45 citations as recorded by crossref.

1. Seasonal patterns and hydrological regulations of root zone storage capacity across United States S. Du et al. https://doi.org/10.1016/j.agrformet.2025.110428
2. A comprehensive calibration and validation of SWAT-T using local datasets, evapotranspiration and streamflow in a tropical montane cloud forest area with permeable substrate in central Veracruz, Mexico S. López-Ramírez et al. https://doi.org/10.1016/j.jhydrol.2021.126781
3. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. https://doi.org/10.1016/j.jhydrol.2018.12.050
4. Omnipresent distribution of herbicides and their transformation products in all water body types of an agricultural landscape in the North German Lowland U. Ulrich et al. https://doi.org/10.1007/s11356-021-13626-x
5. An enhanced SWAT wetland module to quantify hydraulic interactions between riparian depressional wetlands, rivers and aquifers M. Rahman et al. https://doi.org/10.1016/j.envsoft.2016.07.003
6. Ten strategies towards successful calibration of environmental models J. Mai https://doi.org/10.1016/j.jhydrol.2023.129414
7. Identifying the connective strength between model parameters and performance criteria B. Guse et al. https://doi.org/10.5194/hess-21-5663-2017
8. Time-scale effects on runoff simulation and parameters sensitivity using SWAT model L. Yu et al. https://doi.org/10.1016/j.ejrh.2025.102604
9. Assessing parameter identifiability for multiple performance criteria to constrain model parameters B. Guse et al. https://doi.org/10.1080/02626667.2020.1734204
10. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
11. 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
12. Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation L. Melsen & B. Guse https://doi.org/10.5194/hess-25-1307-2021
13. Technical Report—Methods: A Diagnostic Approach to Analyze the Direction of Change in Model Outputs Based on Global Variations in the Model Inputs A. Wang et al. https://doi.org/10.1029/2020WR027153
14. Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set L. Santos et al. https://doi.org/10.1080/02626667.2022.2035388
15. Decoding parameter sensitivities across scales: a Wavelet–Sobol framework for hydrological modelling V. Sai Suswara et al. https://doi.org/10.1016/j.jhydrol.2026.135482
16. Assessing Climate-Sensitive Nutrient Pollution Hazards in Urban-Rural Transitional Watersheds: An Artificial Intelligence and Remote Sensing Data Assimilation Approach J. Xue et al. https://doi.org/10.1007/s13753-026-00750-w
17. Reactive ditches: A simple approach to implement denitrifying wood chip bioreactors to reduce nitrate exports into aquatic ecosystems? M. Pfannerstill et al. https://doi.org/10.1007/s12665-016-5856-2
18. Inter-comparison of experimental catchment data and hydrological modelling S. Singh et al. https://doi.org/10.1016/j.jhydrol.2017.04.049
19. Quantifying Spatial Changes in the Structure of Water Quality Constituents in a Large Prairie River within Two Frameworks of a Water Quality Model N. Hosseini et al. https://doi.org/10.3390/w8040158
20. Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. https://doi.org/10.1029/2020WR028511
21. Hydrological tracers, the herbicide metazachlor and its transformation products in a retention pond during transient flow conditions U. Ulrich et al. https://doi.org/10.1007/s11356-019-05815-6
22. Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model H. Xie et al. https://doi.org/10.3390/w11010171
23. Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints S. Höllering et al. https://doi.org/10.5194/hess-22-203-2018
24. Improving hydrological model optimization for riverine species J. Kiesel et al. https://doi.org/10.1016/j.ecolind.2017.04.032
25. Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements M. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130425
26. Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model P. Stahn et al. https://doi.org/10.1007/s12665-017-6701-y
27. Simultaneously determining global sensitivities of model parameters and model structure J. Mai et al. https://doi.org/10.5194/hess-24-5835-2020
28. Variability in flow and tracer-based performance metric sensitivities reveal regional differences in dominant hydrological processes across the Athabasca River basin T. Holmes et al. https://doi.org/10.1016/j.ejrh.2022.101088
29. 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
30. Projected changes in climate and hydrological regimes of the Western Siberian lowlands R. Sada et al. https://doi.org/10.1007/s12665-019-8047-0
31. Verification of PCSWMM's LID processes and their scalability over time and space Z. Zhang & C. Valeo https://doi.org/10.3389/frwa.2022.1058883
32. Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments A. Watson et al. https://doi.org/10.1002/hyp.15065
33. Towards simplification of hydrologic modeling: identification of dominant processes S. Markstrom et al. https://doi.org/10.5194/hess-20-4655-2016
34. Revealing the positive influence of young water fractions derived from stable isotopes on the robustness of karst water resources predictions K. Çallı et al. https://doi.org/10.1016/j.jhydrol.2023.129549
35. PondR: a process-oriented model to simulate the hydrology of drainage ponds S. Willkommen et al. https://doi.org/10.2166/hydro.2017.038
36. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2018.10.040
37. Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model S. Wu et al. https://doi.org/10.1029/2021WR031149
38. Assessing the impacts of Best Management Practices on nitrate pollution in an agricultural dominated lowland catchment considering environmental protection versus economic development M. Haas et al. https://doi.org/10.1016/j.jenvman.2017.02.060
39. What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi https://doi.org/10.1016/j.earscirev.2019.04.006
40. On characterizing the temporal dominance patterns of model parameters and processes B. Guse et al. https://doi.org/10.1002/hyp.10764
41. How weather conditions and physico-chemical properties control the leaching of flufenacet, diflufenican, and pendimethalin in a tile-drained landscape S. Willkommen et al. https://doi.org/10.1016/j.agee.2019.03.017
42. Demasking the integrated information of discharge: Advancing sensitivity analysis to consider different hydrological components and their rates of change B. Guse et al. https://doi.org/10.1002/2016WR018894
43. Assessment of deterministic PMF modelling approaches G. Felder & R. Weingartner https://doi.org/10.1080/02626667.2017.1319065
44. Increasing parameter identifiability through clustered time-varying sensitivity analysis L. Wang et al. https://doi.org/10.1016/j.envsoft.2024.106189
45. Dynamics of hydrological-model parameters: mechanisms, problems and solutions T. Lan et al. https://doi.org/10.5194/hess-24-1347-2020