169 citations as recorded by crossref.

1. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. https://doi.org/10.5194/hess-28-3665-2024
2. New objective functions for streamflow calibrations to specific model applications H. Il Choi https://doi.org/10.1016/j.jhydrol.2024.132109
3. Variability in flow and tracer-based performance metric sensitivities reveal regional differences in dominant hydrological processes across the Athabasca River basin T. Holmes et al. https://doi.org/10.1016/j.ejrh.2022.101088
4. Hydrological Model Calibration in Data-Scarce Mediterranean Catchments: A Comparative Assessment of Three Strategies A. Jahanshahi et al. https://doi.org/10.3390/hydrology13020066
5. Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system A. Newman et al. https://doi.org/10.5194/hess-25-5603-2021
6. Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice G. Williams https://doi.org/10.1016/j.envsoft.2025.106665
7. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. https://doi.org/10.1061/JHYEFF.HEENG-6246
8. Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index J. Li et al. https://doi.org/10.1175/JHM-D-19-0298.1
9. Hydrological Whiplash: Highlighting the Need for Better Understanding and Quantification of Sub‐Seasonal Hydrological Extreme Transitions J. Hammond et al. https://doi.org/10.1002/hyp.70113
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11. How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al. https://doi.org/10.5194/hess-30-825-2026
12. A rational performance criterion for hydrological model D. Liu https://doi.org/10.1016/j.jhydrol.2020.125488
13. Predicting annual peak daily streamflow in natural basins using quantile regression forests K. Kim et al. https://doi.org/10.1016/j.jhydrol.2025.133233
14. The Role of Snowmelt Temporal Pattern in Flood Estimation for a Small Snow‐Dominated Basin in the Sierra Nevada H. Yan et al. https://doi.org/10.1029/2023WR034496
15. Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. https://doi.org/10.5194/hess-25-105-2021
16. Evaluation of the hydrological utility of the GPM IMERG satellite precipitation products H. Meng & T. Zhao https://doi.org/10.1016/j.atmosres.2025.108139
17. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. https://doi.org/10.1016/j.jhydrol.2021.127043
18. Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions T. Nguyen et al. https://doi.org/10.1029/2021WR030797
19. Applicability of an analytical probabilistic stormwater management model for large rural watersheds L. McGregor & S. Hassini https://doi.org/10.1016/j.jhydrol.2026.135770
20. A Data- and Knowledge-Driven Method for Fusing Satellite-Derived and Ground-Based Precipitation Observations F. Chen et al. https://doi.org/10.1109/TGRS.2024.3385647
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22. Interpretable residual-informed hybrid physics-informed machine learning framework for runoff prediction in a typical karst basin in southwest China C. Mo et al. https://doi.org/10.1016/j.envsoft.2026.106986
23. Streamflow simulation improvements enabled by a state‐of‐the‐art algorithm for reservoir routing in the U.S. National Water Model D. Li et al. https://doi.org/10.1111/1752-1688.13244
24. Exploring an Alternative Configuration of the Hydroclimatic Modeling Chain, Based on the Notion of Asynchronous Objective Functions S. Ricard et al. https://doi.org/10.3390/w11102012
25. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al. https://doi.org/10.5194/hess-23-4323-2019
26. Ten strategies towards successful calibration of environmental models J. Mai https://doi.org/10.1016/j.jhydrol.2023.129414
27. Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. https://doi.org/10.1029/2019JD031485
28. Guidance on large scale hydrologic model calibration with isotope tracers T. Holmes et al. https://doi.org/10.1016/j.jhydrol.2023.129604
29. Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size Y. Wan et al. https://doi.org/10.1016/j.jhydrol.2021.127065
30. Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models W. Knoben et al. https://doi.org/10.1029/2021WR031753
31. Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability S. Nemri & C. Kinnard https://doi.org/10.1016/j.jhydrol.2019.124474
32. Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall–runoff models E. Muñoz-Castro et al. https://doi.org/10.1080/02626667.2023.2231434
33. Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework H. Llauca et al. https://doi.org/10.1016/j.ejrh.2023.101381
34. Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets? T. Hallouin et al. https://doi.org/10.5194/hess-24-1031-2020
35. When best is the enemy of good – critical evaluation of performance criteria in hydrological models G. Cinkus et al. https://doi.org/10.5194/hess-27-2397-2023
36. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al. https://doi.org/10.5194/hess-28-611-2024
37. Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
38. Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile P. Acuña & A. Pizarro https://doi.org/10.1016/j.jhydrol.2023.130118
39. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. https://doi.org/10.3390/hydrology8010006
40. Forcing uncertainty and salinity response to dredging in a tidal freshwater river R. Miller https://doi.org/10.1080/15715124.2021.1961795
41. Multi-step regional rainfall-runoff modeling using pyramidal transformer H. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.132935
42. Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework R. Vogeti et al. https://doi.org/10.2166/wcc.2024.594
43. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. https://doi.org/10.1016/j.jhydrol.2024.131434
44. Quality Assessment of Small Urban Catchments Stormwater Models: A New Approach Using Old Metrics L. David & T. Mota https://doi.org/10.3390/hydrology9050087
45. A time-varying distributed unit hydrograph method considering soil moisture B. Yi et al. https://doi.org/10.5194/hess-26-5269-2022
46. Dual-Phase Calibration for Surface–Subsurface Hydrologic Models with Diverse Hydrologic Conditions S. Abbas et al. https://doi.org/10.1061/JHYEFF.HEENG-6479
47. Frequency-based performance measure for hydrologic model evaluation R. Teegavarapu et al. https://doi.org/10.1016/j.jhydrol.2022.127583
48. Evaluating the contributions of climate change and human activities to runoff in typical semi-arid area, China L. Wu et al. https://doi.org/10.1016/j.jhydrol.2020.125555
49. An integrated approach to streamflow estimation and flood inundation mapping using VIC, RAPID and LISFLOOD-FP S. Nandi & M. Reddy https://doi.org/10.1016/j.jhydrol.2022.127842
50. Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process K. Chong et al. https://doi.org/10.1007/s13201-022-01790-5
51. A watershed-scale multi-approach assessment of design flood discharge estimates used in hydrologic risk analyses for forest road stream crossings and culverts S. Mukherjee et al. https://doi.org/10.1016/j.jhydrol.2024.130698
52. Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins A. Pedersen et al. https://doi.org/10.2166/wst.2022.059
53. ‘Teflon Basin’ or Not? A High-Elevation Catchment Transit Time Modeling Approach J. Schmieder et al. https://doi.org/10.3390/hydrology6040092
54. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. https://doi.org/10.1016/j.jhydrol.2024.130861
55. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. https://doi.org/10.1016/j.jhydrol.2022.127781
56. Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia D. Ratri et al. https://doi.org/10.1016/j.ejrh.2022.101305
57. Towards a generic model evaluation metric for non-normally distributed measurements in water quality and ecosystem models T. Fu & C. Zhang https://doi.org/10.1016/j.ecoinf.2024.102470
58. Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin C. Nickles & E. Beighley https://doi.org/10.3390/rs13081590
59. Harnessing multi-source hydro-meteorological data for high flows modelling in a partially glacierized Himalayan basin D. De Santis et al. https://doi.org/10.5194/nhess-26-1075-2026
60. Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2024.130829
61. Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM H. Tran et al. https://doi.org/10.1016/j.jhydrol.2025.133750
62. Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. https://doi.org/10.1016/j.ejrh.2023.101418
63. Revising calibration of a lumped watershed model to simulate extreme streamflow A. Patil et al. https://doi.org/10.1088/3033-4942/ae2654
64. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. https://doi.org/10.1016/j.advwatres.2023.104560
65. A hybrid hydrologic modeling framework-role of spatial resolution, calibration approaches and error modeling S. Guniganti et al. https://doi.org/10.1016/j.ejrh.2025.103053
66. Enhancing flood event predictions: Multi-objective calibration using gauge and satellite data S. Gegenleithner et al. https://doi.org/10.1016/j.jhydrol.2024.130879
67. Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach D. Liu et al. https://doi.org/10.1016/j.jenvman.2024.121433
68. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
69. 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
70. Enhanced Streamflow Prediction Using a Hybrid CNN-LSTM Model: Integrating Climate-Driven Spatio-Temporal Features in a Deep Learning Framework S. Shahariar et al. https://doi.org/10.1007/s11269-026-04491-9
71. Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis M. Montgomery et al. https://doi.org/10.3390/w16060865
72. Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow J. Hidalgo-Hidalgo et al. https://doi.org/10.1007/s41748-025-00975-7
73. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. https://doi.org/10.1029/2020WR029001
74. Which range of streamflow data is most informative in the calibration of an hourly hydrological model? M. Saadi & C. Furusho-Percot https://doi.org/10.1080/02626667.2023.2277835
75. A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019 H. Tran et al. https://doi.org/10.1038/s41597-022-01123-w
76. How could climate change affect the magnitude, duration and frequency of hydrological droughts and floods in West Africa during the 21st century? A storyline approach J. Ekolu et al. https://doi.org/10.1016/j.jhydrol.2025.133482
77. Requirements for Flood-Driven Forecasting Systems for Small and Medium-Sized Catchments in Germany J. Leandro et al. https://doi.org/10.3390/w17223283
78. Performance Evaluation of the SRM and GRxJ—CemaNeige Models for Daily Streamflow Simulation in Two Catchments with Snow and Rain Dominated Hydrological Regimes B. Rivas et al. https://doi.org/10.3390/w17233413
79. Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling K. Dimitrova-Petrova et al. https://doi.org/10.1016/j.jhydrol.2020.124878
80. Instream constructed wetland capacity at controlling phosphorus outflow under a long‐term nutrient loading scenario: approach using SWAT model C. Sohoulande Djebou et al. https://doi.org/10.1007/s40808-023-01763-w
81. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. https://doi.org/10.3390/w16050777
82. A review of hydrologic signatures and their applications H. McMillan https://doi.org/10.1002/wat2.1499
83. A novel multi-objective model calibration method for ecohydrological applications J. Hernandez-Suarez et al. https://doi.org/10.1016/j.envsoft.2021.105161
84. Assessment of Different Satellite-Based Precipitation Products Over Southwestern Haiti M. Clerge Pierre & S. Taner https://doi.org/10.18475/cjos.v55i2.a12
85. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. https://doi.org/10.1016/j.envsoft.2020.104926
86. Lead-time-dependent calibration of a flood forecasting model P. Astagneau et al. https://doi.org/10.1016/j.jhydrol.2024.132119
87. Flood frequency sampling error: insights from regional analysis, stochastic storm transposition, and physics-based modeling M. Abbasian et al. https://doi.org/10.1016/j.jhydrol.2025.133802
88. Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration A. Pizarro et al. https://doi.org/10.5194/hess-29-4913-2025
89. Applications of machine learning for peripheral artery disease diagnosis and management: A systematic review N. Aant et al. https://doi.org/10.1016/j.compbiomed.2025.110744
90. A flood peak prediction in data-scarce mountain river basins considering the time distribution of rainfall S. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135352
91. Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater https://doi.org/10.5194/hess-26-469-2022
92. On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. https://doi.org/10.5194/hess-25-5805-2021
93. Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning A. Bennett et al. https://doi.org/10.3390/w16141949
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95. Performance of different reanalysis rainfall datasets and hydrological models in gap-filling for Mpologoma catchment in Eastern Uganda M. Twinomuhangi et al. https://doi.org/10.1016/j.ejrh.2026.103416
96. Influence of Calibration Parameter Selection on Flash Flood Simulation for Small to Medium Catchments with MISDc-2L Model N. Nguyen et al. https://doi.org/10.3390/w12113255
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98. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. https://doi.org/10.5194/hess-28-4187-2024
99. OpenBench: a land model evaluation system Z. Wei et al. https://doi.org/10.5194/gmd-18-6517-2025
100. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. https://doi.org/10.1016/j.jhydrol.2020.124698
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102. 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
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104. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
105. Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites H. Cheng et al. https://doi.org/10.3390/w13030388
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109. Flood modelling in a montane basin using machine learning, deep learning and sensor network data D. Ba & J. Langhammer https://doi.org/10.2166/hydro.2026.175
110. Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin J. Towner et al. https://doi.org/10.5194/hess-23-3057-2019
111. Over 100 global climate sensitive rivers are experiencing large and severe changes in streamflow volume and timing R. Patil et al. https://doi.org/10.1088/1748-9326/ae1e16
112. Modelling the impacts of future coal mining and coal seam gas extraction on river flows: A methodological framework N. Viney et al. https://doi.org/10.1016/j.jhydrol.2021.126144
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117. Introducing the Model Fidelity Metric (MFM) for robust and diagnostic land surface model evaluation Z. Wu et al. https://doi.org/10.5194/hess-30-2651-2026
118. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. https://doi.org/10.3390/w17030339
119. The effect of the construction method on the tombolo process at Palabuhanratu seaport E. Nugroho et al. https://doi.org/10.1088/1755-1315/1464/1/012019
120. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler https://doi.org/10.5194/hess-26-3299-2022
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125. Hydrological performance of the ERA5 reanalysis for flood modeling in Tunisia with the LISFLOOD and GR4J models E. Cantoni et al. https://doi.org/10.1016/j.ejrh.2022.101169
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127. Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths M. Fathi et al. https://doi.org/10.1038/s41598-025-96577-4
128. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. https://doi.org/10.3390/w13081048
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131. A comprehensive method for error separation in hydrological modelling Y. Zhao et al. https://doi.org/10.1002/hyp.15273
132. A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria F. Willkofer et al. https://doi.org/10.3390/w12092349
133. Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula J. Lee & H. Choi https://doi.org/10.3390/w13121709
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147. Evaluating an Earth system model from a water manager perspective M. Tye et al. https://doi.org/10.5194/hess-29-1117-2025
148. A hydrological model skill score and revised R-squared C. Onyutha https://doi.org/10.2166/nh.2021.071
149. A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. https://doi.org/10.1002/hyp.14446
150. 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
151. Representing hydrological processes in a Norwegian catchment using SWAT+ : leveraging soft data as process-based constraints in calibration M. Shafiei et al. https://doi.org/10.2166/nh.2026.190
152. Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. https://doi.org/10.3390/w15223944
153. Improving process-based prediction of stream water temperature in SWAT using semi-Lagrangian formulation N. Melaku et al. https://doi.org/10.1016/j.jhydrol.2024.132612
154. Regional-scale assessment of groundwater recharge and the water balance for Austria H. Zeitfogel et al. https://doi.org/10.1016/j.ejrh.2025.102297
155. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. https://doi.org/10.5194/hess-25-2187-2021
156. Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models? B. Majone et al. https://doi.org/10.5194/hess-26-3863-2022
157. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. https://doi.org/10.1080/02626667.2022.2104646
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