50 citations as recorded by crossref.

1. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system S. Sharma et al. https://doi.org/10.5194/hess-22-1831-2018
2. Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine J. Verkade et al. https://doi.org/10.1016/j.jhydrol.2017.10.024
3. IoT-Integrated Variance-Combined Bias Correction for Enhancing Hydrological Forecasting T. Tang et al. https://doi.org/10.1109/JIOT.2024.3521899
4. Verification of Precipitation Forecasts from NCEP’s Short-Range Ensemble Forecast (SREF) System with Reference to Ensemble Streamflow Prediction Using Lumped Hydrologic Models J. Brown et al. https://doi.org/10.1175/JHM-D-11-036.1
5. Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow D. Bourdin & R. Stull https://doi.org/10.1016/j.jhydrol.2013.08.028
6. A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models S. Hagemann et al. https://doi.org/10.5194/os-20-1457-2024
7. Using multiple watershed models to assess the water quality impacts of alternate land development scenarios for a small community A. Sharifi et al. https://doi.org/10.1016/j.catena.2016.11.009
8. On the prediction of persistent processes using the output of deterministic models H. Tyralis & D. Koutsoyiannis https://doi.org/10.1080/02626667.2017.1361535
9. Hydrological post-processing based on approximate Bayesian computation (ABC) J. Romero-Cuellar et al. https://doi.org/10.1007/s00477-019-01694-y
10. Post-processing of ensemble forecasts in low-flow period A. Ye et al. https://doi.org/10.1002/hyp.10374
11. Improved Simulation of Peak Flows under Climate Change: Postprocessing or Composite Objective Calibration? X. Zhang et al. https://doi.org/10.1175/JHM-D-14-0218.1
12. Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods K. Bogner et al. https://doi.org/10.3390/w8040115
13. Development of an electrical exploration data post-processor Y. Ignatov et al. https://doi.org/10.1051/e3sconf/202131503027
14. A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions P. Pokhrel et al. https://doi.org/10.5194/hess-17-795-2013
15. A review on statistical postprocessing methods for hydrometeorological ensemble forecasting W. Li et al. https://doi.org/10.1002/wat2.1246
16. Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach S. Khajehei & H. Moradkhani https://doi.org/10.1016/j.jhydrol.2017.01.026
17. Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia D. Ratri et al. https://doi.org/10.1016/j.ejrh.2022.101305
18. Post‐processing hydrological ensemble predictions intercomparison experiment S. van Andel et al. https://doi.org/10.1002/hyp.9595
19. Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting D. McInerney et al. https://doi.org/10.1029/2019WR026979
20. Ensemble dressing for hydrological applications T. Pagano et al. https://doi.org/10.1002/hyp.9313
21. Hydrologic post-processing of MOPEX streamflow simulations A. Ye et al. https://doi.org/10.1016/j.jhydrol.2013.10.055
22. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. https://doi.org/10.5194/hess-27-4529-2023
23. Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters J. Romero-Cuellar et al. https://doi.org/10.3390/w14081261
24. Bayesian flood forecasting methods: A review S. Han & P. Coulibaly https://doi.org/10.1016/j.jhydrol.2017.06.004
25. The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting S. Barbetta et al. https://doi.org/10.1016/j.jhydrol.2017.06.030
26. Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach A. Bradley et al. https://doi.org/10.1002/2014WR016811
27. <i>Preface</i> Towards practical applications in ensemble hydro-meteorological forecasting Y. He et al. https://doi.org/10.5194/adgeo-29-119-2011
28. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. https://doi.org/10.1029/2020WR028392
29. Statistical Post-Processing to Improve Hydrometeorological Forecasts 青. 段 https://doi.org/10.12677/JWRR.2012.14023
30. Flood Forecasting and Decision Making in the new Millennium. Where are We? E. Todini https://doi.org/10.1007/s11269-017-1693-7
31. Integrating weather and climate predictions for seamless hydrologic ensemble forecasting: A case study in the Yalong River basin A. Ye et al. https://doi.org/10.1016/j.jhydrol.2017.01.053
32. Hydrological post-processing of streamflow forecasts issued from multimodel ensemble prediction systems J. Xu et al. https://doi.org/10.1016/j.jhydrol.2019.124002
33. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous https://doi.org/10.3390/w13233420
34. Towards improved post‐processing of hydrologic forecast ensembles S. Madadgar et al. https://doi.org/10.1002/hyp.9562
35. Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India A. Tiwari & V. Mishra https://doi.org/10.1029/2021JD035737
36. The Science of NOAA's Operational Hydrologic Ensemble Forecast Service J. Demargne et al. https://doi.org/10.1175/BAMS-D-12-00081.1
37. Postprocessing of hydrometeorological ensemble forecasts based on multisource precipitation in Ganjiang River basin, China X. Liu et al. https://doi.org/10.1016/j.jhydrol.2021.127323
38. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
39. A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network Á. Ossandón et al. https://doi.org/10.1175/JHM-D-21-0167.1
40. ImprovedBayesian multimodeling: Integration of copulas andBayesian model averaging S. Madadgar & H. Moradkhani https://doi.org/10.1002/2014WR015965
41. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. https://doi.org/10.2166/hydro.2022.114
42. Bias-correction schemes for calibrated flow in a conceptual hydrological model K. Bum Kim et al. https://doi.org/10.2166/nh.2021.043
43. Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System G. Matthews et al. https://doi.org/10.5194/hess-26-2939-2022
44. Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts – A Hydrologic Model Output Statistics (HMOS) approach S. Regonda et al. https://doi.org/10.1016/j.jhydrol.2013.05.028
45. Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency D. Lucatero et al. https://doi.org/10.5194/hess-22-3601-2018
46. Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales J. Verkade et al. https://doi.org/10.1016/j.jhydrol.2013.07.039
47. 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
48. MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement R. Wu et al. https://doi.org/10.5194/gmd-12-4115-2019
49. Use of Ensemble-Based Forecasting Methods in Reservoir Operation: Issues and Implementation Challenges R. Teegavarapu et al. https://doi.org/10.1061/JHYEFF.HEENG-6467
50. Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies I. Zalachori et al. https://doi.org/10.5194/asr-8-135-2012