226 citations as recorded by crossref.

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2. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. https://doi.org/10.1029/2021WR031860
3. Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments H. Canham et al. https://doi.org/10.5194/hess-29-27-2025
4. Typhoon Nina and the August 1975 Flood over Central China L. Yang et al. https://doi.org/10.1175/JHM-D-16-0152.1
5. Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement K. Fowler et al. https://doi.org/10.1029/2018WR023989
6. EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. https://doi.org/10.5194/gmd-17-4561-2024
7. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton https://doi.org/10.1029/2021WR031575
8. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. https://doi.org/10.1029/2020WR028086
9. CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking O. Delaigue et al. https://doi.org/10.5194/essd-17-1461-2025
10. Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics S. Zhu et al. https://doi.org/10.1016/j.jhydrol.2024.131586
11. On the ability of large-scale hydrological models to simulate land use and land cover change impacts in Amazonian basins D. Rodriguez & J. Tomasella https://doi.org/10.1080/02626667.2015.1051979
12. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. https://doi.org/10.5194/essd-12-2459-2020
13. Robustness of a parsimonious subsurface drainage model at the French national scale A. Jeantet et al. https://doi.org/10.5194/hess-25-5447-2021
14. Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. https://doi.org/10.1029/2023JD038534
15. HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation A. Bahremand https://doi.org/10.5194/hess-20-1433-2016
16. From empirical to physical constraints: Revisiting the structure of monthly water balance models with global evaluation Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2026.134916
17. A review of the current state of process-based and data-driven modelling: guidelines for Lake Erie managers and watershed modellers A. Neumann et al. https://doi.org/10.1139/er-2020-0070
18. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar https://doi.org/10.5194/hess-26-2019-2022
19. Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region H. Gao et al. https://doi.org/10.1002/2016WR019574
20. How does wildfire and climate variability affect streamflow in forested catchments? A regional study in eastern Australia D. Guo et al. https://doi.org/10.1016/j.jhydrol.2023.129979
21. Hydrological design in the HELPING decade – inspiring the community to innovate the hydrological design concept S. Fischer et al. https://doi.org/10.1080/02626667.2024.2436634
22. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. https://doi.org/10.5194/hess-24-2017-2020
23. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. https://doi.org/10.5194/essd-15-4463-2023
24. Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics O. Jaffar et al. https://doi.org/10.1016/j.ejrh.2024.101899
25. Towards seamless large‐domain parameter estimation for hydrologic models N. Mizukami et al. https://doi.org/10.1002/2017WR020401
26. Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters J. Romero-Cuellar et al. https://doi.org/10.3390/w14081261
27. Spatial Combination Modeling Framework of Saturation-Excess and Infiltration-Excess Runoff for Semihumid Watersheds P. Huang et al. https://doi.org/10.1155/2016/5173984
28. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case I. Pechlivanidis & B. Arheimer https://doi.org/10.5194/hess-19-4559-2015
29. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance A. Newman et al. https://doi.org/10.5194/hess-19-209-2015
30. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. https://doi.org/10.1002/2015WR017635
31. MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models R. Schweppe et al. https://doi.org/10.5194/gmd-15-859-2022
32. Assessing the Spatial Transferability of Annual Soil Erosion Models Across Mediterranean Catchments Using Hierarchical and Ensemble Approaches C. Patino-Alonso et al. https://doi.org/10.3390/w18182255
33. A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in New Zealand C. Cattoën et al. https://doi.org/10.1111/jfr3.12864
34. A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models F. Zheng et al. https://doi.org/10.1029/2022WR033703
35. Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. https://doi.org/10.1016/j.jhydrol.2021.126394
36. Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models Y. Choi et al. https://doi.org/10.1016/j.envsoft.2024.106239
37. Archetypes and Controls of Riverine Nutrient Export Across German Catchments P. Ebeling et al. https://doi.org/10.1029/2020WR028134
38. A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations G. Coxon et al. https://doi.org/10.1002/2014WR016532
39. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. https://doi.org/10.5194/hess-23-2601-2019
40. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al. https://doi.org/10.5194/essd-17-4613-2025
41. Seasonal streamflow forecasting in South America’s largest rivers I. Petry et al. https://doi.org/10.1016/j.ejrh.2023.101487
42. Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al. https://doi.org/10.1016/j.ejrh.2025.102257
43. Performance assessment of methods to estimate initial hydrologic conditions for event-based rainfall-runoff modelling V. Manikanta & N. Umamahesh https://doi.org/10.2166/wcc.2023.043
44. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. https://doi.org/10.1038/s41597-023-01975-w
45. 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
46. Hydrology research articles are becoming more topically diverse M. Rahman et al. https://doi.org/10.1016/j.jhydrol.2022.128551
47. Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia S. Clark et al. https://doi.org/10.5194/hess-28-1191-2024
48. Panta Rhei 2013–2015: global perspectives on hydrology, society and change H. McMillan et al. https://doi.org/10.1080/02626667.2016.1159308
49. Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza https://doi.org/10.1016/j.ejrh.2025.102390
50. Integrating remote sensing data in optimization of a national water resources model to improve the spatial pattern performance of evapotranspiration M. Soltani et al. https://doi.org/10.1016/j.jhydrol.2021.127026
51. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. https://doi.org/10.1016/j.jhydrol.2018.09.027
52. Replication of ecologically relevant hydrological indicators following a modified covariance approach to hydrological model parameterization A. Visser-Quinn et al. https://doi.org/10.5194/hess-23-3279-2019
53. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. https://doi.org/10.5194/hess-25-5517-2021
54. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen https://doi.org/10.5194/essd-16-2741-2024
55. Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent I. Pechlivanidis et al. https://doi.org/10.3390/w8050177
56. CHOSEN: A synthesis of hydrometeorological data from intensively monitored catchments and comparative analysis of hydrologic extremes L. Zhang et al. https://doi.org/10.1002/hyp.14429
57. Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? A. Piotrowski et al. https://doi.org/10.1080/02626667.2016.1234712
58. 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
59. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. https://doi.org/10.1016/j.ejrh.2023.101478
60. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
61. Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds T. Mathevet et al. https://doi.org/10.5802/crgeos.189
62. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al. https://doi.org/10.11922/11-6035.csd.2025.0144.zh
63. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al. https://doi.org/10.1038/s41597-020-00583-2
64. A Systematic Evaluation of Noah‐MP in Simulating Land‐Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States N. Ma et al. https://doi.org/10.1002/2017JD027597
65. Characteristics and controls of variability in soil moisture and groundwater in a headwater catchment H. McMillan & M. Srinivasan https://doi.org/10.5194/hess-19-1767-2015
66. Parameter regionalization of large-scale distributed rainfall–runoff models using a conditional probability method T. Sayama et al. https://doi.org/10.1186/s40645-025-00691-w
67. On generalization, language, interpretability and the future of geo-scientific machine learning H. Gupta https://doi.org/10.1016/j.envsoft.2025.106834
68. The CAMELS data set: catchment attributes and meteorology for large-sample studies N. Addor et al. https://doi.org/10.5194/hess-21-5293-2017
69. Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set L. Santos et al. https://doi.org/10.1080/02626667.2022.2035388
70. Global‐scale regionalization of hydrologic model parameters H. Beck et al. https://doi.org/10.1002/2015WR018247
71. Multi-model approach in a variable spatial framework for streamflow simulation C. Thébault et al. https://doi.org/10.5194/hess-28-1539-2024
72. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. https://doi.org/10.1175/JHM-D-16-0284.1
73. A process‐based approach to attribution of historical streamflow decline in a data‐scarce and human‐dominated watershed G. Penny et al. https://doi.org/10.1002/hyp.13707
74. Validation of a national hydrological model H. McMillan et al. https://doi.org/10.1016/j.jhydrol.2016.07.043
75. 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
76. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. https://doi.org/10.1029/2019WR026987
77. Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins O. Rakovec et al. https://doi.org/10.1175/JHM-D-15-0054.1
78. Large-sample detection of reservoir impacts on flow regime alteration through improved paired-catchment approach X. Bai et al. https://doi.org/10.1016/j.jhydrol.2024.131872
79. Global evaluation of runoff from 10 state-of-the-art hydrological models H. Beck et al. https://doi.org/10.5194/hess-21-2881-2017
80. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. https://doi.org/10.1002/hyp.14288
81. Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model R. Lane et al. https://doi.org/10.1029/2020WR028393
82. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan https://doi.org/10.1002/hyp.15215
83. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
84. Heteroscedastic and symmetric efficiency for hydrological model evaluation criteria C. Zhan et al. https://doi.org/10.2166/nh.2019.121
85. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. https://doi.org/10.1029/2020WR027948
86. Temporal interpolation of groundwater level hydrographs for regional drought analysis using mixed models B. Marchant et al. https://doi.org/10.1007/s10040-022-02528-y
87. Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models C. Thébault et al. https://doi.org/10.1080/02626667.2023.2234893
88. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. https://doi.org/10.5194/hess-22-5817-2018
89. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. https://doi.org/10.1016/j.gsf.2022.101349
90. Testing a low-complexity spatially distributed model to simulate the intra-annual dynamics of soil erosion and sediment delivery F. Matthews et al. https://doi.org/10.1016/j.catena.2025.109054
91. On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation D. Guo et al. https://doi.org/10.1029/2019WR026752
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93. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. https://doi.org/10.1002/2016WR019430
94. All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling A. Pedersen et al. https://doi.org/10.5194/hess-26-5879-2022
95. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. https://doi.org/10.5194/essd-14-3715-2022
96. Bushfire‐Induced Water Balance Changes Detected by a Modified Paired Catchment Method Z. Xu et al. https://doi.org/10.1029/2021WR031013
97. On the visual detection of non-natural records in streamflow time series: challenges and impacts L. Strohmenger et al. https://doi.org/10.5194/hess-27-3375-2023
98. Improving the theoretical underpinnings of process‐based hydrologic models M. Clark et al. https://doi.org/10.1002/2015WR017910
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102. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. https://doi.org/10.1029/2021WR030619
103. Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments B. Guse et al. https://doi.org/10.1016/j.jhydrol.2018.12.050
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108. The Impacts of Native Forests and Forest Plantations on Water Supply in Chile C. Alvarez-Garreton et al. https://doi.org/10.3390/f10060473
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110. Evaluating a finer resolution global hydrological model’s simulation of discharge in four West-African river basins T. Babalola et al. https://doi.org/10.1007/s40808-020-00948-x
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112. Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations S. Yatheendradas et al. https://doi.org/10.1007/s10596-018-9804-y
113. Moving sociohydrology forward: a synthesis across studies T. Troy et al. https://doi.org/10.5194/hess-19-3667-2015
114. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. https://doi.org/10.1002/2015WR017498
115. Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States C. Simeone et al. https://doi.org/10.3390/w16202996
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117. Looking beyond general metrics for model comparison – lessons from an international model intercomparison study T. de Boer-Euser et al. https://doi.org/10.5194/hess-21-423-2017
118. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. https://doi.org/10.1029/2021WR031751
119. Revisiting the Basis of Sensitivity Analysis for Dynamical Earth System Models H. Gupta & S. Razavi https://doi.org/10.1029/2018WR022668
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123. Event‐based classification for global study of river flood generating processes L. Stein et al. https://doi.org/10.1002/hyp.13678
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128. The September 2024 Danube flood compared to the 1899, 2002, and 2013 events: a hydrometeorological analysis in a changing climate J. Komma et al. https://doi.org/10.5194/hess-30-4075-2026
129. Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models K. Fowler et al. https://doi.org/10.1002/2015WR018068
130. Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison K. Sink & T. Brikowski https://doi.org/10.5194/hess-29-4015-2025
131. Challenges of Operational River Forecasting T. Pagano et al. https://doi.org/10.1175/JHM-D-13-0188.1
132. Using R in hydrology: a review of recent developments and future directions L. Slater et al. https://doi.org/10.5194/hess-23-2939-2019
133. The rise of the Nash-Sutcliffe efficiency in hydrology L. Melsen et al. https://doi.org/10.1080/02626667.2025.2475105
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135. Seasonal streamflow forecasting by conditioning climatology with precipitation indices L. Crochemore et al. https://doi.org/10.5194/hess-21-1573-2017
136. Quantifying Positive and Negative Human-Modified Droughts in the Anthropocene: Illustration with Two Iranian Catchments E. Kakaei et al. https://doi.org/10.3390/w11050884
137. Evaluating the Influence of Topographic and Process Representation on Runoff and River Flow in Small‐Valley Scenarios Using a Coupled Rainfall–Runoff–Inundation and River‐Flow Model T. Ito et al. https://doi.org/10.1111/jfr3.70034
138. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling H. Beck et al. https://doi.org/10.5194/hess-21-6201-2017
139. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. https://doi.org/10.1080/02626667.2021.1923720
140. Diagnosis of insidious data disasters J. Lundquist et al. https://doi.org/10.1002/2014WR016585
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147. Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification S. Gharari et al. https://doi.org/10.5194/hess-28-4383-2024
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150. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments P. Nicolle et al. https://doi.org/10.5194/hess-18-2829-2014
151. Étude de la sensibilité des paramètres d'un modèle «rural» sur des bassins versants urbanisés M. Saadi et al. https://doi.org/10.1051/lhb/2019013
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153. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al. https://doi.org/10.5194/hess-24-1543-2020
154. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. https://doi.org/10.5194/hess-23-5089-2019
155. 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
156. Using Natural Experiments and Counterfactuals for Causal Assessment: River Salinity and the Ganges Water Agreement G. Penny et al. https://doi.org/10.1029/2019WR026166
157. Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models P. Wagener et al. https://doi.org/10.5194/hess-30-4867-2026
158. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. https://doi.org/10.1002/hyp.13808
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