of Lake Chilwa Basin, Malawi O. Kambombe et al. https://doi.org/10.1007/s13201-023-01965-8
36. SWAT-Based Assessment of the Water Regulation Index Under RCP 4.5 and RCP 8.5 Scenarios in the San Pedro River Basin M. Arteaga Madera et al. https://doi.org/10.3390/hydrology13020045
37. Enhancing reservoir water quality simulation through machine learning-driven remote sensing integration with EFDC: a coupled framework for eutrophication management in data-scarce regions H. Meng et al. https://doi.org/10.1038/s41598-026-46641-4
38. Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques M. Delbari et al. https://doi.org/10.1007/s00704-018-2370-3
39. A watershed scale assessment of phosphorus remediation strategies for achieving water quality restoration targets in the western Everglades Y. Khare et al. https://doi.org/10.1016/j.ecoleng.2019.105663
40. Assessment of CFSR and CMADS Weather Data for Capturing Extreme Hydrologic Events in the Fuhe River Basin of the Poyang Lake J. Lu et al. https://doi.org/10.1111/1752-1688.12866
41. Modeling agricultural land drainage under spring snowmelt conditions with DRAINMOD J. Morrison et al. https://doi.org/10.1139/cjce-2013-0416
42. Developing an Agent-Based Model of Dual-Flush Toilet Adoption E. Ramsey & E. Berglund https://doi.org/10.1061/(ASCE)WR.1943-5452.0001454
43. Modeling the impacts of climate change on mass balance and discharge of Eklutna Glacier, Alaska, 1985–2019 J. Geck et al. https://doi.org/10.1017/jog.2021.41
44. Investigating the contribution of decomposition techniques to machine learning accuracy in SPEI-based drought forecasting for multiple Köppen-Geiger climates E. Anık et al. https://doi.org/10.1007/s11600-025-01773-5
45. Derivation of 2D Velocity Distribution in Watercourses Using Entropy G. Marini et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001492
46. HUP‐BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting P. Darbandsari & P. Coulibaly https://doi.org/10.1029/2020WR029433
47. NDMI-Derived Field-Scale Soil Moisture Prediction Using ERA5 and LSTM for Precision Agriculture E. Koohikeradeh et al. https://doi.org/10.3390/su17062399
48. Hydrologic Modeling of Flow through Sinkholes Located in Streambeds of Cane Run Stream, Kentucky B. Palanisamy & S. Workman https://doi.org/10.1061/(ASCE)HE.1943-5584.0001060
49. Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru E. Pino-Vargas et al. https://doi.org/10.3390/hydrology9010010
50. Applicability of TOPMODEL in the mountainous catchments in the upper Nysa Kłodzka river basin (SW Poland) J. Jeziorska & T. Niedzielski https://doi.org/10.1007/s11600-018-0121-6
51. Evaluation of widespread flooding of the Karkheh Basin in Iran using SWAT model and GLDAS database V. Kuchak et al. https://doi.org/10.1007/s11069-023-05881-7
52. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors H. Sanikhani et al. https://doi.org/10.1016/j.compag.2018.07.008
53. A Study on Radar–Gauge Rainfall Data Merging and Its Impact on Flood Simulation Y. Peng et al. https://doi.org/10.3390/rs18152587
54. Assessing the spatio-temporal impact of landuse landcover change on water yield dynamics of rapidly urbanizing Kathmandu valley watershed of Nepal S. Acharya et al. https://doi.org/10.1016/j.ejrh.2023.101562
55. Assessing the effectiveness of split fertilization and cover crop cultivation in order to conserve soil and water resources and improve crop productivity G. Maharjan et al. https://doi.org/10.1016/j.agwat.2015.10.005
56. Assessment of several nonlinear methods in forecasting suspended sediment concentration in streams M. Zounemat-Kermani https://doi.org/10.2166/nh.2016.219
57. Estimating the reliability of a rainwater catchment system using the output data of general circulation models for the future period (case study: Birjand City, Iran) A. Jafarzadeh et al. https://doi.org/10.1007/s00704-018-2714-z
58. Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco M. Barbouchi et al. https://doi.org/10.1109/JSTARS.2014.2333535
59. Biomass Estimation Models for Cocoa (<i>Theobroma cacao</i>) Plantations in Ghana, West Africa E. Donkor et al. https://doi.org/10.4236/ojapps.2023.139126
60. Parameterization and quantification of recharge in crystalline fractured bedrocks in Galicia-Costa (NW Spain) J. Raposo et al. https://doi.org/10.5194/hess-16-1667-2012
61. Rainfall-runoff modelling using river-stage time series in the absence of reliable discharge information: a case study in the semi-arid Mara River basin P. Hulsman et al. https://doi.org/10.5194/hess-22-5081-2018
62. Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability S. Nemri & C. Kinnard https://doi.org/10.1016/j.jhydrol.2019.124474
63. Insights into sensitivity analysis of Earth and environmental systems models: On the impact of parameter perturbation scale A. Haghnegahdar & S. Razavi https://doi.org/10.1016/j.envsoft.2017.03.031
64. Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies A. Jafarzadeh et al. https://doi.org/10.2166/hydro.2021.058
65. Reducing location error of legacy soil profiles leads to improvement in digital soil mapping G. Shi et al. https://doi.org/10.1016/j.geoderma.2024.116912
66. Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas* S. Jörg-Hess et al. https://doi.org/10.1175/JHM-D-14-0193.1
67. Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models E. Guilpart et al. https://doi.org/10.5194/hess-25-4611-2021
68. Research for forecasting the effect of various meteorological-dynamic conditions on the possible spreading of Cs-137 radioactive substances in case a level 7 incident occurs from Fengcheng nuclear power plant (China) H. Nguyen Minh et al. https://doi.org/10.15625/1859-3097/15964
69. Modeling Infiltration Behavior to Assess the Reliability of Surface Flow Generation in Forested and Deforested Watersheds V. Prajith et al. https://doi.org/10.1061/JHYEFF.HEENG-6646
70. Comments on “Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models” Science of the Total Environment, 705, 135941 (2020) R. Adnan et al. https://doi.org/10.1016/j.scitotenv.2020.139486
71. Evaluating the transferability of measurements from simple constructed non weighable gravitation lysimeters to predict the water regime on field scale—a case study D. Bednorz et al. https://doi.org/10.1002/jpln.201600105
72. Performance evaluation of global hydrological models in six large Pan-Arctic watersheds A. Gädeke et al. https://doi.org/10.1007/s10584-020-02892-2
73. Ant Colony Based Artificial Neural Network for Predicting Spatial and Temporal Variation in Groundwater Quality R. Bhavya et al. https://doi.org/10.3390/w15122222
74. Model-based Scenario Analysis of the Impact of Remediation Measures on Metal Leaching from Soils Contaminated by Historic Smelter Emissions I. Joris et al. https://doi.org/10.2134/jeq2013.07.0287
75. Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets? T. Hallouin et al. https://doi.org/10.5194/hess-24-1031-2020
76. Using gridded rainfall products in simulating streamflow in a tropical catchment – A case study of the Srepok River Catchment, Vietnam V. Thom et al. https://doi.org/10.1515/johh-2016-0047
77. Impact of CO 2 storage flux sampling uncertainty on net ecosystem exchange measured by eddy covariance G. Nicolini et al. https://doi.org/10.1016/j.agrformet.2017.09.025
78. Evaluation of NASA land information system in prediection stream runoff: case study of Atbara and Blue Nile Sub-Basins R. Ali & S. Zahran https://doi.org/10.1007/s40808-022-01663-5
79. Prolonged and Severe Drought in the Most Dammed Tributaries of the Lower Mekong Basin K. Chann et al. https://doi.org/10.3390/su142316254
80. Development of a Multi-Scale Groundwater Drought Prediction Model Using Deep Learning and Hydrometeorological Data D. Kang & K. Byun https://doi.org/10.3390/w16142036
81. Gridded Datasets for Comparative Analysis of Rainfall Patterns Over Eco‐Climatic Zones of Narok County, Kenya O. Ombogo et al. https://doi.org/10.1155/adme/4895679
82. Flood peak estimation for the keureuto watershed using the hydrological modeling system HEC-HMS B. Rama et al. https://doi.org/10.1088/1755-1315/1311/1/012040
83. Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach A. Sikorska & J. Seibert https://doi.org/10.1016/j.jhydrol.2016.06.031
84. Use of an entropy‐based metric in multiobjective calibration to improve model performance I. Pechlivanidis et al. https://doi.org/10.1002/2013WR014537
85. Applications of Linked and Nonlinked Complex Models for TMDL Development: Approaches and Challenges Y. Mohamoud & H. Zhang https://doi.org/10.1061/(ASCE)HE.1943-5584.0001721
86. Impacts of Historic Climate Variability on Seasonal Soil Frost in the Midwestern United States T. Sinha et al. https://doi.org/10.1175/2009JHM1141.1
87. Incorporating Streamflow Forecasts with Aggregate Drought Indices for the Management of Water Supply K. Booras et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000849
88. Hybrid PSO enhanced ANN model and central composite design for modelling and optimization of Low-Intensity magnetic separation of hematite M. Ebrahimi et al. https://doi.org/10.1016/j.mineng.2021.106987
89. Implementing Best Management Practices in Complex Agricultural Watersheds: Insights from High-Resolution Nitrogen Load Dynamics Analysis W. Shen et al. https://doi.org/10.3390/w17060821
90. Transfer Entropy as a Tool for Hydrodynamic Model Validation A. Sendrowski et al. https://doi.org/10.3390/e20010058
91. Interaction between soil moisture and nitrogen dynamics in the root zone of a pear (Pyrus communis ‘Conference’) orchard J. Verellen et al. https://doi.org/10.17660/ActaHortic.2022.1333.44
92. Three-stage hybrid modeling for real-time streamflow prediction in data-scarce regions A. Ali et al. https://doi.org/10.1016/j.ejrh.2025.102337
93. Optimal Control of Total Chlorine and Free Ammonia Levels in a Water Transmission Pipeline Using Artificial Neural Networks and Genetic Algorithms W. Wu et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000486
94. Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes F. Afrasiabi et al. https://doi.org/10.2478/johh-2018-0009
95. Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes R. Muñoz et al. https://doi.org/10.1016/j.ejrh.2021.100932
96. Toward an operational streamflow forecasting system in Mexico: a pioneering ensemble approach with WRF-Hydro in a tropical mountain basin M. Morales-Velázquez et al. https://doi.org/10.1080/02626667.2025.2555857
97. Assessment of hydropower potential using spatial technology and SWAT modelling in the Mat River, southern Mizoram, India A. Pandey et al. https://doi.org/10.1080/02626667.2014.943669
98. A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models H. Bonakdari et al. https://doi.org/10.1007/s11269-020-02644-y
99. Comparative Assessment of SWAT Model Performance in two Distinct Catchments under Various DEM Scenarios of Varying Resolution, Sources and Resampling Methods M. Goyal et al. https://doi.org/10.1007/s11269-017-1840-1
100. Robust optimization of water infrastructure planning under deep uncertainty using metamodels E. Beh et al. https://doi.org/10.1016/j.envsoft.2017.03.013
101. Utilizing sequential modeling in collaborative method for flood forecasting W. Thaisiam et al. https://doi.org/10.1016/j.jhydrol.2024.131290
102. Spatiotemporal variability of streamflow under current and projected climate scenarios of Andit Tid watershed, central highland of Ethiopia A. Woldemariam et al. https://doi.org/10.1007/s43621-024-00440-x
103. The Applicability of Time-Integrated Unit Stream Power for Estimating Bridge Pier Scour Using Noncontact Methods in a Gravel-Bed River L. Hempel et al. https://doi.org/10.3390/rs14091978
104. Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration H. Lamane et al. https://doi.org/10.1016/j.ijsrc.2024.10.002
105. Streamflow Responses to Climate Change: Analysis of Hydrologic Indicators in a New York City Water Supply Watershed S. Pradhanang et al. https://doi.org/10.1111/jawr.12086
106. Analysis of Methods to Estimate Spring Flows in a Karst Aquifer N. Sepúlveda https://doi.org/10.1111/j.1745-6584.2008.00498.x
107. Efficient modelling of compact microstrip antenna using machine learning K. Sharma & G. Pandey https://doi.org/10.1016/j.aeue.2021.153739
108. Assessing Rating Curves in River Gauging Stations for Computing Design Extreme Events for Several Return Periods R. Florian-Noriega et al. https://doi.org/10.3390/w18010115
109. Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms M. Al-Musaylh et al. https://doi.org/10.3390/en13092307
110. Hydrologic Impact of Climate Change in the Jaguari River in the Cantareira Reservoir System L. Domingues et al. https://doi.org/10.3390/w14081286
111. Simulation of Runoff and Sediment Yield for a Kaneri Watershed Using SWAT Model V. Swami & S. Kulkarni https://doi.org/10.4236/gep.2016.41001
112. Flow alterations due a constructed reservoir in the Menik Ganga basin, Sri Lanka S. Hemakumara et al. https://doi.org/10.1007/s43832-023-00049-7
113. Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan K. Nguyen et al. https://doi.org/10.3390/su12052022
114. A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks K. Wang et al. https://doi.org/10.1016/j.cma.2020.113514
115. Modelling Furrow Irrigation-Induced Erosion on a Sandy Loam Soil in Samaru, Northern Nigeria J. Dibal et al. https://doi.org/10.1155/2014/982136
116. Streamflow sensitivity to climate and land cover changes: Meki River, Ethiopia D. Legesse et al. https://doi.org/10.5194/hess-14-2277-2010
117. Modifiye Yaklaşımların Evapotranspirasyon Tahminlerine Etkisi: Van Örneği A. UZUNLAR et al. https://doi.org/10.21605/cukurovaumfd.1230919
118. Simulating spatiotemporal variability of blue and green water resources availability with uncertainty analysis D. Zuo et al. https://doi.org/10.1002/hyp.10307
119. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity A. Masrur Ahmed et al. https://doi.org/10.1016/j.jhydrol.2021.126350
120. Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method M. Ruiz-Aĺvarez et al. https://doi.org/10.3390/w13020222
121. Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings G. Evin et al. https://doi.org/10.5194/hess-28-261-2024
122. A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning Z. Xiang et al. https://doi.org/10.1029/2019WR025326
123. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran M. Panahi et al. https://doi.org/10.1016/j.ejrh.2021.100825
124. Using Sensor Data to Dynamically Map Large‐Scale Models to Site‐Scale Forecasts: A Case Study Using the National Water Model K. Fries & B. Kerkez https://doi.org/10.1029/2017WR022498
125. Quantification of macropore flow in Danish soils using near-saturated hydraulic properties A. Kotlar et al. https://doi.org/10.1016/j.geoderma.2020.114479
126. Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models C. Sezen & M. Šraj https://doi.org/10.1016/j.scitotenv.2024.171684
127. Runoff Losses on Urban Surfaces during Frequent Rainfall Events: A Review of Observations and Modeling Attempts M. Rammal & E. Berthier https://doi.org/10.3390/w12102777
128. Mathematical simulation of surfactant flushing process to remediate diesel contaminated sand column G. Asadollahfardi et al. https://doi.org/10.12989/aer.2016.5.4.213
129. Deep learning–based neural networks for day-ahead power load probability density forecasting Y. Zhou et al. https://doi.org/10.1007/s11356-022-23305-0
130. Disaggregated monthly SWAT+ model versus daily SWAT+ model for estimating environmental flows in Peninsular Spain G. Castellanos-Osorio et al. https://doi.org/10.1016/j.jhydrol.2023.129837
131. Exploring and Modeling the Short-Term Influence of Soil Properties and Covers on Hydrology of Mediterranean Forests after Prescribed Fire and Mulching D. Zema et al. https://doi.org/10.3390/hydrology9020021
132. Climate change under CMIP5-RCP scenarios in Ethiopia: The Bale mountains ecoregion A. Serur https://doi.org/10.1007/s11629-021-7206-4
133. Integration of backscatter thresholds from Sentinel-1 with spectral indices and signatures from Sentinel-2 for the detection of oil spills at sea: The case of Lima, Peru W. Moncada et al. https://doi.org/10.1016/j.rsase.2026.102227
134. Hydrograph matching method for measuring model performance J. Ewen https://doi.org/10.1016/j.jhydrol.2011.07.038
135. A review of alternative climate products for SWAT modelling: Sources, assessment and future directions M. Tan et al. https://doi.org/10.1016/j.scitotenv.2021.148915
136. A hydrological investigation using a lumped water balance model: the Aison River Basin case (Greece) D. Karpouzos et al. https://doi.org/10.1111/j.1747-6593.2010.00222.x
137. Modeling airborne benzene in space and time with self-organizing maps and Bayesian techniques K. Strebel et al. https://doi.org/10.1016/j.envsoft.2012.12.001
138. Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California F. Kabir et al. https://doi.org/10.1002/hyp.13265
139. Assessments of the impacts of land use/land cover change on water resources: Tana Sub-Basin, Ethiopia B. Tesfaw et al. https://doi.org/10.2166/wcc.2023.303
140. Two-Way Calibration-Validation of SWAT Model for a Small Prairie Watershed with Short Observed Record M. Rahbeh et al. https://doi.org/10.4296/cwrj3603884
141. Efficacy of Rainfall-Runoff Models in Loose Coupling Spacial Decision Support Systems Modelbase S. Neto et al. https://doi.org/10.1007/s11269-018-2086-2
142. Quantifying the impact of urban area expansion on groundwater recharge and surface runoff T. Eshtawi et al. https://doi.org/10.1080/02626667.2014.1000916
143. Multi-decadal surface ozone trends at globally distributed remote locations O. Cooper et al. https://doi.org/10.1525/elementa.420
144. Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. https://doi.org/10.1016/j.jhydrol.2024.130948
145. Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG A. Chakraborty et al. https://doi.org/10.1007/s00231-018-2312-8
146. A dynamic contaminant fate model of organic compound: A case study of Nitrobenzene pollution in Songhua River, China C. Wang et al. https://doi.org/10.1016/j.chemosphere.2012.02.065
147. Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach M. Jamei et al. https://doi.org/10.1007/s11269-022-03270-6
148. Climate change impacts on spatial patterns in drought risk in the Willamette River Basin, Oregon, USA I. Jung & H. Chang https://doi.org/10.1007/s00704-011-0531-8
149. The value of commercial fish size distribution recorded at haul by haul compared to trip by trip K. Plet-Hansen et al. https://doi.org/10.1093/icesjms/fsaa141
150. Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India D. Dayal et al. https://doi.org/10.1007/s10661-024-12746-4
151. Interpreting pore-water pressure changes induced by water table fluctuations and mechanical loading due to soil moisture changes C. Anochikwa et al. https://doi.org/10.1139/t11-106
152. ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions T. Marke et al. https://doi.org/10.5194/gmd-9-633-2016
153. Study of sediment movement in an irrigated maize–cotton system combining rainfall simulations, sediment tracers and soil erosion models G. Guzmán et al. https://doi.org/10.1016/j.jhydrol.2015.02.033
154. Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City, Iran, by Using an Artificial Neural Network G. Asadollahfardi et al. https://doi.org/10.1002/tqem.21464
155. A Water Balance Budget for Bung Boraphet—A Flood Plain Wetland-Reservoir Complex in Thailand N. Sriwongsitanon et al. https://doi.org/10.3390/w1010054
156. Response of the river discharge in the Tocantins River Basin, Brazil, to environmental changes and the associated effects on the energy potential R. Von Randow et al. https://doi.org/10.1007/s10113-018-1396-5
157. Impact of land surface model schemes in snow-dominated arid and semiarid watersheds using the WRF-hydro modeling systems W. Hussainzada & H. Lee https://doi.org/10.3934/geosci.2024018
158. Sensitivity exploration of water balance in scenarios of future changes: a case study in an Andean regulated river basin A. Avilés et al. https://doi.org/10.1007/s00704-020-03219-y
159. Calibration of distributed hydrological models considering the heterogeneity of the parameters across the basin: a case study of SWAT model P. Athira & K. Sudheer https://doi.org/10.1007/s12665-021-09434-8
160. Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts R. Maity et al. https://doi.org/10.1016/j.jhydrol.2016.05.042
161. Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach K. Khosravi et al. https://doi.org/10.1016/j.jhydrol.2022.127963
162. Hierarchical prediction of soil water content time series F. Leij et al. https://doi.org/10.1016/j.catena.2021.105841
163. Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology J. Zhang et al. https://doi.org/10.32604/cmes.2021.012686
164. Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria J. Gutierrez et al. https://doi.org/10.1590/2318-0331.272220220046
165. A reanalysis of fine particulate matter air pollution versus life expectancy in the United States G. Krstić https://doi.org/10.1080/10962247.2012.697445
166. Scenario-based Impact Assessment of Land Use/Cover and Climate Changes on Water Resources and Demand: A Case Study in the Srepok River Basin, Vietnam—Cambodia T. Ty et al. https://doi.org/10.1007/s11269-011-9964-1
167. Simulation of Crop Growth and Water-Saving Irrigation Scenarios for Lettuce: A Monsoon-Climate Case Study in Kampong Chhnang, Cambodia P. Ket et al. https://doi.org/10.3390/w10050666
168. Evaluating the flow and sediment effects of gully land consolidation on the Loess Plateau, China Y. Kang et al. https://doi.org/10.1016/j.jhydrol.2021.126535
169. A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM S. Hossain et al. https://doi.org/10.3390/w11030611
170. A new nonparametric copula framework for the joint analysis of river water temperature and low flow characteristics for aquatic habitat risk assessment S. Latif et al. https://doi.org/10.1016/j.jhydrol.2024.131079
171. Agricultural Water Productivity-Based Hydro-Economic Modeling for Optimal Crop Pattern and Water Resources Planning in the Zarrine River Basin, Iran, in the Wake of Climate Change F. Emami & M. Koch https://doi.org/10.3390/su10113953
172. The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods H. Lü et al. https://doi.org/10.3390/w13141891
173. Survey on the resolution and accuracy of input data validity for SWAT-based hydrological models N. Rasheed et al. https://doi.org/10.1016/j.heliyon.2024.e38348
174. Calibration and Evaluation of the SIMPLE Crop Growth Model Applied to the Common Bean under Irrigation M. Servín-Palestina et al. https://doi.org/10.3390/agronomy14050917
175. Spatial and Temporal Variability in Hydrological Responses of the Upper Blue Nile basin, Ethiopia T. Lemann et al. https://doi.org/10.3390/w11010021
176. Modelling blue and green water resources availability in Iran M. Faramarzi et al. https://doi.org/10.1002/hyp.7160
177. Development of an integrated flood hazard assessment model for a complex river system: a case study of the Mun River Basin, Thailand S. Prabnakorn et al. https://doi.org/10.1007/s40808-019-00634-7
178. Generating design flood hydrographs by parameterizing the characteristic flood hydrograph at a site using only flow data M. Goswami https://doi.org/10.1080/02626667.2020.1843656
179. Technical note: Pitfalls in using log-transformed flows within the KGE criterion L. Santos et al. https://doi.org/10.5194/hess-22-4583-2018
180. Sensitivity analysis of relationships between hydrograph components and landscapes metrics extracted from digital elevation models with different spatial resolutions S. Sadeghi et al. https://doi.org/10.1016/j.ecolind.2020.107025
181. Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. https://doi.org/10.3390/rs15051395
182. Application of a Sampling Based on the Combined Objectives of Parameter Identification and Uncertainty Analysis of an Urban Rainfall-Runoff Model D. Zhao et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000522
183. Modeling of river discharge in the Firoozabad watershed using soil and water assessment tool model S. Zomorodian & S. Dowlatabadi https://doi.org/10.1007/s42108-018-0007-y
184. Structural uncertainty assessment in a discharge simulation model X. Zhang et al. https://doi.org/10.1080/02626667.2011.587426
185. Modeling riverine dissolved and particulate organic carbon fluxes from two small watersheds in the northeastern United States J. Qi et al. https://doi.org/10.1016/j.envsoft.2019.104601
186. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition W. Wang et al. https://doi.org/10.1016/j.envres.2015.02.002
187. Use of gridded climate data for hydrological modelling in the Zambezi River Basin, Southern Africa G. Ndhlovu & Y. Woyessa https://doi.org/10.1016/j.jhydrol.2021.126749
188. Embedding risks models in asset pricing from expert-based knowledge: the case of agricultural lands M. Franco & J. Vivo https://doi.org/10.1186/s40854-025-00846-0
189. Assessment of predictability of the Loop Current in the Gulf of Mexico from observing system experiments and observing system simulation experiments D. Dukhovskoy et al. https://doi.org/10.3389/fmars.2023.1153824
190. Adaptation of the century model to simulate C and N dynamics of Caatinga dry forest before and after deforestation T. Althoff et al. https://doi.org/10.1016/j.agee.2017.11.016
191. A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services S. Gebremariam et al. https://doi.org/10.1016/j.envsoft.2014.07.004
192. Assessment of water quality and evaluation of best management practices in a small agricultural watershed adjacent to Coral Reef area in Japan R. Sith et al. https://doi.org/10.1016/j.agwat.2018.11.014
193. Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach S. Dassanayake et al. https://doi.org/10.1016/j.geotexmem.2022.08.005
194. Analysis of Water Regime Modification Induced by Long-Term Development of Vegetation Cover M. Chlumecky https://doi.org/10.15406/ijh.2017.01.00031
195. Impact of correcting sub-daily climate model biases for hydrological studies M. Faghih et al. https://doi.org/10.5194/hess-26-1545-2022
196. Transit time tracing using wetness-adaptive StorAge Selection functions—application to a Mediterranean catchment N. Hachgenei et al. https://doi.org/10.1016/j.jhydrol.2024.131267
197. A process-based modelling of groundwater recharge under contrasting irrigation methods in semi-arid crops T. Attou et al. https://doi.org/10.1016/j.agwat.2025.109584
198. Improvement to the Thornthwaite Method to Study the Runoff at a Basin Scale Using Temporal Remote Sensing Data K. Durga Rao et al. https://doi.org/10.1007/s11269-014-0564-8
199. Future drought characterization using multiple drought indices and CMIP-6 Climate Models within Mille Watershed, Lower Awash Basin, Ethiopia A. Amognehegn et al. https://doi.org/10.1007/s00704-024-05188-y
200. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi https://doi.org/10.5194/hess-26-2387-2022
201. Assessment of the cascade of uncertainty in future snow depth projections across watersheds of mountainous, foothill, and plain areas in northern latitudes M. Zaremehrjardy et al. https://doi.org/10.1016/j.jhydrol.2020.125735
202. Sensitivity of Simulating Hydrologic Processes to Gauge and Radar Rainfall Data in Subtropical Coastal Catchments J. Diaz-Ramirez et al. https://doi.org/10.1007/s11269-012-0088-z
203. Natural and anthropogenic influences on the recent droughts in Yellow River Basin, China A. Omer et al. https://doi.org/10.1016/j.scitotenv.2019.135428
204. Potential of satellite rainfall products to predict Niger River flood events in Niamey C. Casse et al. https://doi.org/10.1016/j.atmosres.2015.01.010
205. The implication of spatial interpolated climate data on biophysical modelling in agricultural systems D. Liu et al. https://doi.org/10.1002/joc.6371
206. Evaluation of baseflow modelling structure in monthly water balance models using 443 Australian catchments S. Cheng et al. https://doi.org/10.1016/j.jhydrol.2020.125572
207. Modelling the Effect of ‘Roller Dynamics’ on Storm Erosion: Sylt, North Sea P. Dissanayake & J. Brown https://doi.org/10.3390/jmse10030305
208. Estimating the impact of temperature and streamflow change on river nitrogen pollution using the land-river integrated modeling system D. Zhu et al. https://doi.org/10.1016/j.jhydrol.2023.130190
209. Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin A. Jabbari & D. Bae https://doi.org/10.3390/w10111626
210. Development of a Demand Driven Hydro-climatic Model for Drought Planning M. Karamouz et al. https://doi.org/10.1007/s11269-011-9920-0
211. Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. https://doi.org/10.5194/hess-27-4135-2023
212. Optimal development of calibration equations for paired catchment projects L. Bren & P. Lane https://doi.org/10.1016/j.jhydrol.2014.07.059
213. Seasonal Storm Controls on Turbidity in an Urban Watershed: Implications for Sediment Best Management Practice (BMP) Design C. Day & D. Rangel https://doi.org/10.3390/land15040597
214. Calibração e validação da equação universal de perda de solos modificada (MUSLE) utilizando dados hidrossedimentológicos locais C. Silva et al. https://doi.org/10.1590/S0100-06832011000400037
215. Assessment of neuro-fuzzy approach based different wavelet families for daily flow rates forecasting Z. Abda et al. https://doi.org/10.1007/s40808-020-00855-1
216. Using SWAT hydrological model for river flow sensitivity indices under different land cover, land use and climate scenarios in the Mara Basin in the period between 1983 and 2014 A. Isaac & O. Clifford https://doi.org/10.5897/AJEST2025.3435
217. Calibrating the Spatiotemporal Root Density Distribution for Macroscopic Water Uptake Models Using Tikhonov Regularization N. Li & X. Yue https://doi.org/10.1002/2017WR020452
218. Hybrid Wavelet-ARX approach for modeling association between rainfall and meteorological forcings at river basin scale M. Suman & R. Maity https://doi.org/10.1016/j.jhydrol.2019.123918
219. Sediment Yield Simulation using the SWAT Model: A Systematic Review and Meta-Analysis of Calibration and Validation Approaches E. Xhafaj et al. https://doi.org/10.37394/232015.2026.22.60
220. Multi-horizon hourly air temperature forecasting over Lake Van using classical, machine learning, and transformer-based models: a comparative benchmark with statistical significance testing M. Güneş https://doi.org/10.1007/s10651-026-00746-1
221. Selection of smoothing parameter estimators for general regression neural networks – Applications to hydrological and water resources modelling X. Li et al. https://doi.org/10.1016/j.envsoft.2014.05.010
222. SWAT modeling of hydrology, sediment and nutrients from the Grand River, Ontario A. Hanief & A. Laursen https://doi.org/10.2166/wqrj.2017.014
223. Estimating uncertainty associated with water stages from a single SAR image G. Schumann et al. https://doi.org/10.1016/j.advwatres.2008.04.008
224. Predictability of daily streamflow for the great rivers of South America based on a simple metric I. Petry et al. https://doi.org/10.1080/02626667.2022.2139620
225. Smart low flow signature metrics for an improved overall performance evaluation of hydrological models M. Pfannerstill et al. https://doi.org/10.1016/j.jhydrol.2013.12.044
226. Multicriteria Evaluation Approach for Assessing Parametric Uncertainty during Extreme Peak and Low Flow Conditions over Snow Glaciated and Inland Catchments V. Singh et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001217
227. Rehabilitation of concrete canals in urban catchments using low impact development techniques B. Palanisamy & T. Chui https://doi.org/10.1016/j.jhydrol.2015.01.034
228. On the ability of large-scale hydrological models to simulate land use and land cover change impacts in Amazonian basins D. Rodriguez & J. Tomasella https://doi.org/10.1080/02626667.2015.1051979
229. Projections of hydrology in the Tocantins-Araguaia Basin, Brazil: uncertainty assessment using the CMIP5 ensemble J. Ho et al. https://doi.org/10.1080/02626667.2015.1057513
230. Estimation of nearshore wave transmission for submerged breakwaters using a data-driven predictive model A. Sharif Ahmadian & R. Simons https://doi.org/10.1007/s00521-016-2587-y
231. Clarification of issues and long-duration hydrologic simulation SCS-CN-based proxy modelling H. Aragaw & S. Mishra https://doi.org/10.1007/s11600-022-00730-w
232. Assessing potential environmental exposure from chemical incidents in coastal Southeast Vietnam using remote sensing and mathematical modelling H. Do et al. https://doi.org/10.1016/j.cscee.2023.100374
233. A current precipitation index-based model for continuous daily runoff simulation in seasonally snow covered sub-arctic catchments J. Akanegbu et al. https://doi.org/10.1016/j.jhydrol.2016.12.020
234. SOIL PROPERTIES MAPPING USING PROXIMAL AND REMOTE SENSING AS COVARIATE M. Pusch et al. https://doi.org/10.1590/1809-4430-eng.agric.v41n6p634-642/2021
235. Entropy approach for 2D velocity distribution in open-channel flow G. Marini et al. https://doi.org/10.1080/00221686.2011.635889
236. Characterization of the fate and distribution of ethiprole in water-fish-sediment microcosm using a fugacity model M. Hu et al. https://doi.org/10.1016/j.scitotenv.2016.10.087
237. Untangling the effects of future climate change and human activity on evapotranspiration in the Heihe agricultural region, Northwest China M. Zou et al. https://doi.org/10.1016/j.jhydrol.2019.124323
238. Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand K. Kuntiyawichai & S. Wongsasri https://doi.org/10.3390/w13192743
239. Neural network for modeling the capture of lead and cadmium ions from wastewater using date palm stones A. Faisal et al. https://doi.org/10.1007/s13762-021-03883-1
240. Vapor Pressure Deficit and Sunlight Explain Seasonality of Leaf Phenology and Photosynthesis Across Amazonian Evergreen Broadleaved Forest X. Chen et al. https://doi.org/10.1029/2020GB006893
241. Streamflow estimation using satellite-retrieved water fluxes and machine learning technique over monsoon-dominated catchments of India D. Dayal et al. https://doi.org/10.1080/02626667.2021.1889557
242. Improving the performance of precipitation outputs from Global Climate Models to predict monthly and seasonal rainfall over the Indian subcontinent A. Nair et al. https://doi.org/10.1016/j.crte.2015.03.004
243. Uncertainty assessments and hydrological implications of climate change in two adjacent agricultural catchments of a rapidly urbanizing watershed S. Oni et al. https://doi.org/10.1016/j.scitotenv.2013.12.032
244. To what extent does river routing matter in hydrological modeling? N. Cortés-Salazar et al. https://doi.org/10.5194/hess-27-3505-2023
245. Climate projections of precipitation and temperature in cities from ABC Paulista, in the Metropolitan Region of São Paulo—Brazil M. Valverde et al. https://doi.org/10.3389/fclim.2023.1127026
246. Improving the streamflow simulations of a data-poor mountainous watershed using parameter transfer based on similar land use and land cover M. Kavya et al. https://doi.org/10.1080/02626667.2026.2621070
247. Hydropower dams of the Mekong River basin: A review of their hydrological impacts J. Hecht et al. https://doi.org/10.1016/j.jhydrol.2018.10.045
248. Perturbation study of climate change impacts in a snow‐fed river basin S. Manandhar et al. https://doi.org/10.1002/hyp.9446
249. Effect of Rainfall Pattern on Sediment Yield and Runoff in Different Textured Soils A. Haydar et al. https://doi.org/10.59341/2707-7799.1747
250. Assessing climate change impacts on water resources in the Benue River Basin, Northern Cameroon R. Nonki et al. https://doi.org/10.1007/s12665-019-8614-4
251. The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. https://doi.org/10.1016/j.jhydrol.2023.129666
252. Optimizing category-based statistical metrics for selecting global climate models in rainfall projections for Peninsular Malaysia Z. Sa’adi et al. https://doi.org/10.1007/s00704-025-05952-8
253. Drainage‐system development in consecutive melt seasons at a polythermal, Arctic glacier, evaluated by flow‐recession analysis and linear‐reservoir simulation R. Hodgkins et al. https://doi.org/10.1002/wrcr.20257
254. Modeling runoff with satellite-based rainfall estimates in the Niger basin G. Oyerinde et al. https://doi.org/10.1080/23311932.2017.1363340
255. Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine H. Bonakdari et al. https://doi.org/10.1007/s11269-019-02346-0
256. Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system Ö. Terzi https://doi.org/10.1007/s00521-012-1027-x
257. Modeling the effects of climate change projections on streamflow in the Nooksack River basin, Northwest Washington S. Dickerson‐Lange & R. Mitchell https://doi.org/10.1002/hyp.10012
258. Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam G. Asadollahfardi et al. https://doi.org/10.1002/tqem.21493
259. Quantifying the Impact of Uncertainty within the Longitudinal Dispersion Coefficient on Concentration Dynamics and Regulatory Compliance in Rivers V. Camacho Suarez et al. https://doi.org/10.1029/2018WR023417
260. Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan K. Eman et al. https://doi.org/10.1002/joc.8558
261. High-resolution modelling of the seasonal evolution of surface water storage on the Greenland Ice Sheet N. Arnold et al. https://doi.org/10.5194/tc-8-1149-2014
262. Application and Flood Discharge Analysis with Hydrological Model (WEB-DHM) in Bago River Basin S. Maung et al. https://doi.org/10.20965/jdr.2020.p0344
263. Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment A. Mubialiwo et al. https://doi.org/10.1007/s42452-021-04514-7
264. Enhancing the accuracy of groundwater level prediction at different scales using spatio-temporal graph convolutional model L. Chen et al. https://doi.org/10.1007/s12145-025-01741-z
265. Assessing the impacts of sustainable agricultural practices for water quality improvements in the Vouga catchment (Portugal) using the SWAT model J. Rocha et al. https://doi.org/10.1016/j.scitotenv.2015.07.038
266. Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt H. Aboelkhair et al. https://doi.org/10.1016/j.asr.2019.03.032
267. Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths M. Fathi et al. https://doi.org/10.1038/s41598-025-96577-4
268. Application of Stochastic Flood Forecasting Model Using Regression Method for Kelantan Catchment S. Osman et al. https://doi.org/10.1051/matecconf/201820307001
269. A Hydro-Economic Model for Water Level Fluctuations: Combining Limnology with Economics for Sustainable Development of Hydropower P. Hirsch et al. https://doi.org/10.1371/journal.pone.0114889
270. Tradeoffs among watershed model calibration targets for parameter estimation K. Price et al. https://doi.org/10.1029/2012WR012005
271. Numerical Simulations of Suspended Sediment Dynamics Due to Seasonal Forcing in the Mekong Coastal Area V. Duy Vinh et al. https://doi.org/10.3390/w8060255
272. Precipitation forecasting using random forest over an ecuadorian andes basin M. Montenegro et al. https://doi.org/10.1007/s00703-024-01053-9
273. Statistical downscaling method to determine the Antamok Watershed hydrological response to climate change scenario C. Murayama & F. Tan https://doi.org/10.1088/1755-1315/1500/1/012030
274. How to Constrain Multi‐Objective Calibrations of the SWAT Model Using Water Balance Components M. Pfannerstill et al. https://doi.org/10.1111/1752-1688.12524
275. Dendroecological testing of the pyroclimatic hypothesis in the central Great Basin, Nevada, USA F. Biondi et al. https://doi.org/10.1890/ES10-00068.1
276. Modelling epilithic biofilms combining hydrodynamics, invertebrate grazing and algal traits M. Graba et al. https://doi.org/10.1111/fwb.12341
277. Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans O. Adeboye et al. https://doi.org/10.1016/j.inpa.2020.10.002
278. Modelling macronutrient dynamics in the Hampshire Avon river: A Bayesian approach to estimate seasonal variability and total flux M. Pirani et al. https://doi.org/10.1016/j.scitotenv.2016.04.129
279. Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data H. Haas et al. https://doi.org/10.3390/w13212947
280. Activation soil moisture accounting (ASMA) for runoff estimation using soil conservation service curve number (SCS-CN) method S. Verma et al. https://doi.org/10.1016/j.jhydrol.2020.125114
281. Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern M. Jamei et al. https://doi.org/10.1016/j.agwat.2022.107715
282. Climatology and nutrient fluxes in the Tagus estuary: A coupled model application H. de Pablo et al. https://doi.org/10.1016/j.ecss.2022.108129
283. Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations L. M. C. Rápalo et al. https://doi.org/10.1016/j.jhydrol.2024.131929
284. Representative parameter estimation for hydrological models using a lexicographic calibration strategy M. Gelleszun et al. https://doi.org/10.1016/j.jhydrol.2017.08.015
285. Modeling the Impact of Climate Change on Low Flows in Xiangjiang River Basin with Bayesian Averaging Method Y. Tian et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001557
286. Framework for Offline Flood Inundation Forecasts for Two-Dimensional Hydrodynamic Models P. Bhola et al. https://doi.org/10.3390/geosciences8090346
287. Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps) M. Boreggio et al. https://doi.org/10.3389/feart.2018.00089
288. Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) D. Maciejewski et al. https://doi.org/10.3390/en17246401
289. A comprehensive method for error separation in hydrological modelling Y. Zhao et al. https://doi.org/10.1002/hyp.15273
290. Modeling soil organic carbon in the Brazilian amazon with geostatistical and machine learning techniques G. Tiruneh et al. https://doi.org/10.1016/j.tfp.2026.101150
291. Hybrid Serving of DOE and RNN-Based Methods to Optimize and Simulate a Copper Flotation Circuit A. Gholami et al. https://doi.org/10.3390/min12070857
292. Current extent, temporal trends, and rates of gully erosion in the Gumara watershed, Northwestern Ethiopia M. Belayneh et al. https://doi.org/10.1016/j.gecco.2020.e01255
293. Dynamics of Water Vapor Content around Isolated Sprinklers: Description and Validation of Model J. Jiao et al. https://doi.org/10.3390/w9050307
294. Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain) Y. Chahor et al. https://doi.org/10.1016/j.agwat.2013.11.014
295. Comparative study of semi-distributed and 2D-distributed hydrological models for streamflow prediction in a data scarce mountainous watershed Z. Khaing et al. https://doi.org/10.1007/s40808-021-01271-9
296. Effects of length and application rate of rice straw mulch on surface runoff and soil loss under laboratory simulated rainfall M. Parhizkar et al. https://doi.org/10.1016/j.ijsrc.2020.12.002
297. Measurement and simulation of subsurface tracer migration to tile drains in low permeability, macroporous soil J. Bishop et al. https://doi.org/10.1002/2014WR016310
298. A preliminary assessment of glacier melt-model parameter sensitivity and transferability in a dry subarctic environment A. MacDougall et al. https://doi.org/10.5194/tc-5-1011-2011
299. Exploring and modelling soil detachment capacity and its relationships with hydraulic parameters and sediment size in rice paddy fields of Northern Iran M. Parhizkar et al. https://doi.org/10.1007/s10333-026-01097-0
300. Modeling the impact of rangeland management on forage production of sagebrush species in arid and semi-arid regions of Iran M. Azimi et al. https://doi.org/10.1016/j.ecolmodel.2012.10.017
301. Integration of soil hydraulic characteristics derived from pedotransfer functions into hydrological models: evaluation of its effects on simulation uncertainty W. Sun et al. https://doi.org/10.2166/nh.2016.150
302. Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting K. Sittichok et al. https://doi.org/10.1080/02626667.2018.1445854
303. Analysis of drought conditions and their impacts in a headwater stream in the Central European lower mountain ranges L. Kaule & S. Frei https://doi.org/10.1007/s10113-022-01926-y
304. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. https://doi.org/10.1080/02626667.2022.2104646
305. Spatial analysis and evolution of four air pollutants in England and Wales Á. Prieto et al. https://doi.org/10.1016/j.scitotenv.2021.145665
306. SWAT Model of Climate Change Impacts on Indian Water Resources: Synthesis, Model Performance, and Research Gaps A. Kumar et al. https://doi.org/10.1061/JIDEDH.IRENG-10575
307. Enhancing field scale water productivity for several rice cultivars under limited water supply R. Jonubi et al. https://doi.org/10.1007/s10333-017-0622-y
308. Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions P. Allahbakhshian-Farsani et al. https://doi.org/10.1007/s11269-020-02589-2
309. Effects of Loess Cavity on Soil Moisture Movement: Revealed by Soil Column and HYDRUS Model L. Porn et al. https://doi.org/10.4236/jwarp.2019.114022
310. Effect of hydrological variability on suspended sediment load at a river junction: a case study N. Khanam & S. Biswal https://doi.org/10.2166/wpt.2024.044
311. An ensemble-based projection of future hydro-climatic extremes in Iran A. Jahanshahi et al. https://doi.org/10.1016/j.jhydrol.2024.131892
312. Quantitative assessment of best management practices for soil and water conservation: A case study from the Tarquinia plain M. Yaseen et al. https://doi.org/10.1016/j.agwat.2025.109952
313. Ecohydrologic separation alters interpreted hydrologic stores and fluxes in a headwater mountain catchment M. Cain et al. https://doi.org/10.1002/hyp.13518
314. Topography- and depth-driven variability of β-glucosidase activity in Amazonian Forest soils (Brazil): A machine learning and structural equation modeling approach G. Tiruneh et al. https://doi.org/10.1016/j.catena.2026.110312
315. HYDRORECESSION: A Matlab toolbox for streamflow recession analysis S. Arciniega-Esparza et al. https://doi.org/10.1016/j.cageo.2016.10.005
316. Thermal, Moisture, and Solute Transport Responses Effects on Unsaturated Soil Hydraulic Parameters Estimation E. Lemoubou et al. https://doi.org/10.1029/2019WR025542
317. The impact of future climate and land use changes on runoff in the Min-Tuo River Basin N. Jiang et al. https://doi.org/10.2166/wcc.2024.384
318. Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River J. Hu et al. https://doi.org/10.1016/j.jhydrol.2023.130369
319. Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms D. Kouchi et al. https://doi.org/10.3390/w9060384
320. Impact of Watershed Subdivision and Soil Data Resolution on SWAT Model Calibration and Parameter Uncertainty1 S. Kumar & V. Merwade https://doi.org/10.1111/j.1752-1688.2009.00353.x
321. Simulation of impacts of Poyang Lake Hydraulic Project on the flow of the Yangtze River using an EFDC model L. Geying et al. https://doi.org/10.18307/2017.0301
322. Assessing the Impacts of Climatic and Water Management Scenarios in a Small Mountainous Greek River A. Mentzafou et al. https://doi.org/10.3390/hydrology12010013
323. How to relevantly characterize hydraulic properties of saline and sodic soils for water and solute transport simulations M. Rezaei et al. https://doi.org/10.1016/j.jhydrol.2020.125777
324. Surface water quality and cropping systems sustainability under a changing climate in the Upper Mississippi River Basin Y. Panagopoulos et al. https://doi.org/10.2489/jswc.69.6.483
325. Operational Comparison of Rainfall-Runoff Models through Hypothesis Testing J. Fidal & T. Kjeldsen https://doi.org/10.1061/(ASCE)HE.1943-5584.0001892
326. Using edge detection techniques and machine learning classifications for accurate lithological discrimination and structure lineaments extraction: a comparative case study from Gattar area, Northern Eastern Desert of Egypt R. El-Arafy et al. https://doi.org/10.1007/s12517-023-11732-3
327. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim https://doi.org/10.1016/j.ejrh.2025.102854
328. Soya bitkisinin (Glycine max. L. Merill) verim parametreleri ile bazı fiziksel toprak özellikleri arasındaki deneysel ilişkilerin belirlenmesi İ. EKBERLİ & N. KARS https://doi.org/10.33409/tbbbd.989044
329. Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan A. Ullah et al. https://doi.org/10.1016/j.ecolind.2018.03.053
330. Assessing freshwater water balance in Cimanuk River Basin M. Trinugroho & D. Nguyen https://doi.org/10.1088/1755-1315/648/1/012094
331. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions H. Tao et al. https://doi.org/10.1016/j.engappai.2023.107559
332. A review of efficiency criteria suitable for evaluating low-flow simulations R. Pushpalatha et al. https://doi.org/10.1016/j.jhydrol.2011.11.055
333. Hydrologic Simulation for Water Balance Improvement in an Outcrop Area of the Guarani Aquifer System A. Machado et al. https://doi.org/10.1007/s40710-016-0128-4
334. Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam D. Khoi et al. https://doi.org/10.3390/w14101552
335. Modeling impacts of climate change on freshwater availability in Africa M. Faramarzi et al. https://doi.org/10.1016/j.jhydrol.2012.12.016
336. Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model L. Joseph et al. https://doi.org/10.1016/j.apenergy.2024.122624
337. The Rate of Oxygen Release from Freshwater Bodies K. Edel’shtein et al. https://doi.org/10.1134/S009780781803003X
338. Estimating the Water Budget of the Upper Blue Nile River Basin With Water and Energy Processes (WEP) Model S. Abebe et al. https://doi.org/10.3389/feart.2022.923252
339. Forecasting daily flow rate-based intelligent hybrid models combining wavelet and Hilbert–Huang transforms in the mediterranean basin in northern Algeria Z. Abda & M. Chettih https://doi.org/10.1007/s11600-018-0188-0
340. Long-term Prediction of Saltwater Intrusion Based on Sequence Learning Framework T. Li et al. https://doi.org/10.1016/j.jenvman.2026.128854
341. Effects of Treatments With Biochar From Crop Residues on Soil Detachment Capacity in Rills After Deforestation M. Parhizkar et al. https://doi.org/10.1111/sum.70070
342. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland C. Zhang et al. https://doi.org/10.1016/j.scitotenv.2022.154969
343. Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil L. Kuana et al. https://doi.org/10.5194/hess-28-3367-2024
344. A Cumulative Rainfall Function for Subhourly Design Storm in Mediterranean Urban Areas M. Carbone et al. https://doi.org/10.1155/2015/528564
345. A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction K. Smith et al. https://doi.org/10.5194/hess-23-3247-2019
346. Assessing the potential value of the regionalised input constraint indices for constraining hydrological model simulations in the Congo River Basin P. Kabuya et al. https://doi.org/10.1016/j.advwatres.2021.104093
347. Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: A case study of the United States W. Jang et al. https://doi.org/10.1016/j.compag.2019.02.004
348. Predicting Forest Microclimate in Heterogeneous Landscapes T. Vanwalleghem & R. Meentemeyer https://doi.org/10.1007/s10021-009-9281-1
349. Surface energy balance model of transpiration from variable canopy cover and evaporation from residue-covered or bare-soil systems L. Lagos et al. https://doi.org/10.1007/s00271-009-0181-0
350. Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment T. Wöhling et al. https://doi.org/10.1007/s12665-013-2306-2
351. Quantifying Positive and Negative Human-Modified Droughts in the Anthropocene: Illustration with Two Iranian Catchments E. Kakaei et al. https://doi.org/10.3390/w11050884
352. Modulation of the goodness of fit in hydrological modelling based on inner balance errors F. Pellicer-Martínez et al. https://doi.org/10.1371/journal.pone.0260117
353. Considering flood scaling property in multi-objective calibration of the SWAT model: a case study in Zijinguan watershed, Northern China Y. Zheng et al. https://doi.org/10.1007/s11069-023-05859-5
354. Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models M. Jha & P. Gassman https://doi.org/10.1002/hyp.9836
355. Evaluation and Interconversion of Various Indicator PCB Schemes for ∑PCB and Dioxin-Like PCB Toxic Equivalent Levels in Fish N. Gandhi et al. https://doi.org/10.1021/es503427r
356. A simplified approach to estimate water retention for Sicilian soils by the Arya–Paris model C. Antinoro et al. https://doi.org/10.1016/j.geoderma.2013.08.004
357. Estimating the impacts of climate change on crop yields and N2O emissions for conventional and no-tillage in Southwestern Ontario, Canada W. He et al. https://doi.org/10.1016/j.agsy.2017.01.025
358. Modelling climate change impact on soil erosion in a watershed of north-western Lesser Himalayan region K. Sooryamol et al. https://doi.org/10.1007/s43217-022-00089-4
359. Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach T. Zhao et al. https://doi.org/10.5194/hess-25-5717-2021
360. Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam D. Dao et al. https://doi.org/10.3390/w13091314
361. Influence of Land Use Change on Hydrological Cycle: Application of SWAT to Su-Mi-Huai Area in Beijing, China L. Zhang et al. https://doi.org/10.3390/w12113164
362. The impact of climate change on Al-wala basin based on geomatics, hydrology and climate models F. Kloub et al. https://doi.org/10.1007/s12518-024-00578-3
363. Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge H. Lauri et al. https://doi.org/10.5194/hess-16-4603-2012
364. Modeling growth, yield, irrigation water use and soil moisture dynamics of OPSIS-irrigated sugarcane M. Gunarathna & M. Kumari https://doi.org/10.3389/fsufs.2025.1446545
365. Evaluation of the Impact of Climate Change on Runoff Generation in an Andean Glacier Watershed R. Escanilla-Minchel et al. https://doi.org/10.3390/w12123547
366. A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises R. Afsari et al. https://doi.org/10.3390/w16050711
367. Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model S. Guniganti et al. https://doi.org/10.1080/02626667.2024.2335294
368. A hybrid hydrologic modeling framework-role of spatial resolution, calibration approaches and error modeling S. Guniganti et al. https://doi.org/10.1016/j.ejrh.2025.103053
369. Comparison of Size Distribution of Fish Obtained from Gill Netting and the Distributions of Echoes from Hydroacoustics in Lake Dejguny (Poland) A. Hutorowicz et al. https://doi.org/10.3390/w15061117
370. Synergetic Integration of SWAT and Multi-Objective Optimization Algorithms for Evaluating Efficiencies of Agricultural Best Management Practices to Improve Water Quality Z. Hashemi Aslani et al. https://doi.org/10.3390/land12020401
371. In situUV-Vis spectroscopy to estimate COD and TSS in wastewater drainage systems R. Brito et al. https://doi.org/10.1080/1573062X.2013.783087
372. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods C. Broderick et al. https://doi.org/10.1002/2016WR018850
373. Analysing the Effects of Forest Cover and Irrigation Farm Dams on Streamflows of Water-Scarce Catchments in South Australia through the SWAT Model H. Nguyen et al. https://doi.org/10.3390/w9010033
374. Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems S. Golestaneh et al. https://doi.org/10.1155/2013/628313
375. A Qualitative Assessment of River Plumes Coupling SWAT Model Simulations and a Beach Optical Monitoring System N. Joumar et al. https://doi.org/10.3390/hydrology10020038
376. A coupled surface water storage and subsurface water dynamics model in SWAT for characterizing hydroperiod of geographically isolated wetlands J. Qi et al. https://doi.org/10.1016/j.advwatres.2019.103380
377. Changes in future drought characteristics in the Ili River Basin, China, using the new comprehensive standardized drought index Z. Li et al. https://doi.org/10.1016/j.ecolind.2025.113412
378. Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al. https://doi.org/10.1016/j.jhydrol.2024.132194
379. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions A. Laaboudi et al. https://doi.org/10.1007/s00484-011-0485-7
380. Estimating sediment and phosphorus loads from streambanks with and without riparian protection R. Miller et al. https://doi.org/10.1016/j.agee.2014.03.016
381. Evaluation of the wind energy potential of two south west sites in Nigeria O. Amoo https://doi.org/10.1007/s11708-012-0201-2
382. Development of a Water Temperature Modeling Platform to Support Short- and Long-Term Water Temperature Management in Reservoir–River Systems M. Deas et al. https://doi.org/10.3390/w17182714
383. Temporal and Spatial Assessment of Supply and Demand of the Water-yield Ecosystem Service for Water Scarcity Management in Arid to Semi-arid Ecosystems M. Sharafatmandrad & A. Khosravi Mashizi https://doi.org/10.1007/s11269-020-02706-1
384. Water quality impact assessment of large-scale biofuel crops expansion in agricultural regions of Michigan B. Love & A. Nejadhashemi https://doi.org/10.1016/j.biombioe.2011.02.041
385. Historical and future changes of frozen ground in the upper Yellow River Basin T. Wang et al. https://doi.org/10.1016/j.gloplacha.2018.01.009
386. Evaluation of Four Reanalysis Datasets against Radiosonde over Southwest Asia A. Alghamdi https://doi.org/10.3390/atmos11040402
387. Estimation of Daily Evaporation from Shallow Groundwater Using Empirical Models with a Temperature Coefficient H. Gao et al. https://doi.org/10.1175/JHM-D-22-0014.1
388. Influence of Hydrological Model Selection on Simulation of Moderate and Extreme Flow Events: A Case Study of the Blue Nile Basin C. Onyutha https://doi.org/10.1155/2016/7148326
389. Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches Y. Brouziyne et al. https://doi.org/10.3390/w12092333
390. Validation of a Soil Temperature and Moisture Model in Southern Quebec, Canada S. Perreault et al. https://doi.org/10.2136/sssaj2012.0311
391. Estimating characteristic coefficient of vertical leaf nitrogen profile within wheat canopy from spectral reflectance H. Li et al. https://doi.org/10.1016/j.compag.2023.107652
392. Water Quality Model Calibration via a Full-Factorial Analysis of Algal Growth Kinetic Parameters J. Bowen & N. Harrigan https://doi.org/10.3390/jmse6040137
393. Bayesian estimation of inflow hydrographs in ungauged sites of multiple reach systems M. D’Oria et al. https://doi.org/10.1016/j.advwatres.2013.11.007
394. Incorporating a non-reactive heavy metal simulation module into SWAT model and its application in the Athabasca oil sands region X. Du et al. https://doi.org/10.1007/s11356-019-05334-4
395. Exploring hydrological system performance for alpine low flows in local and continental prediction systems A. Chang et al. https://doi.org/10.1016/j.ejrh.2024.102056
396. Variations in water conservation function and attributions in the Three-River Source Region of the Qinghai–Tibet Plateau based on the SWAT model M. Li et al. https://doi.org/10.1016/j.agrformet.2024.109956
397. Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region S. Nepal et al. https://doi.org/10.1002/hyp.11199
398. Multivariate mapping of soil with structural equation modelling M. Angelini et al. https://doi.org/10.1111/ejss.12446
399. Modeling soil erosion and sediment yield under climate change: a comparison of RUSLE and MUSLE integrated with SDR using variable soil data resolutions H. Lamane et al. https://doi.org/10.1007/s40808-025-02544-3
400. Estimation of Glacier Melt Water Contribution for Human Consumption in the Royal Andes Considering Temperature Measurement Errors V. Quiroga et al. https://doi.org/10.4236/ojmh.2014.42003
401. Simulating event-scale rainfall erosivity across European climatic regions F. Matthews et al. https://doi.org/10.1016/j.catena.2022.106157
402. Projections of 21st Century Sierra Nevada Local Hydrologic Flow Components Using an Ensemble of General Circulation Models1 D. Ficklin et al. https://doi.org/10.1111/j.1752-1688.2012.00675.x
403. An extreme learning machine model for geosynthetic-reinforced sandy soil foundations M. Raja & S. Shukla https://doi.org/10.1680/jgeen.19.00297
404. Assessing the Impact of Land Use and Climate Change on Surface Runoff Response Using Gridded Observations and SWAT+ P. Kiprotich et al. https://doi.org/10.3390/hydrology8010048
405. Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch? A. Sun et al. https://doi.org/10.1029/2018WR023333
406. Estimating impacts of land cover change on erosion in a data-scarce catchment: Bot River, South Africa J. de Waal et al. https://doi.org/10.1080/02626667.2024.2398653
407. Development and application of a hydroclimatological stream temperature model within the Soil and Water Assessment Tool D. Ficklin et al. https://doi.org/10.1029/2011WR011256
408. Application of SWAT99.2 to sensitivity analysis of water balance components in unique plots in a hilly region J. Dai et al. https://doi.org/10.1016/j.wse.2017.09.002
409. Predicting Event-Based Sediment and Heavy Metal Loads in Untreated Urban Runoff from Impermeable Surfaces F. Charters et al. https://doi.org/10.3390/w12040969
410. Anomalous trend in soil evaporation in a semi-arid, snow-dominated watershed R. Wang et al. https://doi.org/10.1016/j.advwatres.2013.03.004
411. Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model M. Shin & Y. Choi https://doi.org/10.3390/w10121839
412. Simulated Climate Change Impacts on Corn and Soybean Yields in Buchanan County, Iowa E. Osei et al. https://doi.org/10.3390/agriculture13020268
413. Identifying model consistency through stepwise calibration to capture streamflow variability D. Kundu et al. https://doi.org/10.1016/j.envsoft.2016.06.013
414. Assessing Shrub and Grassland Degradation Portfolios as Benchmarks for Potential Water Quantity Benefits: Application of the RIOS and SWAT Model to Rimac Basin, Peru A. Salinas-Castro et al. https://doi.org/10.3390/land15040638
415. The Impact of US Biofuels Policy on Agricultural Production and Nitrogen Loads in Alabama E. Affuso & L. Duzy https://doi.org/10.1155/2013/521254
416. A test of how coupling of vegetation to the atmosphere and climate spatial variation affects water yield modelling in mountainous catchments M. Gharun et al. https://doi.org/10.1016/j.jhydrol.2014.04.037
417. Hydrological simulation of a small ungauged agricultural watershed Semrakalwana of Northern India H. Mishra et al. https://doi.org/10.1007/s13201-017-0531-7
418. Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment L. Wang et al. https://doi.org/10.1016/j.jhydrol.2015.05.049
419. Revisiting water retention curves for simple hydrological modelling of peat D. Dimitrov & P. Lafleur https://doi.org/10.1080/02626667.2020.1853132
420. Hydrological simulation of a small forested catchment under different land use and forest management L. Oliveira et al. https://doi.org/10.3832/ifor3221-013
421. Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea D. Lee & K. Ahn https://doi.org/10.3390/rs14153535
422. New indices to quantify patterns of relative errors produced by spatial interpolation models – A comparative study by modelling soil properties U. Bronowicka-Mielniczuk & J. Mielniczuk https://doi.org/10.1016/j.ecolind.2023.110551
423. Partitioning of flows in the unsaturated zone N. Krüger & C. Külls https://doi.org/10.1016/j.jhydrol.2024.132643
424. Quantitative attribution of major driving forces on soil organic carbon dynamics Y. Wu et al. https://doi.org/10.1002/2014MS000361
425. Evaluating the rainfall erosivity (R-factor) from daily rainfall data: an application for assessing climate change impact on soil loss in Westrapti River basin, Nepal R. Talchabhadel et al. https://doi.org/10.1007/s40808-020-00787-w
426. Evaluation of Evapotranspiration in Brazilian Cerrado Biome Simulated with the SWAT Model A. Ferreira et al. https://doi.org/10.3390/w13152037
427. Impact of missing precipitation values on hydrological model output: a case study from the Eddleston Water catchment, Scotland S. Ruman et al. https://doi.org/10.1007/s11600-020-00409-0
428. Climate Change Impacts on Streamflow and Subbasin-Scale Hydrology in the Upper Colorado River Basin D. Ficklin et al. https://doi.org/10.1371/journal.pone.0071297
429. When and where to reduce nutrient for controlling harmful algal blooms in large eutrophic lake Chaohu, China? J. Huang et al. https://doi.org/10.1016/j.ecolind.2018.01.056
430. Cereal yield stabilization in Terai (Nepal) by water and soil fertility management modeling N. Shrestha et al. https://doi.org/10.1016/j.agwat.2013.03.003
431. Relationship between water levels and flood pulse induced by river–lake interaction in the Tonle Sap basin, Cambodia H. Yang et al. https://doi.org/10.1007/s12665-022-10353-5
432. Application of USLE in a GIS environment to estimate soil erosion in the Irga watershed, Jharkhand, India P. Roy https://doi.org/10.1080/02723646.2018.1550301
433. A simulation tool for managing environmental flows in regulated rivers C. Steinfeld et al. https://doi.org/10.1016/j.envsoft.2015.08.006
434. Estimation of climate and watershed management-induced potential soil loss using the USLE in Demjer watershed, Blue Nile basin, Ethiopia A. Moges & G. Tegbaru https://doi.org/10.1080/24749508.2024.2359783
435. Salt to stream: a process-based integrated watershed model for urban stream chloride from winter salts using SWMM B. Mazumder et al. https://doi.org/10.2166/wqrj.2026.081
436. Responses of natural runoff to recent climatic variations in the Yellow River basin, China Y. Tang et al. https://doi.org/10.5194/hess-17-4471-2013
437. Review and comparison of performance indices for automatic model induction J. Chadalawada & V. Babovic https://doi.org/10.2166/hydro.2017.078
438. How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance B. Guse et al. https://doi.org/10.1002/hyp.9777
439. Smart Wastewater Management: Data-Driven Prediction of Wastewater Using Optimized Learning Techniques A. Aldrees et al. https://doi.org/10.1007/s41101-026-00543-8
440. Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin S. Khan et al. https://doi.org/10.2166/nh.2021.073
441. Enhanced bioretention cell modeling with DRAINMOD-Urban: Moving from water balances to hydrograph production W. Lisenbee et al. https://doi.org/10.1016/j.jhydrol.2019.124491
442. Using an integrated hydrological model to estimate the impacts of droughts in a semiarid transboundary river basin: the case of study of the Tijuana River Basin J. Ruiz-Gibert et al. https://doi.org/10.1080/15715124.2019.1597727
443. Conceptual rainfall-runoff model with a two-parameter, infinite characteristic time transfer function V. Guinot et al. https://doi.org/10.1002/hyp.10523
444. A new multiple regression model for predictions of urban water use S. Eslamian et al. https://doi.org/10.1016/j.scs.2016.08.003
445. Event Mean Concentration-based Modelling Approach for a Large Scale Spatially Heterogenous Catchment using SWMM M. Bhusal & S. Muthukumaran https://doi.org/10.1007/s11269-025-04407-z
446. Kinetics and simulation of nitrification at various pH values of a polluted river in the tropics T. Le et al. https://doi.org/10.1016/j.ecohyd.2018.06.006
447. Application of Export Coefficient Model and QUAL2K for Water Environmental Management in a Rural Watershed Z. Xin et al. https://doi.org/10.3390/su11216022
448. Machine Learning-Based Multivariate Time Series Imputation for Long Continuous Gaps in Surface Water Salinity Monitoring Data N. Thuy et al. https://doi.org/10.1007/s41748-026-01090-x
449. UNISON decision framework for hybrid optimization of wastewater treatment and recycle for Industry 3.5 and cleaner semiconductor manufacturing Y. Lin et al. https://doi.org/10.1016/j.resconrec.2022.106282
450. Geospatially based distributed rainfall‐runoff modelling for simulation of internal and outlet responses in a semi‐forested lower Himalayan watershed R. Ramsankaran et al. https://doi.org/10.1002/hyp.8271
451. Surface energy balance model of transpiration from variable canopy cover and evaporation from residue-covered or bare soil systems: model evaluation L. Lagos et al. https://doi.org/10.1007/s00271-011-0298-9
452. Importance of bedrock hydrology for the rainfall–runoff process in a forested mountain catchment T. Kameyama et al. https://doi.org/10.1016/j.jhydrol.2025.134205
453. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia https://doi.org/10.1016/j.jhydrol.2023.130421
454. Sensitivities of hydrological processes under climate warming and Landuse/landcover change in the Lhasa basin, Tibetan plateau K. Guédé et al. https://doi.org/10.1016/j.ejrh.2024.101731
455. Integrated impact assessment of soil and water conservation structures on runoff and sediment yield through measurements and modeling in the Northern Ethiopian highlands N. Melaku et al. https://doi.org/10.1016/j.catena.2018.05.035
456. Detection of dominant nitrate processes in ecohydrological modeling with temporal parameter sensitivity analysis M. Haas et al. https://doi.org/10.1016/j.ecolmodel.2015.07.009
457. Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins M. Tan et al. https://doi.org/10.3390/w9030229
458. Cement kiln dust (CKD)-filter sand permeable reactive barrier for the removal of Cu(II) and Zn(II) from simulated acidic groundwater A. Sulaymon et al. https://doi.org/10.1016/j.jhazmat.2015.04.061
459. Parameterizing the AquaCrop model for potato growth modeling in a semi-arid region S. Ahmadi et al. https://doi.org/10.1016/j.fcr.2022.108680
460. Application of periodic autoregressive process to the modeling of the Garonne river flows E. Ursu & J. Pereau https://doi.org/10.1007/s00477-015-1193-3
461. Semi-automated buildup and calibration of conceptual sewer models S. Kroll et al. https://doi.org/10.1016/j.envsoft.2017.02.030
462. The Brigalow Catchment Study: A comparison of four methods to estimate peak runoff rate for small catchments before and after land use change in the Brigalow Belt bioregion of central Queensland, Australia C. Thornton & B. Yu https://doi.org/10.1016/j.ejrh.2023.101592
463. Assessing past and future trends of extreme precipitation indices over capital cities of the Gangetic Plain using statistically downscaled CMIP6 models P. Sarthi et al. https://doi.org/10.1007/s00704-026-06245-4
464. Excavating black hole continuum spectrum: Possible signatures of scalar hairs and of higher dimensions I. Banerjee et al. https://doi.org/10.1103/PhysRevD.96.084035
465. Hydroclimatic Impact Assessment Using the SWAT Model in India—State of the Art Review S. Dubey et al. https://doi.org/10.3390/su152215779
466. Elaborating rating curves for implementation of the MGB hydrological model in a river basin, Amazon region, Brazil A. de Melo et al. https://doi.org/10.1007/s40899-022-00715-z
467. Generalised spatial error for validation H. Liu et al. https://doi.org/10.1080/15481603.2026.2690327
468. Water availability and response of Tarbela Reservoir under the changing climate in the Upper Indus Basin, Pakistan F. Khan https://doi.org/10.1038/s41598-022-20159-x
469. Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model Y. Abduljaleel et al. https://doi.org/10.1038/s41598-023-32556-x
470. Hydrologic Drought Atlas for Texas D. Rajsekhar et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001074
471. Uncertainty in radar‐rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008 B. Seo et al. https://doi.org/10.1002/wrcr.20244
472. Comparison of Interpolation Methods for Spatial Distribution of Monthly Precipitation in the State of Pernambuco, Brazil A. da Silva et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001743
473. Simulation of potential evapotranspiration values based on vine copula A. Khashei‐Siuki et al. https://doi.org/10.1002/met.2027
474. Model efficiency performance assessment through a standard triangular diagram (STD) Z. Şen https://doi.org/10.1007/s40808-020-00993-6
475. Evaluating climate change impact on the hydrology of Kessie Watershed, Upper Blue Nile Basin, Ethiopia A. Amognehegn et al. https://doi.org/10.1007/s13201-023-01947-w
476. Major reductions in riverine suspended sediment levels caused by changes in agricultural practices and dams in Southern Brazil (1984–2024) O. Montanher et al. https://doi.org/10.1016/j.scitotenv.2025.180195
477. Impact of suspended sediment and nutrient loading from land uses against water quality in the Hii River basin, Japan H. Somura et al. https://doi.org/10.1016/j.jhydrol.2012.05.032
478. Simulating the impact of flooding events on non-point source pollution and the effects of filter strips in an intensive agricultural watershed in China Y. Chen et al. https://doi.org/10.1007/s10201-014-0443-2
479. Data and Calibration Challenges for Spill Response Models R. Bahadur & W. Samuels https://doi.org/10.1061/(ASCE)EE.1943-7870.0001319
480. Physically-based modelling of UK river flows under climate change B. Smith et al. https://doi.org/10.3389/frwa.2024.1468855
481. Conceptualizing and assessing the effects of installation and operation of photovoltaic power plants on major hydrologic budget constituents V. Pisinaras et al. https://doi.org/10.1016/j.scitotenv.2014.05.132
482. Hydrologic responses to projected climate change in ecologically diverse watersheds of the Gulf Coast, United States R. Neupane et al. https://doi.org/10.1002/joc.5947
483. Experimental Assessment of a 2-D Entropy-Based Model for Velocity Distribution in Open Channel Flow N. Fontana et al. https://doi.org/10.3390/e15030988
484. A novel multi-objective model calibration method for ecohydrological applications J. Hernandez-Suarez et al. https://doi.org/10.1016/j.envsoft.2021.105161
485. ESCIMO.spread – a spreadsheet-based point snow surface energy balance model to calculate hourly snow water equivalent and melt rates for historical and changing climate conditions U. Strasser & T. Marke https://doi.org/10.5194/gmd-3-643-2010
486. The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables A. Ahmed et al. https://doi.org/10.1007/s11356-022-22601-z
487. Extra Dimensions to the Calibration of Hargreaves-Samani Equation Under Data-Scarce Environment A. Musa & N. Elagib https://doi.org/10.1007/s11269-025-04151-4
488. Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement K. Fowler et al. https://doi.org/10.1029/2018WR023989
489. Quantifying spatial and temporal patterns of flow intermittency using spatially contiguous runoff data S. Yu (于松延) et al. https://doi.org/10.1016/j.jhydrol.2018.03.009
490. Assessment of Spatiotemporal Variability of Meteorological Droughts in Northern Iraq Using Satellite Rainfall Data H. Ahmad et al. https://doi.org/10.1007/s12205-021-2046-x
491. Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China X. Yang et al. https://doi.org/10.1007/s00477-015-1099-0
492. Basin-wide hydrological system assessment under climate change scenarios through conceptual modelling C. Donmez et al. https://doi.org/10.1080/17538947.2019.1597188
493. Hydrological modelling in small ungauged catchments U. COMINI et al. https://doi.org/10.1590/0001-3765202020180687
494. Frequency-based performance measure for hydrologic model evaluation R. Teegavarapu et al. https://doi.org/10.1016/j.jhydrol.2022.127583
495. Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model R. Sultana et al. https://doi.org/10.5194/hess-18-3553-2014
496. Formulating the spring discharge-function for the recession period by analyzing its recession curve: A case study of the Ranichauri spring (India) A. Vashisht & B. Bam https://doi.org/10.1007/s12040-013-0356-1
497. Extreme Heat Events over Southeast Europe Based on NEX-GDDP Ensemble: Present Climate Evaluation and Future Projections H. Chervenkov & K. Malcheva https://doi.org/10.3390/atmos14061000
498. Optimizing climate model selection for hydrological modeling: A case study in the Maumee River basin using the SWAT S. Yuan et al. https://doi.org/10.1016/j.jhydrol.2020.125064
499. Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models P. Wagener et al. https://doi.org/10.5194/hess-30-4867-2026
500. Improving Hydrological Modelling With Topkapi Through Irrigation and Open‐Channel Flow in Semi‐Arid Agricultural Catchments S. Abbenante et al. https://doi.org/10.1002/hyp.70140
501. A Heuristic Method for Measurement Site Selection in Sewer Systems T. Vonach et al. https://doi.org/10.3390/w10020122
502. Climate Change Impacts on Coastal Wave Dynamics at Vougot Beach, France P. Dissanayake et al. https://doi.org/10.3390/jmse9091009
503. Hydropower energy recovery in irrigation networks: Validation of a methodology for flow prediction and pump as turbine selection M. Crespo Chacón et al. https://doi.org/10.1016/j.renene.2019.09.119
504. Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD₅ Prediction S. Kim et al. https://doi.org/10.3934/mbe.2022595
505. Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh M. Maniruzzaman et al. https://doi.org/10.1016/j.agwat.2015.06.022
506. Impact of Future Climate Change on Water Temperature and Thermal Habitat for Keystone Fishes in the Lower Saint John River, Canada S. Dugdale et al. https://doi.org/10.1007/s11269-018-2057-7
507. Corn Yield Simulation under Different Nitrogen Loading and Climate Change Scenarios N. Joshi et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000895
508. Hydrological system analysis and modelling of the Kara River basin (West Africa) using a lumped metric conceptual model H. Badjana et al. https://doi.org/10.1080/02626667.2017.1307571
509. Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty Y. Her & I. Chaubey https://doi.org/10.1002/hyp.10487
510. Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China Q. Wang et al. https://doi.org/10.3390/w16121729
511. Regional scale hydrologic modeling of a karst-dominant geomorphology: The case study of the Island of Crete A. Malagò et al. https://doi.org/10.1016/j.jhydrol.2016.05.061
512. Response of climate change impact on streamflow: the case of the Upper Awash sub-basin, Ethiopia S. Abdulahi et al. https://doi.org/10.2166/wcc.2021.251
513. Evaluating the Applicability of Drainage Routing Schemes for Paddy Fields J. Song et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001491
514. Hydrological model using ground- and satellite-based data for river flow simulation towards supporting water resource management in the Red River Basin, Vietnam N. Hiep et al. https://doi.org/10.1016/j.jenvman.2018.03.100
515. Improved Rainfall‐Runoff Calibration for Drying Climate: Choice of Objective Function K. Fowler et al. https://doi.org/10.1029/2017WR022466
516. Characterising urban zinc generation to identify surface pollutant hotspots in a low intensity rainfall climate F. Charters et al. https://doi.org/10.2166/wst.2017.306
517. Investigation of the Curve Number Method For Surface Runoff Estimation In Tropical Regions Y. Dile et al. https://doi.org/10.1111/1752-1688.12446
518. Using radar-based quantitative precipitation data with coupled soil- and groundwater balance models for stream flow simulation in a karst area P. Knöll et al. https://doi.org/10.1016/j.jhydrol.2020.124884
519. Long Term Soil and Water Assessment Tool (SWAT) Calibration from an Ecohydrology Perspective S. Mylevaganam et al. https://doi.org/10.4236/ojapps.2015.57035
520. Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations? D. Myers et al. https://doi.org/10.1002/hyp.14045
521. Effect of sensitivity analysis on parameter optimization: Case study based on streamflow simulations using the SWAT model in China M. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126896
522. Modelling water resources for planning irrigation development in drought-prone southern Chile I. McNamara et al. https://doi.org/10.1080/07900627.2020.1768828
523. Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland P. Gilewski & M. Nawalany https://doi.org/10.3390/w10111665
524. Methodology for including reservoir regulation in water scarcity evaluation J. Xie et al. https://doi.org/10.1016/j.jclepro.2022.132657
525. Developing an explainable deep learning module based on the LSTM framework for flood prediction Z. Zhang et al. https://doi.org/10.3389/frwa.2025.1562842
526. Assessment of synthetic series to design the capacity of flow regulation reservoirs F. Heitor et al. https://doi.org/10.1016/j.jsames.2024.104920
527. Optimal Calibration and Uncertainty Analysis of SWAT for an Arid Climate M. Jajarmizadeh et al. https://doi.org/10.1177/1178622117731792
528. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances J. Xue et al. https://doi.org/10.1016/j.rse.2020.112055
529. Comparison and evaluation of hydrologic performance of satellite, reanalysis and climate model-based precipitation products in the Upper Awash River Basin, Ethiopia S. Abdulahi et al. https://doi.org/10.2166/wpt.2024.134
530. Evaluation of SWAT performance in modeling nutrients of Awash River basin, Ethiopia A. Keraga et al. https://doi.org/10.1007/s40808-018-0533-y
531. Towards improved criteria for hydrological model calibration: theoretical analysis of distance- and weak form-based functions V. Guinot et al. https://doi.org/10.1016/j.jhydrol.2011.02.004
532. A genetic algorithm's novel rainfall distribution method for optimized hydrological modeling at basin scales C. Skoulikaris & N. Nagkoulis https://doi.org/10.2166/hydro.2024.224
533. The Magnitude and Origin of Groundwater Discharge to Eastern U.S. and Gulf of Mexico Coastal Waters K. Befus et al. https://doi.org/10.1002/2017GL075238
534. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. https://doi.org/10.3390/w15112020
535. How much hydrology is embedded in low-cost sensors? Machine-learning emulation of the mesoscale hydrological model from citizen soil moisture observations A. Elsaidy et al. https://doi.org/10.1016/j.hydroa.2026.100225
536. A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al. https://doi.org/10.5194/essd-17-517-2025
537. Interpolation of the gaps in current maps generated by high-frequency radar D. Saha et al. https://doi.org/10.1080/01431161.2016.1230281
538. Simulation of hydrological processes of mountainous watersheds in inland river basins: taking the Heihe Mainstream River as an example Z. Yin et al. https://doi.org/10.1007/s40333-013-0197-4
539. Exploring the accuracy of Random Forest and Multiple Regression models to predict rill detachment in soils under different plant species and soil treatments in deforested lands M. Parhizkar et al. https://doi.org/10.1007/s40808-023-01919-8
540. Modeling of seasonal vertical variation of dissolved oxygen and its impacts on water environment in Shahe Reservoir within Tianmuhu Reservoir C. Xiaoyi et al. https://doi.org/10.18307/2013.0605
541. Comparative analysis of runoff simulations using multiple hydrological models in the Ganjiang River Basin X. Jiang et al. https://doi.org/10.2166/nh.2025.020
542. Assessing the potential of deep learning-based dam inflow forecasting in a heavy snowfall region of Japan using reanalysis and satellite-derived snow cover data H. Yanagihara & R. Arai https://doi.org/10.3178/hrl.26-00002
543. SOWING STRATEGIES FOR BARLEY (HORDEUM VULGARE L.) BASED ON MODELLED YIELD RESPONSE TO WATER WITH AQUACROP B. ABRHA et al. https://doi.org/10.1017/S0014479711001190
544. Two‐dimensional physically based finite element runoff model for small agricultural watersheds: II. Model testing and field application A. Bhardwaj & M. Kaushal https://doi.org/10.1002/hyp.7170
545. Cost-effectiveness of reverse auctions for watershed nutrient reductions in the presence of climate variability: An empirical approach for the Boone River watershed A. Valcu-Lisman et al. https://doi.org/10.2489/jswc.72.3.280
546. Fish and Invertebrate Flow‐Biology Relationships to Support the Determination of Ecological Flows for North Carolina J. Phelan et al. https://doi.org/10.1111/1752-1688.12497
547. Impacts of climate change on reservoir water availability, quality and irrigation needs in a water scarce Mediterranean region (southern Portugal) J. Rocha et al. https://doi.org/10.1016/j.scitotenv.2020.139477
548. Incorporating canopy radiation enhances the explanation of maize yield change and increases model accuracy under film mulching H. Quan et al. https://doi.org/10.1016/j.eja.2024.127198
549. Disentangling the effects of long‐term changes in precipitation and land use on hydrological response in a monsoonal catchment N. Adnan & P. Atkinson https://doi.org/10.1111/jfr3.12294
550. Raised bed furrow irrigation strategies and soil moisture dynamics for sustainable maize production in Bangladesh: Climate-informed DSSAT modeling under CMIP6 projections A. Adham et al. https://doi.org/10.1016/j.rineng.2026.111185
551. Land-use controls on soil organic carbon dynamics across Amazonian ecosystems, Brazil G. Tiruneh et al. https://doi.org/10.1038/s41598-026-43978-8
552. A modeling study on the hydrodynamics of a coastal embayment occupied by mussel farms (Ria de Ares-Betanzos, NW Iberian Peninsula) P. Duarte et al. https://doi.org/10.1016/j.ecss.2014.05.021
553. Exploring Lake Victoria ecosystem functioning using the Atlantis modeling framework C. Nyamweya et al. https://doi.org/10.1016/j.envsoft.2016.09.019
554. Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model R. Deo et al. https://doi.org/10.1016/j.atmosres.2016.10.004
555. Water Balance Uncertainty of a Hydrologic Model to Lengthy Drought and Storm Events in Managed Forest Catchments, Eastern Australia R. Jamshidi & D. Dragovich https://doi.org/10.3390/land12010003
556. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. https://doi.org/10.2166/hydro.2021.179
557. A refined index of model performance C. Willmott et al. https://doi.org/10.1002/joc.2419
558. Identification of the relevant input variables for predicting the parabolic trough solar collector's outlet temperature using an artificial neural network and a multiple linear regression model W. Ajbar et al. https://doi.org/10.1063/5.0055992
559. Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models O. Kisi et al. https://doi.org/10.1080/02626667.2019.1632460
560. Relation Between Runoff Curve Number and Irrigation Water Requirement S. Chakraborty et al. https://doi.org/10.1007/s40003-015-0184-4
561. Can Moistube irrigation (MTI) self-regulate to ambient temperature and soil-atmosphere interface conditions? MTI performances under simulated temperature, wind, radiation, transpiration, and precipitation C. Wang et al. https://doi.org/10.1016/j.agwat.2025.109938
562. Three Gorges Reservoir operations regulate nitrogen transport and transformation via hydrology-sediment-carbon interactions B. Nie et al. https://doi.org/10.1016/j.jhydrol.2026.135996
563. Drainage-Process Analyses for Agricultural Non-Point-Source Pollution from Irrigated Paddy Systems K. Wang et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000662
564. GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model O. Terranova et al. https://doi.org/10.5194/gmd-8-1955-2015
565. Enhanced Streamflow Prediction Using a Hybrid CNN-LSTM Model: Integrating Climate-Driven Spatio-Temporal Features in a Deep Learning Framework S. Shahariar et al. https://doi.org/10.1007/s11269-026-04491-9
566. Estimation models for soil water contents and soil temperature during drought season in autumn K. Eom https://doi.org/10.15531/KSCCR.2024.15.2.207
567. Contributions of Climate Variability and Human Activities to Runoff Changes in the Upper Catchment of the Red River Basin, China Y. Li et al. https://doi.org/10.3390/w8090414
568. Sensitivity analysis of CN using SCS-CN approach, rain gauges and TRMM satellite data assessment into HEC-HMS hydrological model in the upper basin of Oum Er Rbia, Morocco E. Faouzi et al. https://doi.org/10.1007/s40808-022-01404-8
569. Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru C. Fernandez-Palomino et al. https://doi.org/10.1080/02626667.2020.1846740
570. Improvement and comparison of the rainfall–runoff methods in SWAT at the monsoonal watershed of Baocun, Eastern China Q. Cheng et al. https://doi.org/10.1080/02626667.2015.1051485
571. Identification of critical watershed using hydrological model and drought indices: a case study of upper Girna, Maharashtra, India P. Ramkar & S. Yadav https://doi.org/10.1080/09715010.2019.1594416
572. Availability of wind energy resource potential for power generation at Jos, Nigeria O. Ajayi et al. https://doi.org/10.1007/s11708-011-0167-5
573. MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow1 B. Weissling & H. Xie https://doi.org/10.1111/j.1752-1688.2009.00300.x
574. A pseudo-reservoir concept in SWAT model for the simulation of an alluvial floodplain in a complex tropical river system W. Phiri et al. https://doi.org/10.1016/j.ejrh.2020.100770
575. Exploring parsimonious daily rainfall-runoff model structure using the hyperbolic tangent function and Tank model J. Song et al. https://doi.org/10.1016/j.jhydrol.2019.04.054
576. Predicting Carbon Monoxide Concentrations in the Air of Pardis City, Iran, Using an Artificial Neural Network G. Asadollahfardi et al. https://doi.org/10.1002/tqem.21482
577. Improving hydrological simulations by incorporating GRACE data for model calibration P. Bai et al. https://doi.org/10.1016/j.jhydrol.2017.12.025
578. Assessment of Flood-Prone Areas in the Lacramarca River Basin in the Santa Clemencia and Pampadura Region, Peru, Under Climate Change Effects G. Pérez Campomanes et al. https://doi.org/10.3390/hydrology13040103
579. Improved CN-Based Long-Term Hydrologic Simulation Model M. Jain et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000592
580. Determining potential rainwater harvesting sites using a continuous runoff potential accounting procedure and GIS techniques in central Italy M. Napoli et al. https://doi.org/10.1016/j.agwat.2014.04.012
581. Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches J. Brédy et al. https://doi.org/10.1016/j.agwat.2020.106090
582. Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning J. Lee et al. https://doi.org/10.3390/environments12030094
583. Bagged stepwise cluster analysis for probabilistic river flow prediction Q. Zhang et al. https://doi.org/10.1016/j.jhydrol.2023.129995
584. Climate change effects on extreme flows of water supply area in Istanbul: utility of regional climate models and downscaling method F. Kara & I. Yucel https://doi.org/10.1007/s10661-015-4808-8
585. Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study S. Anil et al. https://doi.org/10.1002/joc.7164
586. Evaluation of the SWAT model performance for simulating river discharge in the Himalayan and tropical basins of Asia S. Shrestha et al. https://doi.org/10.2166/nh.2017.189
587. Impacts of native forest harvesting on flows into the Murray–Darling Basin system L. Bren et al. https://doi.org/10.1080/00049158.2013.794494
588. Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran F. Emami & M. Koch https://doi.org/10.3390/cli6020030
589. Machine learning approaches for predicting the performance of stormwater biofilters in heavy metal removal and risk mitigation H. Fang et al. https://doi.org/10.1016/j.watres.2021.117273
590. Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers R. Kumar et al. https://doi.org/10.1016/j.jenvman.2024.121233
591. Quantifying the role of traditional rice terraces in regulating water resources: implications for management and conservation efforts M. Soriano & S. Herath https://doi.org/10.1080/21683565.2018.1437497
592. Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models R. Jiang et al. https://doi.org/10.1016/j.compag.2019.104988
593. Effect of ENSO-based upstream water withdrawals for irrigation on downstream water withdrawals L. Sangha et al. https://doi.org/10.2166/nh.2020.156
594. Influence of lag time on event-based rainfall–runoff modeling using the data driven approach A. Talei & L. Chua https://doi.org/10.1016/j.jhydrol.2012.03.027
595. Quantifying the spatiotemporal dynamics of recharge in a composite Great Lakes watershed using a high-resolution hydrology model and multi-source data G. Kang et al. https://doi.org/10.1016/j.jhydrol.2021.126594
596. ASSESSMENT OF FAILURE ON DRAINAGE STRUCTURES ALONG THE ETHIOPIAN NATIONAL RAILWAY LINE OF SEBETA-MIESO (CASE STUDY OF AKAKI RIVER CROSSING DRAINAGE STRUCTURE) Y. Hirpessa & I. Hailu https://doi.org/10.29121/granthaalayah.v7.i9.2019.568
597. Hydrological modelling of the Kangsabati river under changed climate scenario: case study in India S. Dhar & A. Mazumdar https://doi.org/10.1002/hyp.7351
598. PRISM and Radar Estimation for Precipitation (PREP): PRISM enhancement through ANN and radar data integration in complex terrain D. Kang et al. https://doi.org/10.1016/j.atmosres.2024.107476
599. Assessing the power of non-parametric data-driven approaches to analyse the impact of drought measures J. De Meester & P. Willems https://doi.org/10.1016/j.envsoft.2023.105923
600. Measuring performance of low impact development practices for the surface runoff management W. Yang et al. https://doi.org/10.1016/j.ese.2020.100010
601. Explainable AI in drought forecasting A. Dikshit & B. Pradhan https://doi.org/10.1016/j.mlwa.2021.100192
602. Bayesian scrutiny of simple rainfall–runoff models used in forest water management A. Greenwood et al. https://doi.org/10.1016/j.jhydrol.2014.01.074
603. GMDH-HS hybrid framework for optimization of combined drought indices in arid ecosystems N. Shahbeygi et al. https://doi.org/10.1016/j.asej.2026.104268
604. A new hybrid data-driven model for event-based rainfall–runoff simulation G. Kan et al. https://doi.org/10.1007/s00521-016-2200-4
605. Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model1 I. Masih et al. https://doi.org/10.1111/j.1752-1688.2010.00502.x
606. Evaluation of the soil and water assessment tool (SWAT) for simulating E. coli concentrations at the watershed-scale R. Sowah et al. https://doi.org/10.1016/j.scitotenv.2020.140669
607. Water availability in Pakistan from Hindukush–Karakoram–Himalayan watersheds at 1.5 °C and 2 °C Paris Agreement targets S. Hasson et al. https://doi.org/10.1016/j.advwatres.2019.06.010
608. Sowing and irrigation strategies for improving rainfed tef (Eragrostis tef (Zucc.) Trotter) production in the water scarce Tigray region, Ethiopia A. Tsegay et al. https://doi.org/10.1016/j.agwat.2014.11.014
609. Future hydro-climate extremes in the cypress creek watershed in Texas under different CMIP6 scenarios N. Abeysingha et al. https://doi.org/10.1007/s40899-025-01285-6
610. Parameter Estimation of a Distributed Hydrological Model Using a Genetic Algorithm J. Boisvert et al. https://doi.org/10.4236/ojmh.2016.63013
611. From calibration to real‐time operations: an assessment of three precipitation benchmarks for a Swiss river system N. Andres et al. https://doi.org/10.1002/met.1569
612. Waterworks sludge-filter sand permeable reactive barrier for removal of toxic lead ions from contaminated groundwater A. Faisal et al. https://doi.org/10.1016/j.jwpe.2019.101112
613. Improved SWAT Model Performance With Time‐Dynamic Voronoi Tessellation of Climatic Input Data in Southern Africa1 J. Andersson et al. https://doi.org/10.1111/j.1752-1688.2011.00627.x
614. How Cs-137 dispersions due to atmospheric deposition to East Vietnam Sea: a hypothetical level 7 incident from the Fangchenggang Nuclear Power Plant (China) D. Vu et al. https://doi.org/10.15625/1859-3097/18309
615. Using UKCP09 probabilistic climate information for UK water resource planning B. Christierson et al. https://doi.org/10.1016/j.jhydrol.2011.12.020
616. Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin F. Ehmele et al. https://doi.org/10.5194/nhess-22-677-2022
617. An Evaluation Matrix to Compare Computer Hydrological Models for Flood Predictions P. Filianoti et al. https://doi.org/10.3390/hydrology7030042
618. Simulation of adsorption of antimony on zero-valent iron nanoparticles coated on the industrial minerals (kaolinite, bentonite and perlite) in mineral effluent S. Saeidnia et al. https://doi.org/10.1080/19443994.2015.1130656
619. Copula Theory as a Generalized Framework for Flow‐Duration Curve Based Streamflow Estimates in Ungaged and Partially Gaged Catchments S. Worland et al. https://doi.org/10.1029/2019WR025138
620. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski https://doi.org/10.1016/j.envsoft.2020.104718
621. Monthly Streamflow Forecasting for the Irtysh River Based on a Deep Learning Model Combined with Runoff Decomposition K. Yong et al. https://doi.org/10.3390/w17091375
622. Assessment on Hydrologic Response by Climate Change in the Chao Phraya River Basin, Thailand M. Ligaray et al. https://doi.org/10.3390/w7126665
623. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model Z. Yaseen et al. https://doi.org/10.1016/j.jhydrol.2017.09.007
624. Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes G. Papacharalampous et al. https://doi.org/10.1007/s00477-018-1638-6
625. Enhancing precision in sediment yield Estimation: The synergistic effects of runoff and vegetation dynamics S. Sadeghi et al. https://doi.org/10.1016/j.pce.2025.104016
626. Numerische Modellierung der Grundwasserströmung im Einzugsgebiet der Gallusquelle unter Festlegung eines Drainagesystems U. Mohrlok https://doi.org/10.1007/s00767-013-0249-x
627. An integrated map correlation method and multiple-source sites drainage-area ratio method for estimating streamflows at ungauged catchments: A case study of the Western Black Sea Region, Turkey K. Ergen & E. Kentel https://doi.org/10.1016/j.jenvman.2015.10.036
628. Investigating the possibilities of temperature concentration distribution in Zayanderood based on climate change M. Nazeri Tahroudi et al. https://doi.org/10.1016/j.dynatmoce.2024.101454
629. A comparative evaluation of four evapotranspiration models based on Eddy Covariance measurement over a grass covered surface in Ile-Ife, Southwestern Nigeria O. Babatunde et al. https://doi.org/10.1007/s40808-017-0389-6
630. BaHSYM: Parsimonious Bayesian hierarchical model to predict river sediment yield O. Zoboli et al. https://doi.org/10.1016/j.envsoft.2020.104738
631. Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications L. Wang et al. https://doi.org/10.5194/hess-19-4001-2015
632. Impact of particle transfer on flow properties of crushed mudstones D. Ma et al. https://doi.org/10.1007/s12665-016-5382-2
633. Quantifying leachate discharge and assessing environmental risks of gully-type coal-based solid waste dumps in small watersheds: A refined hydrological modeling approach for mitigation strategies X. Wang et al. https://doi.org/10.1016/j.watres.2025.123655
634. Simulation of national-scale groundwater dynamics in geologically complex aquifer systems: an example from Great Britain M. Bianchi et al. https://doi.org/10.1080/02626667.2024.2320847
635. Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest Z. He & J. Pomeroy https://doi.org/10.1016/j.jhydrol.2023.129897
636. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi https://doi.org/10.1016/j.jhydrol.2024.131835
637. A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States A. Erickson & N. Strigul https://doi.org/10.3390/f10020180
638. Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data A. Le & N. Pricope https://doi.org/10.3390/w9020114
639. Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting M. AL-Musaylh et al. https://doi.org/10.1016/j.apenergy.2018.02.140
640. The potential impact of climate variability on siltation of Andean reservoirs M. Rosas et al. https://doi.org/10.1016/j.jhydrol.2019.124396
641. Spring Precipitation as a Predictor for Peak Standing Crop of Mixed-Grass Prairie L. Wiles et al. https://doi.org/10.2111/REM-D-09-00024.1
642. Enhancing a Multi-Step Discharge Prediction with Deep Learning and a Response Time Parameter W. Thaisiam et al. https://doi.org/10.3390/w14182898
643. Solar radiation estimation methods using ANN and empirical models V. Antonopoulos et al. https://doi.org/10.1016/j.compag.2019.03.022
644. Distributive rainfall–runoff modelling to understand runoff-to-baseflow proportioning and its impact on the determination of reserve requirements of the Verlorenvlei estuarine lake, west coast, South Africa A. Watson et al. https://doi.org/10.5194/hess-23-2679-2019
645. Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization G. Zavala et al. https://doi.org/10.3390/app10010251
646. The implementation of biofiltration systems, rainwater tanks and urban irrigation in a single-layer urban canopy model M. Demuzere et al. https://doi.org/10.1016/j.uclim.2014.10.012
647. Continuous Simulation of Highly Urbanized Watershed to Quantify Nutrients’ Loadings N. Mahdi & K. Pagilla https://doi.org/10.3390/w13202910
648. Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach C. Fan et al. https://doi.org/10.1029/2020WR028831
649. The Impact of Climate Change on Tomato Water Footprint under Irrigation with Saline Water in a Kairouan Irrigated Area (Tunisia Center) K. Khaskhoussy et al. https://doi.org/10.3390/horticulturae10121267
650. Evaluating the effects of forest tree species on rill detachment capacity in a semi-arid environment M. Parhizkar et al. https://doi.org/10.1016/j.ecoleng.2021.106158
651. Modeling Hydrological Response to Climate Change in a Data-Scarce Glacierized High Mountain Astore Basin Using a Fully Distributed TOPKAPI Model I. Atif et al. https://doi.org/10.3390/cli7110127
652. Do Nash values have value? Discussion and alternate proposals R. Criss & W. Winston https://doi.org/10.1002/hyp.7072
653. Comparison between mechanical and hydrological reinforcement effects of cultivated plants on shallow slope stability M. Bordoni et al. https://doi.org/10.1016/j.scitotenv.2023.168999
654. A remote sensing method for estimating regional reservoir area and evaporative loss H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2017.10.007
655. Non‐point source pollution modelling using Soil and Water Assessment Tool and its parameter sensitivity analysis in Xin'anjiang catchment, China X. Zhai et al. https://doi.org/10.1002/hyp.9688
656. Loess Plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration F. Jiang et al. https://doi.org/10.1016/j.agrformet.2021.108669
657. Assessment of Climate Change Impacts on the Water Resources of Megech River Catchment, Abbay Basin, Ethiopia E. Abebe & A. Kebede https://doi.org/10.4236/ojmh.2017.72008
658. Towards the development of a modeling framework to track nitrogen export from lowland artificial watersheds (polders) J. Huang et al. https://doi.org/10.1016/j.watres.2018.01.011
659. Quantification and simulation of nutrient sources at watershed scale in Mississippi A. Risal & P. Parajuli https://doi.org/10.1016/j.scitotenv.2019.03.233
660. Surveyed and modelled one‐year morphodynamics in the braided lower Tana River E. Lotsari et al. https://doi.org/10.1002/hyp.9750
661. A dual-continuum model (TOUGH2) for characterizing flow and discharge in a mechanically disrupted sandstone overburden D. Bedoya-Gonzalez et al. https://doi.org/10.1007/s10040-022-02507-3
662. Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments N. Griessinger et al. https://doi.org/10.5194/hess-20-3895-2016
663. Evaluation of a Stepwise, Multiobjective, Multivariable Parameter Optimization Method for the APEX Model G. Senaviratne et al. https://doi.org/10.2134/jeq2013.12.0509
664. Simulation of water resource allocation for sustainable urban development: An integrated optimization approach F. Wei et al. https://doi.org/10.1016/j.jclepro.2020.122537
665. A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. https://doi.org/10.1016/j.jhydrol.2022.128339
666. A new adaptation of linear reservoir models in parallel sets to assess actual hydrological events J. Mateo Lázaro et al. https://doi.org/10.1016/j.jhydrol.2015.03.009
667. The Modified SEBAL for Mapping Daily Spatial Evapotranspiration of South Korea Using Three Flux Towers and Terra MODIS Data Y. Lee & S. Kim https://doi.org/10.3390/rs8120983
668. Hydrologic Modeling as a Service (HMaaS): A New Approach to Address Hydroinformatic Challenges in Developing Countries M. Souffront Alcantara et al. https://doi.org/10.3389/fenvs.2019.00158
669. Climate-informed irrigation and nitrogen strategies for Boro rice sustainability: DSSAT-CERES modeling under CMIP6 projections M. Islam et al. https://doi.org/10.1016/j.agwat.2026.110642
670. Using shallow groundwater modeling to frame the restoration of a wet prairie in the Oak Openings Region, Ohio, USA: GSSHA model implementation D. Wijayarathne & E. Gomezdelcampo https://doi.org/10.1007/s12665-019-8208-1
671. Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin J. Ougahi & S. Mahmood https://doi.org/10.2166/wcc.2022.410
672. Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model Y. Zhou et al. https://doi.org/10.3390/su13063588
673. An attention-based LSTM model for long-term runoff forecasting and factor recognition D. Han et al. https://doi.org/10.1088/1748-9326/acaedd
674. Impact of Land Use Change on Hydrologic Processes in a Large Plain Irrigation District X. Luan et al. https://doi.org/10.1007/s11269-018-1986-5
675. Modeling of conservation practices’ effects on nutrients and sediment load reduction from commercial cotton fields using APEX A. Thapa et al. https://doi.org/10.1016/j.jafr.2025.101889
676. Efficiency of Artificial Groundwater Recharge, Quantification Through Conceptual Modelling M. Zammouri & N. Brini https://doi.org/10.1007/s11269-020-02617-1
677. Assessment of basin-wise future agricultural drought status across India under changing climate M. Suman & R. Maity https://doi.org/10.2166/wcc.2021.369
678. Estimating erosion rates using 137Cs measurements and WATEM/SEDEM in a Mediterranean cultivated field L. Quijano et al. https://doi.org/10.1016/j.catena.2015.11.009
679. Transient recovery dynamics of a predator–prey system under press and pulse disturbances C. Karakoç et al. https://doi.org/10.1186/s12898-017-0123-2
680. Simulating Groundwater‐Streamflow Connections in the Upper Colorado River Basin H. Tran et al. https://doi.org/10.1111/gwat.13000
681. Restoration of ponds in rural landscapes: Modelling the effect on nitrate contamination of surface water (the Seine River Basin, France) P. Passy et al. https://doi.org/10.1016/j.scitotenv.2012.04.035
682. Rainfall erosivity estimation models for the Tibetan Plateau S. Lu et al. https://doi.org/10.1016/j.catena.2023.107186
683. An Improved Monthly Water Balance GR2M Model with a Seasonally Variable Parameter M. Fathi et al. https://doi.org/10.1016/j.jhydrol.2023.129127
684. Urbanization Development under Climate Change: Hydrological Responses in a Peri‐Urban Mediterranean Catchment Z. Kalantari et al. https://doi.org/10.1002/ldr.2747
685. Impact of Soil and Water Conservation Interventions on Watershed Runoff Response in a Tropical Humid Highland of Ethiopia D. Sultan et al. https://doi.org/10.1007/s00267-018-1005-x
686. Soil moisture estimation based on MODIS NDVI and LST productions in areas with no data M. Eghtedarnezhad et al. https://doi.org/10.61186/jeer.14.2.9
687. Impact of Hydrometeorological and Climatic Pressures on Greek Rivers: Implementation into the Water Framework Directive 2000/60 A. Mentzafou et al. https://doi.org/10.3390/hydrology13090232
688. EVALUATING THE PERFORMANCE OF EFAS HYDROLOGICAL PREDICTIONS IN LATVIAN RIVER BASINS: A COMPARISON WITH OBSERVATIONAL DATA A. Skromulis et al. https://doi.org/10.17770/etr2025vol1.8705
689. Hydrological response of a High-Arctic catchment to changing climate over the past 35 years: a case study of Bayelva watershed, Svalbard A. Nowak & A. Hodson https://doi.org/10.3402/polar.v32i0.19691
690. Source contribution analysis of nutrient pollution in a P-rich watershed: Implications for integrated water quality management J. Han et al. https://doi.org/10.1016/j.envpol.2021.116885
691. Assessing spatial predictive models in the environmental sciences: Accuracy measures, data variation and variance explained J. Li https://doi.org/10.1016/j.envsoft.2016.02.004
692. Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System M. Dehghani et al. https://doi.org/10.3390/en12020289
693. Future predictions of precipitation and temperature in Iraq using the statistical downscaling model M. Al-Mukhtar & M. Qasim https://doi.org/10.1007/s12517-018-4187-x
694. Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America E. Maurer et al. https://doi.org/10.5194/hess-13-183-2009
695. Baseline elevation and siltation of Ribb reservoir in the Upper Abbay Basin, Ethiopia: a good threshold for change detection and sustainability A. Ashenef et al. https://doi.org/10.1007/s43217-024-00172-y
696. Evaluating river basin plans’ effects on water stress in Thailand’s economic development area S. Chuanpongpanich et al. https://doi.org/10.1080/07900627.2024.2401126
697. Evaluation and prediction of meteorological drought conditions using time-series and genetic programming models E. Omidvar & Z. Tahroodi https://doi.org/10.1007/s12040-019-1103-z
698. Performances of Rainfall-Runoff Models Calibrated over Single and Continuous Storm Flow Events S. Tan et al. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(597)
699. Probabilistic programming: A review for environmental modellers C. Krapu & M. Borsuk https://doi.org/10.1016/j.envsoft.2019.01.014
700. Jordan recurrent neural network versus IHACRES in modelling daily streamflows E. Carcano et al. https://doi.org/10.1016/j.jhydrol.2008.08.026
701. Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems D. Zema et al. https://doi.org/10.1016/j.catena.2019.104415
702. Assessment of TOPKAPI-X Applicability for Flood Events Simulation in Two Small Catchments in Saxony F. Janabi et al. https://doi.org/10.3390/hydrology8030109
703. Distributed Energy Balance Flux Modelling of Mass Balances in the Artesonraju Glacier and Discharge in the Basin of Artesoncocha, Cordillera Blanca, Peru M. Lozano Gacha & M. Koch https://doi.org/10.3390/cli9090143
704. Calibration Parameter Selection and Watershed Hydrology Model Evaluation in Time and Frequency Domains K. Kumarasamy & P. Belmont https://doi.org/10.3390/w10060710
705. Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate M. Gizaw & T. Gan https://doi.org/10.1016/j.jhydrol.2016.04.041
706. Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India M. Kumar et al. https://doi.org/10.3390/su12197877
707. Insights to the water balance of a Boreal watershed using a SWAT model K. Islam et al. https://doi.org/10.1088/2515-7620/ad495c
708. Application of Remote Sensing and GIS for Modeling and Assessment of Land Use/Cover Change in Amman/Jordan J. Al-Bakri et al. https://doi.org/10.4236/jgis.2013.55048
709. Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia R. Nijbroek et al. https://doi.org/10.3390/su10051610
710. Investigating the need for complex vs. simple scenarios to improve predictions of aquatic ecosystem exposure with the SoilPlus model D. Ghirardello et al. https://doi.org/10.1016/j.envpol.2013.10.002
711. Crash tests for forward-looking flood control in the city of Zürich (Switzerland) M. Zappa et al. https://doi.org/10.5194/piahs-370-235-2015
712. An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia M. Samsudin et al. https://doi.org/10.1007/s00521-024-09699-7
713. A modified F-test for evaluating model performance by including both experimental and simulation uncertainties N. Sima et al. https://doi.org/10.1016/j.envsoft.2018.03.011
714. Evaluation of targeting methods for implementation of best management practices in the Saginaw River Watershed S. Giri et al. https://doi.org/10.1016/j.jenvman.2012.02.033
715. Assessing Surface Water Quality and Its Relation with Urban Land Cover Changes in the Lake Calumet Area, Greater Chicago C. Wilson & Q. Weng https://doi.org/10.1007/s00267-010-9482-6
716. Optimization of process parameters for ethanol production from sugar cane molasses by Zymomonas mobilis using response surface methodology and genetic algorithm B. Maiti et al. https://doi.org/10.1007/s00253-011-3158-x
717. Impacts of construction and operation of Lake Poyang Hydraulic Project on flood dynamics in Lake Poyang and its inlet area J. Tingli et al. https://doi.org/10.18307/2024.0142
718. Field calibrations of a Diviner 2000 capacitive soil water content probe on a shallow groundwater site and the application in a weighable groundwater lysimeter O. Dietrich & J. Steidl https://doi.org/10.1016/j.agwat.2021.106874
719. Applying AQUATOX to Simulate the Response of Eutrophic State to Nutrient Input in Yincun Port River X. Wei et al. https://doi.org/10.4028/www.scientific.net/AMM.737.481
720. Comparative Evaluation of NARX Model Training Algorithms for PM₁₀ Forecasting in Amasya, TÜRKİYE G. Özdoğan Sarıkoç https://doi.org/10.65520/erciyesfen.1911367
721. Exploration on hydrological model calibration by considering the hydro-meteorological variability B. Zhao et al. https://doi.org/10.2166/nh.2019.047
722. Assessment of nickel leaching phenomena from landfill waste mixed paving block for eco-friendly field application M. Haque https://doi.org/10.1016/j.jclepro.2016.08.028
723. Assessment of discharge and sediment transport from different forest cover types in lower Himalaya using Soil and Water Assessment Tool (SWAT) V. J et al. https://doi.org/10.5897/IJWREE2013.0448
724. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components B. Kamali et al. https://doi.org/10.3390/w9090709
725. Computation of hourly sediment discharges and annual sediment yields by means of two soil erosion models in a mountainous basin K. Kaffas & V. Hrissanthou https://doi.org/10.1080/15715124.2017.1402777
726. Large-Scale Hydrological Models and Transboundary River Basins C. Skoulikaris https://doi.org/10.3390/w16060878
727. OpenBench: a land model evaluation system Z. Wei et al. https://doi.org/10.5194/gmd-18-6517-2025
728. Hydrodynamic Modeling of a Tropical Tidal River Using the Dynamic Estuary Model (DYNHYD5): A Case Study in Sibu Laut River, Sarawak, Malaysia C. Soo et al. https://doi.org/10.1155/2018/8726752
729. Modeling bioretention hydrology: Quantifying the performance of DRAINMOD-Urban and the SWMM LID module W. Lisenbee et al. https://doi.org/10.1016/j.jhydrol.2022.128179
730. Modelling aquatic vegetation and dissolved oxygen after a flood event in the Bow River, Alberta, Canada K. Robinson et al. https://doi.org/10.1139/L08-126
731. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling W. Wu et al. https://doi.org/10.1016/j.envsoft.2013.12.016
732. A comprehensive approach to conducting multi-criteria sensitivity analysis of computationally expensive multidimensional hydrodynamic-ecology models (MHEs) S. Gebremariam https://doi.org/10.1080/02626667.2025.2497883
733. Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics J. Dempewolf et al. https://doi.org/10.3390/rs6109653
734. Modeling the Nexus of Climate Change and Deforestation: Implications for the Blue Water Resources of the Jari River, Amazonia P. Rufino et al. https://doi.org/10.3390/w17050660
735. A Statistical Comparison of Mathematical Models for Heavy Metal Leaching Phenomena from Solidified Landfill Waste Mortar M. Haque https://doi.org/10.1515/cppm-2015-0046
736. An adaptive design and interpolation technique for extracting highly nonlinear response surfaces from deterministic models D. Shahsavani & A. Grimvall https://doi.org/10.1016/j.ress.2008.10.013
737. Simulating short-term dispersion of Cs-137 in the East Vietnam Sea: atmospheric fallout from a hypothetical nuclear accident as a source term Q. Tran et al. https://doi.org/10.1007/s11069-025-07421-x
738. Using the Agricultural Policy/Environmental eXtender to develop and validate physically based indices for the delineation of critical management areas A. Mudgal et al. https://doi.org/10.2489/jswc.67.4.284
739. Calibration of SWAT2009 Using Crop Biomass, Evapotranspiration, and Deep Recharge: Calera Watershed in Zacatecas, Mexico Case Study J. R. Ávila-Carrasco et al. https://doi.org/10.4236/jwarp.2012.47051
740. Multi-metric calibration of hydrological model to capture overall flow regimes Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2016.05.053
741. The Importance of High Resolution Digital Elevation Models for Improved Hydrological Simulations of a Mediterranean Forested Catchment J. Rocha et al. https://doi.org/10.3390/rs12203287
742. Integrated Surface–Groundwater Model for Storm-Water Harvesting Using Basic Mass Balance Principles I. Gogo-Abite et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000518
743. Enhancing water management in Northern European lowland chalk streams: A parsimonious, high-resolution hydrological model using groundwater level as a proxy for baseflow T. Homan et al. https://doi.org/10.1016/j.ejrh.2024.102022
744. Uncertainty in flow and sediment projections due to future climate scenarios for the 3S Rivers in the Mekong Basin B. Shrestha et al. https://doi.org/10.1016/j.jhydrol.2016.07.019
745. Comparison of performance of tile drainage routines in SWAT 2009 and 2012 in an extensively tile-drained watershed in the Midwest T. Guo et al. https://doi.org/10.5194/hess-22-89-2018
746. Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System S. Samadi et al. https://doi.org/10.1111/1752-1688.12596
747. Analysis of the scale-dependence of the hydraulic conductivity in complex fractured media A. Piña et al. https://doi.org/10.1016/j.jhydrol.2018.12.006
748. Generic error model for calibration and uncertainty estimation of hydrological models J. Götzinger & A. Bárdossy https://doi.org/10.1029/2007WR006691
749. Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs M. Li et al. https://doi.org/10.1016/j.jhydrol.2017.10.057
750. Multi-model assessment of glacio-hydrological changes in central Karakoram, Pakistan J. Hassan et al. https://doi.org/10.1007/s11629-021-6748-9
751. Modelling indicators of water security, water pollution and aquatic biodiversity in Europe E. Dumont et al. https://doi.org/10.1080/02626667.2012.715747
752. V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction Z. Cao et al. https://doi.org/10.3390/s21093215
753. A long-term lysimeter experiment to investigate the environmental dispersion of the herbicide chloridazon and its metabolites—comparison of lysimeter types A. Schuhmann et al. https://doi.org/10.1007/s11368-015-1311-3
754. Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile N. Flores et al. https://doi.org/10.3390/w13223191
755. Performance and Limitations of Machine Learning Models for Groundwater Level Prediction Using Hydro-Climatic Variables N. Zeydalinejad et al. https://doi.org/10.1007/s41748-026-01281-6
756. Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions T. Baddoo et al. https://doi.org/10.3390/ijerph17114132
757. Hydrological simulation of the Betwa River basin (India) using the SWAT model S. Suryavanshi et al. https://doi.org/10.1080/02626667.2016.1271420
758. Comment on “Water and nonpoint source pollution estimation in the watershed with limited data availability based on hydrological simulation and regression model H. Wang & Z. Wu & C. Hu & X. Du. Environ Sci Pollut Res, 22(18), 14095-14103. DOI: 10.1007/s11356-015-4450-6” S. Khani & N. Shiraz https://doi.org/10.1007/s11356-016-6781-3
759. Examining the ecosystem service of nutrient removal in a coastal watershed C. Berg et al. https://doi.org/10.1016/j.ecoser.2016.06.007
760. Evaluating performance of macroscopic water uptake models at productive growth stages of durum wheat under saline conditions V. Jalali et al. https://doi.org/10.1016/j.agwat.2016.10.015
761. Effectiveness of Nature-Based Solutions in Mitigating Flood Hazard in a Mediterranean Peri-Urban Catchment C. S.Ferreira et al. https://doi.org/10.3390/w12102893
762. A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies S. Budhathoki et al. https://doi.org/10.1080/02626667.2020.1715982
763. Comparing conservative and nonconservative tracers in karst and using them to estimate flow path geometry A. Luhmann et al. https://doi.org/10.1016/j.jhydrol.2012.04.044
764. Streamflow modeling in a fluctuant climate using SWAT: Yass River catchment in south eastern Australia P. Saha et al. https://doi.org/10.1007/s12665-013-2926-6
765. An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool T. Nguyen et al. https://doi.org/10.1016/j.envsoft.2022.105497
766. Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data V. Gholami et al. https://doi.org/10.1016/j.catena.2017.12.027
767. Effects of model complexity on karst catchment runoff modeling for flood warning systems P. Knöll et al. https://doi.org/10.1016/j.hydroa.2024.100194
768. Using water age to study the biogeochemistry of nutrients in a large-river estuary and the adjacent shelf area L. Gao & H. Zong https://doi.org/10.1016/j.jmarsys.2020.103469
769. Selection of hydrologic modeling approaches for climate change assessment: A comparison of model scale and structures C. Surfleet et al. https://doi.org/10.1016/j.jhydrol.2012.07.012
770. A 2D Real-Time Flood Forecast Framework Based on a Hybrid Historical and Synthetic Runoff Database G. Crotti et al. https://doi.org/10.3390/w12010114
771. Advancing understanding of in-river phosphorus dynamics using an advection–dispersion model (ADModel-P) E. Timis et al. https://doi.org/10.1016/j.jhydrol.2022.128173
772. Improving process representation in conceptual hydrological model calibration using climate simulations M. Minville et al. https://doi.org/10.1002/2013WR013857
773. Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high‐resolution regional climate models Z. Yin et al. https://doi.org/10.1002/joc.6435
774. Information content of stream level class data for hydrological model calibration H. van Meerveld et al. https://doi.org/10.5194/hess-21-4895-2017
775. Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed T. Mengistu et al. https://doi.org/10.3390/w14132068
776. Assessing the impacts of diversified crop rotation systems on yields and nitrous oxide emissions in Canada using the DNDC model R. Jiang et al. https://doi.org/10.1016/j.scitotenv.2020.143433
777. A modeling approach for evaluating the impacts of Land Use/Land Cover change for Ziway Lake Watershed hydrology in the Ethiopian Rift A. Mechal et al. https://doi.org/10.1007/s40808-022-01472-w
778. Projected Climate Change Impacts on the Number of Dry and Very Heavy Precipitation Days by Century’s End: A Case Study of Iran’s Metropolises R. Afsari et al. https://doi.org/10.3390/w16162226
779. Modeling the Impacts of Climate Change on Watershed Hydrology Using Climate and Hydrological Models: The Case of the Ziway Lake Watershed, Ethiopian Rift A. Mechal & A. Bayisa https://doi.org/10.1007/s10666-024-10010-0
780. Long-Term Modelling of an Agricultural and Urban River Catchment with SWMM Upgraded by the Evapotranspiration Model UrbanEVA F. Kachholz & J. Tränckner https://doi.org/10.3390/w12113089
781. Validation and downscaling of Advanced Scatterometer (ASCAT) soil moisture using ground measurements in the Western Cape, South Africa J. Moller et al. https://doi.org/10.1080/02571862.2017.1318962
782. Evaluating the interactions between surface water and groundwater in the arid mid-eastern Yanqi Basin, northwestern China M. Wu et al. https://doi.org/10.1080/02626667.2018.1500744
783. Curonian Lagoon drainage basin modelling and assessment of climate change impact N. Čerkasova et al. https://doi.org/10.1016/j.oceano.2016.01.003
784. Improving soil property maps for precision agriculture in the presence of outliers using covariates M. Pusch et al. https://doi.org/10.1007/s11119-022-09898-z
785. Clay resource estimation using a geostatistical approach in Tam Binh district, Vinh Long province, Viet Nam V. Minh et al. https://doi.org/10.1080/24749508.2024.2395210
786. A novel scaling factor correction for GRACE terrestrial water storage anomalies in the Yellow River Basin H. Yu et al. https://doi.org/10.1016/j.ejrh.2025.102466
787. A Method to Improve the Flood Maps Forecasted by On-Line Use of 1D Model P. Filianoti et al. https://doi.org/10.3390/w12061525
788. A comprehensive approach to building a continuous hydrologic model with Soil Moisture Accounting using Earth Observation data V. Thomopoulou et al. https://doi.org/10.2166/nh.2024.190
789. A spatially distributed model for the assessment of land use impacts on stream temperature in small urban watersheds N. Sun et al. https://doi.org/10.1002/hyp.10363
790. Implementing contour bank farming practices into the J2000 model to improve hydrological and erosion modelling in semi-arid Western Cape Province of South Africa T. Steudel et al. https://doi.org/10.2166/nh.2013.164
791. Evaluation of time stability indices for soil water storage upscaling W. Hu et al. https://doi.org/10.1016/j.jhydrol.2012.09.050
792. Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain J. Pérez-Sánchez et al. https://doi.org/10.3390/su11102872
793. Particle Swarm Optimized–Support Vector Regression Hybrid Model for Daily Horizon Electricity Demand Forecasting Using Climate Dataset S. Mohanad et al. https://doi.org/10.1051/e3sconf/20186408001
794. Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets M. Dembélé et al. https://doi.org/10.1029/2019WR026085
795. Presenting a New Approach to Increase the Efficiency of the Sediment Rating Curve Model in Estimating Suspended Sediment Load in Watersheds (Case Study: Mahabad-Chai River, Lake Urmia Basin, West Azarbayejan Province, Iran) M. Tabatabaei et al. https://doi.org/10.29252/jwmr.10.19.181
796. Operational framework to predict field level crop biomass using remote sensing and data driven models H. Servia et al. https://doi.org/10.1016/j.jag.2022.102725
797. Long-term daily stream temperature record for Scotland reveals spatio-temporal patterns in warming of rivers in the past and further warming in the future E. Loerke et al. https://doi.org/10.1016/j.scitotenv.2023.164194
798. Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Laboratory experiments and model application S. Weinrich et al. https://doi.org/10.1016/j.biortech.2021.125104
799. An improved process-based evapotranspiration/heat fluxes remote sensing algorithm based on the Bayesian and Sobol’ uncertainty analysis framework using eddy covariance observations of Tibetan grasslands J. Feng et al. https://doi.org/10.1016/j.jhydrol.2022.128384
800. Comparison of potential evapotranspiration methods in Ethiopia A. Mekoya & M. Workneh https://doi.org/10.1007/s44292-024-00003-9
801. Vulnerability of ski tourism towards internal climate variability and climate change in the Swiss Alps F. Willibald et al. https://doi.org/10.1016/j.scitotenv.2021.147054
802. Limiting global warming to 2 °C benefits building climate resilience in rice-wheat systems in India through crop calendar management X. Wang et al. https://doi.org/10.1016/j.agsy.2023.103806
803. Comparing Two Hydro-Economic Approaches for Multi-Objective Agricultural Water Resources Planning A. Alamanos et al. https://doi.org/10.1007/s11269-020-02690-6
804. Toprağın zamana bağlı olmayan bir boyutlu ısı iletkenliğinin incelenmesi İ. EKBERLİ et al. https://doi.org/10.7161/omuanajas.828364
805. Trend analysis and outlier distribution of CO2 and CH4: A case study at a rural site in northern Spain I. Pérez et al. https://doi.org/10.1016/j.scitotenv.2022.153129
806. Development and testing of a modified SWAT model based on slope condition and precipitation intensity S. Pang et al. https://doi.org/10.1016/j.jhydrol.2020.125098
807. Evaluation of AccuPAR LP 80 in Estimating Leaf Area Index of Soybeans Canopy in Ile-Ife, Nigeria O. Adeboye et al. https://doi.org/10.1007/s40003-018-0371-1
808. River catchment rainfall series analysis using additive Holt–Winters method Y. Puah et al. https://doi.org/10.1007/s12040-016-0661-6
809. Multivariate bias corrections of mechanistic water quality model predictions D. Libera & A. Sankarasubramanian https://doi.org/10.1016/j.jhydrol.2018.07.043
810. A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes M. Tan et al. https://doi.org/10.1016/j.advwatres.2020.103662
811. Performance of a pesticide fate model for predicting multi-year surface runoff contamination in a Mediterranean vineyard G. Métayer et al. https://doi.org/10.1016/j.scitotenv.2023.167357
812. Modelling of Escherichia coli removal by a low-cost combined drinking water treatment system S. Siwila & I. Brink https://doi.org/10.2166/ws.2020.084
813. Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment H. Ballinas-González et al. https://doi.org/10.3390/hydrology7040073
814. Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems X. Wen et al. https://doi.org/10.1016/j.jhydrol.2018.12.060
815. Uncertainty assessment in river flow projections for Ethiopia’s Upper Awash Basin using multiple GCMs and hydrological models W. Chan et al. https://doi.org/10.1080/02626667.2020.1767782
816. Development of probabilistic inundation mapping for dam failure induced floods C. Tsai et al. https://doi.org/10.1007/s00477-018-1636-8
817. Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey M. Ghorbani et al. https://doi.org/10.1007/s00477-017-1474-0
818. Modeling projected impacts of climate and land use/land cover changes on streamflow in Gelana Catchment, Southern Ethiopia A. Osore Aga & M. Wldmchel Shomre https://doi.org/10.1016/j.wsee.2024.09.003
819. Impacts of climate change on stream flow and hydro power generation in the Alpine region T. Wagner et al. https://doi.org/10.1007/s12665-016-6318-6
820. Rock slope damage level prediction by using multivariate adaptive regression splines (MARS) T. Erdik & A. Pektas https://doi.org/10.1007/s00521-017-3186-2
821. Water availability assessment in cultivation and non-cultivation seasons to identify water security in a tropical catchment: Gin catchment, Sri Lanka T. Wickramaarachchi https://doi.org/10.1007/s10333-023-00954-6
822. The Watershed Flow and Allocation Model: An NHDPlus‐Based Watershed Modeling Approach for Multiple Scales and Conditions M. Eddy et al. https://doi.org/10.1111/1752-1688.12496
823. Best Management Practices Affect Water Quality in Coastal Watersheds S. Bhattarai & P. Parajuli https://doi.org/10.3390/su15054045
824. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. https://doi.org/10.5194/hess-25-4373-2021
825. Testing the water balance model criteria using TDR measurements, micrometeorological data and satellite-based information S. Consoli et al. https://doi.org/10.1016/j.agwat.2015.11.003
826. Application of machine learning techniques with empirical wavelet transform and variational mode decomposition for sediment concentration estimation H. Citakoglu et al. https://doi.org/10.1016/j.pce.2025.104136
827. A process-based typology of hydrological drought A. Van Loon & H. Van Lanen https://doi.org/10.5194/hess-16-1915-2012
828. Attribution of the changes in annual streamflow in the Yangtze River Basin over the past 146 years D. Zhang et al. https://doi.org/10.1007/s00704-014-1121-3
829. Enhancing hydrological modeling of ungauged watersheds through machine learning and physical similarity-based regionalization of calibration parameters A. Bawa et al. https://doi.org/10.1016/j.envsoft.2025.106335
830. Modeling hydromorphological processes in a mountainous basin using a composite mathematical model and ArcSWAT K. Kaffas et al. https://doi.org/10.1016/j.catena.2017.11.017
831. Public policies on water resource management and its impacts on the context of climatic changes and alterations in land use and land cover in small and protected rainforest river basins J. Dill et al. https://doi.org/10.1016/j.envsci.2022.08.021
832. Evaluation of FAO-56 crop coefficient procedures for deficit irrigation management of cotton in a humid climate A. Suleiman et al. https://doi.org/10.1016/j.agwat.2007.03.006
833. Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? J. Li & Q. Zhang https://doi.org/10.1371/journal.pone.0183250
834. Evaluating hydrological and soil erosion processes in different time scales and land uses in southern Brazilian paired watersheds T. Silva et al. https://doi.org/10.1080/02626667.2023.2217333
835. Water Harvesting and Reservoir Optimization in Selected Areas of South Sinjar Mountain, Iraq N. Al-Ansari et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000712
836. Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems E. Sadrossadat et al. https://doi.org/10.1016/j.conbuildmat.2016.07.008
837. Functional ecohydrological differences among native and exotic grassland covers in sub-urban landscapes of Chihuahua city, Mexico V. Reyes Gómez et al. https://doi.org/10.1016/j.landurbplan.2015.03.005
838. Variability of extreme precipitation over Texas and its relation with climatic cycles N. Bhatia et al. https://doi.org/10.1007/s00704-019-02840-w
839. Modeling the Responses of Water and Sediment Discharge to Climate Change in the Upper Yellow River Basin, China X. Yu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001590
840. Application of Meteorological and Hydrological Drought Indices to Establish Drought Classification Maps of the Ba River Basin in Vietnam D. Quang Tri et al. https://doi.org/10.3390/hydrology6020049
841. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data S. Das et al. https://doi.org/10.1038/s41597-024-03756-5
842. Buğday Bitkisinin Verim Parametreleri ile Bazı Toprak Özellikleri Arasındaki Pedotransfer Modellerin Uygulanabilirliği N. KARS & İ. EKBERLİ https://doi.org/10.19159/tutad.489733
843. Dynamic Time Warping for Quantitative Analysis of Tracer Study Time-Series Water Quality Data H. Woo et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001115
844. Influence of Climate Variability on Soybean Yield in MATOPIBA, Brazil L. Reis et al. https://doi.org/10.3390/atmos11101130
845. Could operational hydrological models be made compatible with satellite soil moisture observations? L. Zhuo & D. Han https://doi.org/10.1002/hyp.10804
846. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. https://doi.org/10.1016/j.jhydrol.2023.130350
847. Evaluating and Modelling Soil Detachment Capacity in Rills After Treatment With Biochar From Solid Waste of Olive Oil Mills M. Parhizkar et al. https://doi.org/10.1002/hyp.70067
848. Hydrological evaluation of hourly merged satellite–station precipitation product in the mountainous basin of China using a distributed hydrological model D. Zhu et al. https://doi.org/10.1002/met.1909
849. Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment B. Bhattarai et al. https://doi.org/10.3390/w12092339
850. A new diagram for performance evaluation of complex models A. Izzaddin et al. https://doi.org/10.1007/s00477-024-02678-3
851. Modeling the effect of uncertainties in rainfall characteristics on flash flood warning based on rainfall thresholds S. Wu et al. https://doi.org/10.1007/s11069-014-1390-2
852. Characterization of Droughts in Humid Subtropical Region, Upper Kafue River Basin (Southern Africa) M. Lweendo et al. https://doi.org/10.3390/w9040242
853. Effects of climate change on stream temperature, dissolved oxygen, and sediment concentration in the Sierra Nevada in California D. Ficklin et al. https://doi.org/10.1002/wrcr.20248
854. Large-Scale Hydrological Modeling for Calculating Water Stress Indices: Implications of Improved Spatiotemporal Resolution, Surface-Groundwater Differentiation, and Uncertainty Characterization L. Scherer et al. https://doi.org/10.1021/acs.est.5b00429
855. Measurement and Calculation of Sediment Transport on an Ephemeral Stream L. Avgeris et al. https://doi.org/10.3390/hydrology11070096
856. Estimation of Sediment Yield Change in a Loess Plateau Basin, China L. Zhang et al. https://doi.org/10.3390/w9090683
857. A unique vadose zone model for shallow aquifers: the Hetao irrigation district, China Z. Liu et al. https://doi.org/10.5194/hess-23-3097-2019
858. Unraveling the Hydrology of the Glacierized Kaidu Basin by Integrating Multisource Data in the Tianshan Mountains, Northwestern China Y. Shen et al. https://doi.org/10.1002/2017WR021806
859. Impact of different hydrological models on hydroelectric operation planning J. Páez Mendieta et al. https://doi.org/10.1016/j.renene.2024.120975
860. Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. https://doi.org/10.1016/j.jhydrol.2021.126394
861. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues https://doi.org/10.1016/j.jhydrol.2021.126674
862. Impacts of climate change on the hydrological regime of the Koshi river basin in the Himalayan region S. Nepal https://doi.org/10.1016/j.jher.2015.12.001
863. Seasonal Prediction of Winter Precipitation Anomalies over Central Southwest Asia: A Canonical Correlation Analysis Approach S. Rana et al. https://doi.org/10.1175/JCLI-D-17-0131.1
864. Multi-objective optimization for construction of prediction interval of hydrological models based on ensemble simulations L. Ye et al. https://doi.org/10.1016/j.jhydrol.2014.08.026
865. Future climate change projects positive impacts on sugarcane productivity in southern China H. Ruan et al. https://doi.org/10.1016/j.eja.2018.03.007
866. Parallelization of the flow-path network model using a particle-set strategy F. Zhang & Q. Zhou https://doi.org/10.1080/13658816.2019.1599126
867. Assessing the Impact of CFSR and Local Climate Datasets on Hydrological Modeling Performance in the Mountainous Black Sea Catchment G. Cuceloglu & I. Ozturk https://doi.org/10.3390/w11112277
868. TanDEM-X data for aboveground biomass retrieval in a tropical peat swamp forest M. Schlund et al. https://doi.org/10.1016/j.rse.2014.11.016
869. Modelling the effect of soil and water conservation on discharge and sediment yield in the upper Blue Nile basin, Ethiopia T. Lemann et al. https://doi.org/10.1016/j.apgeog.2016.06.008
870. River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia M. Mustafa et al. https://doi.org/10.1007/s11269-012-9992-5
871. Application of the Hargreaves method for calculating the potential evapotranspiration in the hydrological modelling M. Koprivšek et al. https://doi.org/10.15292/acta.hydro.2021.06
872. Groundwater Flow Model along a Vertical Profile of the Sardas Landfill in Sabiñánigo, Huesca, Spain J. Samper et al. https://doi.org/10.3390/w15193457
873. Hybrid Models Based on Singular Values and Autoregressive Methods for Multistep Ahead Forecasting of Traffic Accidents L. Barba & N. Rodríguez https://doi.org/10.1155/2016/2030647
874. Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model A. Rahmani-Rezaeieh et al. https://doi.org/10.1007/s00704-019-02982-x
875. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece P. Nastos et al. https://doi.org/10.1016/j.atmosres.2013.11.013
876. Estimating sediment yield at Kaduna watershed, Nigeria using soil and water assessment tool (SWAT) model J. Daramola et al. https://doi.org/10.1016/j.heliyon.2019.e02106
877. Predicting wood production and net ecosystem carbon exchange of Pinus radiata plantations in south-western Australia: Application of a process-based model G. Simioni et al. https://doi.org/10.1016/j.foreco.2007.09.084
878. Impact of Infiltration Process Modeling on Soil Water Content Simulations for Irrigation Management M. Feki et al. https://doi.org/10.3390/w10070850
879. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
880. River Water‐Quality Concentration and Flux Estimation Can be Improved by Accounting for Serial Correlation Through an Autoregressive Model Q. Zhang & R. Hirsch https://doi.org/10.1029/2019WR025338
881. Simulating wastewater treatment plants for heavy metals using machine learning models M. Kheimi et al. https://doi.org/10.1007/s12517-022-10736-9
882. Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products L. Ji et al. https://doi.org/10.1175/JHM-D-14-0230.1
883. Evaluation of Surface Runoff Prediction by AnnAGNPS Model in a Large Mediterranean Watershed Covered by Olive Groves D. Zema et al. https://doi.org/10.1002/ldr.2390
884. Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik https://doi.org/10.1007/s11356-024-33732-w
885. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. https://doi.org/10.1016/j.jhydrol.2024.130861
886. The contribution of red clover to nitrate loading in an agricultural watershed Y. Jiang et al. https://doi.org/10.3389/fenvs.2025.1471213
887. Review of studies on hydrological modelling in Malaysia J. Abdulkareem et al. https://doi.org/10.1007/s40808-018-0509-y
888. Stochastic (S[ARIMA]), shallow (NARnet, NAR-GMDH, OS-ELM), and deep learning (LSTM, Stacked-LSTM, CNN-GRU) models, application to river flow forecasting M. Kheimi et al. https://doi.org/10.1007/s11600-023-01157-7
889. Improving model prediction reliability through enhanced representation of wetland soil processes and constrained model auto calibration – A paired watershed study A. Sharifi et al. https://doi.org/10.1016/j.jhydrol.2016.08.022
890. Influence of Wind Speed on CO2 and CH4 Concentrations at a Rural Site I. Pérez et al. https://doi.org/10.3390/ijerph18168397
891. Satellite-Based Probabilistic Assessment of Soil Moisture Using C-Band Quad-Polarized RISAT1 Data M. Pal et al. https://doi.org/10.1109/TGRS.2016.2623378
892. Assessment of the Impact of Climate Change on Snow Distribution and River Flows in a Snow-Dominated Mountainous Watershed in the Western Hindukush–Himalaya, Afghanistan A. Azizi & Y. Asaoka https://doi.org/10.3390/hydrology7040074
893. A user-driven case-based reasoning tool for infilling missing values in daily mean river flow records L. Giustarini et al. https://doi.org/10.1016/j.envsoft.2016.04.013
894. Evaluation of an erosion-sediment transport model for a hillslope using laboratory flume data A. Arguelles et al. https://doi.org/10.1007/s40333-014-0066-9
895. Optimizing irrigation schedule in a large agricultural region under different hydrologic scenarios D. Guo et al. https://doi.org/10.1016/j.agwat.2020.106575
896. Land use disturbance indicators and water quality variability in the Biscayne Bay Watershed, Florida R. Carey et al. https://doi.org/10.1016/j.ecolind.2010.12.009
897. Variability of Bed Load Transport During Six Summers of Continuous Measurements in Two Austrian Mountain Streams (Fischbach and Ruetz) D. Rickenmann https://doi.org/10.1002/2017WR021376
898. Evaluation of observed and satellite-based climate products for hydrological simulation in data-scarce Baro -Akob River Basin, Ethiopia A. Mengistu et al. https://doi.org/10.1016/j.ecohyd.2021.11.006
899. Building and evaluation of a dynamic model for assessing impact of smallholder endowments on food security in agricultural systems in highland areas of central America (SASHACA) O. Marín-González et al. https://doi.org/10.1016/j.agsy.2018.02.005
900. One-day offset in daily hydrologic modeling: An exploration of the issue in automatic model calibration M. Asadzadeh et al. https://doi.org/10.1016/j.jhydrol.2015.12.056
901. A dynamic soft-constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage M. Uz et al. https://doi.org/10.1016/j.jhydrol.2026.135015
902. Forest-fire aerosol–weather feedbacks over western North America using a high-resolution, online coupled air-quality model P. Makar et al. https://doi.org/10.5194/acp-21-10557-2021
903. Potential for small hydropower development in the Tano River Basin of Ghana using SWAT and RETScreen G. Addai et al. https://doi.org/10.1016/j.esd.2025.101720
904. Modeling pesticide diuron loading from the San Joaquin watershed into the Sacramento-San Joaquin Delta using SWAT H. Chen et al. https://doi.org/10.1016/j.watres.2017.05.032
905. Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa P. Upadhyay et al. https://doi.org/10.1080/10106049.2019.1573852
906. Integrated assessment of extreme climate and landuse change impact on sediment yield in a mountainous transboundary watershed of India and Pakistan M. Babur et al. https://doi.org/10.1007/s11629-019-5547-z
907. Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods J. Wu et al. https://doi.org/10.1016/j.envres.2023.117272
908. Regional-scale assessment of groundwater recharge and the water balance for Austria H. Zeitfogel et al. https://doi.org/10.1016/j.ejrh.2025.102297
909. Application of Artificial Neural Networks in Forecasting a Standardized Precipitation Evapotranspiration Index for the Upper Blue Nile Basin G. Mulualem & Y. Liou https://doi.org/10.3390/w12030643
910. Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit M. Miani et al. https://doi.org/10.3390/su13179681
911. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling H. Gupta et al. https://doi.org/10.1016/j.jhydrol.2009.08.003
912. Simulation of long-term spring wheat yields, soil organic C, N and water dynamics using DSSAT-CSM in a semi-arid region of the Canadian prairies Z. Li et al. https://doi.org/10.1007/s10705-015-9688-3
913. Bayesian Modeling of the Effects of Extreme Flooding and the Grazer Community on Algal Biomass Dynamics in a Monsoonal Taiwan Stream M. Chiu et al. https://doi.org/10.1007/s00248-016-0791-z
914. Physically-based distributed modelling of the hydrology and soil erosion under changes in landuse and climate of a humid tropical river basin S. Sreedevi et al. https://doi.org/10.1016/j.catena.2022.106427
915. Development of the DNDC model to improve soil hydrology and incorporate mechanistic tile drainage: A comparative analysis with RZWQM2 W. Smith et al. https://doi.org/10.1016/j.envsoft.2019.104577
916. Modelling impacts of climate change and anthropogenic activities on inflows and sediment loads of wetlands: case study of the Anzali wetland M. Mahdian et al. https://doi.org/10.1038/s41598-023-32343-8
917. Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model H. Bhuiyan et al. https://doi.org/10.3390/hydrology4010009
918. Can a simple water quality model effectively estimate runoff-driven nutrient loads to estuarine systems? A national-scale comparison of STEPLgrid and SPARROW L. Montefiore & N. Nelson https://doi.org/10.1016/j.envsoft.2022.105344
919. Impacts of Impervious Cover and Other Factors on Storm-Water Quality in Austin, Tex. R. Glick https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(316)
920. Hydrological responses to climate shifts for a minimally disturbed mountainous watershed in northwestern China J. Joo et al. https://doi.org/10.1080/02626667.2017.1316851
921. Impact of Artificial Reservoir Size and Land Use/Land Cover Patterns on Probable Maximum Precipitation and Flood: Case of Folsom Dam on the American River W. Yigzaw et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000722
922. Modeling Sediment Yield with Current and Projected Climatic Scenarios in Andit Tid Watershed, Central Highland of Ethiopia A. Woldemariam et al. https://doi.org/10.36253/jaeid-16073
923. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. https://doi.org/10.1007/s11269-021-02770-1
924. Statistical check of USLE‐M and USLE‐MM to predict bare plot soil loss in two Italian environments V. Bagarello et al. https://doi.org/10.1002/ldr.3072
925. Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland D. Młyński et al. https://doi.org/10.3390/atmos10020043
926. DAILY AVERAGED 2D WATER TEMPERATURE MODEL FOR THE ST. LAWRENCE RIVER V. Ouellet et al. https://doi.org/10.1002/rra.2664
927. Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools1 S. Nair et al. https://doi.org/10.1111/j.1752-1688.2011.00570.x
928. How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps M. Antonetti & M. Zappa https://doi.org/10.5194/hess-22-4425-2018
929. Modeling probabilistic lag time equation in a watershed based on uncertainties in rainfall, hydraulic and geographical factors S. Wu et al. https://doi.org/10.2166/nh.2016.134
930. Use of soft data for multicriteria calibration and validation of Agricultural Policy Environmental eXtender: Impact on model simulations A. Nelson et al. https://doi.org/10.2489/jswc.73.6.623
931. A flow-weighted approach to generate daily total phosphorus loads in streams based on seasonal loads Y. Ouyang https://doi.org/10.1007/s10661-021-09199-4
932. Integrating soil water and tracer balances, numerical modelling and GIS tools to estimate regional groundwater recharge: Application to the Alcadozo Aquifer System (SE Spain) J. Hornero et al. https://doi.org/10.1016/j.scitotenv.2016.06.011
933. Assessment of Climate Change Impact on Reservoir Inflows Using Multi Climate-Models under RCPs—The Case of Mangla Dam in Pakistan M. Babur et al. https://doi.org/10.3390/w8090389
934. The relevance of in-situ and laboratory characterization of sandy soil hydraulic properties for soil water simulations M. Rezaei et al. https://doi.org/10.1016/j.jhydrol.2015.12.062
935. Avaliação do modelo J2000/JAMS para modelagem hidrológica em bacias hidrográficas brasileiras A. Machado et al. https://doi.org/10.1590/s1413-41522016145177
936. Simulating seasonal variations of tile drainage discharge in an agricultural catchment G. De Schepper et al. https://doi.org/10.1002/2016WR020209
937. Calibration of a rainfall–runoff model at regional scale by optimising river discharge statistics: Performance analysis for the average/low flow regime L. Lombardi et al. https://doi.org/10.1016/j.pce.2011.05.013
938. Hydrological Forecasting under Climate Variability Using Modeling and Earth Observations in the Naryn River Basin, Kyrgyzstan M. Pamirbek kyzy et al. https://doi.org/10.3390/w14172733
939. Runoff and sediment yield modeling by means of WEPP in the Bautzen dam catchment, Germany M. Al-Mukhtar et al. https://doi.org/10.1007/s12665-014-3113-0
940. Spatio-temporal analysis of sediment yield with a physically based model for a data-scarce headwater in Konya Closed Basin, Turkey C. Koycegiz et al. https://doi.org/10.2166/ws.2021.016
941. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model K. Abbaspour et al. https://doi.org/10.1016/j.jhydrol.2015.03.027
942. Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches A. Teweldebrhan et al. https://doi.org/10.5194/hess-22-5021-2018
943. Changes in compound drought-heat events over Brazil’s Pantanal wetland: an assessment using remote sensing data and multiple drought indicators A. Cunha et al. https://doi.org/10.1007/s00382-023-06937-x
944. A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale X. Yang et al. https://doi.org/10.1029/2017WR022380
945. Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin L. Melsen et al. https://doi.org/10.5194/hess-20-2207-2016
946. Improving the integrated hydrological simulation on a data-scarce catchment with multi-objective calibration Q. He & F. Molkenthin https://doi.org/10.2166/hydro.2021.132
947. Using numerical modeling error analysis methods to indicate changes in a watershed K. Mátyás & K. Bene https://doi.org/10.1556/606.2018.13.3.17
948. Evaluating Streamwater Dissolved Organic Carbon Dynamics in Context of Variable Flowpath Contributions With a Tracer‐Based Mixing Model J. Saiers et al. https://doi.org/10.1029/2021WR030529
949. Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations E. Sadrossadat et al. https://doi.org/10.1108/EC-07-2017-0258
950. Hypothesis Testing for the Difference between Two Nash–Sutcliffe Efficiencies for Comparing Hydrological Model Performance D. Liu https://doi.org/10.1061/JHYEFF.HEENG-6035
951. Understanding watershed phosphorus source apportionment in mining regions: Insights from five-year data of phosphorus industry J. Wei et al. https://doi.org/10.1016/j.wroa.2026.100575
952. Identification of a general light use efficiency model for gross primary production J. Horn & K. Schulz https://doi.org/10.5194/bg-8-999-2011
953. LENS-GRM Applicability Analysis and Evaluation S. Lee et al. https://doi.org/10.3390/w14233897
954. Improved estimators of correlation and R2 for skewed hydrologic data C. Barber et al. https://doi.org/10.1080/02626667.2019.1686639
955. Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence H. Ssegane et al. https://doi.org/10.1111/jawr.12077
956. Development of decomposition-based model using Copula-GARCH approach to simulate instantaneous peak discharge M. Nazeri Tahroudi & R. Mirabbasi https://doi.org/10.1007/s13201-023-01982-7
957. Climate Change Impact on Inflow and Nutrient Loads to a Warm Monomictic Lake B. Parmas et al. https://doi.org/10.3390/w15173162
958. Using multiple satellite-gauge merged precipitation products ensemble for hydrologic uncertainty analysis over the Huaihe River basin R. Sun et al. https://doi.org/10.1016/j.jhydrol.2018.09.024
959. Hydrological change detection using modeling: Half a century of runoff from four rivers in the Blue Nile Basin S. Gebrehiwot et al. https://doi.org/10.1002/wrcr.20319
960. Hydrological Model Calibration in Data-Scarce Mediterranean Catchments: A Comparative Assessment of Three Strategies A. Jahanshahi et al. https://doi.org/10.3390/hydrology13020066
961. A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series M. Grillakis et al. https://doi.org/10.3390/w12082304
962. Application of cross-wavelet–linear programming–Kalman filter and GIUH methods in rainfall–runoff modeling F. Mohammadi et al. https://doi.org/10.1007/s12665-019-8133-3
963. Multi‐Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins M. Funato & Y. Sawada https://doi.org/10.1029/2025JH001196
964. When best is the enemy of good – critical evaluation of performance criteria in hydrological models G. Cinkus et al. https://doi.org/10.5194/hess-27-2397-2023
965. Reconstructing bed topography of a shallow river from close-range aerial imagery: Multi-UAV experimental campaign in the Izera river (SW Poland/N Czechia) M. Witek et al. https://doi.org/10.1016/j.geomorph.2024.109544
966. Hydro-Meteorological Assessment of Three GPM Satellite Precipitation Products in the Kelantan River Basin, Malaysia M. Tan et al. https://doi.org/10.3390/rs10071011
967. Eutrophication modelling chain for improved management strategies to prevent algal blooms in the Bay of Seine P. Passy et al. https://doi.org/10.3354/meps11533
968. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model Z. Yaseen et al. https://doi.org/10.1016/j.advengsoft.2017.09.004
969. Relationship between skills of multimodel hydrologic ensemble predictions and atmospheric circulation patterns: a case study from the Nysa Kłodzka river basin (SW Poland) T. Niedzielski et al. https://doi.org/10.1007/s11600-025-01733-z
970. Enhancing AquaCrop model precision for accurate simulation of sweet potato and taro landraces T. Mthembu et al. https://doi.org/10.3389/fsufs.2025.1698211
971. Prediction of dissolved oxygen concentration in aquatic systems based on transfer learning N. Zhu et al. https://doi.org/10.1016/j.compag.2020.105888
972. AI‐based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada P. Parisouj et al. https://doi.org/10.1111/1752-1688.13098
973. A joint modelling framework for daily extremes of river discharge and precipitation in urban areas K. Breinl et al. https://doi.org/10.1111/jfr3.12150
974. Volume estimation models for avocado fruit M. Mokria et al. https://doi.org/10.1371/journal.pone.0263564
975. Separating spatial and temporal sources of variation for model testing in precision agriculture E. Sadler et al. https://doi.org/10.1007/s11119-007-9046-9
976. A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria F. Willkofer et al. https://doi.org/10.3390/w12092349
977. Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multi-component metrics M. Choudhury et al. https://doi.org/10.1016/j.agwat.2021.107007
978. Combining digital soil mapping and hydrological modeling in a data scarce watershed in north-central Portugal F. Tavares Wahren et al. https://doi.org/10.1016/j.geoderma.2015.08.023
979. Application of artificial neural networks to predict total dissolved solids in the river Zayanderud, Iran A. Gholamreza et al. https://doi.org/10.4491/eer.2015.096
980. Scenario Studies on Effects of Soil Infiltration Rates, Land Slope, and Furrow Irrigation Characteristics on Furrow Irrigation-Induced Erosion J. Dibal et al. https://doi.org/10.1155/2014/942732
981. Quantifying the impacts of climate change on water resources in northern Tuscany, Italy, using high‐resolution regional projections M. D'Oria et al. https://doi.org/10.1002/hyp.13378
982. Sensitivity and uncertainty analysis for streamflow prediction based on multiple optimization algorithms in Yalong River Basin of southwestern China Y. Liang et al. https://doi.org/10.1016/j.jhydrol.2021.126598
983. Comparative analysis of a geomorphology‐based instantaneous unit hydrograph in small mountainous watersheds J. López et al. https://doi.org/10.1002/hyp.8383
984. Development, implementation and validation of Sediment Transport and Erosion Prediction (STEP) model . Yanto & M. Dimyati https://doi.org/10.1016/j.envsoft.2023.105686
985. Using the General Lake Model (GLM) to simulate water temperatures and ice cover of a medium-sized lake: a case study of Lake Ammersee, Germany T. Bueche et al. https://doi.org/10.1007/s12665-017-6790-7
986. Evaluation of the extended applicability of a simulated hydrological Tank model at the Ca River Basin sub-catchments H. Phuong & L. Huu https://doi.org/10.2166/wpt.2023.104
987. Comparing expert judgement and numerical criteria for hydrograph evaluation L. Crochemore et al. https://doi.org/10.1080/02626667.2014.903331
988. Large scale modelling of bankfull flow: An example for Europe C. Schneider et al. https://doi.org/10.1016/j.jhydrol.2011.08.004
989. Assessing water availability using remote sensing in South Rukuru and North Rumphi River Basin, Malawi G. Gondwe et al. https://doi.org/10.2166/h2oj.2025.061
990. Towards a generic model evaluation metric for non-normally distributed measurements in water quality and ecosystem models T. Fu & C. Zhang https://doi.org/10.1016/j.ecoinf.2024.102470
991. Understanding the effects of soil data quality on SWAT model performance and hydrological processes in Tamedroust watershed (Morocco) Y. Bouslihim et al. https://doi.org/10.1016/j.jafrearsci.2019.103616
992. Coupling strategies of snowmelt runoff model and machine learning in the Lhasa River Basin T. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103400
993. Comparison of the abilities of WEPP and the USLE-M to predict event soil loss on steep loessal slopes in China P. Kinnell et al. https://doi.org/10.1016/j.catena.2018.07.007
994. Modeling Streamflow and Sediment Loads with a Photogrammetrically Derived UAS Digital Terrain Model: Empirical Evaluation from a Fluvial Aggregate Excavation Operation J. Hupy & C. Wilson https://doi.org/10.3390/drones5010020
995. A mathematical model for simulation the removal of cadmium and chromium from groundwater using scrap iron and aluminum as permeable reactive barrier A. Faisal et al. https://doi.org/10.5004/dwt.2022.28483
996. Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine Y. Seo et al. https://doi.org/10.3390/hydrology5040054
997. Explainable ensemble machine learning for WWTPs: A systematic review and compliance framework Y. Gegana et al. https://doi.org/10.1016/j.rineng.2026.112376
998. Assessment of water availability vulnerability in the Cerrado D. Althoff et al. https://doi.org/10.1007/s13201-021-01521-2
999. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models J. Koch et al. https://doi.org/10.5194/gmd-11-1873-2018
1000. Hydrological Simulation in a Rift-Bounded Lake System and Implication of Water Abstraction: Central Rift Valley Lakes Basin, Ethiopia S. Balcha et al. https://doi.org/10.3390/w14233929
1001. An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 2: Application to a small agricultural catchment A. Neill et al. https://doi.org/10.1016/j.jenvman.2020.110905
1002. Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency S. Pool et al. https://doi.org/10.1080/02626667.2018.1552002
1003. Re-initiating depth-discharge monitoring in small-sized ungauged watersheds by combining remote sensing and hydrological modelling: a case study in Madagascar J. Andriambeloson et al. https://doi.org/10.1080/02626667.2020.1833013
1004. Effect of pH on the performance of olive pips reactive barrier through the migration of copper-contaminated groundwater A. Faisal https://doi.org/10.1080/19443994.2014.999132
1005. Using the SWAT model to identify erosion prone areas and to estimate soil loss and sediment transport in Mogi Guaçu River basin in Sao Paulo State, Brazil F. dos Santos et al. https://doi.org/10.1016/j.catena.2022.106872
1006. Fitting rainfall interception models to forest ecosystems of Mexico J. Návar https://doi.org/10.1016/j.jhydrol.2017.03.025
1007. Evaluation and calibration of simple methods for daily reference evapotranspiration estimation in humid East China J. Xu et al. https://doi.org/10.1080/03650340.2012.683425
1008. Identifying Drivers for Maize Response to Fertilizer in Ghana P. Bindraban et al. https://doi.org/10.2139/ssrn.4093525
1009. Response of streamflow to climate change in a sub-basin of the source region of the Yellow River based on a tank model P. Wu et al. https://doi.org/10.5194/piahs-379-231-2018
1010. Exploring the effect of catchment morphology on streamflow characteristics with virtual experiments J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.127606
1011. Experimental and numerical investigation of thermally induced convection along a high-temperature borehole heat exchanger V. Ngninjio et al. https://doi.org/10.1016/j.applthermaleng.2023.121345
1012. Evaluation of multisite performance of SWAT model in the Gomti River Basin, India B. Das et al. https://doi.org/10.1007/s13201-019-1013-x
1013. Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design J. Keum et al. https://doi.org/10.1016/j.jhydrol.2018.04.037
1014. Streamflow Impacts of Management and Environmental Change in the Upper Wabash River Basin S. Rahman & L. Bowling https://doi.org/10.1061/(ASCE)HE.1943-5584.0001750
1015. Inverse Estimation of Spatiotemporal Flux Boundary Conditions in Unsaturated Water Flow Modeling N. Li et al. https://doi.org/10.1029/2020WR028030
1016. Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU) W. Li et al. https://doi.org/10.1016/j.inpa.2020.02.002
1017. The impact of projected changes in climate variability on the reliability of surface water supply in Scotland M. Afzal et al. https://doi.org/10.2166/ws.2015.027
1018. Isotope hydrology and baseflow geochemistry in natural and human-altered watersheds in the Inland Pacific Northwest, USA R. Sánchez-Murillo et al. https://doi.org/10.1080/10256016.2015.1008468
1019. Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates M. Şan et al. https://doi.org/10.1007/s00477-022-02345-5
1020. Hydrogeological modelling of a coastal karst aquifer using an integrated SWAT-MODFLOW approach G. Fiorese et al. https://doi.org/10.1016/j.envsoft.2024.106249
1021. Proper methods and its calibration for estimating reference evapotranspiration using limited climatic data in Southwestern China X. Gao et al. https://doi.org/10.1080/03650340.2014.933810
1022. Physically based approaches incorporating evaporation for early warning predictions of rainfall-induced landslides A. Reder et al. https://doi.org/10.5194/nhess-18-613-2018
1023. A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms E. Sharma et al. https://doi.org/10.1016/j.scitotenv.2019.135934
1024. Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations E. Mockler et al. https://doi.org/10.1016/j.advwatres.2016.10.008
1025. Streamflow Regionalization Considering Water Balance with Actual Evapotranspiration Estimated from Remote Sensing R. Ribeiro et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002183
1026. Evaluation of Baseflow Modeling with BlueM.Sim for Long-Term Hydrological Studies in the German Low Mountain Range of Hesse, Germany M. Kissel et al. https://doi.org/10.3390/hydrology10120222
1027. Identifying Water Stress Hotspots in Chilean Patagonia Using Spatially Explicit Water Yield Modeling and Anthropization Proxies I. Irarrazaval et al. https://doi.org/10.3390/w18091041
1028. Simulation of long‐term soil water dynamics at Reynolds Creek, Idaho: implications for rangeland productivity J. Finzel et al. https://doi.org/10.1002/eco.1666
1029. Assessing the vulnerability of water resources in the context of climate changes in a small forested watershed using SWAT: A review M. Marin et al. https://doi.org/10.1016/j.envres.2020.109330
1030. A system dynamic model to quantify the impacts of water resources allocation on water–energy–food–society (WEFS) nexus Y. Zeng et al. https://doi.org/10.5194/hess-26-3965-2022
1031. Effect of climate and land use change on discharge in the Betwa River basin, India, using SWAT based on CMIP6 and the Land Change Modeler model projections R. Singh et al. https://doi.org/10.2166/wcc.2025.598
1032. Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model L. Cunha et al. https://doi.org/10.1029/2012WR012138
1033. A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al. https://doi.org/10.1007/s13201-023-01924-3
1034. Water resources of the Black Sea Basin at high spatial and temporal resolution E. Rouholahnejad et al. https://doi.org/10.1002/2013WR014132
1035. Multi-site multi-objective calibration of SWAT model using a large dataset for improved performance in Ethiopia H. Aragaw & S. Mishra https://doi.org/10.1007/s12517-022-09602-5
1036. Assessing the role of location and scale of Nature Based Solutions for the enhancement of low flows J. Fennell et al. https://doi.org/10.1080/15715124.2022.2092490
1037. Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan M. Setiawati & F. Miura https://doi.org/10.4236/gep.2016.412008
1038. Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia G. Ayele et al. https://doi.org/10.3390/w9100782
1039. Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia W. Gomes et al. https://doi.org/10.2166/wcc.2024.262
1040. Method for indirect determination of soil parameters for numerical simulation of dikes and earth dams T. Fichtner et al. https://doi.org/10.1007/s13201-022-01766-5
1041. Watershed modelling of hydrology and water quality in the Sacramento River watershed, California D. Ficklin et al. https://doi.org/10.1002/hyp.9222
1042. Investigation and simulations of changes in the relationship of precipitation-runoff in drought years W. Tian et al. https://doi.org/10.1016/j.jhydrol.2018.08.015
1043. Simulating hydrological responses using high resolution satellite inputs for a forest dominated hilly catchment of Uttarakhand Himalayas U. Kumar & D. Singh https://doi.org/10.1007/s12517-023-11505-y
1044. Joint analysis of drought affected by climate change in Zarinehrood watershed, Iran, using copula functions M. Samadian et al. https://doi.org/10.1007/s11600-024-01306-6
1045. Evaluation of suspended load changes using AnnAGNPS and SWAT semi-empirical erosion models Z. Kliment et al. https://doi.org/10.1016/j.catena.2007.11.005
1046. Is there a superior conceptual groundwater model structure for baseflow simulation? M. Stoelzle et al. https://doi.org/10.1002/hyp.10251
1047. Evaluating global precipitation datasets over Sicily: From daily estimates to extreme events M. Yıldız et al. https://doi.org/10.1016/j.ejrh.2025.103062
1048. Conceptual modelling to assess the influence of hydro-climatic variability on runoff processes in data scarce semi-arid Andean catchments A. Nauditt et al. https://doi.org/10.1080/02626667.2016.1240870
1049. Temporal flood forecasting for trans-boundary Jhelum River of Greater Himalayas S. Parvaze et al. https://doi.org/10.1007/s00704-021-03562-8
1050. The Impact of Soil Heterogeneity on Nitrate Dynamic and Losses in Tile-Drained Arable Fields D. Bednorz et al. https://doi.org/10.1007/s11270-016-3095-5
1051. Comparison of results from two 3D hydrodynamic models with field data: internal seiches and horizontal currents P. Dissanayake et al. https://doi.org/10.1080/20442041.2019.1580079
1052. Projection of Mean Annual and Maximum 24-h Precipitation under Future Climatic Scenarios in Semi-Arid Regions P. Allahbakhshian-Farsani et al. https://doi.org/10.1007/s11269-025-04091-z
1053. A simple model structure enhances parameter identification and improves runoff prediction in ungauged high-latitude catchments J. Akanegbu et al. https://doi.org/10.1016/j.jhydrol.2018.06.022
1054. A non-parametric bootstrapping framework embedded in a toolkit for assessing water quality model performance D. Libera et al. https://doi.org/10.1016/j.envsoft.2018.05.013
1055. Uncertainty Analysis in Data-Scarce Urban Catchments H. Ballinas-González et al. https://doi.org/10.3390/w8110524
1056. Attributing historical streamflow changes in the Jhelum River basin to climate change M. Javed et al. https://doi.org/10.1007/s10584-023-03628-8
1057. A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon, USA S. Leibowitz et al. https://doi.org/10.1002/hyp.8176
1058. Using precipitation data ensemble for uncertainty analysis in SWAT streamflow simulation M. Strauch et al. https://doi.org/10.1016/j.jhydrol.2011.11.014
1059. Nitrogen removal kinetics in wastewater stabilization pond systems in Greece M. Gratziou https://doi.org/10.5004/dwt.2017.0255
1060. Hydrologic Modeling for Sustainable Water Resources Management in Urbanized Karst Areas H. Cardoso de Salis et al. https://doi.org/10.3390/ijerph16142542
1061. A Study on Regionalization of Parameters for Sacramento Continuous Rainfall-Runoff Model Using Watershed Characteristics T. Kim et al. https://doi.org/10.3741/JKWRA.2015.48.10.793
1062. Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties T. Li et al. https://doi.org/10.1371/journal.pone.0164456
1063. Discovering the performance criteria of education development centers of medical sciences universities: A qualitative study M. Taghavinia & M. Maleki https://doi.org/10.4103/jehp.jehp_638_19
1064. Accounting for model parameter uncertainty provides more robust projections of dissolved organic carbon dynamics to aid drinking water management R. Paíz et al. https://doi.org/10.1016/j.watres.2025.123238
1065. Regionalization of a Rainfall-Runoff Model: Limitations and Potentials J. Song et al. https://doi.org/10.3390/w11112257
1066. Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models M. Wagena et al. https://doi.org/10.1016/j.envsoft.2020.104669
1067. Hydrodynamic modelling of the Amazon River: Factors of uncertainty E. Chávarri et al. https://doi.org/10.1016/j.jsames.2012.10.010
1068. Sensitivity and uncertainty analysis for streamflow prediction using multiple optimization algorithms and objective functions: San Joaquin Watershed, California M. Paul & M. Negahban-Azar https://doi.org/10.1007/s40808-018-0483-4
1069. Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges B. Alizadeh et al. https://doi.org/10.1175/JHM-D-19-0164.1
1070. Evaluation and uncertainty assessment of weather data and model calibration on daily streamflow simulation in a large-scale regulated and snow-dominated river basin Y. Chen et al. https://doi.org/10.1016/j.jhydrol.2023.129103
1071. Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle R. Deo et al. https://doi.org/10.1016/j.envres.2017.01.035
1072. Multi‐century tree‐ring precipitation record reveals increasing frequency of extreme dry events in the upper Blue Nile River catchment M. Mokria et al. https://doi.org/10.1111/gcb.13809
1073. Inverse analysis to estimate site‐specific parameters of a mathematical model for simulating pesticide dissipations in paddy test systems K. Kondo et al. https://doi.org/10.1002/ps.5276
1074. Climate change impacts on renewable groundwater resources in the andosol-dominated Andean highlands, Ecuador J. Senent-Aparicio et al. https://doi.org/10.1016/j.catena.2023.107766
1075. A framework for reflecting on decisions in the modelling cycle incorporating a fitness-for-purpose and pathway perspective M. Liu et al. https://doi.org/10.1016/j.envsoft.2026.107108
1076. A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model P. Durgut & M. Ayvaz https://doi.org/10.1016/j.jhydrol.2022.128976
1077. A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena M. Dis https://doi.org/10.3390/su152115542
1078. Simulating typhoon-induced storm hydrographs in subtropical mountainous watershed: an integrated 3-layer TOPMODEL J. Huang et al. https://doi.org/10.5194/hess-13-27-2009
1079. Pathogen transport and fate modeling in the Upper Salem River Watershed using SWAT model M. Niazi et al. https://doi.org/10.1016/j.jenvman.2014.12.042
1080. Application of HYDRUS-1D model to provide antecedent soil water contents for analysis of runoff and soil erosion from a slope on the Loess Plateau Y. Caiqiong & F. Jun https://doi.org/10.1016/j.catena.2015.11.017
1081. Hydrological Responses to Climate Change and Land-Use Dynamics in Central Asia's Semi-arid Regions: An SWAT Model Analysis of the Tuul River Basin S. Dolgorsuren et al. https://doi.org/10.1007/s41748-023-00367-9
1082. Application of the water balance model J2000 to estimate groundwater recharge in a semi-arid environment: a case study in the Zarqa River catchment, NW-Jordan S. Schulz et al. https://doi.org/10.1007/s12665-013-2342-y
1083. Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices L. Kouadio et al. https://doi.org/10.1016/j.agrformet.2021.108449
1084. Applications of machine learning in enhancing evaporation estimation for small reservoirs: a case study in semi-arid South Texas S. Abdullah et al. https://doi.org/10.1007/s40808-026-02773-0
1085. Climate change and hydropower resilience in Nepal: an integrated modeling approach in the Madi River Basin P. Pokharel & R. Regmi https://doi.org/10.2166/h2oj.2024.110
1086. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. https://doi.org/10.5194/hess-20-4655-2016
1087. Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh https://doi.org/10.3390/w15061036
1088. The impacts of climate change on the runoff volume of Melen and Munzur Rivers in Turkey based on calibration of WASMOD model with multiobjective genetic algorithm M. Nuri Balov & A. Altunkaynak https://doi.org/10.1007/s00703-019-00676-7
1089. Hydrological Modeling of the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment Hunza K. Garee et al. https://doi.org/10.3390/w9010017
1090. Simulating the hydrological response of a small tropical forest watershed (Mata Atlantica, Brazil) by the AnnAGNPS model D. Zema et al. https://doi.org/10.1016/j.scitotenv.2018.04.339
1091. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data Y. Zhang et al. https://doi.org/10.1016/j.agrformet.2016.04.003
1092. Improving fertigation efficiency by numerical modelling in a lettuce subsurface drip irrigation farm F. Nayebloie et al. https://doi.org/10.1016/j.agwat.2022.107721
1093. Efficiency criteria for environmental model quality assessment: A review and its application to wastewater treatment H. Hauduc et al. https://doi.org/10.1016/j.envsoft.2015.02.004
1094. Evaluation of the SWAT Model for the Simulation of Flow and Water Balance Based on Orbital Data in a Poorly Monitored Basin in the Brazilian Amazon P. Rufino et al. https://doi.org/10.3390/geographies3010001
1095. Optimizing surface fertigation practices for application in farmers’ field in the North China Plain X. Sun et al. https://doi.org/10.1016/j.agwat.2023.108342
1096. Modelling runoff in a Swiss glacierized catchment—part I: methodology and application in the Findelen basin under a long‐lasting stable climate B. Uhlmann et al. https://doi.org/10.1002/joc.3501
1097. Enhancing simulations of biomass and nitrous oxide emissions in vineyard, orchard, and vegetable cropping systems M. Hong et al. https://doi.org/10.1016/j.agsy.2024.104243
1098. Impacts of storm events on watershed phosphorus critical source area identification based on SWAT model H. Zhao et al. https://doi.org/10.1016/j.jhydrol.2026.135032
1099. Revised universal soil loss equation-based runoff model for the potential soil loss estimation in Wadi Soubella Watershed, Northeast of Algeria B. Bilal & K. Cherif https://doi.org/10.1007/s40808-021-01291-5
1100. Comparison of SWAT and GSSHA for High Time Resolution Prediction of Stream Flow and Sediment Concentration in a Small Agricultural Watershed R. Sith & K. Nadaoka https://doi.org/10.3390/hydrology4020027
1101. Evaluation of the APSIM model in cropping systems of Asia D. Gaydon et al. https://doi.org/10.1016/j.fcr.2016.12.015
1102. Influence of DEM Resolution on the Hydrological Responses of a Terraced Catchment: An Exploratory Modelling Approach J. Rocha et al. https://doi.org/10.3390/rs15010169
1103. Coupled hydrogeological modeling and nitrate transport modeling in an anthropized valley, a case study of the lower Soummam valley (Bejaïa Northeast of Algeria) D. Benabbes et al. https://doi.org/10.1016/j.jafrearsci.2024.105183
1104. The Effects of Offshore Wind Interpolation Methods on Wind Power Density and Energy Assessment T. Soukissian & V. Apostolou https://doi.org/10.3390/en19040971
1105. Assessment of surface water resources and evapotranspiration in the Haihe River basin of China using SWAT model C. Sun & L. Ren https://doi.org/10.1002/hyp.9213
1106. Application of precipitation products for flood modeling of transboundary river basin: a case study of Jhelum Basin M. Umer et al. https://doi.org/10.1007/s00704-020-03471-2
1107. Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed J. Daramola et al. https://doi.org/10.3390/w14030325
1108. Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria O. Adeboye et al. https://doi.org/10.1016/j.agwat.2018.11.006
1109. Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture Q. Que et al. https://doi.org/10.1016/j.wroa.2024.100269
1110. Assessing Future Impacts of Climate Change on Streamflow within the Alabama River Basin J. Quansah et al. https://doi.org/10.3390/cli9040055
1111. Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China A. Zhao et al. https://doi.org/10.1016/j.catena.2015.09.018
1112. Inverting Topography for Landscape Evolution Model Process Representation: 1. Conceptualization and Sensitivity Analysis K. Barnhart et al. https://doi.org/10.1029/2018JF004961
1113. Linking basin-scale hydrology with climatic parameters in western Himalaya: Application of satellite data, temperature index modelling and in-situ observations S. Srivastava et al. https://doi.org/10.1016/j.gsf.2024.101936
1114. Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters M. Rezaei et al. https://doi.org/10.5194/hess-20-487-2016
1115. Assessment of Suitable Land for Surface Irrigation in Ungauged Catchments: Blue Nile Basin, Ethiopia G. Nigussie et al. https://doi.org/10.3390/w11071465
1116. Time series foundation model chronos enhances nitrogen forecasting under data scarcity K. Zheng et al. https://doi.org/10.1016/j.wroa.2025.100469
1117. Identifying the connective strength between model parameters and performance criteria B. Guse et al. https://doi.org/10.5194/hess-21-5663-2017
1118. Value of river discharge data for global-scale hydrological modeling M. Hunger & P. Döll https://doi.org/10.5194/hess-12-841-2008
1119. Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China S. Chen et al. https://doi.org/10.1007/s00382-018-04611-1
1120. Forecasting Sea Level Rise-driven Inundation in Diked and Tidally Restricted Coastal Lowlands K. Befus et al. https://doi.org/10.1007/s12237-023-01174-1
1121. Comparative analysis of CMIP5 and CMIP6 in conjunction with the hydrological processes of reservoir catchment, Chhattisgarh, India S. Verma et al. https://doi.org/10.1016/j.ejrh.2023.101533
1122. Simulation-based cost-risk analysis of phosphorus reduction alternatives: application to a mountainous watershed S. Nourinezhad & M. Rajabi https://doi.org/10.1007/s40808-022-01532-1
1123. Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves S. Ashrafi et al. https://doi.org/10.1080/02626667.2020.1784902
1124. Climate Change Contributions to Water Conservation Capacity in the Upper Mekong River Basin Y. Luo et al. https://doi.org/10.3390/w16182601
1125. Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation J. Huo & L. Liu https://doi.org/10.1155/2020/8594727
1126. Using remote sensing information to enhance the understanding of the coupling of terrestrial ecosystem evapotranspiration and photosynthesis on a global scale Y. Bai et al. https://doi.org/10.1016/j.jag.2021.102329
1127. Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane https://doi.org/10.1080/15715124.2016.1203331
1128. Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation R. Deo et al. https://doi.org/10.1002/fes3.151
1129. Irrigation, crop stress and drainage reduction under uncertainty: A scenario study F. Mondaca-Duarte et al. https://doi.org/10.1016/j.agwat.2019.105990
1130. Hydrological modelling of ungauged urban watershed using SWAT model E. Sisay et al. https://doi.org/10.1007/s40808-017-0328-6
1131. Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya R. Wambua https://doi.org/10.4018/IJAGR.2019100102
1132. High-resolution climate datasets in hydrological impact studies: Assessing their value in alpine and pre-alpine catchments in southeastern Austria S. Peßenteiner et al. https://doi.org/10.1016/j.ejrh.2021.100962
1133. Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment Z. Yin et al. https://doi.org/10.1007/s00477-018-1585-2
1134. The accuracy of the Sentinel-3A altimetry over Polish rivers M. Halicki & T. Niedzielski https://doi.org/10.1016/j.jhydrol.2021.127355
1135. Groundwater model diagnostic calibration and uncertainty analysis using information theory M. Khorshidi et al. https://doi.org/10.1080/02626667.2024.2347974
1136. An evaluation of automated GPD threshold selection methods for hydrological extremes across different scales S. Curceac et al. https://doi.org/10.1016/j.jhydrol.2020.124845
1137. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018) D. Althoff et al. https://doi.org/10.1029/2020WR027562
1138. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. https://doi.org/10.3390/hydrology8040144
1139. A comparison of the abilities of the USLE-M, RUSLE2 and WEPP to model event erosion from bare fallow areas P. Kinnell https://doi.org/10.1016/j.scitotenv.2017.04.046
1140. Reply to: Comments on “Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models” by Adnan et al. H. Yan et al. https://doi.org/10.1016/j.scitotenv.2020.141207
1141. The Effect of Incorporation of Embankment Information for Flood Simulation of the Gin River, Sri Lanka J. Jayapadma et al. https://doi.org/10.20965/jdr.2022.p0475
1142. Coupled model to predict wave-induced liquefaction and morphological changes V. Pessanha et al. https://doi.org/10.1016/j.seares.2023.102351
1143. Pros and cons of using wavelets in conjunction with genetic programming and generalised linear models in statistical downscaling of precipitation D. Sachindra et al. https://doi.org/10.1007/s00704-019-02848-2
1144. Case Study on Improvement Measures for Increasing Accuracy of AI-Based River Water-Level Prediction Model S. Kim et al. https://doi.org/10.3390/earth6040146
1145. Evaluation of the WEAP model in simulating subbasin hydrology in the Central Rift Valley basin, Ethiopia D. Abera Abdi & T. Ayenew https://doi.org/10.1186/s13717-021-00305-5
1146. What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology F. Heße et al. https://doi.org/10.3389/feart.2019.00118
1147. Assessment of farmers' water and fertilizer practices and perceptions in the North China Plain X. Sun et al. https://doi.org/10.1002/ird.2719
1148. A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds X. Wang et al. https://doi.org/10.1016/j.jenvman.2023.119655
1149. Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks A. Alsumaiei https://doi.org/10.36909/jer.v9i3B.10893
1150. An improved model of shade-affected stream temperature in Soil & Water Assessment Tool E. Noa-Yarasca et al. https://doi.org/10.5194/hess-27-739-2023
1151. Can a regionalized model parameterisation be improved with a limited number of runoff measurements? D. Viviroli & J. Seibert https://doi.org/10.1016/j.jhydrol.2015.07.009
1152. Multi-driver ensemble to evaluate the water utility business interruption cost induced by hydrological drought risk scenarios in Brazil D. Guzmán et al. https://doi.org/10.1080/1573062X.2022.2058564
1153. Modeling the nexus across water supply, power generation and environment systems using the system dynamics approach: Hehuang Region, China M. Feng et al. https://doi.org/10.1016/j.jhydrol.2016.10.011
1154. Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climates in Iran E. Rahnama et al. https://doi.org/10.1007/s12517-020-06146-4
1155. Influence of winds, geostrophy and typhoons on the seasonal variability of the circulation in the Gulf of Tonkin: A high-resolution 3D regional modeling study V. Piton et al. https://doi.org/10.1016/j.rsma.2021.101849
1156. Ein besseres Verständnis des Lurbach-Karstsystems durch ein konzeptionelles Niederschlags-Abfluss-Modell T. Wagner et al. https://doi.org/10.1007/s00767-013-0234-4
1157. Soil Salinity and Tomato Yield Simulation Using SALTMED Model in Drip Irrigation M. Dastranj et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001243
1158. Evaluation of the impact of best management practices on ephemeral gully and sheet/rill erosion using the AnnAGNPS model H. Mohebzadeh et al. https://doi.org/10.1016/j.catena.2024.108436
1159. Effects of damming and climatic change on the eco-hydrological system: A case study in the Yalong River, southwest China Y. Wang et al. https://doi.org/10.1016/j.ecolind.2018.07.039
1160. Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran M. Taie Semiromi & M. Koch https://doi.org/10.1080/02626667.2019.1669793
1161. Synthetic Pollutograph by Prediction Indices: An Evaluation in Several Urban Sub-Catchments J. García et al. https://doi.org/10.3390/su10082634
1162. Water Balance Assessment under Different Glacier Coverage Scenarios in the Hunza Basin S. Shrestha & S. Nepal https://doi.org/10.3390/w11061124
1163. Untangling hybrid hydrological models with explainable artificial intelligence D. Althoff et al. https://doi.org/10.2166/h2oj.2021.066
1164. Flood Hazard Assessment for the Tori Levee Breach of the Indus River Basin, Pakistan B. Naeem et al. https://doi.org/10.3390/w13050604
1165. Multi-Annual Dendroclimatic Patterns for the Desert National Wildlife Refuge, Southern Nevada, USA F. Biondi & J. Roberts https://doi.org/10.3390/f16071142
1166. Effects of climate and land use changes on water quantity and quality of coastal watersheds of Narragansett Bay E. Ross & T. Randhir https://doi.org/10.1016/j.scitotenv.2021.151082
1167. Soil erosion modelling of burned and mulched soils following a Mediterranean forest wildfire J. Gonzalez‐Romero et al. https://doi.org/10.1111/sum.12884
1168. Market chain analysis of potato and factors affecting market supply in West Gojam Zone, Ethiopia A. Wondim & M. Desselgn https://doi.org/10.5897/JDAE2018.0988
1169. Estimation of elastic wave velocity and DCPI distributions using outlier analysis S. Taiwo & H. Yoon https://doi.org/10.1016/j.enggeo.2018.10.027
1170. Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes J. Guerrero et al. https://doi.org/10.5194/hess-21-6345-2017
1171. Predictive models for forecasting hourly urban water demand M. Herrera et al. https://doi.org/10.1016/j.jhydrol.2010.04.005
1172. Groundwater recharge estimation in the Ziway Lake Watershed, Ethiopian Rift: an approach using SWAT and CMB techniques A. Mechal et al. https://doi.org/10.1007/s40899-024-01108-0
1173. Historical and future changes of blue water and green water resources in the Yangtze River source region, China Z. Yuan et al. https://doi.org/10.1007/s00704-019-02883-z
1174. Impact of climate change on streamflow and water quality in the upper Dong Nai river basin, Vietnam L. Thang et al. https://doi.org/10.1051/lhb/2018010
1175. Sanitation Network Sulfide Modeling as a Tool for Asset Management. The Case of the City of Murcia (Spain) J. García et al. https://doi.org/10.3390/su12187643
1176. Assessment of land use/cover impacts on runoff and sediment yield using hydrologic models: A review E. Ndulue et al. https://doi.org/10.5897/JENE2014.0482
1177. Effect of LULC data resolution on hydrological and erosion modeling using SWAT model S. Ait M’Barek et al. https://doi.org/10.1007/s40808-022-01537-w
1178. Do model parameters change under changing climate and land use in the upstream of the Lancang River Basin, China? D. Ma et al. https://doi.org/10.1080/02626667.2020.1782915
1179. Snow profile temperature measurements in spatiotemporal analysis of snowmelt in a subarctic forest-mire hillslope L. Meriö et al. https://doi.org/10.1016/j.coldregions.2018.03.013
1180. Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction J. Senent-Aparicio et al. https://doi.org/10.1016/j.biosystemseng.2018.04.022
1181. Modeling drought stress impacts under current and future climate for peanut in the semiarid pampas region of Argentina F. Palmero et al. https://doi.org/10.1016/j.fcr.2022.108615
1182. A theoretical model to predict the amounts of sugar and water in a binary mixture and its experimental validation by time domain nuclear magnetic resonance A. Sultana et al. https://doi.org/10.1093/ijfood/vvag049
1183. Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields S. Vogl et al. https://doi.org/10.5194/hess-16-2311-2012
1184. Downscaled rainfall projections in south Florida using self-organizing maps P. Sinha et al. https://doi.org/10.1016/j.scitotenv.2018.04.144
1185. Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani https://doi.org/10.1016/j.gsd.2022.100882
1186. Influence of Land Use Changes on the Longaví Catchment Hydrology in South-Center Chile H. Moya et al. https://doi.org/10.3390/hydrology9100169
1187. Regional-scale analysis of karst underground flow deduced from tracing experiments: examples from carbonate aquifers in Malaga province, southern Spain J. Barberá et al. https://doi.org/10.1007/s10040-017-1638-5
1188. Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China S. Wang et al. https://doi.org/10.1016/j.agwat.2019.03.027
1189. Impact of precipitation and temperature changes on hydrological responses of small-scale catchments in the Ethiopian Highlands T. Lemann et al. https://doi.org/10.1080/02626667.2016.1217415
1190. Regression Modeling of Baseflow and Baseflow Index for Michigan USA Y. Zhang et al. https://doi.org/10.3390/w5041797
1191. Structural changes to forests during regeneration affect water flux partitioning, water ages and hydrological connectivity: Insights from tracer-aided ecohydrological modelling A. Neill et al. https://doi.org/10.5194/hess-25-4861-2021
1192. What can we learn from multi-data calibration of a process-based ecohydrological model? S. Kuppel et al. https://doi.org/10.1016/j.envsoft.2018.01.001
1193. Dynamic Modelling of the Impact of Climate Change and Power Flow Management Options using STELLA: Application to the Steephill Falls Reservoir, Ontario, Canada S. Oni et al. https://doi.org/10.4296/cwrj3702831
1194. Performance Evaluation of the SRM and GRxJ—CemaNeige Models for Daily Streamflow Simulation in Two Catchments with Snow and Rain Dominated Hydrological Regimes B. Rivas et al. https://doi.org/10.3390/w17233413
1195. Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system J. Wu et al. https://doi.org/10.1007/s11069-014-1204-6
1196. Assessing the uncertainty of maize yield without nitrogen fertilization A. Correndo et al. https://doi.org/10.1016/j.fcr.2020.107985
1197. Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model R. Deo et al. https://doi.org/10.1007/s00477-016-1265-z
1198. Development of a model to simulate soil heavy metals lateral migration quantity based on SWAT in Huanjiang watershed, China P. Qiao et al. https://doi.org/10.1016/j.jes.2018.06.020
1199. Practice makes the model: A critical review of stormwater green infrastructure modelling practice V. Pons et al. https://doi.org/10.1016/j.watres.2023.119958
1200. Evaluation of furrow irrigation performance: Case of Kasinthula Cane Growers Scheme in Malawi S. Phiri et al. https://doi.org/10.1080/20421338.2021.1889767
1201. A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models F. Zheng et al. https://doi.org/10.1029/2022WR033703
1202. On the use of streamflow transformations for hydrological model calibration G. Thirel et al. https://doi.org/10.5194/hess-28-4837-2024
1203. Impacts of afforestation on groundwater resource: a case study for Upper Yazoo River watershed, Mississippi, USA Y. Ouyang et al. https://doi.org/10.1080/02626667.2021.1876235
1204. Incorporating sediment connectivity index into MUSLE model to explore soil erosion and sediment yield relationships at event scale R. Hao et al. https://doi.org/10.1016/j.jhydrol.2022.128579
1205. Variations in near‐surface debris temperature through the summer monsoon on Khumbu Glacier, Nepal Himalaya M. Gibson et al. https://doi.org/10.1002/esp.4425
1206. Effects of runoff generation methods and simulation time steps on flood simulation: a case study in Liulin experimental watershed J. Li et al. https://doi.org/10.1007/s11069-024-06427-1
1207. On the Direct Calculation of Snow Water Balances Using Snow Cover Information A. Pistocchi et al. https://doi.org/10.3390/w9110848
1208. State and parameter update of a hydrodynamic-phytoplankton model using ensemble Kalman filter J. Huang et al. https://doi.org/10.1016/j.ecolmodel.2013.04.022
1209. Silifke-Göksu Deltasında Yüzey Suyu-Yer Altı Suyu İlişkisinin Modellenmesi M. KAVŞUT & R. YURTAL https://doi.org/10.47495/okufbed.1083851
1210. Introductory overview: Error metrics for hydrologic modelling – A review of common practices and an open source library to facilitate use and adoption E. Jackson et al. https://doi.org/10.1016/j.envsoft.2019.05.001
1211. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui https://doi.org/10.1029/2022WR033684
1212. Future spatial and temporal variation of blue and green water in the Jing River Basin of central China under changing climatic conditions Y. Zhan et al. https://doi.org/10.3389/fenvs.2024.1352544
1213. Emptying Water Towers? Impacts of Future Climate and Glacier Change on River Discharge in the Northern Tien Shan, Central Asia M. Shahgedanova et al. https://doi.org/10.3390/w12030627
1214. Reconstructing pre-instrumental streamflow in Eastern Australia using a water balance approach C. Tozer et al. https://doi.org/10.1016/j.jhydrol.2018.01.064
1215. Assessing the impacts of agricultural conservation practices on freshwater biodiversity under changing climate J. Acero Triana et al. https://doi.org/10.1016/j.ecolmodel.2021.109604
1216. Coupling a Regional Climate Model and a Distributed Hydrological Model to Assess Future Water Resources in Jinhua River Basin, East China Y. Xu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001007
1217. Wind-driven flow dynamics off the Northwestern Arabian Gulf Coast F. Al Senafi & A. Anis https://doi.org/10.1016/j.ecss.2019.106511
1218. Assessment of the AnnAGNPS model in simulating runoff and nutrients in a typical small watershed in the Taihu Lake basin, China Z. Li et al. https://doi.org/10.1016/j.catena.2015.06.007
1219. Impact of the Hoa Binh dam (Vietnam) on water and sediment budgets in the Red River basin and delta V. Vinh et al. https://doi.org/10.5194/hess-18-3987-2014
1220. Assessment of hydropower potential using GIS and hydrological modeling technique in Kopili River basin in Assam (India) B. Kusre et al. https://doi.org/10.1016/j.apenergy.2009.07.019
1221. Evaluation of reanalysis and global meteorological products in Beas river basin of North-Western Himalaya T. Bhattacharya et al. https://doi.org/10.1186/s40068-020-00186-1
1222. A multi-hydrological model ensemble prediction uncertainty estimation (e-PRUNE) framework C. Onyutha https://doi.org/10.2166/nh.2025.116
1223. Impact of climate change and climate anomalies on hydrologic and biogeochemical processes in an agricultural catchment of the Chesapeake Bay watershed, USA M. Wagena et al. https://doi.org/10.1016/j.scitotenv.2018.05.116
1224. Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery A. El-Alem et al. https://doi.org/10.3390/rs4082373
1225. The Impact of a Barrier Island Loss on Extreme Events in the Tampa Bay M. Ulm et al. https://doi.org/10.3389/fmars.2016.00056
1226. Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin, USA K. Yazzie & H. Chang https://doi.org/10.3390/cli5010007
1227. Assessment of the Environmental Fate of the Herbicides Flufenacet and Metazachlor with the SWAT Model N. Fohrer et al. https://doi.org/10.2134/jeq2011.0382
1228. Global Sensitivity Analysis-based Design of Low Impact Development Practices for Urban Runoff Management Under Uncertainty M. Hashemi & N. Mahjouri https://doi.org/10.1007/s11269-022-03140-1
1229. A multi-objective comparative framework for Enhanced GRACE-Groundwater comparative analysis M. Akl et al. https://doi.org/10.1016/j.jhydrol.2025.134403
1230. Impact of precipitation data density and duration on simulated flow dynamics and implications for ecohydrological modelling of semi-arid catchments of Southern Africa A. Watson et al. https://doi.org/10.1016/j.jhydrol.2020.125280
1231. Improving Hydrological Models With the Assimilation of Crowdsourced Data P. Avellaneda et al. https://doi.org/10.1029/2019WR026325
1232. Options for increasing Boro rice production in the saline coastal zone of Bangladesh D. Gaydon et al. https://doi.org/10.1016/j.fcr.2021.108089
1233. Ecosystem Evapotranspiration Partitioning and Its Spatial–Temporal Variation Based on Eddy Covariance Observation and Machine Learning Method L. Lu et al. https://doi.org/10.3390/rs15194831
1234. A Comparative Study of Potential Evapotranspiration Estimation by Eight Methods with FAO Penman–Monteith Method in Southwestern China D. Lang et al. https://doi.org/10.3390/w9100734
1235. Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX) A. Wunsch et al. https://doi.org/10.1016/j.jhydrol.2018.01.045
1236. Simulating carbon fluxes in boreal catchments: WSFS-Vemala model development and key insights M. Korppoo et al. https://doi.org/10.5194/hess-30-3095-2026
1237. Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network D. Zema et al. https://doi.org/10.1016/j.compag.2020.105280
1238. Evaluation of Simulated Strategies for Reducing Nitrate–Nitrogen Losses through Subsurface Drainage Systems S. Ale et al. https://doi.org/10.2134/jeq2010.0466
1239. Calibration and validation of SWAT model using stream flow and sediment load for Mojo watershed, Ethiopia Z. Biru & D. Kumar https://doi.org/10.1007/s40899-017-0189-1
1240. Desagregação de Chuvas Diárias no Estado de Mato Grosso M. Sabino et al. https://doi.org/10.1590/0102-77863740077
1241. Implications of Einstein–Maxwell dilaton–axion gravity from the black hole continuum spectrum I. Banerjee et al. https://doi.org/10.1093/mnras/staa3232
1242. Estimation of Peak Flow in Ungauged Catchments Using the Relationship between Runoff Coefficient and Curve Number N. Kim & M. Shin https://doi.org/10.3390/w10111669
1243. Potential predictability of suspended sediment concentration in the data constrained regions of the Mahanadi River basin, Eastern India R. Kar & A. Sarkar https://doi.org/10.1080/15715124.2021.2016782
1244. Modelling the effect of riparian vegetation restoration on sediment transport in a human‐impacted Brazilian catchment J. Monteiro et al. https://doi.org/10.1002/eco.1726
1245. Crop Residue Modeling and Mapping Using Landsat, ALI, Hyperion and Airborne Remote Sensing Data M. Galloza et al. https://doi.org/10.1109/JSTARS.2012.2222355
1246. Land Use and Land Cover Impact on Probable Maximum Flood and Sedimentation for Artificial Reservoirs: Case Study in the Western United States W. Yigzaw & F. Hossain https://doi.org/10.1061/(ASCE)HE.1943-5584.0001287
1247. The Role of Mangroves in the Retention of Heavy Metal (Chromium): A Simulation Study in the Thi Vai River Catchment, Vietnam A. Nguyen et al. https://doi.org/10.3390/ijerph17165823
1248. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. https://doi.org/10.1029/2021WR031065
1249. Improvement and comparison of likelihood functions for model calibration and parameter uncertainty analysis within a Markov chain Monte Carlo scheme Q. Cheng et al. https://doi.org/10.1016/j.jhydrol.2014.10.008
1250. Understanding Morphodynamic Changes of a Tidal River Confluence through Field Measurements and Numerical Modeling Q. Xie et al. https://doi.org/10.3390/w10101424
1251. Connectivity between estuaries influences nutrient transport, cycling and water quality K. Wild-Allen & J. Andrewartha https://doi.org/10.1016/j.marchem.2016.05.011
1252. Multivariate drought index: An information theory based approach for integrated drought assessment D. Rajsekhar et al. https://doi.org/10.1016/j.jhydrol.2014.11.031
1253. Configurational entropy theory for streamflow forecasting H. Cui & V. Singh https://doi.org/10.1016/j.jhydrol.2014.11.065
1254. Modeling the terrestrial N processes in a small mountain catchment through INCA-N: A case study in Taiwan M. Lu et al. https://doi.org/10.1016/j.scitotenv.2017.03.178
1255. Land use change scenarios and their effects on hydropower energy in the Amazon E. de Oliveira Serrão et al. https://doi.org/10.1016/j.scitotenv.2020.140981
1256. Application of Spatially Distributed Calibrated Hydrological Model in Evapotranspiration Simulation of Three Gorges Reservoir Area of China: A Case Study in the Madu River Basin J. Chen et al. https://doi.org/10.1007/s11769-022-1318-9
1257. Quantifying the contribution of tile drainage to basin-scale water yield using analytical and numerical models K. Schilling et al. https://doi.org/10.1016/j.scitotenv.2018.11.340
1258. Quantitative risk management for energy retrofit projects Y. Heo et al. https://doi.org/10.1080/19401493.2012.706388
1259. Evaluating the transferability of monthly water balance models under changing climate conditions Ž. Topalović et al. https://doi.org/10.1080/02626667.2020.1725238
1260. The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model G. Andiego et al. https://doi.org/10.4236/cweee.2018.71002
1261. Modeling the impact of nature-based solutions on dry season water availability: exploration of spatio-temporal configurations G. Mandavya et al. https://doi.org/10.1016/j.jhydrol.2026.135823
1262. The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean G. Zittis et al. https://doi.org/10.1016/j.atmosres.2017.03.002
1263. Modeling plant density and ponding water effects on flooded rice evapotranspiration and crop coefficients: critical discussion about the concepts used in current methods V. Aschonitis et al. https://doi.org/10.1007/s00704-017-2164-z
1264. Evaluation of precipitation reanalysis products for regional hydrological modelling in the Yellow River Basin C. Jiang et al. https://doi.org/10.1007/s00704-023-04758-w
1265. Water temperature modelling in a controlled environment: comparative study of heat budget equations V. Ouellet et al. https://doi.org/10.1002/hyp.9571
1266. Improvement of the R-SWAT-FME framework to support multiple variables and multi-objective functions Y. Wu & S. Liu https://doi.org/10.1016/j.scitotenv.2013.07.048
1267. A multi-temporal analysis of streamflow using multiple CMIP5 GCMs in the Upper Ayerawaddy Basin, Myanmar U. Ghimire et al. https://doi.org/10.1007/s10584-019-02444-3
1268. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. https://doi.org/10.1016/j.jhydrol.2023.129682
1269. Evaluation of Sediment Deposition in a Mediterranean Reservoir: Comparison of Long Term Bathymetric Measurements and SWAT Estimations F. Licciardello et al. https://doi.org/10.1002/ldr.2557
1270. Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows R. Ragab et al. https://doi.org/10.1080/02626667.2020.1764961
1271. Reprint:Examining the ecosystem service of nutrient removal in a coastal watershed C. Berg et al. https://doi.org/10.1016/j.ecoser.2016.12.001
1272. Daily river water levels from multi-mission altimetry: A reach-based regression method using the unique SWOT data geometry M. Halicki et al. https://doi.org/10.1016/j.jhydrol.2026.135367
1273. A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations T. Lan et al. https://doi.org/10.1029/2018WR023160
1274. Methodology for the Selection of the Representative Climate Scenario at Yongdam Watershed M. Felix et al. https://doi.org/10.1007/s12205-023-1834-x
1275. Climate change and traditional upland paddy farming: a Philippine case study M. Soriano & S. Herath https://doi.org/10.1007/s10333-019-00784-5
1276. A hydrological model skill score and revised R-squared C. Onyutha https://doi.org/10.2166/nh.2021.071
1277. A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models M. Gunarathna et al. https://doi.org/10.3390/agronomy10020285
1278. Quality Assessment of Small Urban Catchments Stormwater Models: A New Approach Using Old Metrics L. David & T. Mota https://doi.org/10.3390/hydrology9050087
1279. Serial droughts and loss of hydrologic resilience in a subtropical basin: The case of water inflow into the Cantareira reservoir system in Brazil during 2013–2021 L. Domingues & H. da Rocha https://doi.org/10.1016/j.ejrh.2022.101235
1280. Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach A. Kouame et al. https://doi.org/10.1016/j.agsy.2023.103667
1281. Quantifying the integrated water and carbon cycle in a data-limited karst basin using a process-based hydrologic model H. Qiu et al. https://doi.org/10.1007/s12665-019-8324-y
1282. Combining SWAT Model and Regionalization Approach to Estimate Soil Erosion under Limited Data Availability Conditions Y. Bouslihim et al. https://doi.org/10.1134/S1064229320090021
1283. Efficient flow calibration method for accurate estimation of baseflow using a watershed scale hydrological model (SWAT) W. Jang et al. https://doi.org/10.1016/j.ecoleng.2018.10.007
1284. Assessing the impact of flood inundation dynamics on an urban environment P. Gao et al. https://doi.org/10.1007/s11069-021-04868-6
1285. A rational performance criterion for hydrological model D. Liu https://doi.org/10.1016/j.jhydrol.2020.125488
1286. A model reconstruction of riverine nutrient fluxes and eutrophication in the Belgian Coastal Zone since 1984 P. Passy et al. https://doi.org/10.1016/j.jmarsys.2013.05.005
1287. Effect of rooting depth, plant density and planting date on maize (Zea mays L.) yield and water use efficiency in semi-arid Zimbabwe: Modelling with AquaCrop I. Nyakudya & L. Stroosnijder https://doi.org/10.1016/j.agwat.2014.08.024
1288. Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece G. Papacharalampous et al. https://doi.org/10.1007/s11269-018-2155-6
1289. Compartment Model for Estimating Element Content in a Water–Soil–Cotton System J. Huang et al. https://doi.org/10.2134/agronj2015.0293
1290. Validation of hydrological models: Conceptual basis, methodological approaches and a proposal for a code of practice D. Biondi et al. https://doi.org/10.1016/j.pce.2011.07.037
1291. A spatial impact assessment of human-induced intervention on hydrological regimes: a case study in the upper Srepok River basin, Central Highlands of Vietnam T. Ty et al. https://doi.org/10.1080/15715124.2011.595720
1292. Evaluation of large-scale precipitation data sets for water resources modelling in Central Asia M. Malsy et al. https://doi.org/10.1007/s12665-014-3107-y
1293. Flood Plain Modelling of Krishna Lower Basin Using Arcgis, Hec-Georas And Hec-Ras B. Jagadeesh & K. Veni https://doi.org/10.1088/1757-899X/1112/1/012024
1294. Artificial neural network modeling of self-compacting concrete mixed and cured with seawater for compressive strength and chloride penetration prediction T. De Alwis S et al. https://doi.org/10.1080/24705314.2025.2558425
1295. Multi-model data fusion as a tool for PUB: example in a Swedish mesoscale catchment J. Exbrayat et al. https://doi.org/10.5194/adgeo-29-43-2011
1296. Towards closure relations in the Representative Elementary Watershed (REW) framework containing observable parameters: Relations for Hortonian overland flow E. Vannametee et al. https://doi.org/10.1016/j.advwatres.2012.03.029
1297. Modeling Metal(loid)s Transport in Arid Mountain Headwater Andean Basin: A WASP-Based Approach D. Castillo et al. https://doi.org/10.3390/w17131905
1298. Simulating the Response of Sugarcane Production to Water Deficit Irrigation Using the AquaCrop Model O. Bahmani & S. Eghbalian https://doi.org/10.1007/s40003-018-0311-0
1299. Analyzing variation of the water table level with three-dimensional numerical simulations to assess reclamation techniques for an acidic tailings impoundment S. Broda et al. https://doi.org/10.1139/cgj-2019-0694
1300. High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 M. Uz et al. https://doi.org/10.1038/s41597-023-02887-5
1301. Evaluating Simulated Groundwater Contributions to Streamflow in a Data‐Scarce, Semi‐Arid Catchment in South Africa M. de Witt et al. https://doi.org/10.1002/hyp.70385
1302. Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam Y. Bui et al. https://doi.org/10.1002/hyp.9860
1303. Gauging the Ungauged: Regionalization of Flow Indices at Grid Level D. Althoff et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002067
1304. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. https://doi.org/10.1029/2022WR034092
1305. Climate change and stream temperature projections in the Columbia River basin: habitat implications of spatial variation in hydrologic drivers D. Ficklin et al. https://doi.org/10.5194/hess-18-4897-2014
1306. Probabilistic Predictions of Ecologically Relevant Hydrologic Indices Using a Hydrological Model J. Hernandez‐Suarez & A. Nejadhashemi https://doi.org/10.1029/2021WR031104
1307. Experimental investigation on local scour downstream of stepped chutes with a flip bucket A. Rajaei et al. https://doi.org/10.1080/23249676.2024.2346657
1308. Modeling Agricultural Watersheds with the Soil and Water Assessment Tool (SWAT): Calibration and Validation with a Novel Procedure for Spatially Explicit HRUs A. Teshager et al. https://doi.org/10.1007/s00267-015-0636-4
1309. Assessing impacts on irrigated agriculture using an ecosystem-based approach in a Mediterranean reservoir/lake N. Papastamkou & C. Doulgeris https://doi.org/10.1016/j.jhydrol.2025.134436
1310. Long-term water balance and sustainable production of Miscanthus energy crops in the Loess Plateau of China W. Liu et al. https://doi.org/10.1016/j.biombioe.2014.01.018
1311. An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion P. Lu et al. https://doi.org/10.1016/j.jhydrol.2021.126225
1312. A new Budyko framework extension using time series SARIMAX model M. Fathi et al. https://doi.org/10.1016/j.jhydrol.2019.01.037
1313. Stream classification using hierarchical artificial neural networks: A fluvial hazard management tool L. Besaw et al. https://doi.org/10.1016/j.jhydrol.2009.04.007
1314. Sediment transport modeling by the SWAT model using two scenarios in the watershed of Beni Haroun dam in Algeria Z. Kateb et al. https://doi.org/10.1007/s12517-020-05623-0
1315. Evaluation of runoff, peak flow and sediment yield for events simulated by the AnnAGNPS model in a belgian agricultural watershed D. Zema et al. https://doi.org/10.1002/ldr.1068
1316. Modelling the impact of Global Change on the hydrological system of the Aral Sea basin T. Aus der Beek et al. https://doi.org/10.1016/j.pce.2011.03.004
1317. Modelling the increased frequency of extreme sea levels in the Ganges–Brahmaputra–Meghna delta due to sea level rise and other effects of climate change S. Kay et al. https://doi.org/10.1039/C4EM00683F
1318. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland R. Deo & M. Şahin https://doi.org/10.1007/s10661-016-5094-9
1319. Improving riverine constituent concentration and flux estimation by accounting for antecedent discharge conditions Q. Zhang & W. Ball https://doi.org/10.1016/j.jhydrol.2016.12.052
1320. Downscaling future climate changes under RCP emission scenarios using CanESM2 climate model over the Bouregreg catchment, Morocco K. Mahdaoui et al. https://doi.org/10.1007/s40808-022-01683-1
1321. Hydrological response to climate change in Baro basin, Ethiopia, using representative concentration pathway scenarios T. Ebissa et al. https://doi.org/10.1186/s40068-024-00370-7
1322. Quantification of uncertainties in the hydrological models for streamflow prediction in a humid tropical river basins in India: a case study of the Chaliyar river basin, Kerala K. Naveena et al. https://doi.org/10.1007/s11600-026-01869-6
1323. Selecting hydrological models for developing countries: Perspective of global, continental, and country scale models over catchment scale models P. Paul et al. https://doi.org/10.1016/j.jhydrol.2021.126561
1324. Analysis of the correlation between the red EDGE vegetation indices and the gross primary productivity of winter wheat crop according to gas and spectrometric measurements in Baryshevsky district of Kiev region V. Lyalko et al. https://doi.org/10.36023/ujrs.2016.8.68
1325. Wheat Yield Responses to NPK Fertilizers and Nutrient Omissions for QUEFTS Model Validation in Tigray, North Ethiopia S. Mesfin et al. https://doi.org/10.3390/soilsystems10020027
1326. Prediction of soil and water conservation structure impacts on runoff and erosion processes using SWAT model in the northern Ethiopian highlands N. Melaku et al. https://doi.org/10.1007/s11368-017-1901-3
1327. Assessment of Integrated BMPs for Subbasin-Scale Soil Erosion Reduction Considering Spatially Distributed Farmland Characteristics J. Lee et al. https://doi.org/10.3390/agriculture15080893
1328. Uncertainty of Hydro-meteorological Predictions Due to Climate Change in the Republic of Korea T. Nkomozepi & S. Chung https://doi.org/10.3741/JKWRA.2014.47.3.257
1329. Quantifying Uncertainties in Modeling Climate Change Impacts on Hydropower Production G. Oyerinde et al. https://doi.org/10.3390/cli4030034
1330. Assessment of climate change impacts on runoff and hydrological drought risk using the VIC-3L model and four-variate D-vine copulas in the Upper Bhima basin, India R. Datta et al. https://doi.org/10.2166/wcc.2025.135
1331. A simulation research of impacts of the Lake Poyang hydraulic project on hydrology and hydrodynamics L. Geying et al. https://doi.org/10.18307/2015.0116
1332. Modeling the hydrodynamic interactions between the main channel and the floodplain at McCarran Ranch in the lower Truckee River, Nevada X. Chen et al. https://doi.org/10.5194/nhess-15-2161-2015
1333. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model C. Oeurng et al. https://doi.org/10.1016/j.jhydrol.2011.02.017
1334. Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. https://doi.org/10.1029/2019WR026933
1335. VARS and HDMR Sensitivity Analysis of Groundwater Flow Modeling through an Alluvial Aquifer Subject to Tidal Effects J. Samper et al. https://doi.org/10.3390/w16172526
1336. Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models M. Shin & C. Kim https://doi.org/10.3390/w13152126
1337. Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods Q. Ouyang & W. Lu https://doi.org/10.1007/s11269-017-1832-1
1338. Storm Water Management Model: Performance Review and Gap Analysis M. Niazi et al. https://doi.org/10.1061/JSWBAY.0000817
1339. A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches J. Yeh & Y. Wang https://doi.org/10.4018/JGIM.327277
1340. Technical analysis of wind energy potentials using a modified Weibull and Raleigh distribution model parameters approach in the Gambia T. Ayua & M. Emetere https://doi.org/10.1016/j.heliyon.2023.e20315
1341. Upscaling Tracer‐Aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization X. Yang et al. https://doi.org/10.1029/2022WR033033
1342. A Modified LAPSUS Model to Enhance the Effective Rainfall Estimation by SCS-CN Method M. Eshghizadeh et al. https://doi.org/10.1007/s11269-018-2002-9
1343. Multi-Variables-Driven Model Based on Random Forest and Gaussian Process Regression for Monthly Streamflow Forecasting N. Sun et al. https://doi.org/10.3390/w14111828
1344. Effects of Input Data Content on the Uncertainty of Simulating Water Resources C. Camargos et al. https://doi.org/10.3390/w10050621
1345. Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images V. Bindhu & B. Narasimhan https://doi.org/10.1016/j.isprsjprs.2014.12.005
1346. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia M. Al-Musaylh et al. https://doi.org/10.1016/j.aei.2017.11.002
1347. Drainage Discharge Design for Improved Hydrologic Performance of a Blue-Green Roof C. Chan et al. https://doi.org/10.14796/JWMM.C540
1348. Effects of projected climate change on the hydrology in the Mono Lake Basin, California D. Ficklin et al. https://doi.org/10.1007/s10584-012-0566-6
1349. Harmonization service and global library of models to support country-driven global information on salt-affected soils C. Omuto et al. https://doi.org/10.1038/s41598-023-40078-9
1350. Shifting water and energy controls on hydrological processes under climate change in the topographically complex Min–Tuo River Basin, southwest China M. Chen et al. https://doi.org/10.1016/j.ejrh.2026.103352
1351. Understanding hydrological flow paths in conceptual catchment models using uncertainty and sensitivity analysis E. Mockler et al. https://doi.org/10.1016/j.cageo.2015.08.015
1352. Quantitative accuracy assessment of the revised sparse Gash model using distinct time-step climatic parameters Y. Li et al. https://doi.org/10.2166/nh.2021.085
1353. Baseline-Conditioned Spatial Heterogeneity in Ensemble-Learning Correction for Global Hourly Sea-Level Reconstruction Y. Hao et al. https://doi.org/10.3390/jmse14080697
1354. A Fuzzy Transformation of the Classic Stream Sediment Transport Formula of Yang K. Kaffas et al. https://doi.org/10.3390/w12010257
1355. Integrated modeling for assessing climate change impacts on water resources and hydropower potential in the Himalayas R. Baniya et al. https://doi.org/10.1007/s00704-024-04863-4
1356. Modeling water scarcity and droughts for policy adaptation to climate change in arid and semiarid regions M. Kahil et al. https://doi.org/10.1016/j.jhydrol.2014.12.042
1357. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. https://doi.org/10.1016/j.jhydrol.2020.124897
1358. Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products M. Kallio et al. https://doi.org/10.5194/gmd-14-5155-2021
1359. Evaluation of the DNDCv.CAN model for simulating greenhouse gas emissions under crop rotations that include winter cover crops J. Singh et al. https://doi.org/10.1071/SR21075
1360. Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador A. Sucozhañay & R. Célleri https://doi.org/10.3390/w10091169
1361. Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data? A. Pelosi & G. Chirico https://doi.org/10.1016/j.agwat.2021.107169
1362. Using maximum likelihood to derive various distance-based goodness-of-fit indicators for hydrologic modeling assessment Q. Cheng et al. https://doi.org/10.1007/s00477-017-1507-8
1363. A reanalysis of fine particulate matter air pollution versus life expectancy in the United States G. Krstić https://doi.org/10.1080/10962247.2013.760357
1364. Advances in soil salinity diagnosis for mangrove swamp rice production in Guinea Bissau, West Africa G. Garbanzo et al. https://doi.org/10.1016/j.srs.2025.100231
1365. A Guideline for Successful Calibration and Uncertainty Analysis for Soil and Water Assessment: A Review of Papers from the 2016 International SWAT Conference K. Abbaspour et al. https://doi.org/10.3390/w10010006
1366. Particulate PhozzyLogic Index for policy makers—an index for a more accurate and transparent identification of critical source areas G. Hepp et al. https://doi.org/10.1016/j.jenvman.2022.114514
1367. Assessing improvements in environmental flow estimation through the use of a coupled surface-groundwater model G. Castellanos-Osorio et al. https://doi.org/10.1016/j.gsd.2025.101426
1368. Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT M. Muche et al. https://doi.org/10.1111/1752-1688.12819
1369. A multi‐storage groundwater concept for the SWAT model to emphasize nonlinear groundwater dynamics in lowland catchments M. Pfannerstill et al. https://doi.org/10.1002/hyp.10062
1370. Assessment of Best Management Practices on Hydrology and Sediment Yield at Watershed Scale in Mississippi Using SWAT D. Nepal & P. Parajuli https://doi.org/10.3390/agriculture12040518
1371. Comparison of SWAT and DLBRM for Hydrological Modeling of a Mountainous Watershed in Arid Northwest China L. Zhang et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001313
1372. Water detention structures as a flood mitigation strategy: A case study of the Elgin Creek Basin M. Asadzadeh et al. https://doi.org/10.1016/j.ejrh.2026.103347
1373. Estimation of energy-saving potential and indoor thermal comfort by the central control of the heating curve in old apartment buildings H. Hajian et al. https://doi.org/10.1051/e3sconf/202124609002
1374. Assessing Post-Monsoon Seasonal Soil Loss over Un-Gauged Stations of the Dwarkeswar and Shilabati Rivers, West Bengal, India A. Mukherjee et al. https://doi.org/10.3390/earth5010003
1375. Efficacy of rain barrels and rain gardens to reduce urban pluvial flooding in densely built-up residential areas: a case study on Miyahara-Cho in Saitama City, Japan J. Lin et al. https://doi.org/10.1007/s44213-023-00024-x
1376. Hydro-climate and biogeochemical processes control watershed organic carbon inflows: Development of an in-stream organic carbon module coupled with a process-based hydrologic model X. Du et al. https://doi.org/10.1016/j.scitotenv.2020.137281
1377. Calibration of hydrological models using flow-duration curves I. Westerberg et al. https://doi.org/10.5194/hess-15-2205-2011
1378. Numerical investigation of a high temperature heat injection test S. Heldt et al. https://doi.org/10.1016/j.jhydrol.2021.126229
1379. Hydrogeological investigation of an oasis-system aquifer in arid southeastern Morocco by development of a groundwater flow model I. Bouaamlat et al. https://doi.org/10.1007/s10040-016-1409-8
1380. Capturing episodic impacts of environmental signals M. Mendiolar et al. https://doi.org/10.1016/j.envsoft.2023.105837
1381. Towards generalized hydrological forecasting using transformer models for 120 h streamflow prediction B. Demiray & I. Demir https://doi.org/10.1088/3049-4753/ae81b8
1382. Alterations in Hydrological Responses under Changing Climate and Land Use/Land Cover across Contrasting Agroecological Environments: A Case Study on the Chemoga Watershed in the Upper Blue Nile Basin, Ethiopia T. Meshesha et al. https://doi.org/10.3390/w16071037
1383. Spatial and temporal patterns of drought under different scenarios for Türkiye in the 21st century M. Yıldız et al. https://doi.org/10.1016/j.pce.2025.104028
1384. Changes in potential evapotranspiration and surface runoff in 1981–2010 and the driving factors in Upper Heihe River Basin in Northwest China K. Luo et al. https://doi.org/10.1002/hyp.10974
1385. Multi-Scenario Integration Comparison of CMADS and TMPA Datasets for Hydro-Climatic Simulation over Ganjiang River Basin, China Q. Wang et al. https://doi.org/10.3390/w12113243
1386. Hydrological simulation of ungauged basins via forcing by large‐scale hydrology models C. Skoulikaris & M. Piliouras https://doi.org/10.1002/hyp.15044
1387. Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling A. Parajuli et al. https://doi.org/10.1002/hyp.13756
1388. Evaluation of Different Objective Functions Used in the SUFI-2 Calibration Process of SWAT-CUP on Water Balance Analysis: A Case Study of the Pursat River Basin, Cambodia D. Sao et al. https://doi.org/10.3390/w12102901
1389. Correlating check dam sedimentation and rainstorm characteristics on the Loess Plateau, China X. Li et al. https://doi.org/10.1016/j.geomorph.2016.04.017
1390. Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data J. Álvarez-Estrella et al. https://doi.org/10.3390/w16070968
1391. Assessing the Impact of Storm Drains at Road Embankments on Diffuse Particulate Phosphorus Emissions in Agricultural Catchments G. Hepp & M. Zessner https://doi.org/10.3390/w11102161
1392. Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. https://doi.org/10.1029/2019WR025605
1393. Multisite calibration of a semi-distributed hydrologic and thermal model in a large Canadian watershed M. Khorsandi et al. https://doi.org/10.1080/02626667.2022.2132161
1394. Hydrologic processes simulation using the conceptual model Zygos: the example of Xynias drained Lake catchment (central Greece) N. Charizopoulos & A. Psilovikos https://doi.org/10.1007/s12665-016-5565-x
1395. Assessing Digital Soil Inventories for Predicting Streamflow in the Headwaters of the Blue Nile A. Adem et al. https://doi.org/10.3390/hydrology7010008
1396. Assessing applicability of two gridded precipitation datasets for hydrological simulation in a poorly gauged river basin towards supporting sustainable water resources management L. Nguyen et al. https://doi.org/10.1016/j.envres.2023.116956
1397. A user-friendly software package for VIC hydrologic model development S. Wi et al. https://doi.org/10.1016/j.envsoft.2017.09.006
1398. Discharge Assessment by Using Integrated Hydrologic Model for Environmental Technology Development M. Ali et al. https://doi.org/10.4028/www.scientific.net/AMR.911.378
1399. Potentials and limitations of modelling spatio‐temporal patterns of soil moisture in a high mountain catchment using WaSiM‐ETH O. Rößler & J. Löffler https://doi.org/10.1002/hyp.7663
1400. Raingauge vs IMERG precipitation data for SWAT modelling: balancing accuracy and cost for reliable hydrological observatories in a tropical river basin Z. Ramahaimandimby et al. https://doi.org/10.1080/02626667.2025.2608134
1401. Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin H. Moya et al. https://doi.org/10.3390/w16081138
1402. Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region E. Quichimbo et al. https://doi.org/10.1002/hyp.15047
1403. Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling S. Shin et al. https://doi.org/10.1016/j.jhydrol.2023.129458
1404. Impacts of Climate Change on Low Flows at Tang Panj Sezar Subbasin, Southwest of Iran M. Mozayyan et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001578
1405. Modelling the Effects of Changes in Forest Cover and Climate on Hydrology of Headwater Catchments in South-Central Chile G. Barrientos et al. https://doi.org/10.3390/w12061828
1406. Misrepresentation and amendment of soil moisture in conceptual hydrological modelling L. Zhuo & D. Han https://doi.org/10.1016/j.jhydrol.2016.02.033
1407. Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M U. Okkan & O. Fistikoglu https://doi.org/10.1007/s00704-013-1005-y
1408. Hydrogeological responses in tropical mountainous springs J. Salas-Navarro et al. https://doi.org/10.1080/10256016.2018.1546701
1409. Effects of landuse change on the hydrologic regime of the Mae Chaem river basin, NW Thailand P. Thanapakpawin et al. https://doi.org/10.1016/j.jhydrol.2006.10.012
1410. Modelling water quantity parameters using Artificial Intelligence techniques, A case study Abu-Ziriq Marsh in south of Iraq. M. Al-Mukhtar et al. https://doi.org/10.1088/1757-899X/737/1/012156
1411. Determining return water levels at ungauged coastal sites: a case study for northern Germany A. Arns et al. https://doi.org/10.1007/s10236-015-0814-1
1412. Effects of Land Use Changes on Streamflow and Sediment Yield in Atibaia River Basin—SP, Brazil F. Mendonça dos Santos et al. https://doi.org/10.3390/w12061711
1413. Evaluating the ability of a hydrologic model to replicate hydro‐ecologically relevant indicators R. Shrestha et al. https://doi.org/10.1002/hyp.9997
1414. Reliability of Dynamic Models A. Haque & S. Sumaiya https://doi.org/10.37394/232026.2025.7.9
1415. Optimality of antecedent precipitation index and its application X. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126027
1416. Effects of Fibrous Roots of Four Herbaceous Species on Water Flow Velocity and Rill Detachment Capacity M. Parhizkar et al. https://doi.org/10.1002/eco.70016
1417. Simulating the hydrological performance of low impact development in shallow groundwater via a modified SWMM K. Zhang et al. https://doi.org/10.1016/j.jhydrol.2018.09.006
1418. Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions H. Apaydin et al. https://doi.org/10.1016/j.jhydrol.2021.126506
1419. Exploring the hydrological robustness of model-parameter values with alpha shapes J. Guerrero et al. https://doi.org/10.1002/wrcr.20533
1420. Exploring the spatial and temporal changes of compound disasters: A case study in Gaoping River, Taiwan T. Chen et al. https://doi.org/10.1016/j.crm.2024.100617
1421. Evaluating best machine learning model for discharge prediction under dam-regulated river using a novel approach of hydro feature engineering P. Dubey et al. https://doi.org/10.1016/j.watres.2026.126564
1422. Evaluation of compression index of marine fine-grained soils by the use of index tests K. Kootahi & G. Moradi https://doi.org/10.1080/1064119X.2016.1213775
1423. A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques T. Paepae et al. https://doi.org/10.3390/s22197338
1424. A wavelet-based approach to assessing timing errors in hydrologic predictions Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2010.11.040
1425. Evaluation of different precipitation inputs on streamflow simulation in Himalayan River basin R. Khatakho et al. https://doi.org/10.1016/j.jhydrol.2021.126390
1426. Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers C. Szczypta et al. https://doi.org/10.5194/hess-16-3351-2012
1427. Determining Hydrological Variability Using a Multi-Catchment Model Approach for the Western Cape, South Africa A. Watson et al. https://doi.org/10.3390/su132414058
1428. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
1429. Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows P. Roy et al. https://doi.org/10.1007/s00477-017-1470-4
1430. Development and validation of a basin scale model PCPF-1@SWAT for simulating fate and transport of rice pesticides J. Boulange et al. https://doi.org/10.1016/j.jhydrol.2014.05.013
1431. Increasing parameter identifiability through clustered time-varying sensitivity analysis L. Wang et al. https://doi.org/10.1016/j.envsoft.2024.106189
1432. Simulating the impact of watershed management for surface water quality protection: A case study on reducing inorganic nitrogen load at a watershed scale Y. Chen et al. https://doi.org/10.1016/j.ecoleng.2013.10.023
1433. Modelling of Response of the Growth and Yield of Soybean to Full and Deficit Irrigation by Using Aquacrop O. Adeboye et al. https://doi.org/10.1002/ird.2073
1434. Application of Soft Computing Models to Daily Average Temperature Analysis M. Göçken et al. https://doi.org/10.19072/ijet.105706
1435. Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal R. Talchabhadel et al. https://doi.org/10.1029/2020EA001518
1436. A method to devise multiple model structures for urban flood inundation uncertainty T. Quintana-Romero & J. Leandro https://doi.org/10.1016/j.jhydrol.2021.127246
1437. Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale G. Lyle et al. https://doi.org/10.3390/rs5041549
1438. How do climate and land use changes affect the water cycle? Modelling study including future drought events prediction using reliable drought indices M. Afzal & R. Ragab https://doi.org/10.1002/ird.2467
1439. Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data J. Lamontagne et al. https://doi.org/10.1029/2020WR027101
1440. Advances in ungauged streamflow prediction using artificial neural networks L. Besaw et al. https://doi.org/10.1016/j.jhydrol.2010.02.037
1441. Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM K. Solander et al. https://doi.org/10.1175/JHM-D-15-0056.1
1442. Information-theoretic summary statistics for diagnostic calibration of the groundwater models using approximate Bayesian computation M. Khorshidi et al. https://doi.org/10.1007/s12665-023-11264-9
1443. Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany C. Gornott & F. Wechsung https://doi.org/10.1016/j.agrformet.2015.10.005
1444. Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors A. Ahmed et al. https://doi.org/10.3390/rs14051136
1445. Application of SWAT model to investigate nitrate leaching in Hamadan–Bahar Watershed, Iran S. Akhavan et al. https://doi.org/10.1016/j.agee.2010.10.015
1446. A comparison of ASCE and FAO-56 reference evapotranspiration for a 15-min time step in humid climate conditions A. Suleiman & G. Hoogenboom https://doi.org/10.1016/j.jhydrol.2009.06.020
1447. Sensitivity of Reservoir Performance to Hydroclimatic Forcing Pathways: A Coupled SWAT+–MSPA-2024 Framework I. Saket Oskoui et al. https://doi.org/10.3390/w18161930
1448. Experimental evaluation of the effect of storm movement on peak discharge J. Liang & C. Melching https://doi.org/10.1016/j.ijsrc.2015.03.004
1449. The urgency for investment on local data for advancing food assessments in Africa: A review case study for APSIM crop modeling A. Carcedo et al. https://doi.org/10.1016/j.envsoft.2023.105633
1450. Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia G. Tiruneh et al. https://doi.org/10.1007/s12517-021-08814-5
1451. Simulation of the improving effect of graphene visible‐light photocatalysis using the MIKE11 model of an urban landscape river in the Chaohu Lake Basin, China H. Xiong et al. https://doi.org/10.1111/nrm.12344
1452. Sea‐level rise impacts on the temporal and spatial variability of extreme water levels: A case study for St. Peter‐Ording, Germany S. Santamaria‐Aguilar et al. https://doi.org/10.1002/2016JC012579
1453. Modeling climate change impacts on blue and green water in the Ethiopian upper Blue Nile basin D. Ayalew et al. https://doi.org/10.1016/j.ejrh.2026.103871
1454. Analyzing the Impact of Streamflow Drought on Hydroelectricity Production: A Global‐Scale Study W. Wan et al. https://doi.org/10.1029/2020WR028087
1455. A Novel Approach for the Integral Management of Water Extremes in Plain Areas C. Guevara Ochoa et al. https://doi.org/10.3390/hydrology6030070
1456. The application of CHIRPS-based Pitman modelling in South Africa J. Kibii & J. Du Plessis https://doi.org/10.1016/j.pce.2023.103475
1457. Random Forests for Global and Regional Crop Yield Predictions J. Jeong et al. https://doi.org/10.1371/journal.pone.0156571
1458. Water, Watt, and Warming: Hydropower Futures in the Eastern Nepal Himalayas under Climate Change A. Poudel et al. https://doi.org/10.2166/wcc.2026.359
1459. Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016 Q. Wu et al. https://doi.org/10.3390/rs15051172
1460. Optimize the real-time operation strategy of urban reservoirs in order to reduce flooding W. Feng et al. https://doi.org/10.1007/s40974-022-00266-1
1461. Yield response of sugar beets to water stress under Western European conditions N. Shrestha et al. https://doi.org/10.1016/j.agwat.2009.10.005
1462. Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions M. Safeeq & A. Fares https://doi.org/10.1007/s00704-011-0438-4
1463. A joined multi-metric calibration of river discharge and nitrate loads with different performance measures M. Haas et al. https://doi.org/10.1016/j.jhydrol.2016.03.001
1464. Crop simulation mediated assessment of climate change impact on rice grown under temperate high-altitude valley of Kashmir A. Shakoor et al. https://doi.org/10.1007/s00704-021-03880-x
1465. Data-limited models to predict river temperatures for aquatic species at risk1 J. Rosencranz et al. https://doi.org/10.1139/cjfas-2020-0294
1466. Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada S. Liu et al. https://doi.org/10.4141/cjss2013-096
1467. Assessment of SMOS soil moisture retrieval parameters using tau–omega algorithms for soil moisture deficit estimation P. Srivastava et al. https://doi.org/10.1016/j.jhydrol.2014.07.056
1468. Modelación del desempeño hidrológico de techos verdes en ciudades andinas tropicales usando SWMM J. Tabares-Catimay et al. https://doi.org/10.22507/pml.v14n1a2
1469. Graphical decomposition model to estimate hourly global solar radiation considering weather stochasticity Y. Li et al. https://doi.org/10.1016/j.enconman.2023.116719
1470. A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China J. Li et al. https://doi.org/10.3390/su10082819
1471. The impact of sea level rise on storm surge water levels in the northern part of the German Bight A. Arns et al. https://doi.org/10.1016/j.coastaleng.2014.12.002
1472. Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. https://doi.org/10.1007/s10661-023-11649-0
1473. Quasar continuum spectrum disfavors black holes with a magnetic monopole charge I. Banerjee et al. https://doi.org/10.1103/PhysRevD.105.064073
1474. A new framework for modeling decentralized low impact developments using Soil and Water Assessment Tool Y. Her et al. https://doi.org/10.1016/j.envsoft.2017.06.005
1475. Application of an Integrated SWAT–MODFLOW Model to Evaluate Potential Impacts of Climate Change and Water Withdrawals on Groundwater–Surface Water Interactions in West-Central Alberta D. Chunn et al. https://doi.org/10.3390/w11010110
1476. Predicting Mechanical Properties of Polymer Materials Using Rate-Dependent Material Models: Finite Element Analysis of Bespoke Upper Limb Orthoses S. Mian et al. https://doi.org/10.3390/polym16091220
1477. Development and application of a catchment scale pesticide fate and transport model for use in drinking water risk assessment S. Pullan et al. https://doi.org/10.1016/j.scitotenv.2016.04.135
1478. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. https://doi.org/10.5194/hess-21-6201-2017
1479. Developing tailored climate change scenarios for hydrological impact assessments V. Ntegeka et al. https://doi.org/10.1016/j.jhydrol.2013.11.001
1480. A goodness of fit measure related to r2 for model performance assessment W. Bardsley https://doi.org/10.1002/hyp.9914
1481. Long-term daily prediction of saltwater intrusion based on the long memory-double autoregressive model K. Lin et al. https://doi.org/10.2166/hydro.2025.047
1482. Parallel multi-objective calibration of a component-based river temperature model C. Buahin et al. https://doi.org/10.1016/j.envsoft.2019.02.012
1483. Potential of rainfall data hybridization in a data-scarce region F. Wambura https://doi.org/10.1016/j.sciaf.2020.e00449
1484. Küçük Melen İğneler Havzası’ndaki taşkınların hidrolojik modelleme yöntemiyle (HEC-HMS) belirlenmesi T. Ergül & O. Aydın https://doi.org/10.29128/geomatik.1492923
1485. Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments S. Abbenante et al. https://doi.org/10.1080/02626667.2024.2413422
1486. Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model B. Trang & M. Van Trinh https://doi.org/10.1515/gps-2023-0187
1487. Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability G. Papacharalampous & H. Tyralis https://doi.org/10.1016/j.jhydrol.2020.125205
1488. Data-driven methods to improve baseflow prediction of a regional groundwater model T. Xu & A. Valocchi https://doi.org/10.1016/j.cageo.2015.05.016
1489. Evaluation of water movement and nitrate dynamics in a lysimeter planted with an orange tree V. Phogat et al. https://doi.org/10.1016/j.agwat.2013.05.017
1490. Investigating regionalization techniques for large-scale hydrological modelling L. Pagliero et al. https://doi.org/10.1016/j.jhydrol.2018.12.071
1491. A simplified modeling approach for optimization of urban river systems W. Feng et al. https://doi.org/10.1016/j.jhydrol.2023.129689
1492. An operational dynamical neuro-forecasting model for hydrological disasters G. de Lima et al. https://doi.org/10.1007/s40808-016-0145-3
1493. Assessing the impact of a multimetric calibration procedure on modelling performance in a headwater catchment in Mau Forest, Kenya A. Kamamia et al. https://doi.org/10.1016/j.ejrh.2018.12.005
1494. Evaluating the performance of hydrological models with joint multifractal spectra Z. Bai et al. https://doi.org/10.1080/02626667.2022.2114834
1495. Climate change impact on the hydrological budget of a large Mediterranean island S. Nerantzaki et al. https://doi.org/10.1080/02626667.2019.1630741
1496. Comparison of Experimental and Numerical Model Results of Oscillating Water Column System Under Regular Wave Conditions F. Mahnamfar et al. https://doi.org/10.1007/s40996-019-00259-x
1497. PickT: A Decision-Making Tool for the Optimal Pickling Process Operation C. Crișan et al. https://doi.org/10.3390/ma16165567
1498. Including effects of watershed heterogeneity in the curve number method using variable initial abstraction V. Santikari & L. Murdoch https://doi.org/10.5194/hess-22-4725-2018
1499. Застосування гiперспектральних iндексiв для визначення змiн трав’яного покриву за даними спектрометричної зйомки В. Лялько et al. https://doi.org/10.15407/dopovidi2014.04.105
1500. Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure E. van der Laan et al. https://doi.org/10.1016/j.scitotenv.2024.172678
1501. Climate change impacts on potential evapotranspiration and crop water demand in the Tana Subbasin, upper blue Nile Basin, Ethiopia H. Eshetie et al. https://doi.org/10.2166/wcc.2026.023
1502. Setting up a hydrological model of Alberta: Data discrimination analyses prior to calibration M. Faramarzi et al. https://doi.org/10.1016/j.envsoft.2015.09.006
1503. Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia A. Berhane et al. https://doi.org/10.1016/j.heliyon.2024.e36084
1504. Developing an optimization model in drip fertigation management to consider environmental issues and supply plant requirements N. Azad et al. https://doi.org/10.1016/j.agwat.2018.06.030
1505. Hydrology of peat estimated from near-surface water contents D. Dimitrov et al. https://doi.org/10.1080/02626667.2022.2099281
1506. Quantifying the Natural Climate Solution Potential of Agricultural Systems by Combining Eddy Covariance and Remote Sensing S. Wiesner et al. https://doi.org/10.1029/2022JG006895
1507. Application of R-Squared in The Analysis of Estimated Water Encroachment into a Reservoir O. Oloro https://doi.org/10.36263/nijest.2020.01.0155
1508. Evaluating the impacts of climate and land use/land cover (LU/LC) dynamics on the Hydrological Responses of the Upper Blue Nile in the Central Highlands of Ethiopia M. Kidane et al. https://doi.org/10.1007/s41324-018-0222-y
1509. Multi-Source Remote Sensing Reveals Multi-Timescale Variations of Lakes on the Tibetan Plateau: A Case Study of Three Representative Lakes J. Wu et al. https://doi.org/10.3390/w18151899
1510. Impacts of supplementary drainage on the water balance of a poorly drained agricultural field O. Häggblom et al. https://doi.org/10.1016/j.agwat.2019.03.039
1511. A methodology for assessing spatio-temporal dynamics of flood regulating services S. Mori et al. https://doi.org/10.1016/j.ecolind.2021.107963
1512. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh https://doi.org/10.1029/2022WR034188
1513. Role of impoundment and irrigation in intensive agriculture watersheds N. Mitra et al. https://doi.org/10.1016/j.jhydrol.2025.134075
1514. Positive feedback of forest cover in maintaining hydrological equilibrium under recent climate change projections and land use change scenarios N. Kumar et al. https://doi.org/10.1016/j.jastp.2026.106880
1515. Impact of 1.5 and 2.0 °C global warming on the hydrology of the Faleme river basin M. Mbaye et al. https://doi.org/10.1016/j.ejrh.2020.100719
1516. Development of a geo-information system embedding a spatially distributed hydrological model for the preliminary assessment of the hydropower potential of historical hydro sites in poorly gauged areas K. Soulis et al. https://doi.org/10.1016/j.renene.2016.02.013
1517. Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms C. Onyutha & G. Deiana https://doi.org/10.1155/2022/7104752
1518. Predicting future salinity variability in the Ca Mau Peninsula due to Climate Change D. Anh et al. https://doi.org/10.1051/e3sconf/202449603002
1519. Impacts of land use and climate changes on hydrological responses in a Peruvian Andean watershed D. Saavedra et al. https://doi.org/10.2166/wcc.2025.820
1520. Hydrologic Modelling Calibration for Operational Flood Forecasting J. Mendes & R. Maia https://doi.org/10.1007/s11269-016-1509-1
1521. Evaluating the dynamics of hydroclimate and streamflow for data-scarce areas using MIKE11-NAM model in Bilate river basin, Ethiopia A. Nannawo et al. https://doi.org/10.1007/s40808-022-01455-x
1522. High-resolution global grids of revised Priestley–Taylor and Hargreaves–Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation V. Aschonitis et al. https://doi.org/10.5194/essd-9-615-2017
1523. On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling J. Bergeron et al. https://doi.org/10.3390/hydrology8010036
1524. Enhancing flood forecasts: A comprehensive neural network approach for groundwater flooding in lowland karst areas R. Tabbussum et al. https://doi.org/10.1016/j.envsoft.2025.106591
1525. A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales N. Tien Thanh https://doi.org/10.3390/cli6040079
1526. Generalized Split-Sample Test Interpretation Using Rainfall Runoff Information Gain A. Ben Jaafar & Z. Bargaoui https://doi.org/10.1061/(ASCE)HE.1943-5584.0001868
1527. Modelling runoff on ceramic tile roofs using the kinematic wave equations A. Silveira et al. https://doi.org/10.2166/wst.2016.148
1528. Water yield and biomass production for on a eucalypt-dominated Mediterranean catchment under different climate scenarios J. Rocha et al. https://doi.org/10.1007/s11676-022-01590-2
1529. Assessing the climate change adaptability of sustainable land management practices regarding water availability and quality: A case study in the Sorraia catchment, Portugal E. van der Laan et al. https://doi.org/10.1016/j.scitotenv.2023.165438
1530. Runoff simulation and flood risk assessment of heavy rainfall-flood events in the Juhe River Basin, Beijing Q. Qin et al. https://doi.org/10.1007/s12665-026-13120-y
1531. Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products N. Belabid et al. https://doi.org/10.3390/rs11030252
1532. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction Z. Yaseen et al. https://doi.org/10.1016/j.jhydrol.2018.11.069
1533. Evaluation of the impact of climate and land use / land cover change on hydrological response in Gelna watershed M. Wldmchel et al. https://doi.org/10.1371/journal.pclm.0000483
1534. Comparative Assessment of Environmental Flow Estimation Methods in a Mediterranean Mountain River C. Papadaki et al. https://doi.org/10.1007/s00267-017-0878-4
1535. Potential impacts of land use/cover and climate changes on ecologically relevant flows F. Dosdogru et al. https://doi.org/10.1016/j.jhydrol.2020.124654
1536. Deriving Rain Threshold for Early Warning Based on a Coupled Hydrological-Hydraulic Model V. Ivanescu & R. Drobot https://doi.org/10.1515/mmce-2016-0014
1537. Estimate of Continuous Sediment Graphs in a Basin, Using a Composite Mathematical Model K. Kaffas & V. Hrissanthou https://doi.org/10.1007/s40710-015-0069-3
1538. Investigating the applicability and assumptions of the regression relationship between flow discharge and nitrogen concentrations for load estimation J. Song et al. https://doi.org/10.1016/j.heliyon.2023.e23603
1539. The effects of peat thickness and water table depth on CO2 and N2O emissions from agricultural peatlands – a process-based modelling approach H. Kajasilta et al. https://doi.org/10.5194/bg-23-3567-2026
1540. Projections of future floods and hydrological droughts in Europe under a +2°C global warming P. Roudier et al. https://doi.org/10.1007/s10584-015-1570-4
1541. Improvement and comparative assessment of a hydrological modelling approach on 20 catchments of various sizes under different climate conditions C. Gaba et al. https://doi.org/10.1080/02626667.2017.1330542
1542. The nexus of inhabitants and impervious surfaces at city scale — wastewater and stormwater travel time distributions and an approach to calibrate diurnal variations T. Kaeseberg et al. https://doi.org/10.1080/1573062X.2018.1529189
1543. Evaluating a parsimonious watershed model versus SWAT to estimate streamflow, soil loss and river contamination in two case studies in Tietê river basin, São Paulo, Brazil F. Santos et al. https://doi.org/10.1016/j.ejrh.2020.100685
1544. A novel integrated approach to predict the sodium absorption ratio (SAR) of groundwater sustainability using deep learning models and SHAP approach K. Moharir et al. https://doi.org/10.1007/s13201-025-02521-2
1545. Prediction of effluent concentration in a wastewater treatment plant using machine learning models H. Guo et al. https://doi.org/10.1016/j.jes.2015.01.007
1546. Using supervised machine learning for regional hydrological hazard estimation in metropolitan France Q. Ding & P. Arnaud https://doi.org/10.1016/j.ejrh.2024.101872
1547. Surface mining and reclamation effects on flood response of watersheds in the central Appalachian Plateau region J. Ferrari et al. https://doi.org/10.1029/2008WR007109
1548. Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia L. Tarasova et al. https://doi.org/10.1002/2015WR018551
1549. Drought interval simulation using functional data analysis U. Beyaztas & Z. Yaseen https://doi.org/10.1016/j.jhydrol.2019.124141
1550. Adequacy of Linear Models for Estimating Stormwater Best Management Practice Treatment Performance R. Gilliom et al. https://doi.org/10.1061/JSWBAY.0000921
1551. Multiscale investigations in a mesoscale catchment – hydrological modelling in the Gera catchment P. Krause et al. https://doi.org/10.5194/adgeo-9-53-2006
1552. Evaluation of METRIC-derived ET fluxes over irrigated alfalfa crop in desert conditions using scintillometer measurements K. Al-Gaadi et al. https://doi.org/10.1007/s12517-016-2469-8
1553. Estudo da autodepuração do rio Jordão, localizado na bacia hidrográfica do rio Dourados M. Salla et al. https://doi.org/10.1590/S1413-41522013000200002
1554. A More Efficient Rainfall Intensity-Duration-Frequency Relationship by Using an “at-site” Regional Frequency Analysis: Application at Mediterranean Climate Locations J. Ayuso-Muñoz et al. https://doi.org/10.1007/s11269-015-0993-z
1555. Using the nonlinear aquifer storage–discharge relationship to simulate the base flow of glacier- and snowmelt-dominated basins in northwest China R. Gan & Y. Luo https://doi.org/10.5194/hess-17-3577-2013
1556. Productivity growth and labor reallocation: Latin America versus East Asia M. Üngör https://doi.org/10.1016/j.red.2016.12.004
1557. Hydrologic modeling of the Aliakmon River in Greece using HEC–HMS and open data D. Frysali et al. https://doi.org/10.1007/s41207-023-00374-2
1558. Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis H. Kim & S. Lee https://doi.org/10.1002/hyp.9785
1559. The impact of the variability and periodicity of rainfall on surface water supply systems in Scotland M. Afzal et al. https://doi.org/10.2166/wcc.2015.136
1560. Comparison between Calculation and Measurement of Total Sediment Load: Application to Streams of NE Greece L. Avgeris et al. https://doi.org/10.3390/geosciences12020091
1561. Soil Texture‐Based Parameterisation and Hydrological Insights of a Fully Coupled Surface and Subsurface Model at the Hubbard Brook Experimental Forest, USA K. Norouzi‐Moghanjoghi et al. https://doi.org/10.1002/hyp.70045
1562. Effects of Repeated Low‐Severity Fires on Particle Detachment Capacity and Soil Properties in Rills of Semi‐Arid Forests M. Parhizkar et al. https://doi.org/10.1002/ldr.5593
1563. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions Z. Yaseen https://doi.org/10.1016/j.chemosphere.2021.130126
1564. Streamflow prediction in ungauged basins: How dissimilar are drainage basins? P. Istalkar & B. Biswal https://doi.org/10.1016/j.jhydrol.2024.131357
1565. A novel high-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds – development and hydrological evaluation https://doi.org/10.1175/JHM-D-20-0285.1
1566. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble D. Althoff et al. https://doi.org/10.1007/s00477-021-01980-8
1567. Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning A. Talei et al. https://doi.org/10.1016/j.jhydrol.2013.02.022
1568. Eutrophication modelling of Amirkabir Reservoir (Iran) using an artificial neural network approach S. Homayoun Aria et al. https://doi.org/10.1111/lre.12254
1569. Improving runoff estimates from regional climate models: a performance analysis in Spain D. González-Zeas et al. https://doi.org/10.5194/hess-16-1709-2012
1570. SWMM-UrbanEVA: A Model for the Evapotranspiration of Urban Vegetation B. Hörnschemeyer et al. https://doi.org/10.3390/w13020243
1571. Estimation of grassland productivity in the period 1978 − 2021 and prediction under different climate scenarios in the period 2021 − 2100 in the Qinghai-Tibet Plateau in China S. Xu et al. https://doi.org/10.1016/j.gecco.2023.e02449
1572. Decision tree learning with random forest models using agricultural and ecological field data incorporating multi-factor studies and covariate structure M. Dhanoa et al. https://doi.org/10.1079/cabireviews.2024.0030
1573. SWAT and IHACRES models for the simulation of rainfall-runoff of Dez watershed M. Jaberzadeh et al. https://doi.org/10.1007/s00382-022-06215-2
1574. A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks B. Aydin et al. https://doi.org/10.3390/w11051101
1575. A Model Combination Approach for Improving Streamflow Prediction A. Kadu & B. Biswal https://doi.org/10.1007/s11269-022-03336-5
1576. Sources of uncertainty in the NRCS CN model: Recognition and solutions P. Durán‐Barroso et al. https://doi.org/10.1002/hyp.11305
1577. HydroLang: An open-source web-based programming framework for hydrological sciences C. Erazo Ramirez et al. https://doi.org/10.1016/j.envsoft.2022.105525
1578. A hydrologic and river ice modelling framework for assessing ungauged sub-basin streamflow impact in a large cold-region river basin Y. Chen & Y. She https://doi.org/10.1080/02626667.2023.2251957
1579. Multi-scalar aridity dynamics and climate–vegetation coupling in the Western Himalayan Cold Desert, India P. Kumar et al. https://doi.org/10.1016/j.scitotenv.2026.181812
1580. Evaluation of Radiation Components in a Global Freshwater Model with Station-Based Observations H. Müller Schmied et al. https://doi.org/10.3390/w8100450
1581. A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers A. Alsumaiei https://doi.org/10.3390/w12030820
1582. Integrated Hydrology and Operations Modeling to Evaluate Climate Change Impacts in an Agricultural Valley Irrigated With Snowmelt Runoff W. Kitlasten et al. https://doi.org/10.1029/2020WR027924
1583. Assessing the impact of future climate change on groundwater recharge in Galicia-Costa, Spain J. Raposo et al. https://doi.org/10.1007/s10040-012-0922-7
1584. Normalized coefficients of prediction accuracy for comparative forecast verification and modeling G. Müller-Plath & H. Lüdecke https://doi.org/10.1080/27684520.2024.2317172
1585. Prioritization of factors affecting annual soil erosion and sediment yield using combined G2-GeoDetector approach F. Kamari Yekdangi et al. https://doi.org/10.1007/s40808-025-02484-y
1586. Projected wind changes in the Caribbean Sea based on CMIP6 models D. Bustos Usta & R. Torres Parra https://doi.org/10.1007/s00382-022-06535-3
1587. Impact of BMPs on water quality: a case study in Big Sunflower River watershed, Mississippi A. Risal et al. https://doi.org/10.1080/15715124.2020.1870993
1588. A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting H. Tongal & M. Booij https://doi.org/10.1007/s11269-016-1237-6
1589. Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US J. Qi et al. https://doi.org/10.1016/j.jhydrol.2019.124415
1590. Comparative evaluation of 24 reference evapotranspiration equations applied on an evergreen-broadleaved forest A. Bourletsikas et al. https://doi.org/10.2166/nh.2017.232
1591. Modeled streamflow response under cloud seeding in the North Platte River watershed A. Acharya et al. https://doi.org/10.1016/j.jhydrol.2011.08.027
1592. Impacts of Dem Source, Resolution and Area Threshold Values on SWAT Generated Stream Network and Streamflow in Two Distinct Nepalese Catchments S. Gautam et al. https://doi.org/10.1007/s40710-019-00379-6
1593. How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction F. Pappenberger et al. https://doi.org/10.1016/j.jhydrol.2015.01.024
1594. 110th Anniversary: Accurate Modeling of the Simultaneous Absorption of H2S and CO2 in Aqueous Amine Solvents I. Alkhatib et al. https://doi.org/10.1021/acs.iecr.9b00862
1595. Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z) Y. Ziggah et al. https://doi.org/10.1007/s11004-016-9638-x
1596. Calibration of building energy models for retrofit analysis under uncertainty Y. Heo et al. https://doi.org/10.1016/j.enbuild.2011.12.029
1597. Assessment of water balance based on SWAT hydrological model: a case study of Oued Cherraa basin (Northeastern Morocco) M. Laaboudi et al. https://doi.org/10.1007/s10653-025-02449-1
1598. Sensitivity analysis and calibration of the Modified Universal Soil Loss Equation (MUSLE) for the upper Malewa Catchment, Kenya V. ODONGO et al. https://doi.org/10.1016/S1001-6279(13)60047-5
1599. Hydrological modeling of the watershed of a RAMSAR site using the SWAT model (Ichkeul National Park—Tunisia of the extreme north) A. Ben Saad et al. https://doi.org/10.1007/s40808-022-01659-1
1600. Estimating flow data in urban drainage using partial least squares regression R. Brito et al. https://doi.org/10.1080/1573062X.2016.1177099
1601. SVM‐PGSL coupled approach for statistical downscaling to predict rainfall from GCM output S. Ghosh https://doi.org/10.1029/2009JD013548
1602. Modeling the Soil Response to Rainstorms after Wildfire and Prescribed Fire in Mediterranean Forests M. Lucas-Borja et al. https://doi.org/10.3390/cli8120150
1603. Possible impacts of the Poyang Lake (China) hydraulic project on lake hydrology and hydrodynamics G. Lai et al. https://doi.org/10.2166/nh.2016.174
1604. On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loíza in Puerto Rico M. Zounemat-Kermani et al. https://doi.org/10.1016/j.jhydrol.2020.124759
1605. Integrated Hydrological Modeling to Analyze the Effects of Precipitation on Surface Water and Groundwater Hydrologic Processes in a Small Watershed S. Paudel & R. Benjankar https://doi.org/10.3390/hydrology9020037
1606. Machine-Learning-Based Electric Power Forecasting G. Chen et al. https://doi.org/10.3390/su151411299
1607. Comparative Study of Evolutionary Algorithms for the Automatic Calibration of the Medbasin-D Conceptual Hydrological Model D. Tigkas et al. https://doi.org/10.1007/s40710-016-0147-1
1608. Diagnostic Evaluation of Hydrologic Models Employing Flow Duration Curve V. Chilkoti et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001778
1609. Application of a hydrometeorological model chain to investigate the effect of global boundaries and downscaling on simulated river discharge T. Marke et al. https://doi.org/10.1007/s12665-013-2876-z
1610. Comparison of Box–Jenkin time series and radial basis function for sodium adsorption rate forecasting; a case study Aras, Sepidrud, Karun, and Mond Rivers E. Rahnama et al. https://doi.org/10.5004/dwt.2021.26987
1611. HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al. https://doi.org/10.1016/j.jhydrol.2024.131591
1612. Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties L. Kouadio et al. https://doi.org/10.1016/j.compag.2018.10.014
1613. Comparing global and local calibration schemes from a differential split-sample test perspective É. Gaborit et al. https://doi.org/10.1139/cjes-2015-0015
1614. Toprak sıcaklığının tahmininde ısı taşınım denklemi ve pedotransfer fonksiyonun karşılaştırılması C. GÜLSER & İ. EKBERLİ https://doi.org/10.33409/tbbbd.668902
1615. Model performance and parameter behavior for varying time aggregations and evaluation criteria in the WASMOD‐M global water balance model E. Widén‐Nilsson et al. https://doi.org/10.1029/2007WR006695
1616. Degree of Symmetry of Spatial Submerged Hydraulic Jumps Using Fuzzy Algorithm H. Zare & R. Baddour https://doi.org/10.4296/cwrj3401007
1617. Robust adaptation assessment – climate change and water supply J. Hall & C. Murphy https://doi.org/10.1108/17568691111153438
1618. In quest of axionic hairs in quasars I. Banerjee et al. https://doi.org/10.1088/1475-7516/2018/03/039
1619. Predicting runoff and sediment yields using soil and water assessment tool (SWAT) model in the Jemma Subbasin of Upper Blue Nile, Central Ethiopia N. Zewde et al. https://doi.org/10.1016/j.envc.2023.100806
1620. Improving parameter regionalization learning for spatialized differentiable hydrological models by assimilation of satellite-based soil moisture data M. Ettalbi et al. https://doi.org/10.1016/j.jhydrol.2025.133300
1621. Discharge Alterations of the Mures River, Romania under Ensembles of Future Climate Projections and Sequential Threats to Aquatic Ecosystem by the End of the Century A. Lobanova et al. https://doi.org/10.3390/w7062753
1622. Environmental Efficacy and Cost Effectiveness of Best Management Practices for Sediment Control in an Arid and Semiarid Watershed L. Wu et al. https://doi.org/10.1061/JHYEFF.HEENG-6221
1623. Response of glacier modelling parameters to time, space, and model complexity: Examples from eastern slopes of Canadian Rocky Mountains G. Silwal et al. https://doi.org/10.1016/j.scitotenv.2023.162156
1624. Modelling the ecohydrological responses of two eucalyptus–dominated catchments in Central and Southern Portugal with contrasting edaphoclimatic characteristics to future climate scenarios J. Rocha et al. https://doi.org/10.1016/j.ejrh.2026.103508
1625. Comparative study of a physically based distributed hydrological model versus a conceptual hydrological model for assessment of climate change response in the Upper Nile, Baro-Akobo basin: a case study of the Sore watershed, Ethiopia A. Kebede et al. https://doi.org/10.1080/15715124.2014.917315
1626. Hybrid CNN–GRU model for hourly flood forecasting index: case studies from the Fiji islands R. Chand et al. https://doi.org/10.1007/s00477-025-02964-8
1627. Experimental assessment of level pool routing in preliminary design of floodplain storage G. De Martino et al. https://doi.org/10.1016/j.scitotenv.2011.11.032
1628. Hydrological control of dissolved organic carbon dynamics in a rehabilitated Sphagnum-dominated peatland: a water-table based modelling approach L. Bernard-Jannin et al. https://doi.org/10.5194/hess-22-4907-2018
1629. Flood hydrograph simulation using the SWMM model: A semiarid zone watershed case study, Shiraz Khoshk River, Iran E. Khaleghi et al. https://doi.org/10.1111/nrm.12269
1630. Comparison of static MLP and dynamic NARX neural networks for forecasting of atmospheric PM10 and SO2 concentrations in an industrial site of Turkey S. Gündoğdu https://doi.org/10.1080/15275922.2020.1771637
1631. Analysis of future meteorological, hydrological, and agricultural drought characterization under climate change in Kessie watershed, Ethiopia A. Amognehegn et al. https://doi.org/10.1080/10106049.2023.2247377
1632. Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River watershed, Chicago Metropolitan Statistical Area, Illinois C. Wilson & Q. Weng https://doi.org/10.1016/j.scitotenv.2011.07.001
1633. Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model L. Joseph et al. https://doi.org/10.1109/ACCESS.2024.3392899
1634. A Decision Support System for Managed Aquifer Recharge Through Non-Conventional Waters in the South of the Mediterranean R. Hadded et al. https://doi.org/10.3390/resources14040063
1635. Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice G. Williams https://doi.org/10.1016/j.envsoft.2025.106665
1636. Hydrological climate change impact assessment in high latitudes: are bucket-type models up to the task? A. Todorović et al. https://doi.org/10.1016/j.envsoft.2026.106964
1637. Verification of short-term runoff forecasts for a small Philippine basin (Marikina) D. Kneis et al. https://doi.org/10.1080/02626667.2016.1183773
1638. Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region S. Bista et al. https://doi.org/10.1016/j.jhydrol.2024.131310
1639. A proportionality-based multi-scale catchment water balance model and its global verification S. Zhang et al. https://doi.org/10.1016/j.jhydrol.2019.124446
1640. Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2024.130829
1641. Understanding the hydrological system dynamics of a glaciated alpine catchment in the Himalayan region using the J2000 hydrological model S. Nepal et al. https://doi.org/10.1002/hyp.9627
1642. Evaluation of AnnAGNPS for simulating the inundation of drained and farmed potholes in the Prairie Pothole Region of Iowa P. Upadhyay et al. https://doi.org/10.1016/j.agwat.2018.03.037
1643. The potential for agricultural land use change to reduce flood risk in a large watershed K. Schilling et al. https://doi.org/10.1002/hyp.9865
1644. Modelling the impact of past and future climate scenarios on streamflow in a highly mountainous watershed: A case study in the West Seti River Basin, Nepal B. Bhatta et al. https://doi.org/10.1016/j.scitotenv.2020.140156
1645. Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment J. Exbrayat et al. https://doi.org/10.5194/hess-14-2383-2010
1646. Assessing the aplicability of OPSIS for sustainable sugarcane production in Sri Lanka M. Gunarathna et al. https://doi.org/10.3389/fsufs.2025.1693737
1647. Simulation of extreme rainfall and streamflow events in small Mediterranean watersheds with a one-way-coupled atmospheric–hydrologic modelling system C. Camera et al. https://doi.org/10.5194/nhess-20-2791-2020
1648. Increasing eutrophication in the coastal seas of China from 1970 to 2050 M. Strokal et al. https://doi.org/10.1016/j.marpolbul.2014.06.011
1649. PHYSICAL ANALYSIS OF REGIONALIZED FLOW AS AN AID IN THE IDENTIFICATION OF HYDROLOGICALLY HOMOGENEOUS REGIONS A. Calegario et al. https://doi.org/10.1590/1809-4430-eng.agric.v40n3p334-343/2020
1650. Assessing the Impact of Rural Multifunctionality on Non-Point Source Pollution: A Case Study of Typical Hilly Watershed, China W. Yan et al. https://doi.org/10.3390/land12101936
1651. Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation S. Ochoa-Rodriguez et al. https://doi.org/10.1016/j.jhydrol.2015.05.035
1652. Uncertainty of potential evapotranspiration modelling in climate change impact studies on low flows in North America G. Dallaire et al. https://doi.org/10.1080/02626667.2021.1888955
1653. Using dry and wet year hydroclimatic extremes to guide future hydrologic projections S. Oni et al. https://doi.org/10.5194/hess-20-2811-2016
1654. Comprehensive effects of climate, land use/cover and management practices on runoff and nutrient variations in a rapidly urbanizing watershed J. Wu et al. https://doi.org/10.1016/j.chemosphere.2023.140934
1655. Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia M. AL-Musaylh et al. https://doi.org/10.1016/j.rser.2019.109293
1656. A discontinuous finite element suspended sediment transport model for water quality assessments in river networks F. Fiengo Pérez et al. https://doi.org/10.1002/hyp.11151
1657. Sensitivity of hydrology and water quality to variation in land use and land cover data A. Risal et al. https://doi.org/10.1016/j.agwat.2020.106366
1658. Proposition of a Multi-Reservoir System Across the Border of Riparian Countries and Specifying Its Operational Outputs by Formulating Simulation Models Y. Al-Aqeeli et al. https://doi.org/10.1007/s11269-021-02996-z
1659. Fruit weight and yield estimation models for five avocado cultivars in Ethiopia M. Mokria et al. https://doi.org/10.1088/2515-7620/ac81a4
1660. Integrating hydraulic, physicochemical and ecological models to assess the effectiveness of water quality management strategies for the River Cuenca in Ecuador J. Holguin-Gonzalez et al. https://doi.org/10.1016/j.ecolmodel.2013.01.011
1661. Revealing the spectral bands that make generic remote estimates of leaf area index in wheat crop over various interference factors and planting conditions H. Li et al. https://doi.org/10.1016/j.compag.2025.110381
1662. Modelling domestic water demand: An agent based approach I. Koutiva & C. Makropoulos https://doi.org/10.1016/j.envsoft.2016.01.005
1663. Forecasting Evaporative Loss by Least-Square Support-Vector Regression and Evaluation with Genetic Programming, Gaussian Process, and Minimax Probability Machine Regression: Case Study of Brisbane City R. Deo & P. Samui https://doi.org/10.1061/(ASCE)HE.1943-5584.0001506
1664. Frequency Analysis of Different Time Series Rainfall at Kohima, Nagaland, India B. Kusre & K. Shyamjai Singh https://doi.org/10.1007/s40030-012-0017-4
1665. River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network S. Meshram et al. https://doi.org/10.1007/s00500-018-3598-7
1666. Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area J. Li & L. Zhang https://doi.org/10.3390/su13168862
1667. Using Granular Dead Anaerobic Sludge as Permeable Reactive Barrier for Remediation of Groundwater Contaminated with Phenol A. Faisal & Z. Ali https://doi.org/10.1061/(ASCE)EE.1943-7870.0000903
1668. Rainfall interception recovery in a subtropical forest damaged by the great 2008 ice and snow storm in southern China L. Wei et al. https://doi.org/10.1016/j.jhydrol.2020.125232
1669. Numerical Approach for Air Temperature, Soil Temperature and the Difference between Them with Julian Date under Napa Cabbage K. Eom https://doi.org/10.7745/KJSSF.2023.56.4.419
1670. Reservoir-based flood forecasting and warning: deep learning versus machine learning S. Yi & J. Yi https://doi.org/10.1007/s13201-024-02298-w
1671. A modeling approach for evaluating and predicting the impacts of land use land cover changes on groundwater recharge in Walga Watershed, Upper Omo Basin, Central Ethiopia B. Tola & G. Deyassa https://doi.org/10.1016/j.ejrh.2024.101659
1672. Multiple changepoint detection for periodic autoregressive models with an application to river flow analysis D. Cucina et al. https://doi.org/10.1007/s00477-019-01692-0
1673. Analysis of alternative climate datasets and evapotranspiration methods for the Upper Mississippi River Basin using SWAT within HAWQS M. Chen et al. https://doi.org/10.1016/j.scitotenv.2020.137562
1674. A model-independent Particle Swarm Optimisation software for model calibration M. Zambrano-Bigiarini & R. Rojas https://doi.org/10.1016/j.envsoft.2013.01.004
1675. Improved red-edge chlorophyll-a detection for Sentinel 2 J. Bramich et al. https://doi.org/10.1016/j.ecolind.2020.106876
1676. What about reservoirs? Questioning anthropogenic and climatic interferences on water availability A. Akbas et al. https://doi.org/10.1002/hyp.13960
1677. Physical dynamics controlling variability in nearshore fecal pollution: Fecal indicator bacteria as passive particles M. Rippy et al. https://doi.org/10.1016/j.marpolbul.2012.09.030
1678. Appropriate temporal resolution of precipitation data for discharge modelling in pre-alpine catchments A. Sikorska & J. Seibert https://doi.org/10.1080/02626667.2017.1410279
1679. An evaluation of the statistical methods for testing the performance of crop models with observed data J. Yang et al. https://doi.org/10.1016/j.agsy.2014.01.008
1680. Snowmelt energy balance in a burned forest plot, Crowsnest Pass, Alberta, Canada K. Burles & S. Boon https://doi.org/10.1002/hyp.8067
1681. Does black hole continuum spectrum signal f(R) gravity in higher dimensions? I. Banerjee et al. https://doi.org/10.1103/PhysRevD.101.024013
1682. High-resolution root-zone soil moisture for agricultural drought assessment using Sentinel-2 and hybrid modeling in the Lake Tana Basin, Ethiopia H. Eshetie et al. https://doi.org/10.1016/j.rsase.2026.101900
1683. On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation D. Guo et al. https://doi.org/10.1029/2019WR026752
1684. Modeling nitrous oxide emission from full-scale hybrid membrane aerated biofilm reactors (MABR) N. Lakshminarasimman et al. https://doi.org/10.1016/j.watres.2025.123128
1685. A water retention model considering biopolymer-soil interactions C. Zhou et al. https://doi.org/10.1016/j.jhydrol.2020.124874
1686. Simulating Discharge in a Non-Dammed River of Southeastern South America Using SWAT Model T. Fujita et al. https://doi.org/10.3390/w14030488
1687. Predicting post-fire hydrological and erosive catchment response during rainfall events. A comparison of OpenLISEM and MOHID Land models M. Basso et al. https://doi.org/10.1016/j.jhydrol.2024.131258
1688. Using Functional Data Analysis to Calibrate and Evaluate Hydrological Model Performance S. Larabi et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001669
1689. Season‐based rainfall–runoff modelling using the probability‐distributed model (PDM) for large basins in southeastern Brazil R. Zhang et al. https://doi.org/10.1002/hyp.13154
1690. Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye A. Akbas & H. Ozdemir https://doi.org/10.1002/joc.8671
1691. A non-stationary impactquant framework for assessing the human activity impacts on hydrological drought in the Upper Red River J. Leng et al. https://doi.org/10.1016/j.atmosres.2024.107419
1692. Assessment the impacts of land cover and climate changes on rainwater harvesting systems using remote sensing and runoff model in some Wadis of West Matrouh – Egypt H. Salem et al. https://doi.org/10.1016/j.hydres.2024.05.002
1693. Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models B. Guo et al. https://doi.org/10.3390/w10111611
1694. Estimation of evapotranspiration fluxes from Sal (Shorea robusta Gaertn.f) forest using METRIC model: case study of Doon Valley, India S. Purohit et al. https://doi.org/10.1080/10106049.2021.1920630
1695. Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions M. Klein et al. https://doi.org/10.3390/atmos12060710
1696. Sediment Yield and Reservoir Sedimentation in Highly Dynamic Watersheds: The Case of Koga Reservoir, Ethiopia G. Ayele et al. https://doi.org/10.3390/w13233374
1697. A numerical modeling framework for simulating the key in-stream fate processes of PAH decay in Muskeg River Watershed, Alberta, Canada Y. Han et al. https://doi.org/10.1016/j.scitotenv.2022.157246
1698. Novel Approaches for the Empirical Assessment of Evapotranspiration over the Mediterranean Region A. Uzunlar & M. Dis https://doi.org/10.3390/w16030507
1699. Performance assessment of stormwater GI practices using artificial neural networks S. Li et al. https://doi.org/10.1016/j.scitotenv.2018.10.155
1700. Modeling the Impact of Climate Change on Water Availability in the Zarrine River Basin and Inflow to the Boukan Dam, Iran F. Emami & M. Koch https://doi.org/10.3390/cli7040051
1701. Modeling runoff-sediment influx responses to alternative BMP interventions in the Gojeb watershed, Ethiopia, using the SWAT hydrological model Y. Anteneh et al. https://doi.org/10.1007/s11356-022-23711-4
1702. Mapping almond stem water potential using machine learning and multispectral imagery P. Savchik et al. https://doi.org/10.1007/s00271-024-00932-8
1703. High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016 B. Seo et al. https://doi.org/10.1175/JHM-D-18-0046.1
1704. The river absorption capacity determination as a tool to evaluate state of surface water P. Wilk et al. https://doi.org/10.5194/hess-22-1033-2018
1705. Spatial-temporal heterogeneity analysis of blue and green water resources for Poyang Lake basin, China M. Liu et al. https://doi.org/10.1016/j.jhydrol.2022.128983
1706. A diagnostic approach to constraining flow partitioning in hydrologic models using a multiobjective optimization framework M. Shafii et al. https://doi.org/10.1002/2016WR019736
1707. Characterising performance of environmental models N. Bennett et al. https://doi.org/10.1016/j.envsoft.2012.09.011
1708. Using random forest and multiple-regression models to predict changes in surface runoff and soil erosion after prescribed fire D. Zema et al. https://doi.org/10.1007/s40808-023-01838-8
1709. Exploring the relations between sequential droughts and stream nitrogen dynamics in central Germany through catchment-scale mechanistic modelling X. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.128615
1710. Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series W. Roberts et al. https://doi.org/10.3390/hydrology5040066
1711. Sediment Yield Simulation Using SWAT Model for Water Environmental Protection in an Agricultural Watershed K. Xu et al. https://doi.org/10.4028/www.scientific.net/AMM.713-715.1894
1712. Conjunctive simulation of surface water and groundwater using SWAT and MODFLOW in Firoozabad watershed S. Dowlatabadi & S. Ali Zomorodian https://doi.org/10.1007/s12205-015-0354-8
1713. Projecting climate change impacts on Scottish river pollution A. Corrochano-Fraile et al. https://doi.org/10.1016/j.crm.2026.100856
1714. Effects of climate change on water resources in the upper Blue Nile Basin of Ethiopia V. Roth et al. https://doi.org/10.1016/j.heliyon.2018.e00771
1715. Conceptual evaluation of continental land-surface model behaviour L. Peeters et al. https://doi.org/10.1016/j.envsoft.2013.01.007
1716. Evaluating the groundwater recharge requirement and restoration in the Kanari river, India, using SWAT model A. Trivedi et al. https://doi.org/10.1007/s10668-023-03235-8
1717. Future prediction of water balance using the SWAT and CA-Markov model using INMCM5 climate projections: a case study of the Silwani watershed (Jharkhand), India M. Kumar et al. https://doi.org/10.1007/s11356-023-27547-4
1718. A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin Water Transfer regimes A. Essenfelder & C. Giupponi https://doi.org/10.1016/j.envsoft.2020.104779
1719. Future Water Availability from Hindukush-Karakoram-Himalaya upper Indus Basin under Conflicting Climate Change Scenarios S. Hasson https://doi.org/10.3390/cli4030040
1720. Pros and cons of various efficiency criteria for hydrological model performance evaluation C. Onyutha https://doi.org/10.5194/piahs-385-181-2024
1721. Comprehensive framework for agricultural water management in data-scarce regions: Integration of hydrological models and remotely sensed crop type data W. Hussainzada et al. https://doi.org/10.1016/j.jhydrol.2026.135073
1722. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2018.10.040
1723. Catchments as simple dynamical systems: A case study on methods and data requirements for parameter identification L. Melsen et al. https://doi.org/10.1002/2013WR014720
1724. Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China J. Huang et al. https://doi.org/10.1007/s10201-015-0454-7
1725. Recharge variability and sensitivity to climate: The example of Gidabo River Basin, Main Ethiopian Rift A. Mechal et al. https://doi.org/10.1016/j.ejrh.2015.09.001
1726. The br2-weighting Method for Estimating the Effects of Air Pollution on Population Health G. Krstic et al. https://doi.org/10.22237/jmasm/1478004000
1727. Fitting of Soil-Water Characteristic Curves (SWCC) of Bukit Mewah, Malaysia soil using field monitoring dataset F. Suhaizan et al. https://doi.org/10.1371/journal.pone.0316488
1728. Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices J. Hernandez-Suarez et al. https://doi.org/10.1016/j.jhydrol.2018.07.056
1729. Climate Change and Curtailment: Evaluating Water Management Practices in the Context of Changing Runoff Regimes in a Snowmelt-Dominated Basin A. Steimke et al. https://doi.org/10.3390/w10101490
1730. Numerical Evaluation of Nitrate Distributions in the Onion Root Zone under Conventional Furrow Fertigation S. Deb et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001304
1731. Assessing causes and associated water levels for an urban flood using hydroinformatic tools J. Pinho et al. https://doi.org/10.2166/hydro.2019.019
1732. Evaluation of precipitation elasticity using precipitation data from ground and satellite-based estimates and watershed modeling in Western Nepal R. Talchabhadel et al. https://doi.org/10.1016/j.ejrh.2020.100768
1733. The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing G. Formetta et al. https://doi.org/10.5194/gmd-4-943-2011
1734. Daily Nitrate Losses: Implication on Long-Term River Quality in an Intensive Agricultural Catchment of Southwestern France L. Boithias et al. https://doi.org/10.2134/jeq2011.0367
1735. Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States Y. Wu et al. https://doi.org/10.1002/2013MS000298
1736. Quantifying relative contributions from sediment sources in Conservation Effects Assessment Project watersheds C. Wilson et al. https://doi.org/10.2489/jswc.63.6.523
1737. Nitrate Runoff Contributing from the Agriculturally Intensive San Joaquin River Watershed to Bay-Delta in California R. Wang et al. https://doi.org/10.3390/su11102845
1738. A new hybrid ensemble approach for the prediction of effluent total nitrogen from a full-scale wastewater treatment plant using a combined trickling filter-activated sludge system S. Sadri Moghaddam & H. Mesghali https://doi.org/10.1007/s11356-022-21864-w
1739. Statistics for sample splitting for the calibration and validation of hydrological models D. Liu et al. https://doi.org/10.1007/s00477-018-1539-8
1740. Generation and regulation of summer runoff in a boreal flat fen J. Kværner & B. Kløve https://doi.org/10.1016/j.jhydrol.2008.07.009
1741. Modeling the Hydrological Process of the Genale Dawa‐3 Dam Watershed, Ethiopia A. Dechasa et al. https://doi.org/10.1155/aess/8848852
1742. Contribution of hydrological modeling to the water resources assessment using soil-moisture accounting model: the case of the wadi Taria (Macta basin, NW Algeria) Y. Anouar et al. https://doi.org/10.5004/dwt.2017.21631
1743. A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia) J. Quilty & J. Adamowski https://doi.org/10.1016/j.envsoft.2021.105119
1744. Comparison of predictive skills offered by Prognocean, Prognocean Plus and MyOcean real-time sea level forecasting systems M. Świerczyńska et al. https://doi.org/10.1016/j.oceaneng.2015.12.023
1745. Hydro-meteorological drought assessment under climate change impact over the Vu Gia–Thu Bon river basin, Vietnam M. Vu et al. https://doi.org/10.1080/02626667.2017.1346374
1746. Integrated evaluation of flood mitigation measures: A multidisciplinary approach combining hydrology, economics, and public perception H. Graham et al. https://doi.org/10.1016/j.ijdrr.2025.105926
1747. Hydrologic and suspended sediment effects of forest roads using field and DHSVM modelling studies C. Surfleet & S. Marks https://doi.org/10.1016/j.foreco.2021.119632
1748. Emulator-based optimization of APSIM-Sugar using the results of sensitivity analysis performed with the software GEM-SA M. Gunarathna et al. https://doi.org/10.3389/fsufs.2023.1157854
1749. EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. https://doi.org/10.5194/gmd-17-4561-2024
1750. Comparison of GLUE Weighting Methods for Uncertainty Estimation in Hydrologic Modeling A. Fathabadi et al. https://doi.org/10.1111/nrm.70008
1751. Impact Assessment of Future Climate Change on Streamflows Upstream of Khanpur Dam, Pakistan using Soil and Water Assessment Tool S. Nauman et al. https://doi.org/10.3390/w11051090
1752. Spatial distributions, driving factors, and threshold effects of soil organic carbon stocks in the Tibetan Plateau Z. Sun et al. https://doi.org/10.1016/j.still.2025.106457
1753. Reservoir volume forecasting using artificial intelligence-based models: Artificial Neural Networks, Support Vector Regression, and Long Short-Term Memory G. Özdoğan-Sarıkoç et al. https://doi.org/10.1016/j.jhydrol.2022.128766
1754. Seawater intrusions for coastal aquifers are increasingly dominated by anthropogenic drivers in the Southern Mediterranean Basin N. Khazri et al. https://doi.org/10.1016/j.ejrh.2025.103028
1755. Dynamics of surface runoff and sedimentation leading to extensive transformation in land use land cover in Megech dam watershed of Ethiopia T. Wukaw et al. https://doi.org/10.1007/s13201-025-02571-6
1756. The influence of model structure on groundwater recharge rates in climate-change impact studies C. Moeck et al. https://doi.org/10.1007/s10040-016-1367-1
1757. Mean and extreme precipitation over Aotearoa New Zealand: A comparison across multiple different estimation techniques G. Vishwanathan et al. https://doi.org/10.1002/joc.8017
1758. Incorporating Land Use Mapping and Participation in Jordan J. Al-Bakri et al. https://doi.org/10.1659/mrd.0863
1759. Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model L. Delhez et al. https://doi.org/10.1016/j.eja.2025.127533
1760. The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model G. Andiego et al. https://doi.org/10.4236/cweee.2018.81002
1761. A new fractal-theory-based criterion for hydrological model calibration Z. Bai et al. https://doi.org/10.5194/hess-25-3675-2021
1762. Determinants of Rice Market Supply and Profitability on Smallholder Farmers in North Western Ethiopia T. Ayele et al. https://doi.org/10.11648/j.ajtab.20251102.11
1763. Assessing differences in snowmelt-dependent hydrologic projections using CMIP3 and CMIP5 climate forcing data for the western United States D. Ficklin et al. https://doi.org/10.2166/nh.2015.101
1764. Aerobic cometabolism of 1,4-dioxane by isobutane-utilizing microorganisms including Rhodococcus rhodochrous strain 21198 in aquifer microcosms: Experimental and modeling study H. Rolston et al. https://doi.org/10.1016/j.scitotenv.2019.133688
1765. The Modified Soil Moisture Constraint Scheme Significantly Enhances the Evapotranspiration Simulation Accuracy of the MOD16 Model M. Guo et al. https://doi.org/10.3390/su151612460
1766. An enhanced Budyko-based water balance model simulating monthly runoff in headwater catchments of the Tarim River Basin S. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102576
1767. Comparativa de modelos tradicionales vs machine learning para el relleno de datos faltantes en series de caudales diarios en cuencas representativas del Perú B. Berrocal & J. Ramirez https://doi.org/10.24850/j-tyca-18-02-02
1768. Restoring a Natural Fire Regime Alters the Water Balance of a Sierra Nevada Catchment G. Boisramé et al. https://doi.org/10.1029/2018WR024098
1769. Forecasting River Water Levels at Virtual Stations of Altimetry Satellites M. Halicki et al. https://doi.org/10.1109/JSTARS.2026.3681863
1770. Analysing the temporal dynamics of model performance for hydrological models D. Reusser et al. https://doi.org/10.5194/hess-13-999-2009
1771. Runoff Simulation in the Upper Reaches of Heihe River Basin Based on the RIEMS–SWAT Model S. Zou et al. https://doi.org/10.3390/w8100455
1772. Examining model performances and parameter uncertainty for streamflow and suspended sediment regime simulation: Comparison of three calibration methods R. Ranjan & A. Mishra https://doi.org/10.1016/j.jhydrol.2022.128304
1773. Evaluation of the Effect of Logarithmic Transformations and Objective Functions on the Performance of Neural Network Models in Estimation of Suspended Sediment Load (Case Study: Sarab Ghare So Watershed, Ghorichai River) M. Tabatabaei & S. Hosseini https://doi.org/10.52547/jwmr.12.24.133
1774. PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration M. Vremec et al. https://doi.org/10.5194/gmd-17-7083-2024
1775. Hydrological Model Adaptability to Rainfall Inputs of Varied Quality J. Wang et al. https://doi.org/10.1029/2022WR032484
1776. Sensitivity of Streamflow to Changing Rainfall and Evapotranspiration in Catchments Across the Nile Basin C. Onyutha et al. https://doi.org/10.3390/atmos15121415
1777. Contrasting growth responses in Himalayan trees to future climate R. Dhyani et al. https://doi.org/10.1016/j.scitotenv.2025.179700
1778. Analysis of Terrestrial Discharge from Agricultural Watersheds and Its Impact on Nearshore and Offshore Reefs in Fiji https://doi.org/10.2112/JCOASTRES-D-11-00149.1
1779. Evaluation of the tabulated, NEH4, least squares and asymptotic fitting methods for the CN estimation of urban watersheds M. Galbetti et al. https://doi.org/10.1080/1573062X.2021.1992639
1780. Forecasting the Propagation from Meteorological to Hydrological and Agricultural Drought in the Huaihe River Basin with Machine Learning Methods R. Hao et al. https://doi.org/10.3390/rs15235524
1781. Global calibration model of UV-Vis spectroscopy for COD estimation in the effluent of rural sewage treatment facilities P. Li et al. https://doi.org/10.1039/C9RA10732K
1782. Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada F. Aguilar Andrade et al. https://doi.org/10.1016/j.jhydrol.2024.131861
1783. Tutorials as a flexible alternative to GUIs: An example for advanced model calibration using Pilot Points C. Moeck et al. https://doi.org/10.1016/j.envsoft.2014.12.018
1784. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm R. Prasad et al. https://doi.org/10.1016/j.atmosres.2017.06.014
1785. A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network A. Faisal et al. https://doi.org/10.1016/j.scp.2020.100220
1786. Estimating Soil Water Retention Curve by Inverse Modelling from Combination of In Situ Dynamic Soil Water Content and Soil Potential Data P. Ket et al. https://doi.org/10.3390/soilsystems2040055
1787. Risk‐based water resources planning: Incorporating probabilistic nonstationary climate uncertainties E. Borgomeo et al. https://doi.org/10.1002/2014WR015558
1788. Improved Inverse Modeling by Separating Model Structural and Observational Errors Q. Cheng et al. https://doi.org/10.3390/w10091151
1789. Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model B. Shehu et al. https://doi.org/10.1016/j.fcr.2019.107585
1790. Development of alternative SWAT-based models for simulating water budget components and streamflow for a karstic-influenced watershed M. Reza Eini et al. https://doi.org/10.1016/j.catena.2020.104801
1791. Changes in runoff and sediment discharge and their influencing factors in the lower Brahmaputra River, Bangladesh E. Shaik & D. Fan https://doi.org/10.1016/j.jhydrol.2025.133924
1792. Event-based rainfall–run-off modeling and uncertainty analysis for lower Tapi Basin, India P. Bhavsar & J. Patel https://doi.org/10.1080/09715010.2018.1464406
1793. Joint frequency analysis and uncertainty estimation of coupled rainfall–runoff series relying on historical and simulated data E. Dodangeh et al. https://doi.org/10.1080/02626667.2019.1704762
1794. Modeling future hydrological responses through parameter optimization and climate change scenarios in Dirima Watershed, Ethiopia S. Atanaw et al. https://doi.org/10.1007/s40808-023-01817-z
1795. A tightly coupled socio-hydrological model with feedback pathways for water availability assessment: A case study of the Weihe River Basin M. Wu et al. https://doi.org/10.1016/j.envsoft.2026.107093
1796. Statistical downscaling of multi‐site daily rainfall in a South Australian catchment using a Generalized Linear Model S. Beecham et al. https://doi.org/10.1002/joc.3933
1797. Estimating instantaneous peak flow from mean daily flow B. Chen et al. https://doi.org/10.2166/nh.2017.200
1798. Modelling climate change impacts at a drinking water reservoir in Turkey and implications for reservoir management in semi-arid regions D. Aibaidula et al. https://doi.org/10.1007/s11356-022-23141-2
1799. Laboratory investigations of the conservativeness of deuterated water as the artificial tracer for hydrogeological tests X. Huang et al. https://doi.org/10.1007/s11356-022-23432-8
1800. Statistical characteristics of rainfall in the Onkaparinga catchment in South Australia M. Rashid et al. https://doi.org/10.2166/wcc.2014.031
1801. Development of a methodology to assess future trends in low flows at the watershed scale using solely climate data É. Foulon et al. https://doi.org/10.1016/j.jhydrol.2017.12.064
1802. Evaluation of spatial plan in controlling stream flow rate in Wakung Watershed, Pemalang, Central Java, Indonesia Y. Anwar et al. https://doi.org/10.1088/1755-1315/148/1/012030
1803. Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates J. Hussain et al. https://doi.org/10.1371/journal.pone.0197546
1804. Spatial optimization of soil and water conservation practices using coupled SWAT model and evolutionary algorithm F. Naseri et al. https://doi.org/10.1016/j.iswcr.2021.04.002
1805. Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning W. Jang et al. https://doi.org/10.1016/j.envsoft.2019.104602
1806. Lateral Subsurface Flow in a Soil Cover over Waste Rock in a Humid Temperate Environment L. Hopp et al. https://doi.org/10.2136/vzj2010.0094
1807. metrica: an R package to evaluate prediction performance of regression and classification point-forecast models A. Correndo et al. https://doi.org/10.21105/joss.04655
1808. Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events U. Ehret & E. Zehe https://doi.org/10.5194/hess-15-877-2011
1809. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. https://doi.org/10.1029/2022WR033918
1810. Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection S. Pool et al. https://doi.org/10.5194/hess-21-5443-2017
1811. Simple Method for Streamflow Disaggregation A. Acharya & J. Ryu https://doi.org/10.1061/(ASCE)HE.1943-5584.0000818
1812. Groundwater-surface water interactions across scales in a boreal landscape investigated using a numerical modelling approach E. Jutebring Sterte et al. https://doi.org/10.1016/j.jhydrol.2018.03.011
1813. Fine Sediment Modeling During Storm-Based Events in the River Bandon, Ireland J. García & J. Harrington https://doi.org/10.3390/w11071523
1814. Hydropower generation, flood control and dam cascades: A national assessment for Vietnam V. Nguyen-Tien et al. https://doi.org/10.1016/j.jhydrol.2018.02.063
1815. Monthly and seasonal hydrological drought forecasting using multiple extreme learning machine models G. Wang et al. https://doi.org/10.1080/19942060.2022.2089732
1816. Estimation of Annual and Seasonal Groundwater Recharge by Using Wetspass‐M and Chloride Mass‐Balance Methods, Upper Bilate Catchment, Rifty Valley Basin, Ethiopia S. Bekele et al. https://doi.org/10.1155/2024/2724015
1817. Catchment specific evaluation of Aphrodite’s and TRMM derived gridded precipitation data products for predicting runoff in a semi gauged watershed of Tropical India S. Santra Mitra et al. https://doi.org/10.1080/10106049.2019.1641563
1818. Impacts of Forest Fires and Climate Variability on the Hydrology of an Alpine Medium Sized Catchment in the Canadian Rocky Mountains J. Springer et al. https://doi.org/10.3390/hydrology2010023
1819. Biogas production estimation using data-driven approaches for cold region municipal wastewater anaerobic digestion M. Asadi et al. https://doi.org/10.1016/j.jenvman.2019.109708
1820. Climate change and probabilistic scenario of streamflow extremes in an alpine region T. Yang et al. https://doi.org/10.1002/2014JD021824
1821. Hybrid AI-Hydrologic Flood Modeling in Prairie Agricultural Watersheds A. Hassanjabbar et al. https://doi.org/10.1007/s41748-026-01175-7
1822. How significant are quadratic criteria? Part 2. On the relative contribution of large flood events to the value of a quadratic criterion L. Berthet et al. https://doi.org/10.1080/02626667.2010.505891
1823. Improving watershed decisions using run-off and yield models at different simulation scales N. Wangusi et al. https://doi.org/10.1007/s10669-013-9454-5
1824. Means and Extremes: Evaluation of a CMIP6 Multi-Model Ensemble in Reproducing Historical Climate Characteristics across Alberta, Canada B. Masud et al. https://doi.org/10.3390/w13050737
1825. Development of a Keetch and Byram—Based drought index sensitive to forest management in Mediterranean conditions A. Garcia-Prats et al. https://doi.org/10.1016/j.agrformet.2015.02.009
1826. Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan S. Mtibaa & S. Asano https://doi.org/10.1016/j.ejrh.2022.101134
1827. A suggestion for computing objective function in model calibration Y. Wu & S. Liu https://doi.org/10.1016/j.ecoinf.2014.08.002
1828. Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed F. Unduche et al. https://doi.org/10.1080/02626667.2018.1474219
1829. Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model K. Pandit et al. https://doi.org/10.5194/gmd-12-4585-2019
1830. Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria A. Ogunrinde et al. https://doi.org/10.1002/eng2.12194
1831. Impact of input data resolution and extent of harvested areas on crop yield estimates in large-scale agricultural modeling for maize in the USA C. Folberth et al. https://doi.org/10.1016/j.ecolmodel.2012.03.035
1832. Precision irrigation of five years date palm trees (Majdool variety) by applying Hydrus-2D software: Jericho area/West Bank (occupied Palestinian territories) O. Majadbeh et al. https://doi.org/10.5004/dwt.2022.28898
1833. Analysis of the water balance in a river catchment using the soil and water assessment tool (SWAT) model: A case study in Southern Africa M. Letsoela et al. https://doi.org/10.1088/1755-1315/1489/1/012019
1834. Assessment of the adequacy of the regional relationships between catchment attributes and catchment response dynamics, calibrated by a multi‐objective approach H. Kim & S. Lee https://doi.org/10.1002/hyp.9942
1835. Coupled hydrodynamic modelling approach to assess land use change induced flood characteristics J. Jayapadma et al. https://doi.org/10.1007/s10661-022-09986-7
1836. Climate change impact on water quality in the integrated Mahabad Dam watershed-reservoir system M. Nazari-Sharabian & M. Taheriyoun https://doi.org/10.1016/j.jher.2021.12.001
1837. Impact of the model structure and calibration strategy on baseflow modeling in the German low mountain range M. Kissel et al. https://doi.org/10.2166/hydro.2024.077
1838. Automated calibration of the EPA-SWMM model for a small suburban catchment using PEST: a case study R. Perin et al. https://doi.org/10.1007/s10661-020-08338-7
1839. Comparison of Flood Frequency at Different Climatic Scenarios in Forested Coastal Watersheds S. Bhattarai et al. https://doi.org/10.3390/cli11020041
1840. Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins R. Morovati & O. Kisi https://doi.org/10.3390/hydrology11040048
1841. Evaluation and modeling of scouring and sedimentation around check dams in a Mediterranean torrent in Calabria, Italy D. Zema et al. https://doi.org/10.2489/jswc.69.4.316
1842. A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China S. Pan et al. https://doi.org/10.1080/02626667.2017.1388917
1843. Hydrological projections over the Upper Indus Basin at 1.5 °C and 2.0 °C temperature increase R. Kiani et al. https://doi.org/10.1016/j.scitotenv.2021.147759
1844. Assessment of Sentinel-1 C-band SAR snow depth assimilation in operational hydrological modeling of high-alpine catchments T. Pulka-Petzolt et al. https://doi.org/10.1016/j.jhydrol.2026.135888
1845. How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty A. Watson et al. https://doi.org/10.3389/fclim.2022.859303
1846. Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks A. Bomers https://doi.org/10.3390/hydrology8020087
1847. Parameter regionalization of a monthly water balance model for the conterminous United States A. Bock et al. https://doi.org/10.5194/hess-20-2861-2016
1848. Effect of near‐bed turbulence on chronic detachment of epilithic biofilm: Experimental and modeling approaches M. Graba et al. https://doi.org/10.1029/2009WR008679
1849. Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States C. Simeone et al. https://doi.org/10.3390/w16202996
1850. Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis M. Shin & C. Kim https://doi.org/10.2166/nh.2016.129
1851. Modeling boreal forest evapotranspiration and water balance at stand and catchment scales: a spatial approach S. Launiainen et al. https://doi.org/10.5194/hess-23-3457-2019
1852. Uncertainty analysis for streamflow modeling using multiple optimization algorithms at a data-scarce semi-arid region: Altınapa Reservoir Watershed, Turkey D. Aibaidula et al. https://doi.org/10.1007/s00477-022-02377-x
1853. Simulated effect of drainage water management operational strategy on hydrology and crop yield for Drummer soil in the Midwestern United States S. Ale et al. https://doi.org/10.1016/j.agwat.2008.10.005
1854. AI-Based Time-Series Ensemble Approach Coupled with a Hydrological Model for Reservoir Storage Prediction in Korea J. Park et al. https://doi.org/10.3390/w17223296
1855. Factors controlling variability in nearshore fecal pollution: The effects of mortality M. Rippy et al. https://doi.org/10.1016/j.marpolbul.2012.09.003
1856. Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments V. Boico et al. https://doi.org/10.1016/j.jhydrol.2022.128120
1857. Coupled Rainfall-Runoff and Hydrodynamic Modeling using MIKE + for Flood Simulation K. Vashist & K. Singh https://doi.org/10.1007/s40996-024-01624-1
1858. Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments A. Ritter & R. Muñoz-Carpena https://doi.org/10.1016/j.jhydrol.2012.12.004
1859. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions . Ateeq-ur-Rauf et al. https://doi.org/10.1007/s10661-018-7012-9
1860. Evaluating precipitation datasets for large-scale distributed hydrological modelling M. Mazzoleni et al. https://doi.org/10.1016/j.jhydrol.2019.124076
1861. Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model Q. Cao et al. https://doi.org/10.1111/jfr3.12827
1862. Assessment of a conservative mixing model for the evaluation of constituent behavior below river confluences, Elqui River Basin, Chile C. Rossi et al. https://doi.org/10.1002/rra.3823
1863. Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada Z. Li et al. https://doi.org/10.1016/j.scitotenv.2016.01.002
1864. Water resources management in the context of future climate and development changes: a South African case study S. Mantel et al. https://doi.org/10.2166/wcc.2015.098
1865. Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard) M. Nowak & I. Sobota https://doi.org/10.1111/geoa.12089
1866. Hydrodynamic response of a large river–lake system under flow regulation: A numerical study of Hongze lake F. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.131015
1867. Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation E. Starkey et al. https://doi.org/10.1016/j.jhydrol.2017.03.019
1868. A Framework to Regionalize Flow Information in a Catchment with Limited Hydrological Data M. Angalika et al. https://doi.org/10.4236/ojmh.2023.131002
1869. Remote sensing of ice albedo using harmonized Landsat and Sentinel 2 datasets: validation S. Feng et al. https://doi.org/10.1080/01431161.2023.2291000
1870. Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions J. Hunter et al. https://doi.org/10.1016/j.jhydrol.2021.126578
1871. Machine learning-driven surface water quality prediction: an intuitive GUI solution for forecasting TDS and DO levels B. Siddiq et al. https://doi.org/10.2166/wqrj.2025.005
1872. Scenario analysis for assessing the impact of hydraulic fracturing on stream low flows using the SWAT model A. Shrestha et al. https://doi.org/10.1080/02626667.2016.1235276
1873. The role of urbanization in the flooding and surface water chemistry of Puerto Rico’s mangroves B. Branoff https://doi.org/10.1080/02626667.2020.1747620
1874. Imprints of the Janis-Newman-Winicour spacetime on observations related to shadow and accretion S. Sau et al. https://doi.org/10.1103/PhysRevD.102.064027
1875. Application and assessment of a multiscale data integration method to saturated hydraulic conductivity in soil N. Li & L. Ren https://doi.org/10.1029/2009WR008645
1876. A methodology for extreme groundwater surge predetermination in carbonate aquifers: Groundwater flood frequency analysis K. Najib et al. https://doi.org/10.1016/j.jhydrol.2007.11.035
1877. Comparing the effects of hydromulching and application of biodegradable plastics on surface runoff and soil erosion in deforested and burned lands M. Parhizkar et al. https://doi.org/10.2478/johh-2024-0028
1878. Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data P. Groenendijk et al. https://doi.org/10.1016/j.scitotenv.2014.07.002
1879. New methodology of evaluation of best management practices performances for an agricultural watershed according to the climate change scenarios: A hybrid use of deterministic and decision support models D. Jeon et al. https://doi.org/10.1016/j.ecoleng.2018.05.006
1880. A new approach for hydrograph data interpolation and outlier removal for vector autoregressive modelling: a case study from the Odra/Oder River M. Halicki & T. Niedzielski https://doi.org/10.1007/s00477-024-02711-5
1881. Impact Assessment of Rainfall-Runoff Simulations on the Flow Duration Curve of the Upper Indus River—A Comparison of Data-Driven and Hydrologic Models A. Rauf & A. Ghumman https://doi.org/10.3390/w10070876
1882. Comparing COSMO-CLM simulations and MODIS data of snow cover extent and distribution over Italian Alps P. Da Ronco et al. https://doi.org/10.1007/s00382-016-3054-2
1883. Modeling Air Pollution In Dong Nai Province, Vietnam T. Nguyen et al. https://doi.org/10.24057/2071-9388-2019-44
1884. The critical role of the routing scheme in simulating peak river discharge in global hydrological models F. Zhao et al. https://doi.org/10.1088/1748-9326/aa7250
1885. Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads A. Kotlar et al. https://doi.org/10.2136/vzj2019.06.0063
1886. Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships M. Eddy et al. https://doi.org/10.1002/eco.2387
1887. Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources J. Domagalski et al. https://doi.org/10.1016/j.scitotenv.2016.02.112
1888. Functioning and precipitation-displacement modelling of rainfall-induced deep-seated landslides subject to creep deformation A. Vallet et al. https://doi.org/10.1007/s10346-015-0592-3
1889. A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework Z. Liang et al. https://doi.org/10.1007/s00704-017-2186-6
1890. Future climate change and impacts on water resources in the Upper Blue Nile basin G. Takele et al. https://doi.org/10.2166/wcc.2021.235
1891. R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial dependence X. Wang & Y. Shen https://doi.org/10.1016/j.jhydrol.2023.129093
1892. Assessing the climate change impacts and adaptation strategies for rice production in Punjab, Pakistan G. Shabbir et al. https://doi.org/10.1007/s11356-020-08846-6
1893. Using hydropedological characteristics to improve modelling accuracy in Afromontane catchments R. Harrison et al. https://doi.org/10.1016/j.ejrh.2021.100986
1894. Impacts of urbanization and climate change on stormwater drainage efficiency: A PCSWMM 7.5 case study in Dilla Town, Ethiopia Y. Girma et al. https://doi.org/10.1016/j.cacint.2025.100280
1895. Large‐scale ocean‐atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts C. Tozer et al. https://doi.org/10.1002/joc.5043
1896. The influence of the calibration metric on design flood estimation using continuous simulation P. Cu & J. Ball https://doi.org/10.1080/15715124.2016.1239623
1897. Delineation of Soil Fertility Management Zones for Site-specific Nutrient Management in the Maize Belt Region of Nigeria K. Aliyu et al. https://doi.org/10.3390/su12219010
1898. Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model A. Chaudhary & M. Hantush https://doi.org/10.1016/j.watres.2016.11.012
1899. PRISM vs. CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models D. Radcliffe & R. Mukundan https://doi.org/10.1111/1752-1688.12484
1900. The impact of Best Management Practices on simulated streamflow and sediment load in a Central Brazilian catchment M. Strauch et al. https://doi.org/10.1016/j.jenvman.2013.01.014
1901. Multi-Criteria Process-Based Calibration Using Functional Data Analysis to Improve Hydrological Model Realism S. Larabi et al. https://doi.org/10.1007/s11269-017-1803-6
1902. A coupled hydrology–biogeochemistry model to simulate dissolved organic carbon exports from a permafrost‐influenced catchment J. Lessels et al. https://doi.org/10.1002/hyp.10566
1903. The effect of land cover change on duration and severity of high and low flows K. Ahn & V. Merwade https://doi.org/10.1002/hyp.10981
1904. A lumped conceptual approach for modeling hydrological processes: the case of Scopia catchment area, Central Greece N. Charizopoulos et al. https://doi.org/10.1007/s12665-017-6967-0
1905. Predicting runoff series in ungauged basins of the Brazilian Cerrado biome D. Althoff et al. https://doi.org/10.1016/j.envsoft.2022.105315
1906. Comparing Performance and Parameterization of a One‐Dimensional Unsaturated Zone Model across Scales V. Sheikh & E. van Loon https://doi.org/10.2136/vzj2006.0077
1907. Integration of frequency analysis and HEC-RAS for probabilistic flood hazard mapping M. Shaikh et al. https://doi.org/10.1680/jwama.24.00046
1908. Inundation and groundwater dynamics for quantification of evaporative water loss in tropical wetlands J. Schwerdtfeger et al. https://doi.org/10.5194/hess-18-4407-2014
1909. Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine M. Zounemat-Kermani et al. https://doi.org/10.1007/s11709-021-0697-9
1910. Decoding signatures of extra dimensions and estimating spin of quasars from the continuum spectrum I. Banerjee et al. https://doi.org/10.1103/PhysRevD.100.044045
1911. Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model D. Li et al. https://doi.org/10.1111/1752-1688.13244
1912. Existing Accessible Modeling Tools Offer Limited Support to Evaluation of Impact Investment in Rangeland Ecosystem Services B. Gordon et al. https://doi.org/10.3389/fsufs.2019.00077
1913. Is hillslope-based catchment decomposition approach superior to hydrologic response unit (HRU) for stream-aquifer interaction modelling: Inference from two process-based coupled models S. Sahoo & B. Sahoo https://doi.org/10.1016/j.jhydrol.2020.125588
1914. Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow J. Senent-Aparicio et al. https://doi.org/10.3390/w12030657
1915. Effect of Time Scale on the Performance of Different Sediment Transport Formulas in a Semiarid Region T. Bisantino et al. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000125
1916. Evaluation of hydrology, suspended sediment and Nickel loads in a small watershed in Basque Country (Northern Spain) using eco-hydrological SWAT model M. Peraza-Castro et al. https://doi.org/10.1051/limn/2015006
1917. A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain L. Barba & N. Rodríguez https://doi.org/10.1155/2017/7951395
1918. Assessment of future water availability and seasonal patterns of dry seasons under climate change in Cidanau Watershed Banten Province, Indonesia S. Saputra et al. https://doi.org/10.1016/j.ejrh.2025.102344
1919. Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales K. Price et al. https://doi.org/10.1002/hyp.9890
1920. Hydrological modeling in the Upper Blue Nile basin using soil and water analysis tool (SWAT) G. Takele et al. https://doi.org/10.1007/s40808-021-01085-9
1921. Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City G. Asadollahfardi et al. https://doi.org/10.5572/ajae.2016.10.2.067
1922. ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database T. Nguyen-Quang et al. https://doi.org/10.5194/gmd-11-4965-2018
1923. Local-scale flood hazard projections in historically vulnerable communities S. Bista et al. https://doi.org/10.1007/s40808-025-02599-2
1924. Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference S. Woodward et al. https://doi.org/10.1016/j.jhydrol.2017.07.021
1925. An advanced machine learning-based mathematical equation for predicting compression index from readily-available soil physical properties B. Nguyen et al. https://doi.org/10.1080/17486025.2025.2455109
1926. Lumped versus Distributed Hydrological Modeling of the Jacaré-Guaçu Basin, Brazil F. dos Santos et al. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001397
1927. Assessing anthropogenic influence on the hydrology of small peri-urban catchments: Development of the object-oriented PUMMA model by integrating urban and rural hydrological models S. Jankowfsky et al. https://doi.org/10.1016/j.jhydrol.2014.06.034
1928. Hydro-Economic Modeling of Water Resources Management Challenges: Current Applications and Future Directions J. Ortiz-Partida et al. https://doi.org/10.1142/S2382624X23400039
1929. Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city B. Kitessa et al. https://doi.org/10.2166/wcc.2021.012
1930. An enhanced temperature index model for debris-covered glaciers accounting for thickness effect M. Carenzo et al. https://doi.org/10.1016/j.advwatres.2016.05.001
1931. Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction A. Meyal et al. https://doi.org/10.3389/frwa.2020.574917
1932. Watershed-scale water environmental capacity estimation assisted by machine learning X. Wang et al. https://doi.org/10.1016/j.jhydrol.2021.126310
1933. Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model A. Bahrami et al. https://doi.org/10.1016/j.jhydrol.2020.125744
1934. Potential impacts of climate change on flow regime and fish habitat in mountain rivers of the south-western Balkans C. Papadaki et al. https://doi.org/10.1016/j.scitotenv.2015.06.134
1935. On the performance of three indices of agreement: an easy-to-use r-code for calculating the Willmott indices H. Pereira et al. https://doi.org/10.1590/1678-4499.2017054
1936. Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia S. Bhagat et al. https://doi.org/10.1016/j.jhazmat.2020.123492
1937. Joint impacts of future climate conditions and invasive species on black ash forested wetlands J. Shannon et al. https://doi.org/10.3389/ffgc.2022.957526
1938. Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment J. Monteiro et al. https://doi.org/10.1002/hyp.10708
1939. Assessing the Applicability of Variable Infiltration Capacity (VIC) Model using Remote Sensing Products for the Analysis of Water Balance: Case Study of the Tons River Basin, India S. Himanshu et al. https://doi.org/10.1007/s12524-023-01768-z
1940. A new dataset of river flood hazard maps for Europe and the Mediterranean Basin F. Dottori et al. https://doi.org/10.5194/essd-14-1549-2022
1941. Uncertainty based assessment of dynamic freshwater scarcity in semi-arid watersheds of Alberta, Canada M. Faramarzi et al. https://doi.org/10.1016/j.ejrh.2016.11.003
1942. Assessment of reduced-complexity landscape evolution model suitability to adequately simulate flood events in complex flow conditions B. Zellou & H. Rahali https://doi.org/10.1007/s11069-016-2671-8
1943. Exploring the factors influencing the hydrological response of soil after low and high-severity fires with post-fire mulching in Mediterranean forests M. Lucas-Borja et al. https://doi.org/10.1016/j.iswcr.2022.08.002
1944. Assessment of blue water-green water interchange under extreme warm and dry events across different ecohydrological regions of western Canada P. Khalili et al. https://doi.org/10.1016/j.jhydrol.2023.130105
1945. Simulating maize yield at county scale in southern Ontario using the decision support system for agrotechnology transfer model S. Liu et al. https://doi.org/10.1139/cjss-2020-0116
1946. Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem A. Endalamaw et al. https://doi.org/10.5194/hess-21-4663-2017
1947. Assessment of impact of climate change on streamflow and soil moisture in Pare watershed of Arunachal Pradesh, India W. Singh et al. https://doi.org/10.1007/s40899-024-01063-w
1948. An interactive Web-GIS fluvial flood forecast and alert system in operation in Portugal S. Mourato et al. https://doi.org/10.1016/j.ijdrr.2021.102201
1949. Comparison between Computations and Measurements of Bed Load Transport Rate in Nestos River, Greece T. Papalaskaris et al. https://doi.org/10.1016/j.proeng.2016.11.034
1950. New objective functions for streamflow calibrations to specific model applications H. Il Choi https://doi.org/10.1016/j.jhydrol.2024.132109
1951. Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran B. Kamali et al. https://doi.org/10.3390/w9040241
1952. The Impact of Para Rubber Expansion on Streamflow and Other Water Balance Components of the Nam Loei River Basin, Thailand W. Wangpimool et al. https://doi.org/10.3390/w9010001
1953. Comparison of Box-Jenkins time series and ANN in predicting total dissolved solid at the Zāyandé-Rūd River, Iran G. Asadollahfardi et al. https://doi.org/10.2166/aqua.2018.108
1954. Hydrologic impacts of projected future climate change in the Lake Michigan region K. Cherkauer & T. Sinha https://doi.org/10.1016/j.jglr.2009.11.012
1955. Response of runoff to climate change in the Wei River basin, China D. Zuo et al. https://doi.org/10.1080/02626667.2014.943668
1956. A multi-catchment evaluation of hydrological model transferability and ensemble averaging under contrasting hydroclimatic conditions in Iran A. Jahanshahi & M. Booij https://doi.org/10.1016/j.jhydrol.2026.135878
1957. Field Studies and 3D Modelling of Morphodynamics in a Meandering River Reach Dominated by Tides and Suspended Load Q. Xie et al. https://doi.org/10.3390/fluids4010015
1958. Does model performance improve with complexity? A case study with three hydrological models R. Orth et al. https://doi.org/10.1016/j.jhydrol.2015.01.044
1959. Development of PSO-SVR and ANN models to evaluate the effect of cross-sectional shape on denitrifying bioreactors S. Faramarzmanesh et al. https://doi.org/10.2166/ws.2025.083
1960. Assessment of runoff and sediment yield in the Miyun Reservoir catchment by using SWAT model Z. Xu et al. https://doi.org/10.1002/hyp.7475
1961. Characterization of resilient mounts for marine diesel engines: Prediction of static response via nonlinear analysis and response surface methodology J. Fragasso et al. https://doi.org/10.1016/j.oceaneng.2018.10.051
1962. A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model J. Gutierrez et al. https://doi.org/10.1007/s10596-019-09870-3
1963. Ecosystem approach to water resources management using the MIKE 11 modeling system in the Strymonas River and Lake Kerkini C. Doulgeris et al. https://doi.org/10.1016/j.jenvman.2011.06.023
1964. A simple method of bias correction for GCM derived streamflow at catchment scale S. Islam et al. https://doi.org/10.1080/02626667.2023.2218036
1965. Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting F. Hernández & X. Liang https://doi.org/10.5194/hess-22-5759-2018
1966. Simulating future trends in hydrological regimes in Western Switzerland M. Milano et al. https://doi.org/10.1016/j.ejrh.2015.10.010
1967. Which objective function to calibrate rainfall–runoff models for low-flow index simulations? F. Garcia et al. https://doi.org/10.1080/02626667.2017.1308511
1968. SWAT Model calibration and uncertainty analysis for streamflow prediction of the Tons River Basin, India, using Sequential Uncertainty Fitting (SUFI-2) algorithm N. Kumar et al. https://doi.org/10.1007/s40808-017-0306-z
1969. Interacting Effects of Precipitation and Potential Evapotranspiration Biases on Hydrological Modeling J. Wang et al. https://doi.org/10.1029/2022WR033323
1970. New modelling technique for improving crop model performance - Application to the GLAM model I. Droutsas et al. https://doi.org/10.1016/j.envsoft.2019.05.005
1971. Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images J. Meng et al. https://doi.org/10.3390/rs8020125
1972. A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa V. Ferreira et al. https://doi.org/10.3390/w11020401
1973. Multivariate adaptive regression splines model for reinforced soil foundations M. Raja & S. Shukla https://doi.org/10.1680/jgein.20.00049
1974. Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape A. Chipanshi et al. https://doi.org/10.1016/j.agrformet.2015.03.007
1975. Precision mapping of evapotranspiration in arid basins: Spatiotemporal optimization and sensitivity to extreme climate conditions based on multi-mechanism model fusion X. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135795
1976. Assessing the impact of climate change on water resources in Iran K. Abbaspour et al. https://doi.org/10.1029/2008WR007615
1977. The NEON Daily Isotopic Composition of Environmental Exchanges Dataset C. Finkenbiner et al. https://doi.org/10.1038/s41597-022-01412-4
1978. Distinguishing the Relative Contribution of Environmental Factors to Runoff Change in the Headwaters of the Yangtze River M. Guo et al. https://doi.org/10.3390/w11071432
1979. Analytical solute transport modeling of furrow fertigation using the STANMOD software package A. Ashrafi et al. https://doi.org/10.2478/johh-2025-0015
1980. The role of conceptual hydrologic model calibration in climate change impact on water resources assessment A. Todorovic & J. Plavsic https://doi.org/10.2166/wcc.2015.086
1981. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. https://doi.org/10.3390/w13070970
1982. Improvement approaches and performance of temperature-based remote sensing evapotranspiration models in agricultural fields: A meta analysis J. Ding et al. https://doi.org/10.1016/j.agrformet.2026.111166
1983. Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (southern Italy) D. Capolongo et al. https://doi.org/10.1016/j.jhydrol.2008.04.002
1984. Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets A. te Linde et al. https://doi.org/10.5194/hess-12-943-2008
1985. Retention Efficiencies of Vegetative Filter Strips in Reducing Agricultural Nonpoint Source Pollution in Jianghan Plain: Experiments and VFSMOD Modeling R. Li et al. https://doi.org/10.1007/s11270-023-06859-8
1986. Applications of the SWAT Model Special Section: Overview and Insights P. Gassman et al. https://doi.org/10.2134/jeq2013.11.0466
1987. Heteroscedastic and symmetric efficiency for hydrological model evaluation criteria C. Zhan et al. https://doi.org/10.2166/nh.2019.121
1988. Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization S. Mozaffari et al. https://doi.org/10.1007/s11269-022-03118-z
1989. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. https://doi.org/10.2166/wst.2024.110
1990. Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0) J. Kupzig et al. https://doi.org/10.5194/gmd-17-6819-2024
1991. Simulating the spatio-temporal dynamics of soil erosion, deposition, and yield using a coupled sediment dynamics and 3D distributed hydrologic model T. Zi et al. https://doi.org/10.1016/j.envsoft.2016.06.004
1992. Climate-change potential effects on the hydrological regime of freshwater springs in the Italian Northern Apennines F. Cervi et al. https://doi.org/10.1016/j.scitotenv.2017.11.231
1993. Identifying changes to key APSIM-wheat constants to sensibly simulate high temperature crop response in Pakistan M. Shahid et al. https://doi.org/10.1016/j.fcr.2024.109265
1994. Modeling Climate Change Impacts on Crop Water Requirement and Yield under Irrigated and Rainfed Conditions in Jordan’s Mediterranean Highlands J. Al-Bakri et al. https://doi.org/10.1007/s11540-026-10028-9
1995. Exploratory analyses for the assessment of climate change impacts on the energy production in an Amazon run-of-river hydropower plant G. Mohor et al. https://doi.org/10.1016/j.ejrh.2015.04.003
1996. Combined predictive and descriptive tests for extreme rainfall probability distribution selection A. Ballarin et al. https://doi.org/10.1080/02626667.2022.2063725
1997. Validation of biophysical models: issues and methodologies. A review G. Bellocchi et al. https://doi.org/10.1051/agro/2009001
1998. Soya Bitkisinin Verim Parametreleri ile Bazı Kimyasal Toprak Özellikleri Arasındaki Pedotransfer Modellerin Uygulanabilirliği N. KARS & İ. EKBERLİ https://doi.org/10.33462/jotaf.842505
1999. Sediment Reallocations due to Erosive Rainfall Events in the Three Gorges Reservoir Area, Central China F. Stumpf et al. https://doi.org/10.1002/ldr.2503
2000. Modeling the effects of farming management practices on soil organic carbon stock under two tillage practices in a semi-arid region, Morocco I. Lembaid et al. https://doi.org/10.1016/j.heliyon.2020.e05889
2001. Rice in cropping systems—Modelling transitions between flooded and non-flooded soil environments D. Gaydon et al. https://doi.org/10.1016/j.eja.2012.01.003
2002. Effects of specimen size and mix ratio on the nickel migration behavior of landfill waste mixed mortar M. Haque https://doi.org/10.1016/j.jenvman.2016.12.061
2003. Modelling the impacts of climatic variables on the hydrology of the Upper Crocodile River Basin, Johannesburg, South Africa K. Leketa & T. Abiye https://doi.org/10.1007/s12665-019-8353-6
2004. The contribution of human agricultural activities to increasing evapotranspiration is significantly greater than climate change effect over Heihe agricultural region M. Zou et al. https://doi.org/10.1038/s41598-017-08952-5
2005. Watershed derived nutrients for Lake Ontario inflows: Model calibration considering typical land operations in Southern Ontario M. Asadzadeh et al. https://doi.org/10.1016/j.jglr.2015.09.002
2006. Generation of Total Runoff Hydrographs Using a Method Derived from a Digital Filter Algorithm S. Tan et al. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:1(101)
2007. Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing Y. Yang & T. Chui https://doi.org/10.1029/2020WR029471
2008. A multi-model study of the subsurface and surface hydrodynamics of a 700 km2 watershed in western Canada (Fox Creek area, Alberta) B. Meneses-Vega et al. https://doi.org/10.1016/j.ejrh.2025.102637
2009. Streamflow Predictions in a Small Urban–Rural Watershed: The Effects of Radar Rainfall Resolution and Urban Rainfall–Runoff Dynamics L. Grimley et al. https://doi.org/10.3390/atmos11080774
2010. Calibration, validation, parameter indentifiability and uncertainty analysis of a 2 – parameter parsimonious monthly rainfall-runoff model in two catchments in Zimbabwe D. Rwasoka et al. https://doi.org/10.1016/j.pce.2013.09.015
2011. Enhanced low flow prediction for water and environmental management S. Aryal et al. https://doi.org/10.1016/j.jhydrol.2020.124658
2012. A ranking system for comparing models' performance combining multiple statistical criteria and scenarios: The case of reference evapotranspiration models V. Aschonitis et al. https://doi.org/10.1016/j.envsoft.2019.01.005
2013. Evaluation of NRCS curve number and MODIS time-series proxies for antecedent moisture condition B. Weissling et al. https://doi.org/10.1080/10286600802005356
2014. Quantifying human impacts on hydrological drought using a combined modelling approach in a tropical river basin in central Vietnam A. Firoz et al. https://doi.org/10.5194/hess-22-547-2018
2015. Improving the characterization of initial conditions for streamflow prediction using a precipitation reconstruction algorithm A. Tahiri et al. https://doi.org/10.1016/j.ifacol.2018.06.193
2016. Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models M. Rathinasamy et al. https://doi.org/10.1002/2013WR014650
2017. Convergence and final performances of optimization algorithms for rainfall- runoff model calibration based on the number of function calls Z. Ersoy et al. https://doi.org/10.1007/s12145-025-01885-y
2018. Comparative assessment between GR model and tank model for rainfall-runoff analysis using Kalman filter- application to Algerian basins-. M. Amireche et al. https://doi.org/10.1051/matecconf/201712005006
2019. Technical note: Evaluation and bias correction of an observation-based global runoff dataset using streamflow observations from small tropical catchments in the Philippines D. Ibarra et al. https://doi.org/10.5194/hess-25-2805-2021
2020. The rise of the Nash-Sutcliffe efficiency in hydrology L. Melsen et al. https://doi.org/10.1080/02626667.2025.2475105
2021. Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis P. Ferreira et al. https://doi.org/10.1590/2318-0331.252020190155
2022. Comparing classical performance measures with signature indices derived from flow duration curves to assess model structures as tools for catchment classification R. Ley et al. https://doi.org/10.2166/nh.2015.221
2023. An integrated statistical and deterministic hydrologic model for analyzing trace organic contaminants in commercial and high-density residential stormwater runoff J. Brown et al. https://doi.org/10.1016/j.scitotenv.2019.03.327
2024. Multivariate statistical monitoring of ETo: A new approach for estimation in nearby locations using geographical inputs P. Martí & M. Zarzo https://doi.org/10.1016/j.agrformet.2011.08.008
2025. Analyzing hydrological alteration and environmental flows in a highly anthropized agricultural river basin system using SWAT+, WEAP and IAHRIS S. Asadi et al. https://doi.org/10.1016/j.ejrh.2024.101738
2026. Water availability, demand and reliability of in situ water harvesting in smallholder rain-fed agriculture in the Thukela River Basin, South Africa J. Andersson et al. https://doi.org/10.5194/hess-13-2329-2009
2027. Effects of Streamside Buffers on Stream Temperatures Associated With Forest Management and Harvesting Using DHSVM-RBM; South Fork Caspar Creek, California J. Ridgeway & C. Surfleet https://doi.org/10.3389/ffgc.2021.611380
2028. Incorporating land-use changes and surface–groundwater interactions in a simple catchment water yield model M. Gilfedder et al. https://doi.org/10.1016/j.envsoft.2012.05.005
2029. 2D model of groundwater flow and total dissolved HCH transport through the Gállego alluvial aquifer downstream the Sardas landfill (Huesca, Spain) B. Sobral et al. https://doi.org/10.1016/j.jconhyd.2024.104370
2030. Model-Based Attribution of High-Resolution Streamflow Trends in Two Alpine Basins of Western Austria C. Kormann et al. https://doi.org/10.3390/hydrology3010007
2031. Impact of Climate Change on Flood Frequency of the Trian Reservoir in Vietnam Using RCMS N. Dong et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001609
2032. Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. https://doi.org/10.3389/frai.2020.565859
2033. Projected Streamflow in the Kurau River Basin of Western Malaysia under Future Climate Scenarios M. Adib et al. https://doi.org/10.1038/s41598-020-65114-w
2034. A webGIS Application to Assess Seawater Quality: A Case Study in a Coastal Area in the Northern Aegean Sea D. Kitsiou et al. https://doi.org/10.3390/jmse9010033
2035. Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region Y. Cai et al. https://doi.org/10.1016/j.jclepro.2023.136891
2036. Simplified modeling of phosphorus removal by vegetative filter strips to control runoff pollution from phosphate mining areas Y. Kuo & R. Muñoz-Carpena https://doi.org/10.1016/j.jhydrol.2009.09.039
2037. Assessing Statistical Indices for Calibrating Building Performance Simulation Models via Continuous Time Series P. Freudenberg et al. https://doi.org/10.1088/1742-6596/2654/1/012056
2038. How EU policies could reduce nutrient pollution in European inland and coastal waters B. Grizzetti et al. https://doi.org/10.1016/j.gloenvcha.2021.102281
2039. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration M. Demirel et al. https://doi.org/10.3390/w11102083
2040. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al. https://doi.org/10.1016/j.advwatres.2021.103965
2041. A Modeling Approach to Diagnose the Impacts of Global Changes on Discharge and Suspended Sediment Concentration within the Red River Basin X. Wei et al. https://doi.org/10.3390/w11050958
2042. Performance Assessment of Model Averaging Techniques to Reduce Structural Uncertainty of Groundwater Modeling A. Jafarzadeh et al. https://doi.org/10.1007/s11269-021-03031-x
2043. Effectiveness of side-inlet vegetated filter strips at trapping pesticides from agricultural runoff M. Arpino et al. https://doi.org/10.1016/j.scitotenv.2023.162881
2044. Development of the estimation models for annual soil temperature according to soil depth K. Eom https://doi.org/10.15531/KSCCR.2024.15.6.1195
2045. Spatiotemporal Modeling of Surface Water–Groundwater Interactions via Multi-Task Transformer-Based Learning H. Jing et al. https://doi.org/10.3390/hydrology12110291
2046. Modeling water balance for the small-forested watershed in Korea E. Combalicer et al. https://doi.org/10.1007/s12205-008-0339-y
2047. Estimating spatiotemporal variability and sustainability of shallow groundwater in a well-irrigated plain of the Haihe River basin using SWAT model X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2016.08.030
2048. Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach M. Hollaway et al. https://doi.org/10.1016/j.envsoft.2021.104993
2049. Post-wildfire recovery of water yield in the Sydney Basin water supply catchments: An assessment of the 2001/2002 wildfires J. Heath et al. https://doi.org/10.1016/j.jhydrol.2014.09.033
2050. Simulated Impacts of Climate Change on Surface Water Yields over the Sondu Basin in Kenya S. Rwigi et al. https://doi.org/10.31686/ijier.vol4.iss8.584
2051. Quantification of Flood Damage under Potential Climate Change Impacts in Central Vietnam Q. Van Dau et al. https://doi.org/10.1002/ird.2160
2052. Development of a Non-Linear Integrated Drought Index (NDI) for Managing Drought and Water Resources Forecasting in the Upper Tana River Basin-Kenya R. Wambua https://doi.org/10.4018/IJESGT.2020010102
2053. Estimation of suspended loads in the Danube River at Göd (1668 river km), Hungary B. Tóth & E. Bódis https://doi.org/10.1016/j.jhydrol.2015.01.031
2054. Modeling the salinity fluctuations in salt marsh lagoons X. Casamitjana et al. https://doi.org/10.1016/j.jhydrol.2019.06.018
2055. Anthropogenic climate change versus internal climate variability: impacts on snow cover in the Swiss Alps F. Willibald et al. https://doi.org/10.5194/tc-14-2909-2020
2056. Simulation of cyanide oxidation using calcium and sodium hypochlorite in the Moteh Gold Mine Tailing Dam, Iran E. Tavasoli et al. https://doi.org/10.5004/dwt.2019.23639
2057. Effects of Data and Model Simplification on the Results of a Wetland Water Resource Management Model O. Dietrich et al. https://doi.org/10.3390/w8060252
2058. Enhancing groundwater resources through managed aquifer recharge: A SWAT application in arid southeastern Tunisia R. Hadded et al. https://doi.org/10.1016/j.ejrh.2025.103027
2059. A model-based site selection approach associated with regional frequency analysis for modeling extreme rainfall depths in Minas Gerais state, Southeast Brazil L. Assis et al. https://doi.org/10.1007/s00477-017-1481-1
2060. Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach F. Han et al. https://doi.org/10.1029/2022WR033673
2061. Modelling Wheat Stomatal Resistance in Hourly Time Steps from Micrometeorological Variables and Soil Water Status D. Neukam et al. https://doi.org/10.1111/jac.12133
2062. Modelling maize yield, soil nitrogen balance and organic carbon changes under long-term fertilization in Northeast China J. Yang et al. https://doi.org/10.1016/j.jenvman.2022.116454
2063. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. https://doi.org/10.1080/02626667.2023.2273402
2064. Quantifying the Impact of Cascade Reservoirs on Streamflow, Drought, and Flood in the Jinsha River Basin K. Zhang et al. https://doi.org/10.3390/su15064989
2065. Searching for an Optimized Single-objective Function Matching Multiple Objectives with Automatic Calibration of Hydrological Models F. Tian et al. https://doi.org/10.1007/s11769-019-1068-5
2066. Mapping high resolution National Soil Information Grids of China F. Liu et al. https://doi.org/10.1016/j.scib.2021.10.013
2067. Cyber-enabled autocalibration of hydrologic models to support Open Science A. Rajib et al. https://doi.org/10.1016/j.envsoft.2022.105561
2068. HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al. https://doi.org/10.3389/feart.2022.884766
2069. Low-Impact Development Effects on Aquifer Recharge Using Coupled Surface and Groundwater Models E. Mooers et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001682
2070. Occurrence of metolachlor and trifluralin losses in the Save river agricultural catchment during floods L. Boithias et al. https://doi.org/10.1016/j.jhazmat.2011.09.012