469 citations as recorded by crossref.

1. Expanding the role of reactive transport models in critical zone processes L. Li et al. https://doi.org/10.1016/j.earscirev.2016.09.001
2. System dynamics modeling of infiltration and drainage in layered coarse soil M. Huang et al. https://doi.org/10.4141/cjss10009
3. Flood Control in the Cuiabá River Basin, Brazil, with Multipurpose Reservoir Operation R. de Paes & J. Brandão https://doi.org/10.1007/s11269-013-0388-y
4. Have applications of continuous rainfall–runoff simulation realized the vision for process‐based flood frequency analysis? R. Lamb et al. https://doi.org/10.1002/hyp.10882
5. 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
6. Interpreting the Manning Roughness Coefficient in Overland Flow Simulations with Coupled Hydrological-Hydraulic Distributed Models M. Sanz-Ramos et al. https://doi.org/10.3390/w13233433
7. Scales and locations of time stability of soil water storage in a hummocky landscape A. Biswas & B. Si https://doi.org/10.1016/j.jhydrol.2011.07.027
8. Proximal-Sensing-Powered Modelling of Energy-Water Fluxes in a Vineyard: A Spatial Resolution Analysis N. Paciolla et al. https://doi.org/10.3390/rs13224699
9. Sequential surface and subsurface flow modeling in a tropical aquifer under different rainfall scenarios M. Jimenez et al. https://doi.org/10.1016/j.envsoft.2022.105328
10. Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters F. Avanzi et al. https://doi.org/10.1016/j.jhydrol.2019.124478
11. Modeling climate-induced changes in hydrometeorological components and analyzing future extreme weather events using SPI in the Kosi River Basin, Uttarakhand B. Das et al. https://doi.org/10.1007/s00704-026-06284-x
12. Return-flow prediction and buffer designation in two rainforest headwaters N. Chappell et al. https://doi.org/10.1016/j.foreco.2005.12.013
13. Identification of prevailing storm runoff generation mechanisms in an intensively cultivated catchment D. Zumr et al. https://doi.org/10.1515/johh-2015-0022
14. A stochastic approach for assessing the uncertainty of rainfall‐runoff simulations A. Montanari & A. Brath https://doi.org/10.1029/2003WR002540
15. A joint modelling framework for daily extremes of river discharge and precipitation in urban areas K. Breinl et al. https://doi.org/10.1111/jfr3.12150
16. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell https://doi.org/10.1016/j.jhydrol.2021.127287
17. Separation of global time-variable gravity signals into maximally independent components E. Forootan & J. Kusche https://doi.org/10.1007/s00190-011-0532-5
18. Groundwater dynamics along a hillslope: A test of the steady state hypothesis J. Seibert et al. https://doi.org/10.1029/2002WR001404
19. White water: Fifty years of snow research in WRR and the outlook for the future M. Sturm https://doi.org/10.1002/2015WR017242
20. Strategies for smarter catchment hydrology models: incorporating scaling and better process representation R. Sidle https://doi.org/10.1186/s40562-021-00193-9
21. The Small Water Cycle in the Czech Landscape: How Has It Been Affected by Land Management Changes Over Time? N. Noreika et al. https://doi.org/10.3390/su132413757
22. Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach U. Khan et al. https://doi.org/10.1016/j.jhydrol.2018.07.066
23. Intensive precipitation observation greatly improves hydrological modelling of the poorly gauged high mountain Mabengnong catchment in the Tibetan Plateau L. Wang et al. https://doi.org/10.1016/j.jhydrol.2017.11.039
24. On the applicability of temporal stability analysis to raingauge network design R. Morbidelli et al. https://doi.org/10.1080/02626667.2019.1645959
25. New objective functions for streamflow calibrations to specific model applications H. Il Choi https://doi.org/10.1016/j.jhydrol.2024.132109
26. Impact of evapotranspiration process representation on runoff projections from conceptual rainfall‐runoff models D. Guo et al. https://doi.org/10.1002/2016WR019627
27. The Tyranny of Small Scales—On Representing Soil Processes in Global Land Surface Models D. Or https://doi.org/10.1029/2019WR024846
28. Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models M. Feigl et al. https://doi.org/10.1029/2020WR027385
29. A framework for classifying and comparing distributed hillslope and catchment hydrologic models S. Kampf & S. Burges https://doi.org/10.1029/2006WR005370
30. From groundwater abstraction to vegetative response in fen ecosystems O. Johansen et al. https://doi.org/10.1002/hyp.9808
31. The role of remote sensing in hydrological modelling of the Okavango Delta, Botswana C. Milzow et al. https://doi.org/10.1016/j.jenvman.2007.06.032
32. A method for the regional estimation of runoff separation parameters for hydrological modelling H. Kling & H. Nachtnebel https://doi.org/10.1016/j.jhydrol.2008.10.015
33. Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems I. Nalbantis et al. https://doi.org/10.5194/hess-15-743-2011
34. Modelling Groundwater Flow with MIKE SHE Using Conventional Climate Data and Satellite Data as Model Forcing in Haihe Plain, China Y. Shu et al. https://doi.org/10.3390/w10101295
35. Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables G. Aronica et al. https://doi.org/10.1016/j.pce.2011.04.001
36. Hydrological model performance comparison through uncertainty recognition and quantification S. Chiang et al. https://doi.org/10.1002/hyp.6678
37. Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.132611
38. Flash flooding prediction in regions of northern Vietnam using the KINEROS2 model H. Nguyen et al. https://doi.org/10.2166/nh.2015.125
39. A process‐based rejectionist framework for evaluating catchment runoff model structure K. Vaché & J. McDonnell https://doi.org/10.1029/2005WR004247
40. Seasonal dynamics of surface runoff in mountain grassland ecosystems differing in land use G. Leitinger et al. https://doi.org/10.1016/j.jhydrol.2010.02.006
41. Hydrologic comparison between a lowland catchment (Kielstau, Germany) and a mountainous catchment (XitaoXi, China) using KIDS model in PCRaster X. Zhang et al. https://doi.org/10.5194/adgeo-21-125-2009
42. Parameter identification using optimization techniques in open-channel inverse problems H. Roux & D. Dartus https://doi.org/10.1080/00221680509500125
43. High-resolution hydrological modeling to assess groundwater recharge dynamics in the Colombian Orinoco O. Rueda-Franco et al. https://doi.org/10.1016/j.ejrh.2025.103066
44. Toward Simple Modeling Practices in the Complex Canadian Prairie Watersheds M. Ahmed et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001922
45. Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
46. A Comprehensive Review and Application of Bayesian Methods in Hydrological Modelling: Past, Present, and Future Directions K. Haddad https://doi.org/10.3390/w17071095
47. A rebalanced performance criterion for hydrological model calibration J. Lee & H. Choi https://doi.org/10.1016/j.jhydrol.2021.127372
48. Testing the mHM-MPR Reliability for Parameter Transferability across Locations in North–Central Nigeria K. Ogbu et al. https://doi.org/10.3390/hydrology9090158
49. Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling C. Sezen & T. Partal https://doi.org/10.2166/hydro.2022.171
50. Integrated MIKE SHE/MIKE+ modelling and isotopic investigations for freshwater assessment of the Suketi catchment in the Northwest Himalayas A. Sharma et al. https://doi.org/10.1080/10256016.2025.2482213
51. Investigating the behaviour of a small Mediterranean catchment using three different hydrological models as hypotheses G. Ruiz‐Pérez et al. https://doi.org/10.1002/hyp.10738
52. Hydrological response of a paired watershed to rainfall storm events in arid region: a study in Dehgin of Hormozgan province, Iran S. Mohamadi & R. Bagheri https://doi.org/10.1007/s11356-022-21543-w
53. Controls of flash flood peak discharge in Mediterranean basins and the special role of runoff-contributing areas Y. Rinat et al. https://doi.org/10.1016/j.jhydrol.2018.08.055
54. What can hydrological modelling gain from spatially explicit parameterization and multi-gauge calibration? X. Zheng et al. https://doi.org/10.5194/hess-30-2493-2026
55. Climate-driven spatiotemporal evolution of China's inland wetlands from 2000 to 2100 A. Li et al. https://doi.org/10.1016/j.eiar.2026.108658
56. The MGB-IPH model for large-scale rainfall—runoff modelling W. COLLISCHONN et al. https://doi.org/10.1623/hysj.52.5.878
57. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. https://doi.org/10.1016/j.jhydrol.2023.130350
58. Assessment of the effects of spatial resolutions on daily water flux simulations X. Liang et al. https://doi.org/10.1016/j.jhydrol.2003.07.007
59. A spatio‐temporal comparison of water balance modelling in an Alpine catchment H. Kling & H. Nachtnebel https://doi.org/10.1002/hyp.7207
60. Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model X. MO et al. https://doi.org/10.1623/hysj.51.1.45
61. A Simple Modelling Framework for Shallow Subsurface Water Storage and Flow L. Verrot et al. https://doi.org/10.3390/w11081725
62. smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al. https://doi.org/10.5194/gmd-18-7003-2025
63. Parameter identification and uncertainty analysis for simulating streamflow in a river basin of Eastern India B. Uniyal et al. https://doi.org/10.1002/hyp.10446
64. Accounting for legacy nitrogen stores does not improve the accuracy of riverine nitrate load model predictions J. Carleton & R. Sabo https://doi.org/10.1088/3033-4942/ae09d6
65. Reconciling theory with observations: elements of a diagnostic approach to model evaluation H. Gupta et al. https://doi.org/10.1002/hyp.6989
66. JAMES BUTTLE REVIEW: Interflow, subsurface stormflow and throughflow: A synthesis of field work and modelling K. McGuire et al. https://doi.org/10.1002/hyp.15263
67. Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models P. Groffman et al. https://doi.org/10.1007/s10533-008-9277-5
68. Simulating the multi-seasonal response of a large-scale watershed with a 3D physically-based hydrologic model Q. Li et al. https://doi.org/10.1016/j.jhydrol.2008.05.024
69. Modelling in support of an interdisciplinary approach to ecosystem restitution M. Rubarenzya et al. https://doi.org/10.2478/v10060-008-0030-5
70. On topographic controls of soil hydraulic parameter scaling at hillslope scales R. Jana & B. Mohanty https://doi.org/10.1029/2011WR011204
71. Transferability of hydrological model parameters between basins in data-sparse areas, subarctic Canada S. van der Linden & M. Woo https://doi.org/10.1016/S0022-1694(02)00295-0
72. Connecting the power-law scaling structure of peak-discharges to spatially variable rainfall and catchment physical properties T. Ayalew et al. https://doi.org/10.1016/j.advwatres.2014.05.009
73. A guide to good practice in modeling semantics for authors and referees K. Beven & P. Young https://doi.org/10.1002/wrcr.20393
74. Annual water, sediment, nutrient, and organic carbon fluxes in river basins: A global meta‐analysis as a function of scale M. Mutema et al. https://doi.org/10.1002/2014WR016668
75. Semi‐distributed modelling of the monthly water balance in an alpine catchment G. Eder et al. https://doi.org/10.1002/hyp.5888
76. Modeling the Genetic Components of the Water and Chemical Runoff of Heavy Metals in the Basin of the Nizhnekamskoe Reservoir T. Fashchevskaya et al. https://doi.org/10.1134/S0097807823040073
77. Streamflow prediction in ungauged catchments using copula‐based dissimilarity measures L. Samaniego et al. https://doi.org/10.1029/2008WR007695
78. Hydrologic Research in Japan: Accomplishments, Future Challenges, and Opportunities for International Collaboration R. Sidle https://doi.org/10.3178/jjshwr.28.17
79. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti https://doi.org/10.5194/hess-17-2669-2013
80. The Variable Saturation Hydraulic Conductivity Method for Improving Soil Water Content Simulation in EPIC and APEX Models L. Doro et al. https://doi.org/10.2136/vzj2017.06.0125
81. Where does water go when it rains? Moving beyond the variable source area concept of rainfall‐runoff response J. McDonnell https://doi.org/10.1002/hyp.5132
82. Farm-Scale Biofuel Crop Adoption and Its Effects on In-Basin Water Balance N. Noreika et al. https://doi.org/10.3390/su122410596
83. Impacts of different precipitation data sources on water budgets J. Guo et al. https://doi.org/10.1016/j.jhydrol.2003.08.020
84. Knowledge, uncertainty and physical geography: towards the development of methodologies for questioning belief J. Brown https://doi.org/10.1111/j.0020-2754.2004.00342.x
85. Distributed Sensitivity Analysis of Flood Inundation Model Calibration J. Hall et al. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:2(117)
86. An application of the distributed hydrologic model CASC2D to a tropical montane watershed M. Marsik & P. Waylen https://doi.org/10.1016/j.jhydrol.2006.04.003
87. Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow I. Daliakopoulos & I. Tsanis https://doi.org/10.1080/02626667.2016.1154151
88. Informal likelihood measures in model assessment: Theoretic development and investigation P. Smith et al. https://doi.org/10.1016/j.advwatres.2008.04.012
89. Influence of land use enclave distribution on discharge in a small catchment J. Moravcova et al. https://doi.org/10.2478/johh-2024-0006
90. Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model L. Ehlers et al. https://doi.org/10.1002/hyp.13367
91. Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis H. Tromp‐van Meerveld & J. McDonnell https://doi.org/10.1029/2004WR003800
92. A high-resolution model of soil and surface water conditions I. Maclean et al. https://doi.org/10.1016/j.ecolmodel.2012.03.029
93. Simulation of the impacts of land use/cover and climatic changes on the runoff characteristics at the mesoscale L. Samaniego & A. Bárdossy https://doi.org/10.1016/j.ecolmodel.2006.01.005
94. A statistical method for source apportionment of riverine nitrogen loads B. Grizzetti et al. https://doi.org/10.1016/j.jhydrol.2004.07.036
95. Macropores and water flow in soils revisited K. Beven & P. Germann https://doi.org/10.1002/wrcr.20156
96. Multi-Objective Calibration of a Pre-Modern Nile Hydrologic Model Using Recovered Records I. Munyejuru & J. Stagge https://doi.org/10.3390/hydrology13040114
97. Testing the hybrid‐3‐D hillslope hydrological model in a controlled environment P. Hazenberg et al. https://doi.org/10.1002/2015WR018106
98. Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters S. Ragettli & F. Pellicciotti https://doi.org/10.1029/2011WR010559
99. A lumped conceptual model to simulate groundwater level time-series J. Mackay et al. https://doi.org/10.1016/j.envsoft.2014.06.003
100. Development and application of GIS-based PRISM integration through a plugin approach W. Lee et al. https://doi.org/10.1016/j.jhydrol.2014.03.029
101. Driving a lumped hydrological model with precipitation output from weather generators of different complexity K. Breinl https://doi.org/10.1080/02626667.2015.1036755
102. The Effects of Agricultural Conservation Practices on the Small Water Cycle: From the Farm- to the Management-Scale N. Noreika et al. https://doi.org/10.3390/land11050683
103. A topography‐based scaling algorithm for soil hydraulic parameters at hillslope scales: Field testing R. Jana & B. Mohanty https://doi.org/10.1029/2011WR011205
104. Automating drainage direction and physiographic inputs to the CEQUEAU hydrological model: sensitivity testing on the lower Saint John River watershed, Canada S. Dugdale et al. https://doi.org/10.2166/hydro.2017.051
105. Estimation of probabilistic flood inundation maps for an extreme event: Pánuco River, México A. Pedrozo‐Acuña et al. https://doi.org/10.1111/jfr3.12067
106. Flow analysis with WaSiM-ETH – model parameter sensitivity at different scales J. Cullmann et al. https://doi.org/10.5194/adgeo-9-73-2006
107. A simple hydrologic model for rapid prediction of runoff from ungauged coastal catchments Y. Wan & K. Konyha https://doi.org/10.1016/j.jhydrol.2015.06.047
108. BARUMODEL: Combined Data Based Mechanistic models of runoff response in a managed rainforest catchment N. Chappell et al. https://doi.org/10.1016/j.foreco.2005.12.008
109. An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales S. Halliday et al. https://doi.org/10.1016/j.scitotenv.2011.10.052
110. Searching for “post” Variable Source Area Concept of Rainfall-Runoff Response in Headwater. Where does Water Go When it Rains? Y. ASANO et al. https://doi.org/10.3178/jjshwr.18.459
111. A MODIS-Based Energy Balance to Estimate Evapotranspiration for Clear-Sky Days in Brazilian Tropical Savannas A. Ruhoff et al. https://doi.org/10.3390/rs4030703
112. Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model W. Guo et al. https://doi.org/10.3390/w7062691
113. Towards integrating tracer studies in conceptual rainfall‐runoff models: recent insights from a sub‐arctic catchment in the Cairngorm Mountains, Scotland C. Soulsby & S. Dunn https://doi.org/10.1002/hyp.1132
114. A comparison of linear and nonlinear model performance of shia_landslide: a forecasting model for rainfall-induced landslides E. Aristizábal-Giraldo et al. https://doi.org/10.17533/udea.redin.n80a09
115. Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales T. Madsen et al. https://doi.org/10.3390/w12051279
116. Regionalisierung hydrologischer Modelle mit Function Space Optimization M. Feigl et al. https://doi.org/10.1007/s00506-021-00766-0
117. An Operational Weather Radar–Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling X. He et al. https://doi.org/10.2136/vzj2010.0034
118. Classification of Runoff in Headwater Catchments: A Physical Problem? M. Hauhs & H. Lange https://doi.org/10.1111/j.1749-8198.2007.00075.x
119. Reduced Soil Macropores and Forest Cover Reduce Warm‐Season Baseflow below Ecological Thresholds in the Upper Delaware River Basin T. Endreny et al. https://doi.org/10.1111/1752-1688.12777
120. A process proof test for model concepts: Modelling the meso-scale H. Hellebrand et al. https://doi.org/10.1016/j.pce.2010.07.019
121. Searching for the Holy Grail of scientific hydrology: Qt=(S, R, Δt)A as closure K. Beven https://doi.org/10.5194/hess-10-609-2006
122. On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit‐for purpose K. Beven & S. Lane https://doi.org/10.1002/hyp.14704
123. Improving Hydrological Modelling With Topkapi Through Irrigation and Open‐Channel Flow in Semi‐Arid Agricultural Catchments S. Abbenante et al. https://doi.org/10.1002/hyp.70140
124. Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness? D. Birhanu et al. https://doi.org/10.3390/su10082837
125. On the development of regionalization relationships for lumped watershed models: The impact of ignoring sub-basin scale variability H. Kling & H. Gupta https://doi.org/10.1016/j.jhydrol.2009.04.031
126. Survival of salmonid eggs in a degraded gravel‐bed stream: effects of groundwater–surface water interactions I. Malcolm et al. https://doi.org/10.1002/rra.706
127. An appraisal of the Great Lakes advanced hydrologic prediction system A. Gronewold et al. https://doi.org/10.1016/j.jglr.2011.06.010
128. Rapid mobilization of old water during urban stormflow S. Ariano & C. Oswald https://doi.org/10.1002/hyp.14745
129. Future trends in transport and fate of diffuse contaminants in catchments, with special emphasis on stable isotope applications J. Turner et al. https://doi.org/10.1002/hyp.6074
130. Developing channel and floodplain dimensions with limited data: a case study in the Amazon Basin R. Beighley & V. Gummadi https://doi.org/10.1002/esp.2132
131. Experimental Study on Anisotropic Attributes of Surface Roughness in Watersheds S. Zhang & Y. Liu https://doi.org/10.1061/(ASCE)HE.1943-5584.0001586
132. Data‐supported robust parameterisations in land surface–atmosphere flux predictions: towards a top‐down approach K. Schulz & K. Beven https://doi.org/10.1002/hyp.1331
133. Reductive dechlorination of 1,2-dichloroethane in the presence of chloroethenes and 1,2-dichloropropane as co-contaminants P. Peng et al. https://doi.org/10.1007/s00253-019-09985-8
134. Comparison of SWAT and DLBRM for Hydrological Modeling of a Mountainous Watershed in Arid Northwest China L. Zhang et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001313
135. Development of a distributed hydrological model to facilitate watershed management S. Li et al. https://doi.org/10.1080/02626667.2017.1351029
136. Hydrogeophysical inversion using a physics-based catchment model with hydrological and electromagnetic induction data M. Pleasants et al. https://doi.org/10.1016/j.jhydrol.2024.132376
137. Modelling nitrogen dynamics for a mesoscale catchment using a minimum information requirement (MIR) concept / Modélisation des dynamiques de l'azote pour un bassin versant de taille moyenne, utilisant le concept de besoin minimum d'information M. EISELE & C. LEIBUNDGUT https://doi.org/10.1080/02626660209492978
138. GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application R. Vázquez et al. https://doi.org/10.1007/s11269-008-9329-6
139. A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system development X. Yuan et al. https://doi.org/10.1002/wat2.1088
140. Impacts of climate change on the hydrological regime of the Koshi river basin in the Himalayan region S. Nepal https://doi.org/10.1016/j.jher.2015.12.001
141. Quantifying the spatial variability of rainfall and flow routing on flood response across scales G. Weijian et al. https://doi.org/10.1007/s12665-015-4456-x
142. Equifinality, a blessing in disguise? H. Savenije https://doi.org/10.1002/hyp.494
143. On hypothesis testing in hydrology: Why falsification of models is still a really good idea K. Beven https://doi.org/10.1002/wat2.1278
144. Performance of the COSERO precipitation–runoff model under non-stationary conditions in basins with different climates H. Kling et al. https://doi.org/10.1080/02626667.2014.959956
145. Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica M. Mendez et al. https://doi.org/10.3390/su142416938
146. The ability of a GCM-forced hydrological model to reproduce global discharge variability F. Sperna Weiland et al. https://doi.org/10.5194/hess-14-1595-2010
147. Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL? S. Blazkova et al. https://doi.org/10.1029/2001WR000912
148. A physically based hydrological model for paddy agriculture dominated hilly watersheds in tropical region S. Mishra et al. https://doi.org/10.1016/j.jhydrol.2008.05.019
149. A Deterministic Combinatorial Approach to Investigate Interactions of Soil Hydraulic Parameters on River Flow Modelling D. Sales et al. https://doi.org/10.3390/w17172627
150. Hydrologic Modeling, Uncertainty, and Sensitivity in the Okavango Basin: Insights for Scenario Assessment A. Linhoss et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000755
151. NutSpaFHy—A Distributed Nutrient Balance Model to Predict Nutrient Export from Managed Boreal Headwater Catchments A. Lauren et al. https://doi.org/10.3390/f12060808
152. From model- to data-centric urban hydroinformatics: an automated workflow for enhancing geospatial data for surface flow modelling W. Svellingen et al. https://doi.org/10.2166/hydro.2026.195
153. Comment on ‘L. Ciarapica and E. Todini, TOPKAPI: a model for the representation of the rainfall – runoff process at different scales. Hydrological Processes 16 (2002) 207 – 229’ J. Latron et al. https://doi.org/10.1002/hyp.1311
154. Geostatistical co-regionalization of soil hydraulic properties in a micro-scale catchment using terrain attributes M. Herbst et al. https://doi.org/10.1016/j.geoderma.2005.05.008
155. The Importance of Measuring Soil Erosion by Water at the Field Scale: A Review A. Nicosia et al. https://doi.org/10.3390/w16233427
156. Hydrological modelling, process understanding and uncertainty in a southern African context: lessons from the northern hemisphere D. Hughes https://doi.org/10.1002/hyp.10721
157. 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
158. Assessing development and climate variability impacts on water resources in the Zambezi River basin: Initial model calibration, uncertainty issues and performance D. Hughes et al. https://doi.org/10.1016/j.ejrh.2020.100765
159. A novel conceptual flood inundation model for large scale data-scarce regions S. Unnithan et al. https://doi.org/10.1016/j.envsoft.2023.105863
160. Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes W. Buytaert et al. https://doi.org/10.1016/j.jhydrol.2006.02.031
161. Calibrating Agro-Hydrological Model under Grazing Activities and Its Challenges and Implications A. Nelson et al. https://doi.org/10.3390/hydrology11040042
162. Concepts of Information Content and Likelihood in Parameter Calibration for Hydrological Simulation Models K. Beven & P. Smith https://doi.org/10.1061/(ASCE)HE.1943-5584.0000991
163. How to make advances in hydrological modelling K. Beven https://doi.org/10.2166/nh.2019.134
164. Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed S. Lee et al. https://doi.org/10.1016/j.agwat.2022.107503
165. Adapting water resource systems to a changing future: challenges for UK hydrology in the 21st century J. Hall & A. Murgatroyd https://doi.org/10.1098/rsta.2024.0278
166. 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
167. Evaluating precipitation datasets for large-scale distributed hydrological modelling M. Mazzoleni et al. https://doi.org/10.1016/j.jhydrol.2019.124076
168. Multi-variable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability W. Cao et al. https://doi.org/10.1002/hyp.5933
169. Which spatial discretization for distributed hydrological models? Proposition of a methodology and illustration for medium to large-scale catchments J. Dehotin & I. Braud https://doi.org/10.5194/hess-12-769-2008
170. Process‐based hydrological modelling: The potential of a bottom‐up approach for runoff predictions in ungauged catchments M. Antonetti et al. https://doi.org/10.1002/hyp.11232
171. Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales G. Baroni et al. https://doi.org/10.5194/hess-21-2301-2017
172. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. https://doi.org/10.1029/2019WR025001
173. On the use of multiple criteria for a posteriori model rejection: Soft data to characterize model performance K. Vaché et al. https://doi.org/10.1029/2004GL021577
174. Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region S. Nepal et al. https://doi.org/10.1002/hyp.11199
175. Modelling water temperature in the lower Olifants River and the implications for climate change A. Adlam et al. https://doi.org/10.17159/sajs.2022/12953
176. Pilot-point based multi-objective calibration in a surface–subsurface distributed hydrological model M. Maneta & W. Wallender https://doi.org/10.1080/02626667.2012.754987
177. Assessment of climate change impact on the Gomti River basin in India under different RCP scenarios B. Das et al. https://doi.org/10.1007/s12517-020-06359-7
178. On environmental models of everywhere on the GRID K. Beven https://doi.org/10.1002/hyp.5090
179. 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
180. An equivalent cross-sectional basis for semidistributed hydrological modeling U. Khan et al. https://doi.org/10.1002/2013WR014741
181. Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads L. Tran et al. https://doi.org/10.1007/s11269-013-0260-0
182. Fully distributed hydrological modelling for catchment-wide hydrological data verification D. Ocio et al. https://doi.org/10.2166/nh.2019.006
183. 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
184. The role and value of distributed precipitation data in hydrological models R. Loritz et al. https://doi.org/10.5194/hess-25-147-2021
185. A discrete particle representation of hillslope hydrology: hypothesis testing in reproducing a tracer experiment at Gårdsjön, Sweden J. Davies et al. https://doi.org/10.1002/hyp.8085
186. Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level W. Sun et al. https://doi.org/10.1002/hyp.8429
187. Physics‐based hydrologic response simulation: platinum bridge, 1958 Edsel, or useful tool K. Loague & J. VanderKwaak https://doi.org/10.1002/hyp.5737
188. Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization H. Mao et al. https://doi.org/10.3390/w16213030
189. Testing the structure of a hydrological model using Genetic Programming B. Selle & N. Muttil https://doi.org/10.1016/j.jhydrol.2010.11.009
190. Implementing a Proxy-Basin Strategy to Assess the Transposability of a Hydrological Model in Geographically Similar Catchments C. Donmez et al. https://doi.org/10.3390/su132011393
191. Challenges and Uncertainties in Hydrological Modeling of Remote Hindu Kush–Karakoram–Himalayan (HKH) Basins: Suggestions for Calibration Strategies F. Pellicciotti et al. https://doi.org/10.1659/MRD-JOURNAL-D-11-00092.1
192. Numerical experiments on the sensitivity of runoff generation to the spatial variation of soil hydraulic properties M. Herbst et al. https://doi.org/10.1016/j.jhydrol.2005.10.036
193. Directions of preferential flow in a hillslope soil, 1. Quasi-steady flow P. Germann & M. Zimmermann https://doi.org/10.1002/hyp.1506
194. Using lumped conceptual rainfall–runoff models to simulate daily isotope variability with fractionation in a nested mesoscale catchment C. Birkel et al. https://doi.org/10.1016/j.advwatres.2010.12.006
195. Analysis of Mapping Techniques for Mountain Precipitation: A Case Study of Alpine Region, Austria A. Laghari et al. https://doi.org/10.48084/etasr.2167
196. Improvement and comparison of likelihood functions for model calibration and parameter uncertainty analysis within a Markov chain Monte Carlo scheme Q. Cheng et al. https://doi.org/10.1016/j.jhydrol.2014.10.008
197. Enhancing water management in Northern European lowland chalk streams: A parsimonious, high-resolution hydrological model using groundwater level as a proxy for baseflow T. Homan et al. https://doi.org/10.1016/j.ejrh.2024.102022
198. Ensemble Evaluation of Hydrologically Enhanced Noah-LSM: Partitioning of the Water Balance in High-Resolution Simulations over the Little Washita River Experimental Watershed E. Rosero et al. https://doi.org/10.1175/2010JHM1228.1
199. Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling Y. Her et al. https://doi.org/10.1111/1752-1688.12533
200. Evaluating seasonal hydrological extremes in mesoscale (pre-)Alpine basins at coarse 0.5° and fine hyperresolution J. Buitink et al. https://doi.org/10.5194/hess-23-1593-2019
201. Advances in Quantifying Streamflow Variability Across Continental Scales: 2. Improved Model Regionalization and Prediction Uncertainties Using Hierarchical Bayesian Methods R. Alexander et al. https://doi.org/10.1029/2019WR025037
202. Multivariate calibration of large scale hydrologic models: The necessity and value of a Pareto optimal approach A. Koppa et al. https://doi.org/10.1016/j.advwatres.2019.06.005
203. The economics of aquifer protection plans under climate water stress: New insights from hydroeconomic modeling F. Ward et al. https://doi.org/10.1016/j.jhydrol.2019.06.081
204. Can we model the hydrological impacts of environmental change? T. Wagener https://doi.org/10.1002/hyp.6873
205. Use of a simple surface–groundwater interaction model to inform water management K. Ivkovic et al. https://doi.org/10.1080/08120090802541945
206. Selecting the appropriate hydraulic model structure using low-resolution satellite imagery P. Prestininzi et al. https://doi.org/10.1016/j.advwatres.2010.09.016
207. Effects of climate change on wet and dry spells in Kelantan River basin using physically-based hydrological model and CMIP6 scenarios N. Mohamad Arbai & M. Irie https://doi.org/10.1016/j.ejrh.2025.102219
208. Assessing the effects of precipitation and temperature changes on hydrological processes in a glacier‐dominated catchment X. Wang et al. https://doi.org/10.1002/hyp.10538
209. Kinetics of dechlorination by Dehalococcoides mccartyi using different carbon sources U. Schneidewind et al. https://doi.org/10.1016/j.jconhyd.2013.10.006
210. DBv2: an improved climate-centric calibration-free model for runoff-generation simulation P. Istalkar & B. Biswal https://doi.org/10.1080/02626667.2025.2581263
211. Prediction limits of a catchment hydrological model using different estimates of ETp R. Vázquez & H. Hampel https://doi.org/10.1016/j.jhydrol.2014.03.051
212. Some hydrological challenges in understanding discharge generation processes in the Rhine and Meuse basins T. Bogaard et al. https://doi.org/10.1016/j.pce.2005.04.002
213. Discharge, Groundwater Gradients, and Streambed Micro‐Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach E. Bonanno et al. https://doi.org/10.1029/2022WR034053
214. A deterministic linearized recurrent neural network for recognizing the transition of rainfall–runoff processes T. Pan et al. https://doi.org/10.1016/j.advwatres.2007.02.009
215. Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models E. Bonanno et al. https://doi.org/10.5194/hess-26-6003-2022
216. Localizing Hydrological Drought Early Warning Using In Situ Groundwater Sensors W. Veness et al. https://doi.org/10.1029/2022WR032165
217. On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration J. Seibert & J. McDonnell https://doi.org/10.1029/2001WR000978
218. Separating natural and epistemic uncertainty in flood frequency analysis B. Merz & A. Thieken https://doi.org/10.1016/j.jhydrol.2004.11.015
219. Current Awareness https://doi.org/10.1002/hyp.538
220. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
221. Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis N. Bulygina et al. https://doi.org/10.5194/hess-13-893-2009
222. Understanding the hydrological system dynamics of a glaciated alpine catchment in the Himalayan region using the J2000 hydrological model S. Nepal et al. https://doi.org/10.1002/hyp.9627
223. Physically-based modelling of UK river flows under climate change B. Smith et al. https://doi.org/10.3389/frwa.2024.1468855
224. Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. https://doi.org/10.5194/hess-25-1617-2021
225. Scale effects in conceptual hydrological modeling R. Merz et al. https://doi.org/10.1029/2009WR007872
226. Hydrological response to climate variability at different time scales: A study in the Ebro basin J. López-Moreno et al. https://doi.org/10.1016/j.jhydrol.2012.11.028
227. Hydrological response of tropical rivers basins to climate change using the GR2M model: the case of the Casamance and Kayanga-Géva rivers basins C. Sadio et al. https://doi.org/10.1186/s12302-023-00822-4
228. Using high‐resolution isotope data and alternative calibration strategies for a tracer‐aided runoff model in a nested catchment C. Tunaley et al. https://doi.org/10.1002/hyp.11313
229. Integrating Tracers and Soft Data Into Multi‐Criteria Calibration: Implications From Distributed Modeling in a Riparian Wetland S. Wu et al. https://doi.org/10.1029/2023WR035509
230. The NextGen Water Resources Modeling Framework: Community Innovation at the Intersection of Hydrologic, Data and Computer Sciences F. Ogden et al. https://doi.org/10.1111/1752-1688.70089
231. Hydrologic Modelling for Flood Threshold and Hazard Prediction in the Black Volta River Basin, West Africa F. Yeboah et al. https://doi.org/10.1007/s10666-023-09946-6
232. Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows X. Chen et al. https://doi.org/10.1007/s11269-012-0244-5
233. Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020 A. Heldmyer et al. https://doi.org/10.5194/hess-30-5925-2026
234. Prediction and uncertainty analysis of a parsimonious floodplain surface water‐groundwater interaction model N. Maier et al. https://doi.org/10.1002/2017WR020749
235. 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
236. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103470
237. A new flood type classification method for use in climate change impact studies T. Turkington et al. https://doi.org/10.1016/j.wace.2016.10.001
238. Assessing Current and Future Freshwater Flood Risk from North Atlantic Tropical Cyclones via Insurance Claims J. Czajkowski et al. https://doi.org/10.1038/srep41609
239. 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
240. The impact of hillslope groundwater dynamics and landscape functioning in event-flow generation: a field study in the Rietholzbach catchment, Switzerland J. von Freyberg et al. https://doi.org/10.1007/s10040-015-1238-1
241. A spatially distributed flash flood forecasting model G. Blöschl et al. https://doi.org/10.1016/j.envsoft.2007.06.010
242. A Review on Theoretical Consideration and Types of Models in Hydrology M. Jajarmizad et al. https://doi.org/10.3923/jest.2012.249.261
243. Influence of uncertain boundary conditions and model structure on flood inundation predictions F. Pappenberger et al. https://doi.org/10.1016/j.advwatres.2005.11.012
244. Estimating Catchment Nutrient Flow with the HBV-NP Model: Sensitivity To Input Data L. Andersson et al. https://doi.org/10.1579/0044-7447-34.7.521
245. Inverse modeling of large‐scale spatially distributed vadose zone properties using global optimization J. Vrugt et al. https://doi.org/10.1029/2003WR002706
246. Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al. https://doi.org/10.5194/hess-16-3049-2012
247. Analysis of catchment evapotranspiration at different scales using bottom-up and top-down approaches X. Xu & D. Yang https://doi.org/10.1007/s11709-010-0002-9
248. MODELING EFFECTS OF BRUSH MANAGEMENT ON THE RANGELAND WATER BUDGET: EDWARDS PLATEAU, TEXAS J. Afinowicz et al. https://doi.org/10.1111/j.1752-1688.2005.tb03727.x
249. Combining Hydrometric and Hydrochemical Data Sets for Investigating Runoff Generation Processes: Tautologies, Inconsistencies and Possible Explanations G. Lischeid https://doi.org/10.1111/j.1749-8198.2007.00082.x
250. Scenario-Specific Landslide Warning Thresholds from Uncertainty-Based Clustering of TANK Model Soil Water Index Responses in Republic of Korea D. Kim et al. https://doi.org/10.3390/land15040688
251. Multi-criteria optimization of a regional spatially-distributed subsurface water flow model G. Schoups et al. https://doi.org/10.1016/j.jhydrol.2005.01.001
252. A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolution J. Buitink et al. https://doi.org/10.5194/gmd-13-6093-2020
253. Commemorating the 50th anniversary of the Freeze and Harlan (1969) Blueprint for a physically-based, digitally-simulated hydrologic response model C. Simmons et al. https://doi.org/10.1016/j.jhydrol.2019.124309
254. Impacts of land-cover change on the water flow regulation ecosystem service: Invasive alien plants, fire and their policy implications D. Le Maitre et al. https://doi.org/10.1016/j.landusepol.2013.07.007
255. Distributed Hydrological Modeling and Sensitivity Analysis in Torysa Watershed, Slovakia A. Bahremand & F. De Smedt https://doi.org/10.1007/s11269-007-9168-x
256. Learning from model improvement: On the contribution of complementary data to process understanding F. Fenicia et al. https://doi.org/10.1029/2007WR006386
257. The geography of rainfall in Mauritius: Modelling the relationship between annual and monthly rainfall and landscape characteristics on a small volcanic island C. Staub et al. https://doi.org/10.1016/j.apgeog.2014.08.008
258. Calibration of a distributed hydrological model based on satellite evapotranspiration W. Immerzeel & P. Droogers https://doi.org/10.1016/j.jhydrol.2007.11.017
259. Linking basin-scale hydrology with climatic parameters in western Himalaya: Application of satellite data, temperature index modelling and in-situ observations S. Srivastava et al. https://doi.org/10.1016/j.gsf.2024.101936
260. Soil water balance performs better than climatic water variables in tree species distribution modelling C. Piedallu et al. https://doi.org/10.1111/geb.12012
261. System dynamics approach to assess the sustainability of reclamation of disturbed watersheds A. Elshorbagy et al. https://doi.org/10.1139/l04-112
262. Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed M. Mbuh et al. https://doi.org/10.1080/02723646.2018.1507339
263. Quantifying parameter uncertainty and assessing the skill of exponential dispersion rainfall simulation models A. Gronewold et al. https://doi.org/10.1002/joc.3469
264. Modeling the Genetic Components of the Water and Chemical Runoff of Heavy Metals in the Basin of the Nizhnekamskoe Reservoir T. Fashchevskaya et al. https://doi.org/10.31857/S0321059623040077
265. A topographic index explaining hydrological similarity by accounting for the joint controls of runoff formation R. Loritz et al. https://doi.org/10.5194/hess-23-3807-2019
266. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand https://doi.org/10.5194/hess-20-1433-2016
267. Simultaneously assimilating multivariate data sets into the two-source evapotranspiration model by Bayesian approach: application to spring maize in an arid region of northwestern China G. Zhu et al. https://doi.org/10.5194/gmd-7-1467-2014
268. Analysis of Spatial Similarities Between NEXRAD and NLDAS Precipitation Data Products Z. Nan et al. https://doi.org/10.1109/JSTARS.2010.2048418
269. Advances in the use of observed spatial patterns of catchment hydrological response R. Grayson et al. https://doi.org/10.1016/S0309-1708(02)00060-X
270. Developing a Snowmelt Forecast Model in the Absence of Field Data E. Sproles et al. https://doi.org/10.1007/s11269-016-1271-4
271. Application of catchment scale sediment delivery model INCA-Sed to four small study catchments in Finland K. Rankinen et al. https://doi.org/10.1016/j.catena.2010.07.005
272. A new externally coupled, physically-based multi-model framework for simulating subsurface and overland flow hydrological processes on hillslopes A. Rahman et al. https://doi.org/10.1016/j.jhydrol.2025.133842
273. Upscaling hydrological processes and land management change impacts using a metamodeling procedure C. Fraser et al. https://doi.org/10.1002/wrcr.20432
274. Improving the representation of hydrologic processes in Earth System Models M. Clark et al. https://doi.org/10.1002/2015WR017096
275. Towards a methodology for testing models as hypotheses in the inexact sciences K. Beven https://doi.org/10.1098/rspa.2018.0862
276. Effects of reduced land cover detail on hydrological model response R. Armstrong & L. Martz https://doi.org/10.1002/hyp.6830
277. A distributed Grid-Xinanjiang model with integration of subgrid variability of soil storage capacity W. Guo et al. https://doi.org/10.1016/j.wse.2016.06.003
278. Determining tropical cyclone inland flooding loss on a large scale through a new flood peak ratio-based methodology J. Czajkowski et al. https://doi.org/10.1088/1748-9326/8/4/044056
279. Catchment-scale non-linear groundwater-surface water interactions in densely drained lowland catchments Y. van der Velde et al. https://doi.org/10.5194/hess-13-1867-2009
280. Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA S. Praskievicz & C. Luo https://doi.org/10.1002/hyp.13394
281. A dependency-guided symbolic regression framework for overcoming error compensation in hybrid AI–physical models Q. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136201
282. Hydrological hysteresis and its value for assessing process consistency in catchment conceptual models O. Fovet et al. https://doi.org/10.5194/hess-19-105-2015
283. Temperature index based snowmelt runoff modelling for the Satluj River basin in the western Himalayas S. Tiwari et al. https://doi.org/10.1002/met.1692
284. Hydrologic Modelling of Data Scarce Basin with SWAT Model: Capabilities and Limitations M. Nyeko https://doi.org/10.1007/s11269-014-0828-3
285. Assessment of the impact of climate change on current and future flows of the ungauged Aga-Foua-Djilas watershed: a comparative study of hydrological models CWatM under ISIMIP and HMF-WA P. Dione et al. https://doi.org/10.1007/s13201-024-02219-x
286. 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
287. HESS Opinions: The complementary merits of competing modelling philosophies in hydrology M. Hrachowitz & M. Clark https://doi.org/10.5194/hess-21-3953-2017
288. HydroZIP: How Hydrological Knowledge can Be Used to Improve Compression of Hydrological Data S. Weijs et al. https://doi.org/10.3390/e15041289
289. 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
290. A tracer‐based assessment of hydrological pathways at different spatial scales in a mesoscale Scottish catchment C. Soulsby et al. https://doi.org/10.1002/hyp.1163
291. 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
292. Potential Impacts of Climate Warming on Water Supply Reliability in the Tuolumne and Merced River Basins, California M. Kiparsky et al. https://doi.org/10.1371/journal.pone.0084946
293. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology S. Fatichi et al. https://doi.org/10.1016/j.jhydrol.2016.03.026
294. Conceptual modelling to assess the influence of hydro-climatic variability on runoff processes in data scarce semi-arid Andean catchments A. Nauditt et al. https://doi.org/10.1080/02626667.2016.1240870
295. Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets A. te Linde et al. https://doi.org/10.5194/hess-12-943-2008
296. Development of the forSIM model to quantify positive and negative hydrological impacts of tropical reforestation N. Chappell et al. https://doi.org/10.1016/j.foreco.2007.06.024
297. Multi-GCMs approach for assessing climate change impact on water resources in Thailand P. Deb et al. https://doi.org/10.1007/s40808-018-0428-y
298. Evaluating a parsimonious watershed model versus SWAT to estimate streamflow, soil loss and river contamination in two case studies in Tietê river basin, São Paulo, Brazil F. Santos et al. https://doi.org/10.1016/j.ejrh.2020.100685
299. Assessment of streamflow in ungauged basin by using physical similarity approach S. Barbhuiya et al. https://doi.org/10.1007/s12517-023-11786-3
300. Nutrient Load Mitigation with Wintertime Cover as Estimated by the INCA Model K. Rankinen et al. https://doi.org/10.3390/w13040450
301. Coupling the short-term global forecast system weather data with a variable source area hydrologic model A. Sommerlot et al. https://doi.org/10.1016/j.envsoft.2016.09.008
302. Vadose Zone Flow Model Uncertainty as Conditioned on Geophysical Data A. Binley & K. Beven https://doi.org/10.1111/j.1745-6584.2003.tb02576.x
303. Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States H. Chien et al. https://doi.org/10.1016/j.jhydrol.2013.03.026
304. Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport P. Collender et al. https://doi.org/10.1080/10643389.2016.1269578
305. On doing better hydrological science K. Beven https://doi.org/10.1002/hyp.7108
306. Upscaling Sorption/Desorption Processes in Reactive Transport Models To Describe Metal/Radionuclide Transport: A Critical Review A. Miller et al. https://doi.org/10.1021/es101822v
307. Modelling the Potential of Integrated Vegetation Bands (IVB) to Retain Stormwater Runoff on Steep Hillslopes of Southeast Queensland, Australia J. Ryan et al. https://doi.org/10.3390/land4030711
308. Assessing a Regression-Based Regionalization Approach to Ungauged Sites with Various Hydrologic Models in a Forested Catchment in the Northeastern United States G. Clark et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001582
309. Modelling Spatial Variability Along Drainage Networks with Geostatistics J. Bailly et al. https://doi.org/10.1007/s11004-006-9033-0
310. Computationally Efficient Multivariate Calibration and Validation of a Grid-Based Hydrologic Model in Sparsely Gauged West African River Basins T. Poméon et al. https://doi.org/10.3390/w10101418
311. MIKE SHE modeling of ecohydrological processes: Merits, applications, and challenges L. Ma et al. https://doi.org/10.1016/j.ecoleng.2016.01.008
312. Modelling rapid flow response of a tile‐drained field site using a 2D physically based model: assessment of ‘equifinal’ model setups J. Klaus & E. Zehe https://doi.org/10.1002/hyp.7687
313. Assessing the hydrologic response to wildfires in mountainous regions A. Havel et al. https://doi.org/10.5194/hess-22-2527-2018
314. The quixotic search for a comprehensive understanding of hydrologic response at the surface: Horton, Dunne, Dunton, and the role of concept‐development simulation K. Loague et al. https://doi.org/10.1002/hyp.7834
315. How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps M. Antonetti & M. Zappa https://doi.org/10.5194/hess-22-4425-2018
316. Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters M. Saadi et al. https://doi.org/10.3390/w11081540
317. Determining slack tide with a GPS receiver on an anchored buoy M. Valk et al. https://doi.org/10.5194/hess-18-2599-2014
318. Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data M. Jayaprathiga et al. https://doi.org/10.1007/s00477-024-02758-4
319. Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system K. Beven https://doi.org/10.1002/hyp.343
320. The Court of Miracles of Hydrology: can failure stories contribute to hydrological science? V. Andréassian et al. https://doi.org/10.1080/02626667.2010.506050
321. A blueprint for process‐based modeling of uncertain hydrological systems A. Montanari & D. Koutsoyiannis https://doi.org/10.1029/2011WR011412
322. A probabilistic appraisal of rainfall-runoff modeling approaches within SWAT in mixed land use watersheds A. Tasdighi et al. https://doi.org/10.1016/j.jhydrol.2018.07.035
323. A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models K. Beven https://doi.org/10.1080/02626667.2024.2332616
324. Extreme flood reconstruction by using the 3DNet platform for hydrological modelling M. Stanić et al. https://doi.org/10.2166/hydro.2017.050
325. Evaluation of regionalization parameters for stream flow prediction in ungauged catchments of Rift Valley Lakes Basin, Ethiopia Z. Mada et al. https://doi.org/10.1007/s40808-024-01977-6
326. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin A. Ruhoff et al. https://doi.org/10.1080/02626667.2013.837578
327. Flux parameterization in the representative elementary watershed approach: Application to a natural basin P. Reggiani & T. Rientjes https://doi.org/10.1029/2004WR003693
328. Symbolic Regression for the Estimation of Transfer Functions of Hydrological Models D. Klotz et al. https://doi.org/10.1002/2017WR021253
329. Modeling watershed rainfall–runoff relations using impervious surface-area data with high spatial resolution Y. Zhou et al. https://doi.org/10.1007/s10040-010-0618-9
330. Spatial and temporal variability of climate change impacts on ecosystem services in small agricultural catchments using the Soil and Water Assessment Tool (SWAT) B. Uniyal et al. https://doi.org/10.1016/j.scitotenv.2023.162520
331. Parsimonious modeling of hydrologic responses in engineered watersheds: Structural heterogeneity versus functional homogeneity N. Basu et al. https://doi.org/10.1029/2009WR007803
332. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2018.10.040
333. 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
334. Towards a coherent philosophy for modelling the environment K. Beven https://doi.org/10.1098/rspa.2002.0986
335. Apparent translatory flow in groundwater recharge and runoff generation G. Lischeid et al. https://doi.org/10.1016/S0022-1694(02)00108-7
336. Hydraulic-hydrological calibration of a peri-urban watershed in the SWMM model C. Perius et al. https://doi.org/10.5902/2179460X84083
337. Assessing hydroclimatic impacts of climate change in snowy catchments using a physically based hydrological model F. Talbot et al. https://doi.org/10.1016/j.ejrh.2025.102453
338. Sediment Yield Estimation and Effect of Management Options on Sediment Yield of Kesem Dam Watershed, Awash Basin, Ethiopia T. Tesema & O. Leta https://doi.org/10.1016/j.sciaf.2020.e00425
339. Natural length scales define the range of applicability of the Richards equation for capillary flows D. Or et al. https://doi.org/10.1002/2015WR017034
340. An application of the GLUE methodology for estimating the parameters of the INCA-N model K. Rankinen et al. https://doi.org/10.1016/j.scitotenv.2006.02.034
341. Weather radar and hydrology G. Delrieu et al. https://doi.org/10.1016/j.advwatres.2009.03.006
342. On philosophical foundations of ecohydrology: the challenge of bridging natural and social science G. Owusu https://doi.org/10.1002/eco.1690
343. 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
344. Modelling storage‐driven connectivity between landscapes and riverscapes: towards a simple framework for long‐term ecohydrological assessment C. Soulsby et al. https://doi.org/10.1002/hyp.10862
345. On explanatory depth and predictive power K. Beven https://doi.org/10.1002/hyp.500
346. Modeling the Rainfall–Runoff Relationship with TOPMODEL in the Wadi El Kebir Watershed N. Maref et al. https://doi.org/10.14796/JWMM.C497
347. A methodology for monitoring and modeling of high altitude Alpine catchments A. Soncini et al. https://doi.org/10.1177/0309133317710832
348. Spatial pattern‐based performance evaluation and uncertainty analysis of a distributed hydrological model S. Gaur et al. https://doi.org/10.1002/hyp.14586
349. Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography I. Thomas et al. https://doi.org/10.1016/j.jag.2016.08.012
350. Simulating the transition of a semi-arid rainfed catchment towards irrigation agriculture A. Pérez et al. https://doi.org/10.1016/j.jhydrol.2011.08.061
351. Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.132904
352. Integrating tracer experiments with modeling to assess runoff processes and water transit times K. McGuire et al. https://doi.org/10.1016/j.advwatres.2006.07.004
353. System dynamics model for predicting floods from snowmelt in North American prairie watersheds L. Li & S. Simonovic https://doi.org/10.1002/hyp.1064
354. Regional parameter allocation and predictive uncertainty estimation of a rainfall-runoff model in the poorly gauged Three Gorges Area (PR China) T. Bosshard & M. Zappa https://doi.org/10.1016/j.pce.2008.03.004
355. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. https://doi.org/10.3390/w14131997
356. The need for an improved risk index for phosphorus losses to water from tile-drained agricultural land B. Ulén et al. https://doi.org/10.1016/j.jhydrol.2011.01.038
357. Nitrate transport in Chalk catchments: monitoring, modelling and policy implications B. Jackson et al. https://doi.org/10.1016/j.envsci.2007.10.006
358. Bayesian conditioning of a rainfall‐runoff model for predicting flows in ungauged catchments and under land use changes N. Bulygina et al. https://doi.org/10.1029/2010WR009240
359. Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland K. Rahman et al. https://doi.org/10.1007/s11269-012-0188-9
360. Real-Time Geospatial Data Handling and Forecasting: Examples From Delft-FEWS Forecasting Platform/System A. Weerts et al. https://doi.org/10.1109/JSTARS.2010.2046882
361. Evaluation of a Stepwise, Multiobjective, Multivariable Parameter Optimization Method for the APEX Model G. Senaviratne et al. https://doi.org/10.2134/jeq2013.12.0509
362. An evaluation of approaches for modelling hydrological processes in high‐elevation, glacierized Andean watersheds S. Ragettli et al. https://doi.org/10.1002/hyp.10055
363. GLUE: 20 years on K. Beven & A. Binley https://doi.org/10.1002/hyp.10082
364. Multi-timescale streamflow projections in human-regulated basins of southwest Iran using CWatM and explainable machine learning M. Mohammadnezhad et al. https://doi.org/10.1016/j.ejrh.2026.103534
365. Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale L. Samaniego et al. https://doi.org/10.1029/2008WR007327
366. Estimation of Runoff, Peak Discharge and Sediment Load at the Event Scale in a Medium‐Size Mediterranean Watershed Using the Annagnps Model T. Bisantino et al. https://doi.org/10.1002/ldr.2213
367. Evaluation of inundation areas resulting from the diversion of an extreme discharge towards the sea: case study in Tabasco, Mexico A. Pedrozo‐Acuña et al. https://doi.org/10.1002/hyp.8175
368. Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models W. Armstrong et al. https://doi.org/10.1080/02626667.2025.2471430
369. Use of soft information to describe the relative uncertainty of calibration data in hydrologic models I. Khadam & J. Kaluarachchi https://doi.org/10.1029/2003WR002939
370. Identifying runoff processes on the plot and catchment scale P. Schmocker-Fackel et al. https://doi.org/10.5194/hess-11-891-2007
371. Water-related ecosystem services in Western Siberian lowland basins—Analysing and mapping spatial and seasonal effects on regulating services based on ecohydrological modelling results B. Schmalz et al. https://doi.org/10.1016/j.ecolind.2016.06.050
372. Uncertainty Analysis and Parameter Estimation for the CSM‐CROPGRO‐Cotton Model T. Pathak et al. https://doi.org/10.2134/agronj2011.0349
373. An ET-Based Two-Phase Method for the Calibration and Application of Distributed Hydrological Models Z. Sun et al. https://doi.org/10.1007/s11269-021-02774-x
374. Flow resistance in seepage-affected alluvial channel A. Sharma & B. Kumar https://doi.org/10.1080/09715010.2018.1464407
375. The Importance of Capturing Topographic Features for Modeling Groundwater Flow and Transport in Mountainous Watersheds C. Wang et al. https://doi.org/10.1029/2018WR023863
376. Flow pathways in the Slapton Wood catchment using temperature as a tracer S. Birkinshaw & B. Webb https://doi.org/10.1016/j.jhydrol.2009.12.042
377. Exploring the potential for parameter transfer from daily to hourly time step in the HYPE model for Sweden D. Fuentes-Andino et al. https://doi.org/10.1080/02626667.2022.2121165
378. Hydrological science, society and the sustainable management of Scottish freshwaters resources in the 21st century C. Soulsby et al. https://doi.org/10.1016/S0048-9697(02)00069-4
379. Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. https://doi.org/10.1002/wat2.1569
380. Spatially distributed gradient-based calibration and parametric sensitivity of a spatialized hydrological model over 235 French catchments P. Garambois et al. https://doi.org/10.1016/j.ejrh.2025.102485
381. The role of bedrock topography on subsurface storm flow J. Freer et al. https://doi.org/10.1029/2001WR000872
382. Using micro‐catchment experiments for multi‐local scale modelling of nature‐based solutions B. Hankin et al. https://doi.org/10.1002/hyp.14418
383. Objective quantitative spatial verification of distributed snow cover simulations—an experiment for the whole of Switzerland / Vérification quantitative spatiale objective de simulations distribuées de la couche de neige—une étude pour l'ensemble de la Suisse M. ZAPPA https://doi.org/10.1623/hysj.53.1.179
384. Runoff processes, stream water residence times and controlling landscape characteristics in a mesoscale catchment: An initial evaluation C. Soulsby et al. https://doi.org/10.1016/j.jhydrol.2005.10.024
385. Continuous spatially distributed simulation of surface and subsurface hydrological processes in a small semiarid catchment M. Maneta et al. https://doi.org/10.1002/hyp.6817
386. Application of a fully‐integrated surface‐subsurface flow model at the watershed‐scale: A case study J. Jones et al. https://doi.org/10.1029/2006WR005603
387. Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability J. Han et al. https://doi.org/10.1016/j.scitotenv.2015.02.107
388. Transit times—the link between hydrology and water quality at the catchment scale M. Hrachowitz et al. https://doi.org/10.1002/wat2.1155
389. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. https://doi.org/10.1016/j.jhydrol.2023.129176
390. 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
391. When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events F. Lobligeois et al. https://doi.org/10.5194/hess-18-575-2014
392. Conceptualization in catchment modelling: simply learning? S. Dunn et al. https://doi.org/10.1002/hyp.7070
393. Flood Simulations and Uncertainty Analysis for the Pearl River Basin Using the Coupled Land Surface and Hydrological Model System Y. Zhu et al. https://doi.org/10.3390/w9060391
394. One-Dimensional Velocity Distribution in Seepage Bed Open Channels Using Tsallis Entropy A. Sharma et al. https://doi.org/10.1061/AJRUA6.RUENG-1041
395. Data-driven estimation of the hydrologic response using generalized additive models Q. Duchemin et al. https://doi.org/10.5194/gmd-18-8663-2025
396. Sediment modelling with fine temporal and spatial resolution for a hilly catchment A. Elliott et al. https://doi.org/10.1002/hyp.8445
397. Virtual experiments: a new approach for improving process conceptualization in hillslope hydrology M. Weiler & J. McDonnell https://doi.org/10.1016/S0022-1694(03)00271-3
398. The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions S. Zwieback et al. https://doi.org/10.1175/JHM-D-15-0213.1
399. Using multiple objective calibrations to explore uncertainty in extreme event modeling A. Roche et al. https://doi.org/10.1139/cjce-2020-0275
400. Simple spatially-distributed models for predicting flood inundation: A review N. Hunter et al. https://doi.org/10.1016/j.geomorph.2006.10.021
401. Simpler models in environmental studies and predictions E. Hong et al. https://doi.org/10.1080/10643389.2017.1393264
402. Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty J. Seibert & J. McDonnell https://doi.org/10.1080/02626661003683264
403. Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS D. Gyawali & A. Bárdossy https://doi.org/10.5194/hess-26-3055-2022
404. Temporal stability of soil water storage and its influencing factors on a forestland hillslope during the rainy season in China’s Loess Plateau S. Cheng et al. https://doi.org/10.1007/s12665-017-6850-z
405. Probabilistic Flood Hazard Mapping Using Bivariate Analysis Based on Copulas A. Candela & G. Aronica https://doi.org/10.1061/AJRUA6.0000883
406. Models in geography revisited N. Clifford https://doi.org/10.1016/j.geoforum.2007.01.016
407. Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference J. Yang et al. https://doi.org/10.1016/j.jhydrol.2007.04.006
408. Effects of land-use changes on the hydrologic response of reclamation systems G. Camorani et al. https://doi.org/10.1016/j.pce.2005.07.010
409. A method to obtain the spatial distribution of free water storage capacity based on topo-graphic index T. Bingxing et al. https://doi.org/10.18307/2017.0522
410. Sensitivity analysis-based multi-objective calibration using machine learning emulators for integral water system models M. Mohan et al. https://doi.org/10.1080/02626667.2026.2674165
411. Understanding watershed hydrogeochemistry: 2. Synchronized hydrological and geochemical processes drive stream chemostatic behavior L. Li et al. https://doi.org/10.1002/2016WR018935
412. Projecting climate change impacts on stream flow regimes with tracer-aided runoff models - preliminary assessment of heterogeneity at the mesoscale R. Capell et al. https://doi.org/10.1002/hyp.9612
413. Snow water equivalent modeling components in NewAge-JGrass G. Formetta et al. https://doi.org/10.5194/gmd-7-725-2014
414. On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment M. Jay-Allemand et al. https://doi.org/10.5194/hess-24-5519-2020
415. GRHyMoLAP: A process-driven ODE catchment hydrology model inspired by GR4J and HyMoLAP approaches S. Ezéckiel Houénafa et al. https://doi.org/10.1016/j.jhydrol.2026.135597
416. Modelling nitrogen fluxes from the land to the coastal zone in European systems: a perspective from the INCA project A. Wade et al. https://doi.org/10.1016/j.jhydrol.2004.07.041
417. The influence of the spatial structure of soil properties on water balance modeling in a microscale catchment M. Herbst & B. Diekkrüger https://doi.org/10.1016/S1474-7065(02)00054-2
418. Estimation of stream discharge of ungauged basins using NRCS-CN and remote sensing methods: A case study of Okhuwan and Okhaihe catchments, Benin City, Edo State, Nigeria E. Isagba et al. https://doi.org/10.5897/IJWREE2021.1014
419. Pattern‐process relationships in surface hydrology: hydrological connectivity expressed in landscape metrics B. Van Nieuwenhuyse et al. https://doi.org/10.1002/hyp.8101
420. Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach F. Alduais et al. https://doi.org/10.1155/2022/3250499
421. Toward catchment hydro‐biogeochemical theories L. Li et al. https://doi.org/10.1002/wat2.1495
422. Towards Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning A. Sanjay Potdar et al. https://doi.org/10.1175/JHM-D-20-0302.1
423. Soil spatial scaling: Local measurements to global policy A. McBratney & B. Minasny https://doi.org/10.1016/j.still.2026.107378
424. Groundwater recharge estimation in the Ziway Lake Watershed, Ethiopian Rift: an approach using SWAT and CMB techniques A. Mechal et al. https://doi.org/10.1007/s40899-024-01108-0
425. Effects of single‐ and multi‐site calibration strategies on hydrological model performance and parameter sensitivity of large‐scale semi‐arid and semi‐humid watersheds L. Wu et al. https://doi.org/10.1002/hyp.14616
426. Long‐term Hydrological Forecasting in Cold Regions: Retrospect, Current Status and Prospect A. Gelfan & Y. Motovilov https://doi.org/10.1111/j.1749-8198.2009.00256.x
427. An improved routing algorithm for a large-scale distributed hydrological model with consideration of underlying surface impact J. Li et al. https://doi.org/10.2166/nh.2020.170
428. 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
429. BioRT-Flux-PIHM v1.0: a biogeochemical reactive transport model at the watershed scale W. Zhi et al. https://doi.org/10.5194/gmd-15-315-2022
430. Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. https://doi.org/10.1016/j.advwatres.2025.104955
431. 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
432. Streamflow Simulation Based on a Hybrid Morphometric–Satellite Methodological Framework D. Pérez-Campo et al. https://doi.org/10.3390/w18070786
433. A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1 P. Block et al. https://doi.org/10.1111/j.1752-1688.2009.00327.x
434. Differences in the water-balance components of four lakes in the southern-central Tibetan Plateau S. Biskop et al. https://doi.org/10.5194/hess-20-209-2016
435. Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins W. Sun et al. https://doi.org/10.5194/hess-21-251-2017
436. Estimation of flood inundation probabilities as conditioned on event inundation maps R. Romanowicz & K. Beven https://doi.org/10.1029/2001WR001056
437. High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation B. Fersch et al. https://doi.org/10.5194/hess-24-2457-2020
438. Mathematical Modelling for the Integrated Management of Water Resources in Hydrological Basins H. Silva-Hidalgo et al. https://doi.org/10.1007/s11269-008-9296-y
439. 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
440. Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances T. Conradt et al. https://doi.org/10.5194/hess-17-2947-2013
441. Factors controlling flooding at the Tonalá river mouth (Mexico) A. Pedrozo‐Acuña et al. https://doi.org/10.1111/j.1753-318X.2012.01142.x
442. Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling Y. Chiang et al. https://doi.org/10.1016/j.jhydrol.2003.12.033
443. Efeitos da resolução espacial do modelo VIC na previsão de vazão diária a usinas hidroelétricas do Rio Uruguai V. Siqueira Oliveira Carvalho et al. https://doi.org/10.36659/dae.2021.057
444. Comment on “Bayesian recursive parameter estimation for hydrologic models” by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian K. Beven & P. Young https://doi.org/10.1029/2001WR001183
445. Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes D. Xu et al. https://doi.org/10.1038/s41467-024-45286-z
446. Finite‐element modelling of physics‐based hillslope hydrology, Keith Beven, and beyond K. Loague & B. Ebel https://doi.org/10.1002/hyp.10762
447. A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information C. Yao et al. https://doi.org/10.1016/j.jhydrol.2012.08.025
448. Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change S. Ragettli et al. https://doi.org/10.1002/wrcr.20450
449. Estimation of a Critical Spatial Discretization Limit for Solving Richards' Equation at Large Scales H. Vogel & O. Ippisch https://doi.org/10.2136/vzj2006.0182
450. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. https://doi.org/10.1029/2021WR031794
451. Is a simple model based on two mixing reservoirs able to reproduce the intra-annual dynamics of DOC and NO3 stream concentrations in an agricultural headwater catchment? L. Strohmenger et al. https://doi.org/10.1016/j.scitotenv.2021.148715
452. Accelerating large-scale hydrological modeling with stepwise spatial-temporal multimember parallelization L. Jiang et al. https://doi.org/10.1016/j.envsoft.2025.106495
453. Simulating hydrologic and hydraulic processes throughout the Amazon River Basin R. Beighley et al. https://doi.org/10.1002/hyp.7252
454. The eWaterCycle platform for open and FAIR hydrological collaboration R. Hut et al. https://doi.org/10.5194/gmd-15-5371-2022
455. Advancing land surface model development with satellite-based Earth observations R. Orth et al. https://doi.org/10.5194/hess-21-2483-2017
456. Impacts of hydrometeorological factors on discharge simulation in the North West Himalayas: a SUFI-2 algorithm-driven investigation using the SWAT model T. Naqash et al. https://doi.org/10.1007/s10661-023-11916-0
457. Robust analysis of future climate change impacts on water for agriculture and other sectors: a case study in the Sacramento Valley D. Purkey et al. https://doi.org/10.1007/s10584-007-9375-8
458. A manifesto for the equifinality thesis K. Beven https://doi.org/10.1016/j.jhydrol.2005.07.007
459. Climatic signatures in regulated flow regimes across the Central and Eastern United States M. Ferrazzi et al. https://doi.org/10.1016/j.ejrh.2021.100809
460. Conceptual model building inspired by field-mapped runoff generation mechanisms A. Viglione et al. https://doi.org/10.2478/johh-2018-0010
461. A review of regionalisation for continuous streamflow simulation Y. He et al. https://doi.org/10.5194/hess-15-3539-2011
462. Data-model comparison of temporal variability in long-term time series of large-scale soil moisture L. Verrot & G. Destouni https://doi.org/10.1002/2016JD025209
463. Checking a process‐based catchment model by artificial neural networks G. Lischeid & S. Uhlenbrook https://doi.org/10.1002/hyp.1123
464. Effects of rainfall patterns on runoff and soil erosion in field plots M. Mohamadi & A. Kavian https://doi.org/10.1016/j.iswcr.2015.10.001
465. Identifying and assessing uncertainty in hydrological pathways: a novel approach to end member mixing in a Scottish agricultural catchment C. Soulsby et al. https://doi.org/10.1016/S0022-1694(02)00398-0
466. Remote sensing and flood inundation modelling P. Bates https://doi.org/10.1002/hyp.5649
467. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell https://doi.org/10.1002/wat2.1530
468. On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes S. Stisen et al. https://doi.org/10.5194/hess-16-4157-2012
469. Groundwater recharge modelling in semi-arid regions; a case study of El Khairat alluvial plain (Tunisia) N. Brini & M. Zammouri https://doi.org/10.1144/qjegh2015-084