220 citations as recorded by crossref.

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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
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39. 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
40. Seasonal streamflow forecasting in South America’s largest rivers I. Petry et al. https://doi.org/10.1016/j.ejrh.2023.101487
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44. 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
45. Hydrology research articles are becoming more topically diverse M. Rahman et al. https://doi.org/10.1016/j.jhydrol.2022.128551
46. 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
47. Panta Rhei 2013–2015: global perspectives on hydrology, society and change H. McMillan et al. https://doi.org/10.1080/02626667.2016.1159308
48. 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
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50. The hazards of split-sample validation in hydrological model calibration R. Arsenault et al. https://doi.org/10.1016/j.jhydrol.2018.09.027
51. 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
52. 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
53. 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
54. Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent I. Pechlivanidis et al. https://doi.org/10.3390/w8050177
55. 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
56. Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? A. Piotrowski et al. https://doi.org/10.1080/02626667.2016.1234712
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58. 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
59. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
60. 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
61. 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
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63. 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
64. 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
65. 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
66. On generalization, language, interpretability and the future of geo-scientific machine learning H. Gupta https://doi.org/10.1016/j.envsoft.2025.106834
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69. Global‐scale regionalization of hydrologic model parameters H. Beck et al. https://doi.org/10.1002/2015WR018247
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71. Benchmarking of a Physically Based Hydrologic Model A. Newman et al. https://doi.org/10.1175/JHM-D-16-0284.1
72. 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
73. Validation of a national hydrological model H. McMillan et al. https://doi.org/10.1016/j.jhydrol.2016.07.043
74. 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
75. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. https://doi.org/10.1029/2019WR026987
76. 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
77. 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
78. 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
79. Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. https://doi.org/10.1002/hyp.14288
80. 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
81. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan https://doi.org/10.1002/hyp.15215
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