134 citations as recorded by crossref.

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4. The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long‐Term Streamflow Components A. Ballarin et al. https://doi.org/10.1029/2022WR032413
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8. Characterising baseflow signature variability in the Yellow River Basin S. Lyu et al. https://doi.org/10.1016/j.jenvman.2023.118565
9. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. https://doi.org/10.1029/2018WR022913
10. 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
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12. A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments D. Kim et al. https://doi.org/10.5194/hess-21-5647-2017
13. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. https://doi.org/10.1002/hyp.14987
14. Enhancing the hydrologic system differential response method for flood forecasting correction X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2020.125793
15. Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment? B. Quesada‐Montano et al. https://doi.org/10.1002/hyp.11460
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18. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell https://doi.org/10.1002/wat2.1530
19. Hydrological climate change impact assessment in high latitudes: are bucket-type models up to the task? A. Todorović et al. https://doi.org/10.1016/j.envsoft.2026.106964
20. Panta Rhei 2013–2015: global perspectives on hydrology, society and change H. McMillan et al. https://doi.org/10.1080/02626667.2016.1159308
21. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.04.070
22. 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
23. Event-scale power law recession analysis: quantifying methodological uncertainty D. Dralle et al. https://doi.org/10.5194/hess-21-65-2017
24. Scalable deep learning for watershed model calibration M. Mudunuru et al. https://doi.org/10.3389/feart.2022.1026479
25. Sharing perceptual models of uncertainty: On the use of soft information about discharge data I. Westerberg & R. Karlsen https://doi.org/10.1002/hyp.15145
26. Assessment of bottom-up satellite rainfall products on estimating river discharge and hydrologic signatures in Brazilian catchments A. Almagro et al. https://doi.org/10.1016/j.jhydrol.2021.126897
27. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. https://doi.org/10.3390/rs13030333
28. An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale A. Tashie et al. https://doi.org/10.1029/2019WR025448
29. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model S. Mohammed et al. https://doi.org/10.3390/w13070970
30. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan https://doi.org/10.1029/2022WR033091
31. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. https://doi.org/10.1002/2015WR017635
32. Signature‐Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Empirical Analysis of Fundamental Properties F. Fenicia et al. https://doi.org/10.1002/2017WR021616
33. 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
34. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. https://doi.org/10.1029/2020WR027948
35. What is the near-natural catchment? An application of hydrological signatures assessment H. Xu et al. https://doi.org/10.1016/j.ecolind.2025.113209
36. Tradeoff between economic and environmental costs and benefits of hydropower production at run-of-river-diversion schemes under different environmental flows scenarios M. Bejarano et al. https://doi.org/10.1016/j.jhydrol.2019.03.048
37. Informing hydrological models of poorly gauged river catchments – A parameter regionalization and calibration approach C. Kittel et al. https://doi.org/10.1016/j.jhydrol.2020.124999
38. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
39. Regional Modeling of Long-Term and Annual Flow Duration Curves: Reliability for Information Transfer with Evolutionary Polynomial Regression V. Costa & W. Fernandes https://doi.org/10.1061/(ASCE)HE.1943-5584.0002051
40. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. https://doi.org/10.1080/02626667.2022.2104646
41. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. https://doi.org/10.1016/j.scitotenv.2023.166422
42. Using Functional Data Analysis to Calibrate and Evaluate Hydrological Model Performance S. Larabi et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001669
43. Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review Y. Guo et al. https://doi.org/10.1002/wat2.1487
44. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt https://doi.org/10.1029/2022WR032263
45. Nonlinear control of climate, hydrology, and topography on streamflow response through the use of interpretable machine learning across the contiguous United States Y. Wu & N. Li https://doi.org/10.2166/wcc.2023.279
46. A hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models: a case study in the Ren River basin in China S. Liu et al. https://doi.org/10.1007/s00477-022-02282-3
47. Accelerating advances in continental domain hydrologic modeling S. Archfield et al. https://doi.org/10.1002/2015WR017498
48. Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model A. Santos et al. https://doi.org/10.1002/hyp.13425
49. Hydropedological clustering: improving the representation of low streamflows in a semi-distributed hydrological model F. Gimeno et al. https://doi.org/10.1016/j.jhydrol.2025.134787
50. Assessment of water availability vulnerability in the Cerrado D. Althoff et al. https://doi.org/10.1007/s13201-021-01521-2
51. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. https://doi.org/10.5194/hess-25-5355-2021
52. Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.jhydrol.2023.129807
53. 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
54. Modeling and Forecasting Vibrio Parahaemolyticus Concentrations in Oysters P. Namadi & Z. Deng https://doi.org/10.1016/j.watres.2020.116638
55. A Simple Approach to Account for Stage–Discharge Uncertainty in Hydrological Modelling R. Vázquez & H. Hampel https://doi.org/10.3390/w14071045
56. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. https://doi.org/10.1029/2021WR031751
57. 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
58. Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures M. Vanderhoof et al. https://doi.org/10.1007/s11273-025-10066-z
59. Linking hydrologic signatures to hydrologic processes: A review H. McMillan https://doi.org/10.1002/hyp.13632
60. Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment X. Yang et al. https://doi.org/10.1029/2020WR029094
61. Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty S. Khatami et al. https://doi.org/10.1029/2018WR023750
62. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. https://doi.org/10.5194/hess-25-3105-2021
63. Probabilistic Predictions of Ecologically Relevant Hydrologic Indices Using a Hydrological Model J. Hernandez‐Suarez & A. Nejadhashemi https://doi.org/10.1029/2021WR031104
64. Enhancing urban runoff modelling using water stable isotopes and ages in complex catchments A. Smith et al. https://doi.org/10.1002/hyp.14814
65. High-resolution hydrometeorological data from a network of headwater catchments in the tropical Andes B. Ochoa-Tocachi et al. https://doi.org/10.1038/sdata.2018.80
66. The 3DNet-Catch hydrologic model: Development and evaluation A. Todorović et al. https://doi.org/10.1016/j.jhydrol.2018.10.040
67. Trade-offs between parameter constraints and model realism: a case study F. Jehn et al. https://doi.org/10.1038/s41598-019-46963-6
68. Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica S. Arciniega-Esparza et al. https://doi.org/10.5194/hess-26-975-2022
69. How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications H. McMillan et al. https://doi.org/10.1002/2016WR020328
70. 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
71. 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
72. Bayesian network modeling of the water-energy-food nexus: the case of the Peace River Basin, Canada L. Wu et al. https://doi.org/10.1007/s10113-025-02465-y
73. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. https://doi.org/10.5194/essd-12-2075-2020
74. A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China S. Pan et al. https://doi.org/10.1080/02626667.2017.1388917
75. Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G. Papacharalampous et al. https://doi.org/10.1016/j.scitotenv.2020.144612
76. Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations I. Horner et al. https://doi.org/10.1002/hyp.14497
77. An improved method to estimate the rate of change of streamflow recession and basin synthetic recession parameters from hydrographs M. Gao et al. https://doi.org/10.1016/j.jhydrol.2021.127254
78. Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment M. Masood et al. https://doi.org/10.1155/2023/1685720
79. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al. https://doi.org/10.1007/s11442-024-2249-4
80. Model averaging versus model selection: estimating design floods with uncertain river flow data K. Okoli et al. https://doi.org/10.1080/02626667.2018.1546389
81. Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin Z. Mei et al. https://doi.org/10.1007/s11269-024-03975-w
82. A new fractal-theory-based criterion for hydrological model calibration Z. Bai et al. https://doi.org/10.5194/hess-25-3675-2021
83. Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model T. Sharannya et al. https://doi.org/10.3390/w12092400
84. Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers L. Santos et al. https://doi.org/10.5194/hess-29-683-2025
85. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. https://doi.org/10.1029/2019WR026987
86. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. https://doi.org/10.5194/hess-25-2045-2021
87. The Impact of the Uncertain Input Data of Multi-Purpose Reservoir Volumes under Hydrological Extremes S. Paseka & D. Marton https://doi.org/10.3390/w13101389
88. Characterizing Watersheds to Support Land-use Planning in Indonesia: A Case Study of Brantas Tropical Watershed B. Wiwoho et al. https://doi.org/10.1016/j.ecohyd.2023.06.001
89. A comprehensive intercomparison study between a lumped and a fully distributed hydrological model across a set of 50 catchments in the United Kingdom S. Sinha et al. https://doi.org/10.1002/hyp.14544
90. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. https://doi.org/10.5194/essd-13-4529-2021
91. Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends M. Hameed et al. https://doi.org/10.1029/2023WR035829
92. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. https://doi.org/10.1016/j.jhydrol.2024.131774
93. Assessing Hydrograph Similarity and Rare Runoff Dynamics by Cross Recurrence Plots D. Wendi et al. https://doi.org/10.1029/2018WR024111
94. Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia C. Sezen & M. Šraj https://doi.org/10.1007/s00477-023-02607-w
95. Signature‐Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Theory and Comparison to Existing Applications D. Kavetski et al. https://doi.org/10.1002/2017WR020528
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97. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al. https://doi.org/10.5194/hess-25-2685-2021
98. Uncertainty Impact on Water Management Analysis of Open Water Reservoir D. Marton & S. Paseka https://doi.org/10.3390/environments4010010
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100. 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
101. Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. https://doi.org/10.5194/gmd-14-821-2021
102. 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
103. HESS Opinions: The complementary merits of competing modelling philosophies in hydrology M. Hrachowitz & M. Clark https://doi.org/10.5194/hess-21-3953-2017
104. Models of everywhere revisited: A technological perspective G. Blair et al. https://doi.org/10.1016/j.envsoft.2019.104521
105. Propagation of rating curve uncertainty in design flood estimation G. Steinbakk et al. https://doi.org/10.1002/2015WR018516
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108. A review of hydrologic signatures and their applications H. McMillan https://doi.org/10.1002/wat2.1499
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114. Past, present, and future of the Hurst-Kolmogorov dynamics in Stochastics: A bibliometric analysis of the last 50 years in water resources A. Pizarro et al. https://doi.org/10.1016/j.jhydrol.2024.132008
115. Snow hydrology signatures for model identification within a limits‐of‐acceptability approach B. Schaefli https://doi.org/10.1002/hyp.10972
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117. Simulation of the cumulative hydrological response to green infrastructure P. Avellaneda et al. https://doi.org/10.1002/2016WR019836
118. Synergistic effect of drought and rainfall events of different patterns on watershed systems J. Qiu et al. https://doi.org/10.1038/s41598-021-97574-z
119. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
120. Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times V. Mansanarez et al. https://doi.org/10.1029/2018WR023389
121. Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin D. Hughes & F. Farinosi https://doi.org/10.1002/hyp.14141
122. Global Runoff Signatures Changes and Their Response to Atmospheric Environment, GRACE Water Storage, and Dams S. Yan et al. https://doi.org/10.3390/rs13204084
123. Hydrological data uncertainty and its implications H. McMillan et al. https://doi.org/10.1002/wat2.1319
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125. Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach F. Garavaglia et al. https://doi.org/10.5194/hess-21-3937-2017
126. Five guidelines for selecting hydrological signatures H. McMillan et al. https://doi.org/10.1002/hyp.11300
127. 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
128. The application of CHIRPS-based Pitman modelling in South Africa J. Kibii & J. Du Plessis https://doi.org/10.1016/j.pce.2023.103475
129. Using hydrological and climatic catchment clusters to explore drivers of catchment behavior F. Jehn et al. https://doi.org/10.5194/hess-24-1081-2020
130. Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests C. Prieto et al. https://doi.org/10.1029/2018WR023254
131. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. https://doi.org/10.1016/j.envsoft.2021.104983
132. 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
133. Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships M. Eddy et al. https://doi.org/10.1002/eco.2387
134. 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