45 citations as recorded by crossref.

1. Parameter estimation for a large catchment P. Cu & J. Ball https://doi.org/10.1080/13241583.2017.1398365
2. Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework H. Zhu et al. https://doi.org/10.5194/bg-21-3735-2024
3. Sensitivitätsanalyse zu Infiltrationsprozessen in Böden G. Brunetti et al. https://doi.org/10.1007/s00506-022-00839-8
4. Soil–vegetation–water interactions controlling solute flow and chemical weathering in volcanic ash soils of the high Andes S. Páez-Bimos et al. https://doi.org/10.5194/hess-27-1507-2023
5. Detection of hidden model errors by combining single and multi-criteria calibration T. Houska et al. https://doi.org/10.1016/j.scitotenv.2021.146218
6. 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
7. Impacts of Spatial Climatic Representation on Hydrological Model Calibration and Prediction Uncertainty: A Mountainous Catchment of Three Gorges Reservoir Region, China Y. Li et al. https://doi.org/10.3390/w8030073
8. Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties H. Dokoohaki et al. https://doi.org/10.1016/j.agwat.2018.06.034
9. Challenges and opportunities in precision irrigation decision-support systems for center pivots J. Zhang et al. https://doi.org/10.1088/1748-9326/abe436
10. Greenhouse Gas Footprints of Maize Cultivation Systems in Different Climate Zones: Field Data Validation and Application of CNMM–DNDC as a Hydro-Biogeochemical Model S. Li et al. https://doi.org/10.1007/s00376-025-4420-y
11. Closing the N-Budget: How Simulated Groundwater-Borne Nitrate Supply Affects Plant Growth and Greenhouse Gas Emissions on Temperate Grassland R. Liebermann et al. https://doi.org/10.3390/atmos9100407
12. Parameter Uncertainty and Nonstationarity in Regional Rainfall Frequency in Qu River Basin, East China Q. Zhu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001355
13. Two Model Performance Comparisons with Multisite Observations Based on Uncertainty Methods for Modeling Hydrologic Dynamics J. Zhang et al. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001284
14. 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
15. Diagnostic Framework for Evaluating How Parametric Uncertainty Influences Agro‐Hydrologic Model Projections of Crop Yields Under Climate Change T. Karimi et al. https://doi.org/10.1029/2021WR031249
16. SPOTting Model Parameters Using a Ready-Made Python Package T. Houska et al. https://doi.org/10.1371/journal.pone.0145180
17. A design of experiment aided stochastic parameterization method for modeling aquifer NAPL contamination Z. Li et al. https://doi.org/10.1016/j.envsoft.2017.12.014
18. Assessing soil erosion risk at national scale in developing countries: The technical challenges, a proposed methodology, and a case history M. Rosas & R. Gutierrez https://doi.org/10.1016/j.scitotenv.2019.135474
19. The critical role of uncertainty in projections of hydrological extremes H. Meresa & R. Romanowicz https://doi.org/10.5194/hess-21-4245-2017
20. Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology K. Cucchi et al. https://doi.org/10.1016/j.advwatres.2019.02.003
21. Effects of different functional structure parameters of plant communities on slope runoff in different periods of the year in semiarid grasslands J. Hou et al. https://doi.org/10.1016/j.scitotenv.2020.136705
22. Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States B. Nolan et al. https://doi.org/10.1002/ps.3875
23. Response of wetland soil carbon to groundwater conservation: Probabilistic outcomes from error propagation D. Lewis https://doi.org/10.1016/j.ecolind.2015.06.028
24. Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models A. Pérez-Alarcón et al. https://doi.org/10.1007/s40710-022-00602-x
25. Modeling China's terrestrial ecosystem gross primary productivity with BEPS model: Parameter sensitivity analysis and model calibration X. Xing et al. https://doi.org/10.1016/j.agrformet.2023.109789
26. Understanding the performance of global precipitation products for hydrological modeling in the data-scarce morphologically complex central Himalayan region S. Sandilya et al. https://doi.org/10.1016/j.jastp.2025.106513
27. Calibration of a climate suitability model using a generalized likelihood uncertainty estimation (GLUE): a global case study of orange production S. Hyun et al. https://doi.org/10.1038/s41598-026-44664-5
28. Optimization of planning structure in irrigated district considering water footprint under uncertainty X. Liu et al. https://doi.org/10.1016/j.jclepro.2018.10.339
29. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al. https://doi.org/10.5194/hess-23-4323-2019
30. A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models Y. Fan et al. https://doi.org/10.1016/j.jhydrol.2015.09.035
31. Rainfall–Runoff Processes and Modelling in Regions Characterized by Deficiency in Soil Water Storage P. Shi et al. https://doi.org/10.3390/w11091858
32. Coupled water transport and heat flux in seasonally frozen soils: uncertainties identification in multi-site calibration M. Wu et al. https://doi.org/10.1007/s12665-020-09262-2
33. Incremental model breakdown to assess the multi-hypotheses problem F. Jehn et al. https://doi.org/10.5194/hess-22-4565-2018
34. Role of Sectoral Transformation in the Evolution of Water Management Norms in Agricultural Catchments: A Sociohydrologic Modeling Analysis M. Roobavannan et al. https://doi.org/10.1002/2017WR020671
35. Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model Y. Zhang et al. https://doi.org/10.1371/journal.pone.0302098
36. Rejecting hydro-biogeochemical model structures by multi-criteria evaluation T. Houska et al. https://doi.org/10.1016/j.envsoft.2017.03.005
37. A Review of Tank Model and Its Applicability to Various Korean Catchment Conditions J. Lee et al. https://doi.org/10.3390/w12123588
38. Allocating Environmental Water and Impact on Basin Unemployment: Role of A Diversified Economy M. Roobavannan et al. https://doi.org/10.1016/j.ecolecon.2017.02.006
39. Comparing the ability of different remotely sensed evapotranspiration products in enhancing hydrological model performance and reducing prediction uncertainty S. Taia et al. https://doi.org/10.1016/j.ecoinf.2023.102352
40. A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling R. Camacho et al. https://doi.org/10.1111/1752-1688.12319
41. Exploring impacts of vegetated buffer strips on nitrogen cycling using a spatially explicit hydro-biogeochemical modeling approach S. Klatt et al. https://doi.org/10.1016/j.envsoft.2016.12.002
42. A coupled hydrological-plant growth model for simulating the effect of elevated CO 2 on a temperate grassland J. Kellner et al. https://doi.org/10.1016/j.agrformet.2017.05.017
43. Comparison of IHM and MIKE SHE Model Performance for Modeling Hydrologic Dynamics in Shallow Water Table Settings J. Zhang & M. Ross https://doi.org/10.2136/vzj2014.03.0023
44. Response of maize biomass and soil water fluxes on elevated CO2 and drought—From field experiments to process‐based simulations J. Kellner et al. https://doi.org/10.1111/gcb.14723
45. A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking A. Schöniger et al. https://doi.org/10.1002/2015WR016918