404 citations as recorded by crossref.

1. Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting H. Chen et al. https://doi.org/10.1016/j.advwatres.2013.06.010
2. Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM J. Guo et al. https://doi.org/10.1016/j.jhydrol.2023.129969
3. Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching A. Ficchì et al. https://doi.org/10.1016/j.jhydrol.2019.05.084
4. Hydrological models: mathematics or science? D. Hughes https://doi.org/10.1002/hyp.7805
5. Probabilistic runoff forecasting by integrating improved conceptual hydrological model with interpretable deep learning approach in a typical karst basin, Southwest China S. Lai et al. https://doi.org/10.1016/j.jhydrol.2025.132950
6. Hydrological model performance and parameter estimation in the wavelet-domain B. Schaefli & E. Zehe https://doi.org/10.5194/hess-13-1921-2009
7. Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows D. Gupta & C. Dhanya https://doi.org/10.1061/(ASCE)HE.1943-5584.0002072
8. Interpreting the cross-sectional flow field in a river bank based on a genetic-algorithm two-dimensional heat-transport method (GA-VS2DH) X. Su et al. https://doi.org/10.1007/s10040-016-1459-y
9. Exploration of warm-up period in conceptual hydrological modelling K. Kim et al. https://doi.org/10.1016/j.jhydrol.2017.11.015
10. The use of multiobjective calibration and regional sensitivity analysis in simulating hyporheic exchange R. Naranjo et al. https://doi.org/10.1029/2011WR011179
11. Comparing Streamflow Depletion Estimation Approaches in a Heavily Stressed, Conjunctively Managed Aquifer S. Zipper et al. https://doi.org/10.1029/2020WR027591
12. Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis T. Wagener et al. https://doi.org/10.1002/hyp.1135
13. Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin M. Morlot et al. https://doi.org/10.1016/j.jhydrol.2023.130587
14. Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation M. Parrish et al. https://doi.org/10.1029/2011WR011116
15. Risk-based modelling of surface water quality: a case study of the Charles River, Massachusetts N. McIntyre et al. https://doi.org/10.1016/S0022-1694(02)00417-1
16. Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows / Calage d’un modèle semi-distribué pour la simulation conjointe d’écoulements superficiels et souterrains E. Rozos et al. https://doi.org/10.1623/hysj.49.5.819.55130
17. Strategies for smarter catchment hydrology models: incorporating scaling and better process representation R. Sidle https://doi.org/10.1186/s40562-021-00193-9
18. Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors B. Renard et al. https://doi.org/10.1029/2009WR008328
19. Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes M. He et al. https://doi.org/10.1016/j.advwatres.2010.10.002
20. A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation L. Ammann et al. https://doi.org/10.5194/hess-23-2147-2019
21. 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
22. Unraveling complex hydrogeological processes in Andean basins in south‐central Chile: An integrated assessment to understand hydrological dissimilarity E. Muñoz et al. https://doi.org/10.1002/hyp.11032
23. Bias-correction schemes for calibrated flow in a conceptual hydrological model K. Bum Kim et al. https://doi.org/10.2166/nh.2021.043
24. Regional sensitivity analysis to assess critical parameters in circular economy interventions: An application to the dynamic MFA model Y. Fujii et al. https://doi.org/10.1111/jiec.13623
25. A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior R. Singh et al. https://doi.org/10.5194/hess-15-3591-2011
26. Modelos hidrológicos como ferramenta da implementação da política nacional de recursos hídricos F. Campos et al. https://doi.org/10.59824/rmrh.v6.350
27. Adjustment of a weather generator to represent actual rain erosivity in the northern Black Forest – Germany J. Haas et al. https://doi.org/10.1016/j.catena.2017.12.006
28. Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems I. Nalbantis et al. https://doi.org/10.5194/hess-15-743-2011
29. Calibration of hydrological models using flow-duration curves I. Westerberg et al. https://doi.org/10.5194/hess-15-2205-2011
30. Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals M. Girotto et al. https://doi.org/10.1016/j.scitotenv.2023.167312
31. An Improved Optimization Scheme for Representing Hillslopes and Depressions in Karst Hydrology C. Xu et al. https://doi.org/10.1029/2019WR026038
32. 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
33. Continuous Hydrologic Modeling of Snow-Affected Watersheds in the Great Lakes Basin Using HEC-HMS R. Gyawali & D. Watkins https://doi.org/10.1061/(ASCE)HE.1943-5584.0000591
34. The role of hydrograph indices in parameter estimation of rainfall-runoff models E. Shamir et al. https://doi.org/10.1002/hyp.5676
35. A comparison of simple and complex versions of the Xinanjiang hydrological model in predicting runoff in ungauged basins P. Bai et al. https://doi.org/10.2166/nh.2016.094
36. Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications Y. Tang et al. https://doi.org/10.1016/j.advwatres.2006.06.006
37. Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests C. Prieto et al. https://doi.org/10.1029/2018WR023254
38. Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering N. de Vos et al. https://doi.org/10.1002/hyp.7698
39. Reconciling droughts and landfalling tropical cyclones in the Southeastern United States V. Misra & S. Bastola https://doi.org/10.1007/s00382-015-2645-7
40. Exploration of sub-annual calibration schemes of hydrological models K. Kim & D. Han https://doi.org/10.2166/nh.2016.296
41. A process‐based rejectionist framework for evaluating catchment runoff model structure K. Vaché & J. McDonnell https://doi.org/10.1029/2005WR004247
42. Research on Combination Forecast Mode of Conceptual Hydrological Model M. Dai et al. https://doi.org/10.1007/s11269-016-1401-z
43. An Extended Model Evaluation Method under Uncertainty in Hydrologic Modeling G. Lee et al. https://doi.org/10.14481/jkges.2015.16.5.13
44. Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments P. Garambois et al. https://doi.org/10.5194/hess-17-2305-2013
45. Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
46. Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation M. Casper et al. https://doi.org/10.2478/johh-2019-0005
47. Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events D. Tri et al. https://doi.org/10.1007/s12517-022-10801-3
48. The site dynamics of Corrigiola litoralis (Strapwort) on the Elbe River in Czechia: A combined hydrological and hydrodynamic approach V. Havlíček et al. https://doi.org/10.1002/eco.2586
49. Calibration of hydrological models on hydrologically unusual events S. Singh & A. Bárdossy https://doi.org/10.1016/j.advwatres.2011.12.006
50. Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox V. Noacco et al. https://doi.org/10.1016/j.mex.2019.09.033
51. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al. https://doi.org/10.1029/2019WR026008
52. Long-term effects of forest growth dynamics and climate change on groundwater recharge and evapotranspiration in a steep catchment of western Japan R. Guyo et al. https://doi.org/10.1016/j.ecolind.2025.113652
53. Reconciling theory with observations: elements of a diagnostic approach to model evaluation H. Gupta et al. https://doi.org/10.1002/hyp.6989
54. Karst water resources in a changing world: Review of hydrological modeling approaches A. Hartmann et al. https://doi.org/10.1002/2013RG000443
55. Flood Simulations Using a Sensor Network and Support Vector Machine Model J. Langhammer https://doi.org/10.3390/w15112004
56. Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa Y. Zhang et al. https://doi.org/10.1109/JSTARS.2013.2251321
57. Assessment of a multimodel ensemble against an operational hydrological forecasting system A. Thiboult & F. Anctil https://doi.org/10.1080/07011784.2015.1026402
58. Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model Y. Zhou et al. https://doi.org/10.3390/su13063588
59. On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. https://doi.org/10.5194/hess-25-5805-2021
60. Research on the distributed Clark confluence model based on basic watershed units J. Zhu et al. https://doi.org/10.2166/wcc.2024.638
61. Using Different Classic Turbulence Closure Models to Assess Salt and Temperature Modelling in a Lagunar System: A Sensitivity Study J. Lopes https://doi.org/10.3390/jmse10111750
62. Evaluating flash flood simulation capability with respect to rainfall temporal variability in a small mountainous catchment X. Wang et al. https://doi.org/10.1007/s11442-023-2188-5
63. Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework X. Zhang et al. https://doi.org/10.1002/hyp.8058
64. Assessing uncertainties in surface water security: An empirical multimodel approach D. Rodrigues et al. https://doi.org/10.1002/2014WR016691
65. Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera https://doi.org/10.1016/j.jhydrol.2024.131071
66. A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data R. Sheikholeslami & S. Razavi https://doi.org/10.1029/2020GL089829
67. One decade of multi-objective calibration approaches in hydrological modelling: a review A. Efstratiadis & D. Koutsoyiannis https://doi.org/10.1080/02626660903526292
68. Uneven shifts in flood and drought flows in a Brazilian water supply catchment T. Mattos et al. https://doi.org/10.1016/j.ejrh.2025.102498
69. Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm M. Shafii & F. De Smedt https://doi.org/10.5194/hess-13-2137-2009
70. Creativity, Uncertainty, and Automated Model Building L. Marshall https://doi.org/10.1111/gwat.12552
71. Global sensitivity analysis: A Bayesian learning based polynomial chaos approach B. Bhattacharyya https://doi.org/10.1016/j.jcp.2020.109539
72. 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
73. The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments S. Bastola et al. https://doi.org/10.1016/j.advwatres.2011.01.008
74. Informal likelihood measures in model assessment: Theoretic development and investigation P. Smith et al. https://doi.org/10.1016/j.advwatres.2008.04.012
75. The impact of upland land management on flooding: insights from a multiscale experimental and modelling programme B. Jackson et al. https://doi.org/10.1111/j.1753-318X.2008.00009.x
76. 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
77. Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model E. Demaria et al. https://doi.org/10.1029/2006JD007534
78. Model identification for hydrological forecasting under uncertainty T. Wagener & H. Gupta https://doi.org/10.1007/s00477-005-0006-5
79. Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins? A. Montanari & E. Toth https://doi.org/10.1029/2006WR005184
80. Multiobjective Automatic Parameter Calibration of a Hydrological Model D. Jung et al. https://doi.org/10.3390/w9030187
81. Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty T. Page et al. https://doi.org/10.5194/hess-27-2523-2023
82. Empirical Formulation for Time of Concentration Based on Watershed Morphometry Using the Diffusion Wave Equation (DWE) Model M. Ansori et al. https://doi.org/10.1007/s11269-026-04700-5
83. Is ET often oversimplified in hydrologic models? Using long records to elucidate unaccounted for controls on ET C. Kelleher & S. Shaw https://doi.org/10.1016/j.jhydrol.2017.12.018
84. Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures J. Freer et al. https://doi.org/10.1016/j.jhydrol.2003.12.037
85. Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios S. Bastola et al. https://doi.org/10.1002/hyp.8349
86. Sensitivity and identifiability analysis of a conceptual-lumped model in the headwaters of the Benue River Basin, Cameroon: implications for uncertainty quantification and parameter optimization R. Nonki et al. https://doi.org/10.2166/nh.2023.243
87. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. https://doi.org/10.5194/gmd-12-2463-2019
88. Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox T. Wagener & J. Kollat https://doi.org/10.1016/j.envsoft.2006.06.017
89. Influence of constant rate versus slug injection experiment type on parameter identifiability in a 1‐D transient storage model for stream solute transport A. Wlostowski et al. https://doi.org/10.1002/wrcr.20103
90. Correlation Effects? A Major but Often Neglected Component in Sensitivity and Uncertainty Analysis N. Do & S. Razavi https://doi.org/10.1029/2019WR025436
91. 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
92. Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development F. Fenicia et al. https://doi.org/10.1029/2010WR010174
93. Water balance of a lake with floodplain buffering: Lake Tana, Blue Nile Basin, Ethiopia M. Dessie et al. https://doi.org/10.1016/j.jhydrol.2014.12.049
94. A spatially hybrid hydrological modeling approach using subbasin-specific model structures Y. Wang et al. https://doi.org/10.1016/j.envsoft.2026.106944
95. Development of Real-Time River Flow Forecasting Model with Data Assimilation Technique B. Lee & D. Bae https://doi.org/10.3741/JKWRA.2011.44.3.199
96. A modular and parallelized watershed modeling framework L. Zhu et al. https://doi.org/10.1016/j.envsoft.2019.104526
97. Advancing the calibration of macroscale hydrological models by integrating sensitivity analysis, adaptive pseudo-modeling optimization and multi-site calibration C. Thakur et al. https://doi.org/10.1016/j.jhydrol.2026.135840
98. Contrasting hydrological responses to climate change in two adjacent catchments dominated by karst and nonkarst Y. Chang et al. https://doi.org/10.1016/j.jhydrol.2023.130013
99. Ground truthing global-scale model estimates of groundwater recharge across Africa C. West et al. https://doi.org/10.1016/j.scitotenv.2022.159765
100. Evaluating the Effect of Numerical Schemes on Hydrological Simulations: HYMOD as A Case Study S. Zhang & K. Al-Asadi https://doi.org/10.3390/w11020329
101. Process verification of a hydrological model using a temporal parameter sensitivity analysis M. Pfannerstill et al. https://doi.org/10.5194/hess-19-4365-2015
102. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction Y. Xuan et al. https://doi.org/10.5194/hess-13-293-2009
103. Accounting for three sources of uncertainty in ensemble hydrological forecasting A. Thiboult et al. https://doi.org/10.5194/hess-20-1809-2016
104. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. https://doi.org/10.5194/hess-25-4373-2021
105. Effects of measurement uncertainties of meteorological data on estimates of site water balance components U. Spank et al. https://doi.org/10.1016/j.jhydrol.2013.03.047
106. 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
107. Probabilistic Numerical Modeling of Compound Flooding Caused by Tropical Storm Matthew Over a Data‐Scarce Coastal Environment Y. Zhang & M. Najafi https://doi.org/10.1029/2020WR028565
108. Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation M. McCarthy et al. https://doi.org/10.1016/j.ejrh.2025.102518
109. Dynamic modelling of multiple phytoplankton groups in rivers with an application to the Thames river system in the UK P. Whitehead et al. https://doi.org/10.1016/j.envsoft.2015.09.010
110. The impact of regional and catchment characteristics on long-term runoff in small agricultural catchments in Latvia A. Veinbergs & A. Lagzdins https://doi.org/10.2166/wpt.2022.005
111. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher https://doi.org/10.1029/2020WR028544
112. Intergenerational inequity from hydrological drought in a warming world R. Liu et al. https://doi.org/10.1016/j.jenvman.2025.125988
113. Temporal Networks‐Based Approach for Nonstationary Hydroclimatic Modeling and its Demonstration With Streamflow Prediction R. Dutta & R. Maity https://doi.org/10.1029/2020WR027086
114. Validating ENSO Teleconnections on Southeastern U.S. Winter Hydrology B. Nag et al. https://doi.org/10.1175/EI-D-14-0007.1
115. Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States N. Sun et al. https://doi.org/10.1029/2018JD030140
116. A Comparison of Hydrological and Geophysical Calibration Data in Layered Hydrologic Models of Mountain Hillslopes M. Pleasants et al. https://doi.org/10.1029/2022WR033506
117. Assessing the Performance of Two Hydrologic Models for Forecasting Daily Streamflows in the Cazones River Basin (Mexico) F. González-Leiva et al. https://doi.org/10.4236/ojmh.2016.63014
118. An approach to identify time consistent model parameters: sub-period calibration S. Gharari et al. https://doi.org/10.5194/hess-17-149-2013
119. Fast calculation of groundwater exfiltration salinity in a lowland catchment using a lumped celerity/velocity approach J. Delsman et al. https://doi.org/10.1016/j.envsoft.2017.07.004
120. A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library G. van Voorn et al. https://doi.org/10.3389/fpls.2023.1172359
121. Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria B. Guse et al. https://doi.org/10.1029/2019WR025605
122. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions F. Pianosi & T. Wagener https://doi.org/10.1016/j.envsoft.2015.01.004
123. Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose J. Guillaume et al. https://doi.org/10.1016/j.envsoft.2019.07.007
124. A scenario-incorporating analysis of the propagation of uncertainty to flash flood simulations F. Quintero et al. https://doi.org/10.1016/j.jhydrol.2012.06.045
125. The Curve Number Concept as a Driver for Delineating Hydrological Response Units E. Savvidou et al. https://doi.org/10.3390/w10020194
126. Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression K. Cheng & Z. Lu https://doi.org/10.1016/j.compstruc.2017.09.002
127. Reservoirs Response to Climate Change Under Medium Emission Scenario in Upper Krishna Basin, India Using Geospatial Inputs E. Arivoli et al. https://doi.org/10.1007/s12524-024-01861-x
128. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. https://doi.org/10.5194/hess-24-1319-2020
129. 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
130. Parameter sensitivity analysis and optimization of Noah land surface model with field measurements from Huaihe River Basin, China T. Hou et al. https://doi.org/10.1007/s00477-015-1033-5
131. An open software environment for hydrological model assessment and development F. Andrews et al. https://doi.org/10.1016/j.envsoft.2011.04.006
132. Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application S. Bastola & V. Misra https://doi.org/10.1002/hyp.9734
133. Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations R. Harmel et al. https://doi.org/10.1016/j.envsoft.2014.02.013
134. Flexible watershed simulation with the Raven hydrological modelling framework J. Craig et al. https://doi.org/10.1016/j.envsoft.2020.104728
135. Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132395
136. Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates J. Spaaks & W. Bouten https://doi.org/10.5194/hess-17-3455-2013
137. A qualitative model structure sensitivity analysis method to support model selection S. Van Hoey et al. https://doi.org/10.1016/j.jhydrol.2014.09.052
138. 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
139. Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins M. Yadav et al. https://doi.org/10.1016/j.advwatres.2007.01.005
140. Effect of (quasi-)optimum model parameter sets and model characteristics on future discharge projection of two basins from Europe and Asia A. Chamorro et al. https://doi.org/10.1007/s10584-017-1974-4
141. River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa L. Brocca et al. https://doi.org/10.1038/s41598-020-69343-x
142. Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering H. Zhang et al. https://doi.org/10.1016/j.advwatres.2011.06.005
143. Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method Q. Huang et al. https://doi.org/10.1029/2022WR032492
144. Optimizing hydrological modelling on real urban catchment: impact of calibration data selection and objective function M. Assaf et al. https://doi.org/10.1016/j.jhydrol.2025.133821
145. Identifying advantages and drawbacks of two hydrological models based on a sensitivity analysis: a study in two Chilean watersheds V. Parra et al. https://doi.org/10.1080/02626667.2018.1538593
146. Assessing Streamflow Sensitivity to Precipitation Variability in Karst‐Influenced Catchments With Unclosed Water Balances Y. Liu et al. https://doi.org/10.1029/2020WR028598
147. 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
148. 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
149. An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during multiple water pollutant modeling W. Wu et al. https://doi.org/10.1007/s11356-021-12596-4
150. A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices Y. Zhou et al. https://doi.org/10.1016/j.ymssp.2018.08.015
151. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction N. Ajami et al. https://doi.org/10.1029/2005WR004745
152. A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model K. Yilmaz et al. https://doi.org/10.1029/2007WR006716
153. The sensitivity of fluvial flood risk in Irish catchments to the range of IPCC AR4 climate change scenarios S. Bastola et al. https://doi.org/10.1016/j.scitotenv.2011.08.042
154. Ensemble evaluation of hydrological model hypotheses T. Krueger et al. https://doi.org/10.1029/2009WR007845
155. Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model Y. Liu et al. https://doi.org/10.1155/2016/8215308
156. Multi-objective optimization of empirical hydrological model for streamflow prediction J. Guo et al. https://doi.org/10.1016/j.jhydrol.2014.01.047
157. Inverse problem models for assessment of rainfall-runoff-sediment processes applied to a small river basin in the Amazon C. Falcón et al. https://doi.org/10.1007/s40808-025-02303-4
158. Medium- and Long-Term Hydrological Process Study in the Karst Watershed of the Lijiang River Basin W. Li et al. https://doi.org/10.3390/w16233424
159. Bayesian analysis for uncertainty estimation of a canopy transpiration model S. Samanta et al. https://doi.org/10.1029/2006WR005028
160. HYDROGEIOS: a semi-distributed GIS-based hydrological model for modified river basins A. Efstratiadis et al. https://doi.org/10.5194/hess-12-989-2008
161. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. https://doi.org/10.3390/w14142211
162. Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate M. Aufiero & L. Janson https://doi.org/10.1137/24M1718330
163. Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable‐Complexity Empirical Snow Model in Space and Time A. Lute & C. Luce https://doi.org/10.1002/2017WR020752
164. Application of a pseudo simulator to evaluate the sensitivity of parameters in complex watershed models K. Sudheer et al. https://doi.org/10.1016/j.envsoft.2010.07.007
165. Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis S. Xiao et al. https://doi.org/10.1016/j.ress.2017.03.011
166. Hydrological ensemble forecasting using a multi-model framework P. Dion et al. https://doi.org/10.1016/j.jhydrol.2021.126537
167. Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution Z. Yin et al. https://doi.org/10.3390/su10093209
168. Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models C. PERRIN et al. https://doi.org/10.1623/hysj.52.1.131
169. Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization H. Mao et al. https://doi.org/10.3390/w16213030
170. Simultaneous calibration of hydrological models in geographical space A. Bárdossy et al. https://doi.org/10.5194/hess-20-2913-2016
171. PERSiST: a flexible rainfall-runoff modelling toolkit for use with the INCA family of models M. Futter et al. https://doi.org/10.5194/hess-18-855-2014
172. Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis F. Pianosi & T. Wagener https://doi.org/10.1002/hyp.10968
173. How do potential evapotranspiration formulas influence hydrological projections? G. Seiller & F. Anctil https://doi.org/10.1080/02626667.2015.1100302
174. Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. https://doi.org/10.1029/2020WR028338
175. Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study M. Shafii et al. https://doi.org/10.1007/s00477-014-0855-x
176. 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
177. Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design H. Chien & D. Mackay https://doi.org/10.1002/hyp.13594
178. Comparison of model structural uncertainty using a multi‐objective optimisation method G. Lee et al. https://doi.org/10.1002/hyp.8006
179. Effective and efficient algorithm for multiobjective optimization of hydrologic models J. Vrugt et al. https://doi.org/10.1029/2002WR001746
180. Spatially Distributed Conceptual Hydrological Model Building: A Generic Top‐Down Approach Starting From Lumped Models Q. Tran et al. https://doi.org/10.1029/2018WR023566
181. Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications X. Song et al. https://doi.org/10.1016/j.jhydrol.2015.02.013
182. Ecohydrologic Error Models for Improved Bayesian Inference in Remotely Sensed Catchments Y. Tang et al. https://doi.org/10.1029/2019WR025055
183. DBv2: an improved climate-centric calibration-free model for runoff-generation simulation P. Istalkar & B. Biswal https://doi.org/10.1080/02626667.2025.2581263
184. Overall uncertainty of climate change impacts on watershed hydrology in China S. Zhang et al. https://doi.org/10.1002/joc.7257
185. Current Awareness https://doi.org/10.1002/hyp.538
186. Multi-objective model auto-calibration and reduced parameterization: Exploiting gradient-based optimization tool for a hydrologic model Y. Wang & K. Brubaker https://doi.org/10.1016/j.envsoft.2015.04.001
187. Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering Z. Zahmatkesh et al. https://doi.org/10.1016/j.advwatres.2015.06.012
188. Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions Y. Her et al. https://doi.org/10.1038/s41598-019-41334-7
189. Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance R. Nonki et al. https://doi.org/10.1175/JHM-D-20-0192.1
190. On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil https://doi.org/10.1016/j.jhydrol.2015.09.036
191. Assessing parameter identifiability for multiple performance criteria to constrain model parameters B. Guse et al. https://doi.org/10.1080/02626667.2020.1734204
192. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models J. Zaherpour et al. https://doi.org/10.1016/j.envsoft.2019.01.003
193. Sensitivity and identifiability of stream flow generation parameters of the SWAT model R. Cibin et al. https://doi.org/10.1002/hyp.7568
194. Testing the realism of model structures to identify karst system processes using water quality and quantity signatures A. Hartmann et al. https://doi.org/10.1002/wrcr.20229
195. Increasing parameter certainty and data utility through multi-objective calibration of a spatially distributed temperature and solute model C. Bandaragoda & B. Neilson https://doi.org/10.5194/hess-15-1547-2011
196. Development of a biosphere hydrological model considering vegetation dynamics and its evaluation at basin scale under climate change Q. Li & H. Ishidaira https://doi.org/10.1016/j.jhydrol.2011.08.046
197. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
198. Transposabilité temporelle des paramètres de modèles hydrologiques dans un contexte de changement climatique en Afrique de l’Ouest et Centrale S. Ouermi et al. https://doi.org/10.1080/02626667.2015.1072275
199. Parameter uncertainty and sensitivity analysis of the three Gorges Reservoir and Xiangxi River EFDC model S. Xu et al. https://doi.org/10.1016/j.jhydrol.2022.127881
200. Land Surface Model Calibration Using Satellite Remote Sensing Data M. Khaki https://doi.org/10.3390/s23041848
201. Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season E. Rosero et al. https://doi.org/10.1029/2009JD012035
202. A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow J. Liu et al. https://doi.org/10.1007/s11069-015-1643-8
203. Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach W. Gong et al. https://doi.org/10.1002/wrcr.20161
204. Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter H. Moradkhani et al. https://doi.org/10.1029/2004WR003604
205. Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments – A comparative hydrology approach R. Singh et al. https://doi.org/10.1016/j.jhydrol.2014.06.030
206. 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
207. Investigation of the model complexity required in runoff simulation at different time scales / Etude de la complexité de modélisation requise pour la simulation d'écoulement à différentes échelles temporelles Z. MICOVIC & M. QUICK https://doi.org/10.1623/hysj.54.5.872
208. Laboratory and numerical simulations of infiltration process and solute transport in karst vadose zone R. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.131242
209. Artificial neural network assisted Bayesian calibration of climate models T. Hauser et al. https://doi.org/10.1007/s00382-011-1168-0
210. Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes – Part 2: Intercomparison of models and calibration approaches P. Willems et al. https://doi.org/10.1016/j.jhydrol.2014.01.028
211. Steady-state distributed modeling of dissolved oxygen in data-poor, sewage dominated river systems using drainage networks M. Pilotti et al. https://doi.org/10.1016/j.envsoft.2018.08.027
212. Multi-objective calibration of a river water quality model—Information content of calibration data M. Rode et al. https://doi.org/10.1016/j.ecolmodel.2006.12.037
213. A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index B. Duchemin et al. https://doi.org/10.1016/j.envsoft.2007.10.003
214. Parsimonious rainfall–runoff model construction supported by time series processing and validation of hydrological extremes – Part 1: Step-wise model-structure identification and calibration approach P. Willems https://doi.org/10.1016/j.jhydrol.2014.01.017
215. When are multiobjective calibration trade‐offs in hydrologic models meaningful? J. Kollat et al. https://doi.org/10.1029/2011WR011534
216. A comparison of alternative multiobjective calibration strategies for hydrological modeling F. Fenicia et al. https://doi.org/10.1029/2006WR005098
217. On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes K. Beven et al. https://doi.org/10.1002/hyp.14703
218. How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting? X. Shi et al. https://doi.org/10.1175/2008JHM1001.1
219. Analysis of pattern–process interactions based on landscape models—Overview, general concepts, and methodological issues B. Schröder & R. Seppelt https://doi.org/10.1016/j.ecolmodel.2006.05.036
220. A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization J. Guo et al. https://doi.org/10.1007/s11269-013-0324-1
221. Sensitivity analysis of the concentration transport estimation in a turbulent flow D. Kolyukhin et al. https://doi.org/10.1515/mcma-2022-2116
222. Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland – Part I: Modelling framework and calibration results D. Viviroli et al. https://doi.org/10.1016/j.jhydrol.2009.08.023
223. Assessing the Effectiveness of Water-Saving Plans at the Farm and Basin Level Using Agrohydrological Modeling and Water-Accounting Approaches M. Delavar et al. https://doi.org/10.1061/JIDEDH.IRENG-10110
224. Diagnostic Evaluation of Hydrologic Models Employing Flow Duration Curve V. Chilkoti et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001778
225. Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration A. Wadoux et al. https://doi.org/10.7717/peerj.9558
226. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability C. Song https://doi.org/10.1016/j.jhydrol.2021.127324
227. Redressing the balance: quantifying net intercatchment groundwater flows L. Bouaziz et al. https://doi.org/10.5194/hess-22-6415-2018
228. Learning from model improvement: On the contribution of complementary data to process understanding F. Fenicia et al. https://doi.org/10.1029/2007WR006386
229. A review of foundational methods for checking the structural identifiability of models: Results for rainfall-runoff M. Shin et al. https://doi.org/10.1016/j.jhydrol.2014.11.040
230. Subsurface Irrigation in Regional Water Management: a System Dynamics Approach to Support Decision Making J. de Wit et al. https://doi.org/10.1007/s11269-025-04393-2
231. Comparison of hydrological models for use in climate change studies: A test on 241 catchments in West and Central Africa K. Ouermi et al. https://doi.org/10.1016/j.crte.2019.08.001
232. Assimilating multi-site measurements for semi-distributed hydrological model updating J. Chen et al. https://doi.org/10.1016/j.quaint.2012.01.030
233. Sensitivity analysis of environmental models: A systematic review with practical workflow F. Pianosi et al. https://doi.org/10.1016/j.envsoft.2016.02.008
234. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty T. Wagener & H. Wheater https://doi.org/10.1016/j.jhydrol.2005.07.015
235. 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
236. An experimental approach to reduce the parametric dimensionality for rainfall–runoff models J. Chen et al. https://doi.org/10.2166/nh.2016.145
237. Urban-industrial impacts on river network stability in South-East Serbia: A GIS and regression based study A. Valjarević https://doi.org/10.1016/j.nexres.2026.101465
238. Dynamic Identifiability Analysis-Based Model Structure Evaluation Considering Rating Curve Uncertainty S. Van Hoey et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000995
239. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al. https://doi.org/10.1016/j.hydres.2023.11.001
240. The Wageningen Lowland Runoff Simulator (WALRUS): a lumped rainfall–runoff model for catchments with shallow groundwater C. Brauer et al. https://doi.org/10.5194/gmd-7-2313-2014
241. Satellite remote sensing and conceptual cloud modeling for convective rainfall simulation A. Haile et al. https://doi.org/10.1016/j.advwatres.2010.08.007
242. An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools D. Viviroli et al. https://doi.org/10.1016/j.envsoft.2009.04.001
243. Peering into agricultural rebound phenomenon using a global sensitivity analysis approach M. Ghoreishi et al. https://doi.org/10.1016/j.jhydrol.2021.126739
244. Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes P. Hublart et al. https://doi.org/10.5194/hess-20-3691-2016
245. Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding C. Kelleher et al. https://doi.org/10.5194/hess-21-3325-2017
246. Coupling variable infiltration capacity (VIC) reservoir model and regression model for improving inflow estimation into Srisailam Reservoir, India A. E et al. https://doi.org/10.1080/09715010.2025.2596671
247. Influence of spatial and temporal flow variability on solute transport in catchments J. Selroos & G. Destouni https://doi.org/10.1002/hyp.10464
248. Detection of structural inadequacy in process‐based hydrological models: A particle‐filtering approach P. Smith et al. https://doi.org/10.1029/2006WR005205
249. Effects of Model Spatial Structure and Basin Characteristics on the Performance of Three Hydrologic Models F. Kacar et al. https://doi.org/10.1007/s11269-025-04308-1
250. Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model T. Roy et al. https://doi.org/10.5194/hess-21-879-2017
251. Estimating spatiotemporal pumping amounts using multiple signal decomposition methods H. Tseng et al. https://doi.org/10.1016/j.jhydrol.2024.130856
252. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? J. Vrugt et al. https://doi.org/10.1007/s00477-008-0274-y
253. Verifying model performance using validation of Pareto solutions N. Harvey et al. https://doi.org/10.1016/j.jhydrol.2023.129594
254. A soil moisture accounting-procedure with a Richards' equation-based soil texture-dependent parameterization S. Mathias et al. https://doi.org/10.1002/2014WR016144
255. 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
256. 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
257. Regional runoff prediction through aggregation of first-order hydrological process knowledge: a case study R. VAN DEN BOS et al. https://doi.org/10.1623/hysj.51.6.1021
258. Process consistency in models: The importance of system signatures, expert knowledge, and process complexity M. Hrachowitz et al. https://doi.org/10.1002/2014WR015484
259. Constraining Conceptual Hydrological Models With Multiple Information Sources R. Nijzink et al. https://doi.org/10.1029/2017WR021895
260. Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results J. Mai & B. Tolson https://doi.org/10.1029/2018WR023382
261. A Bayesian alternative for multi-objective ecohydrological model specification Y. Tang et al. https://doi.org/10.1016/j.jhydrol.2017.07.040
262. Hydrological Model Calibration by Sequential Replacement of Weak Parameter Sets Using Depth Function S. Singh & A. Bárdossy https://doi.org/10.3390/hydrology2020069
263. Multi-basin and regional calibration based identification of distributed precipitation–runoff models for hourly runoff simulation: calibration and transfer of full and partial parameters T. Hailegeorgis & K. Alfredsen https://doi.org/10.2166/nh.2015.174
264. Application of hydrological and hydraulic models for hydrological data transfer M. Ostojski https://doi.org/10.2478/s11600-012-0097-6
265. Quantification and allocation of uncertainties of climate change impacts on hydropower potential under 1.5 °C and 2.0 °C global warming levels in the headwaters of the Benue River Basin, Cameroon R. Nonki et al. https://doi.org/10.1016/j.renene.2023.118979
266. Reconstructing the Natural Streamflow of a Regulated River: A Case Study of La Grande Rivière, Québec, Canada M. Hernández-Henríquez et al. https://doi.org/10.4296/cwrj3503301
267. Process-based karst modelling to relate hydrodynamic and hydrochemical characteristics to system properties A. Hartmann et al. https://doi.org/10.5194/hess-17-3305-2013
268. A modular open-source suite of notebooks for multi-process hydrological analysis with GEOframe R. Busti & G. Formetta https://doi.org/10.1016/j.hydrch.2026.100024
269. Investigating the appropriate model structure for simulation of a karst catchment from the aspect of spatial complexity Y. Chang et al. https://doi.org/10.1007/s12665-018-8017-y
270. Estimating the uncertainty of hydrological forecasts: A statistical approach A. Montanari & G. Grossi https://doi.org/10.1029/2008WR006897
271. 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
272. The temporally varying roles of rainfall, snowmelt and soil moisture for debris flow initiation in a snow-dominated system K. Mostbauer et al. https://doi.org/10.5194/hess-22-3493-2018
273. Evaluation of statistical models for forecast errors from the HBV model K. Engeland et al. https://doi.org/10.1016/j.jhydrol.2010.01.018
274. Global Sensitivity Analysis of environmental models: Convergence and validation F. Sarrazin et al. https://doi.org/10.1016/j.envsoft.2016.02.005
275. How does the quantification of uncertainties affect the quality and value of flood early warning systems? A. Thiboult et al. https://doi.org/10.1016/j.jhydrol.2017.05.014
276. Characterizing Disturbed Desert Soils Using Multiobjective Parameter Optimization T. Caldwell et al. https://doi.org/10.2136/vzj2012.0083
277. How much can we gain with increasing model complexity with the same model concepts? H. Li et al. https://doi.org/10.1016/j.jhydrol.2015.05.044
278. Characterization of watershed model behavior across a hydroclimatic gradient K. van Werkhoven et al. https://doi.org/10.1029/2007WR006271
279. The Influence of Land Use and Land Cover Transitions on Hydrology in a Tropical River Basin of Southwest India G. Kumar et al. https://doi.org/10.1007/s41101-024-00301-8
280. Ground Truthing Global-Scale Model Estimates of Groundwater Recharge Across Africa C. West et al. https://doi.org/10.2139/ssrn.4184338
281. Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization Z. Zhang et al. https://doi.org/10.1029/2008WR006833
282. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions G. Seiller et al. https://doi.org/10.5194/hess-16-1171-2012
283. Simulating ecologically relevant hydrological indicators in a temporary river system A. De Girolamo et al. https://doi.org/10.1016/j.agwat.2016.05.034
284. Multivariate output global sensitivity analysis using multi-output support vector regression K. Cheng et al. https://doi.org/10.1007/s00158-018-2184-z
285. Against a ‘wait and see’ approach in adapting to climate change C. Murphy et al. https://doi.org/10.1080/00750778.2011.615707
286. HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights Y. Zhu et al. https://doi.org/10.1016/j.jhydrol.2026.135153
287. Investigating the use of spatial discretization of hydrological processes in conceptual rainfall runoff modelling: a case study for the meso‐scale H. Hellebrand & R. van den Bos https://doi.org/10.1002/hyp.6909
288. GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models T. Gleeson et al. https://doi.org/10.5194/gmd-14-7545-2021
289. The regionalization of a parameter of HYMOD, a conceptual hydrological model, using data from across the globe S. Bastola https://doi.org/10.1016/j.hydres.2022.01.001
290. A historical perspective on modelling the relationship between catchment wetness and streamflow response F. Fenicia et al. https://doi.org/10.1080/02626667.2025.2504429
291. Structural decomposition and Regional Sensitivity Analysis of industrial consumption embedded emissions from Chinese households M. Sajid https://doi.org/10.1016/j.ecolind.2020.107237
292. Investigating hydrological model versatility to simulate extreme flood events D. Peredo et al. https://doi.org/10.1080/02626667.2022.2030864
293. Parameter optimization of distributed hydrological model with a modified dynamically dimensioned search algorithm X. Huang et al. https://doi.org/10.1016/j.envsoft.2013.09.028
294. An approach to measure parameter sensitivity in watershed hydrological modelling T. Ranatunga et al. https://doi.org/10.1080/02626667.2016.1174335
295. Modelling rainfall–runoff processes of the Chemoga and Jedeb meso-scale catchments in the Abay/Upper Blue Nile basin, Ethiopia S. Tekleab et al. https://doi.org/10.1080/02626667.2015.1032292
296. Evidence-based identification of integrated water quality systems E. Akomeah et al. https://doi.org/10.1080/09640568.2022.2028609
297. Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall‐runoff simulations A. Montanari https://doi.org/10.1029/2004WR003826
298. Identification of a karst system’s intrinsic hydrodynamic parameters: upscaling from single springs to the whole aquifer A. Hartmann et al. https://doi.org/10.1007/s12665-011-1033-9
299. Toward improved identifiability of hydrologic model parameters: The information content of experimental data J. Vrugt et al. https://doi.org/10.1029/2001WR001118
300. Diagnostic evaluation of multiple hypotheses of hydrological behaviour in a limits-of-acceptability framework for 24 UK catchments G. Coxon et al. https://doi.org/10.1002/hyp.10096
301. A review of 40 years of hydrological science and practice in southern Africa using the Pitman rainfall-runoff model D. Hughes https://doi.org/10.1016/j.jhydrol.2013.07.043
302. Consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates S. Vero et al. https://doi.org/10.1016/j.jconhyd.2014.10.002
303. Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa E. Kapangaziwiri et al. https://doi.org/10.1080/02626667.2012.690881
304. Improving the temporal transposability of lumped hydrological models on twenty diversified U.S. watersheds G. Seiller et al. https://doi.org/10.1016/j.ejrh.2015.02.012
305. Stochastic Residual-Error Analysis for Estimating Hydrologic Model Predictive Uncertainty M. Hantush & L. Kalin https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(585)
306. A study of non-linearity in rainfall-runoff response using 120 UK catchments S. Mathias et al. https://doi.org/10.1016/j.jhydrol.2016.06.039
307. Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures G. Seiller & F. Anctil https://doi.org/10.5194/hess-18-2033-2014
308. Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models R. Sheikholeslami & S. Razavi https://doi.org/10.1016/j.envsoft.2017.03.010
309. Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria A. Wang & D. Solomatine https://doi.org/10.3390/w11051062
310. Application of the RSPARROW Modeling Tool to Estimate Total Nitrogen Sources to Streams and Evaluate Source Reduction Management Scenarios in the Grande River Basin, Brazil M. Miller et al. https://doi.org/10.3390/w12102911
311. Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico M. Beck et al. https://doi.org/10.1016/j.ecolmodel.2017.08.020
312. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. https://doi.org/10.1016/j.jhydrol.2021.127221
313. Hydrological modelling using data from monthly GCMs in a regional catchment R. Remesan et al. https://doi.org/10.1002/hyp.9872
314. Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation T. Vansteenkiste et al. https://doi.org/10.1016/j.jhydrol.2014.01.050
315. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part II: Impact of Soil Texture Classification and Vegetation Type Mismatches Y. Xia et al. https://doi.org/10.1175/JHM-D-14-0097.1
316. Being Bayesian: Discussions from the Perspectives of Stakeholders and Hydrologists T. Ferre https://doi.org/10.3390/w12020461
317. Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles G. Seiller et al. https://doi.org/10.1016/j.jhydrol.2017.07.002
318. Choix des pas de temps et d'espace pour des modélisations parcimonieuses en hydrologie des crues C. Obled et al. https://doi.org/10.1051/lhb/2009059
319. Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models M. Shin & C. Kim https://doi.org/10.3390/w13152126
320. Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach Y. Zhou et al. https://doi.org/10.1016/j.jhydrol.2024.131834
321. Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space D. Brochero et al. https://doi.org/10.5194/hess-15-3327-2011
322. Modeling oasis dynamics driven by ecological water diversion and implications for oasis restoration in arid endorheic basins F. Huang et al. https://doi.org/10.1016/j.jhydrol.2020.125774
323. A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics T. Lan et al. https://doi.org/10.5194/hess-24-5859-2020
324. 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
325. 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
326. Actual Evapotranspiration in the High Andean Grasslands: A Comparison of Measurement and Estimation Methods A. Ochoa-Sánchez et al. https://doi.org/10.3389/feart.2019.00055
327. Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran H. Salehi et al. https://doi.org/10.3390/w16071028
328. Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al. https://doi.org/10.1016/j.jhydrol.2021.126773
329. An Improved Copula‐Based Framework for Efficient Global Sensitivity Analysis H. Liu et al. https://doi.org/10.1029/2022WR033808
330. Sensitivity Analysis of Fully Distributed Parameterization Reveals Insights Into Heterogeneous Catchment Responses for Water Quality Modeling X. Yang et al. https://doi.org/10.1029/2019WR025575
331. Characterising performance of environmental models N. Bennett et al. https://doi.org/10.1016/j.envsoft.2012.09.011
332. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models K. van Werkhoven et al. https://doi.org/10.1016/j.advwatres.2009.03.002
333. A multi-objective assessment of alternate conceptual ecohydrological models B. Naseem et al. https://doi.org/10.1016/j.jhydrol.2015.08.060
334. The influence of conceptual model structure on model performance: a comparative study for 237 French catchments W. van Esse et al. https://doi.org/10.5194/hess-17-4227-2013
335. Uncertainty in water resource model parameters used for climate change impact assessment R. Wilby https://doi.org/10.1002/hyp.5819
336. Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models J. Herman et al. https://doi.org/10.5194/hess-17-2893-2013
337. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods C. Broderick et al. https://doi.org/10.1002/2016WR018850
338. WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.hydroa.2021.100109
339. A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales C. Massmann et al. https://doi.org/10.1016/j.envsoft.2013.09.033
340. Evaluating hydrological model performance using varying amounts of participatory monitoring water level data F. Mitze et al. https://doi.org/10.1371/journal.pwat.0000405
341. Probabilistic calibration of a distributed hydrological model for flood forecasting L. Mediero et al. https://doi.org/10.1080/02626667.2011.610322
342. Identifying the connective strength between model parameters and performance criteria B. Guse et al. https://doi.org/10.5194/hess-21-5663-2017
343. Comparison of Stationary and Dynamic Conceptual Models in a Mountainous and Data-Sparse Catchment in the South-Central Chilean Andes C. Toledo et al. https://doi.org/10.1155/2015/526158
344. Revisiting evapotranspiration inputs in eco-hydrological modeling for climate change assessment Y. Zhou et al. https://doi.org/10.1016/j.jhydrol.2024.131888
345. Modelling precipitation uncertainties in a multi-objective Bayesian ecohydrological setting Y. Tang et al. https://doi.org/10.1016/j.advwatres.2018.10.015
346. 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
347. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models P. Willems https://doi.org/10.1016/j.envsoft.2008.09.005
348. Software for hydrogeologic time series analysis, interfacing data with physical insight J. von Asmuth et al. https://doi.org/10.1016/j.envsoft.2012.06.003
349. 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
350. 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
351. Flood mapping under uncertainty: a case study in the Canadian prairies R. Bharath & A. Elshorbagy https://doi.org/10.1007/s11069-018-3401-1
352. Multi-variable calibration of hydrological model in the upper Omo-Gibe basin, Ethiopia M. Nesru et al. https://doi.org/10.1007/s11600-020-00417-0
353. From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models J. Herman et al. https://doi.org/10.5194/hess-17-5109-2013
354. A lightweight framework for rapid development of object-based hydrological model engines D. Kneis https://doi.org/10.1016/j.envsoft.2015.02.009
355. Identification of rainfall–runoff models for operational applications / Identification de modèles pluie–débit pour des applications opérationnelles T. Wagener & N. McIntyre https://doi.org/10.1623/hysj.2005.50.5.735
356. Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models M. Bacci et al. https://doi.org/10.1016/j.jhydrol.2022.128057
357. Calibration and sensitivity analysis of a river water quality model under unsteady flow conditions A. Sincock et al. https://doi.org/10.1016/S0022-1694(03)00127-6
358. A catastrophe progression approach based index sensitivity analysis model for the multivariate flooding process L. Wang et al. https://doi.org/10.1007/s00477-016-1339-y
359. Blue and green water simulation in the river basin using remote sensing data fusion and dual-variable hydrological calibration Y. Lu et al. https://doi.org/10.1016/j.agwat.2025.110046
360. Forward-reverse switch between density-based and regional sensitivity analysis S. Xiao et al. https://doi.org/10.1016/j.apm.2020.03.025
361. Diagnosis of Model Errors With a Sliding Time‐Window Bayesian Analysis H. Hsueh et al. https://doi.org/10.1029/2021WR030590
362. Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model N. Di Marco et al. https://doi.org/10.1016/j.jhydrol.2021.126020
363. Dual state–parameter estimation of hydrological models using ensemble Kalman filter H. Moradkhani et al. https://doi.org/10.1016/j.advwatres.2004.09.002
364. Rainfall–runoff modelling in a tropical climate (Citarum basin, West Java, Indonesia): Model and climate data intercomparison G. Vesuviano et al. https://doi.org/10.2166/nh.2025.021
365. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation Y. Tang et al. https://doi.org/10.5194/hess-11-793-2007
366. Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change S. Wi et al. https://doi.org/10.5194/hess-19-857-2015
367. A checklist for model credibility, salience, and legitimacy to improve information transfer in environmental policy assessments G. van Voorn et al. https://doi.org/10.1016/j.envsoft.2016.06.003
368. Calibrating hydrologic models in flow-corrected time T. Smith et al. https://doi.org/10.1002/2013WR014635
369. Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights D. Kavetski & F. Fenicia https://doi.org/10.1029/2011WR010748
370. A multi-model approach for improved simulations of future water availability at a large Eastern Mediterranean karst spring A. Hartmann et al. https://doi.org/10.1016/j.jhydrol.2012.08.024
371. 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
372. Hydrological behaviour of a drained agricultural peat catchment in the tropics. 2: Time series transfer function modelling approach A. Katimon et al. https://doi.org/10.1080/02626667.2013.815758
373. Selection of conceptual models for regionalisation of the rainfall-runoff relationship H. Lee et al. https://doi.org/10.1016/j.jhydrol.2005.02.016
374. A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling T. Lan et al. https://doi.org/10.5194/hess-30-2455-2026
375. Analysis of the drought recovery of Andosols on southern Ecuadorian Andean páramos V. Iñiguez et al. https://doi.org/10.5194/hess-20-2421-2016
376. Identification of the dominant hydrological process and appropriate model structure of a karst catchment through stepwise simplification of a complex conceptual model Y. Chang et al. https://doi.org/10.1016/j.jhydrol.2017.02.050
377. Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication C. Telteu et al. https://doi.org/10.5194/gmd-14-3843-2021
378. 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
379. A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method M. Kheimi & S. Abdelaziz https://doi.org/10.3390/w14020143
380. Pathways for Hydrological Resilience: Strategies for Adaptation in a Changing Climate F. Granata & F. Di Nunno https://doi.org/10.1007/s41748-024-00567-x
381. Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior J. Herman et al. https://doi.org/10.1002/wrcr.20124
382. Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.jhydrol.2021.127261
383. Improved unit hydrograph identification for seven Welsh rivers: implications for estimating continuous streamflow at ungauged sites I. LITTLEWOOD https://doi.org/10.1623/hysj.48.5.743.51454
384. Understanding the role of hydrologic model structures on evapotranspiration-driven sensitivity D. Jayathilake & T. Smith https://doi.org/10.1080/02626667.2020.1754421
385. Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models A. Jacquin & A. Shamseldin https://doi.org/10.5194/hess-13-41-2009
386. Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems P. Young https://doi.org/10.1002/wrcr.20068
387. Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment X. Yang et al. https://doi.org/10.1029/2020WR029094
388. Signature‐Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Empirical Analysis of Fundamental Properties F. Fenicia et al. https://doi.org/10.1002/2017WR021616
389. Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof L. Worthen et al. https://doi.org/10.1002/hyp.14387
390. Assessing water security in the São Paulo metropolitan region under projected climate change G. Gesualdo et al. https://doi.org/10.5194/hess-23-4955-2019
391. Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons S. Bastola et al. https://doi.org/10.1175/2013EI000519.1
392. Dynamics of hydrological-model parameters: mechanisms, problems and solutions T. Lan et al. https://doi.org/10.5194/hess-24-1347-2020
393. Identifiability analysis: towards constrained equifinality and reduced uncertainty in a conceptual model E. Muñoz et al. https://doi.org/10.1080/02626667.2014.892205
394. Linking Land Use Change and Hydrological Responses: The Role of Agriculture in the Decline of Urmia Lake A. Mirdarsoltany et al. https://doi.org/10.3390/hydrology11120209
395. Calibration of rainfall-runoff models in ungauged basins: A regional maximum likelihood approach S. Castiglioni et al. https://doi.org/10.1016/j.advwatres.2010.04.009
396. Distributed rainfall-runoff simulation for a large-scale karst catchment by incorporating landform and topography into the DDRM model parameters S. Chen et al. https://doi.org/10.1016/j.jhydrol.2022.127853
397. 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
398. Interactive Visualization for Evolutionary Optimization of Conceptual Rainfall-Streamflow Models M. Borthwick et al. https://doi.org/10.1061/(ASCE)0887-3801(2008)22:1(40)
399. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters J. Vrugt et al. https://doi.org/10.1029/2002WR001642
400. 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
401. Visualization Workflow for Quantifying Parameter Sensitivities and Uncertainties for Hydrologic Models C. Finkenbiner & K. Semmendinger https://doi.org/10.1111/1752-1688.12946
402. Estimation of Annual Maximum and Minimum Flow Trends in a Data-Scarce Basin. Case Study of the Allipén River Watershed, Chile Y. Medina & E. Muñoz https://doi.org/10.3390/w12010162
403. Multi-objective assessment of three remote sensing vegetation products for streamflow prediction in a conceptual ecohydrological model B. Naseem et al. https://doi.org/10.1016/j.jhydrol.2016.10.038
404. Use of regional sensitivity analysis for diagnosing parsimony of models: A water model case study R. Srikanthan et al. https://doi.org/10.1016/j.envsoft.2025.106727