12 citations as recorded by crossref.

1. Evaluation of denitrification and decomposition from three biogeochemical models using laboratory measurements of N2, N2O and CO2 B. Grosz et al. https://doi.org/10.5194/bg-18-5681-2021
2. Simulating impacts of silage maize (Zea mays) in monoculture and undersown with annual grass (Lolium perenne L.) on the soil water balance in a sandy-humic soil in Northwest Germany Y. Conrad & N. Fohrer https://doi.org/10.1016/j.agwat.2016.09.005
3. Simulated or measured soil moisture: which one is adding more value to regional landslide early warning? A. Wicki et al. https://doi.org/10.5194/hess-25-4585-2021
4. Selection of likelihood parameters for complex models determines the effectiveness of Bayesian calibration K. Rahn et al. https://doi.org/10.1016/j.ecoinf.2011.08.002
5. Calibration of an energy balance model to simulate wintertime soil temperature, soil frost depth, and snow depth for a 14 year period in a highland area of Iran Y. Khoshkhoo et al. https://doi.org/10.1016/j.coldregions.2015.07.008
6. A test of CoupModel for assessing the nitrogen leaching in grassland systems with two different fertilization levels Y. Conrad & N. Fohrer https://doi.org/10.1002/jpln.200800264
7. Key functional soil types explain data aggregation effects on simulated yield, soil carbon, drainage and nitrogen leaching at a regional scale E. Coucheney et al. https://doi.org/10.1016/j.geoderma.2017.11.025
8. Multi‐year simulation and model calibration of soil moisture and temperature profiles in till soil J. Okkonen et al. https://doi.org/10.1111/ejss.12489
9. Modelling nitrous oxide emissions from mown-grass and grain-cropping systems: Testing and sensitivity analysis of DailyDayCent using high frequency measurements N. Senapati et al. https://doi.org/10.1016/j.scitotenv.2016.07.226
10. Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change A. Villa et al. https://doi.org/10.1016/j.eja.2022.126630
11. Improving efficiency of a statistical analysis of complex ecological models, when using the statistical software R by parallelising tasks with Rmpi K. Rahn et al. https://doi.org/10.1016/j.ecoinf.2013.02.005
12. A Survey of Bayesian Calibration and Physics-informed Neural Networks in Scientific Modeling F. Viana & A. Subramaniyan https://doi.org/10.1007/s11831-021-09539-0