76 citations as recorded by crossref.

1. Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA S. Praskievicz & C. Luo 10.1002/hyp.13394
2. River Classification as a Geographic Tool in the Age of Big Data and Global Change S. Praskievicz 10.1111/gere.12251
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4. A generalised ecohydrological landscape classification for assessing ecosystem risk in Australia due to an altering water regime A. Herr et al. 10.5194/hess-28-1957-2024
5. On the non-stationarity of hydrological response in anthropogenically unaffected catchments: an Australian perspective H. Ajami et al. 10.5194/hess-21-281-2017
6. Landscape and climate conditions influence the hydrological sensitivity to climate change in eastern Canada O. Aygün et al. 10.1016/j.jhydrol.2022.128595
7. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen 10.1029/2018WR022643
8. Towards catchment classification in data‐scarce regions D. Auerbach et al. 10.1002/eco.1721
9. Equifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty S. Khatami et al. 10.1029/2018WR023750
10. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
11. Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors Z. Li & S. Quiring 10.1016/j.jhydrol.2021.126621
12. Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests M. Yang & F. Olivera 10.1016/j.jhydrol.2023.129409
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14. Spatial and Temporal Changes in Hydrological Regionalization of Lowland Rivers V. Akstinas et al. 10.1007/s41742-021-00380-8
15. Dominant flood types in Europe and their role in flood statistics S. Fischer & A. Schumann 10.1080/02626667.2025.2450369
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17. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
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19. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. 10.1007/s40808-021-01298-y
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22. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
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25. Intra-catchment comparison and classification of long-term streamflow variability in the Alps using wavelet analysis T. Pérez Ciria & G. Chiogna 10.1016/j.jhydrol.2020.124927
26. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
27. Assessing hydrologic similarity in the semi-arid region: a case study in Piranhas-Açu basin P. Bernardo Costa et al. 10.1007/s40899-025-01196-6
28. Run‐off processes from mountains to foothills: The role of soil stratigraphy and structure in influencing run‐off characteristics across high to low relief landscapes M. Zimmer & J. Gannon 10.1002/hyp.11488
29. Integrating scientific knowledge into machine learning using interactive decision trees G. Sarailidis et al. 10.1016/j.cageo.2022.105248
30. Assessment of Hydrologic Alteration Metrics for Detecting Urbanization Impacts R. McDaniel & F. O’Donnell 10.3390/w11051017
31. Which Potential Evapotranspiration Formula to Use in Hydrological Modeling World‐Wide? R. Pimentel et al. 10.1029/2022WR033447
32. Simulation of river flow in the Thames over 120 years: Evidence of change in rainfall-runoff response? S. Crooks & A. Kay 10.1016/j.ejrh.2015.05.014
33. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
34. A scale-based framework to understand the promises, pitfalls and paradoxes of irrigation efficiency to meet major water challenges B. Lankford et al. 10.1016/j.gloenvcha.2020.102182
35. Assessing baseflow index vulnerability to variation in dry spell length for a range of catchment and climate properties A. Longobardi & A. Van Loon 10.1002/hyp.13147
36. Linking the Budyko framework and the Dunne diagram R. Trancoso et al. 10.1016/j.jhydrol.2016.02.017
37. A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution X. Zeng et al. 10.1016/j.advwatres.2020.103799
38. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. 10.1029/2018WR022913
39. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani 10.1038/s41597-025-04389-y
40. Long‐Term Coevolution of an Urban Human‐Water System Under Climate Change: Critical Role of Human Adaptive Actions B. Li & M. Sivapalan 10.1029/2020WR027931
41. Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G. Papacharalampous et al. 10.1016/j.scitotenv.2020.144612
42. РАСЧЕТЫ РЕЧНОГО СТОКА НА ОСНОВЕ МОДЕЛИ SWAP ДЛЯ ВОДОСБОРОВ С НЕДОСТАТОЧНЫМ ИНФОРМАЦИОННЫМ ОБЕСПЕЧЕНИЕМ. 2. ИСПОЛЬЗОВАНИЕ МЕТОДОВ ФИЗИКО-ГЕОГРАФИЧЕСКОГО ПОДОБИЯ И ПРОСТРАНСТВЕННОЙ ГЕОСТАТИСТИКИ, "Водные ресурсы" Г. Айзель et al. 10.7868/S0321059617040022
43. Exploring Controls on Rainfall‐Runoff Events: 1. Time Series‐Based Event Separation and Temporal Dynamics of Event Runoff Response in Germany L. Tarasova et al. 10.1029/2018WR022587
44. Characterising baseflow signature variability in the Yellow River Basin S. Lyu et al. 10.1016/j.jenvman.2023.118565
45. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
46. A Gaussian sliding windows regression model for hydrological inference S. Schrunner et al. 10.1093/jrsssc/qlaf009
47. Regionalization of runoff models derived by genetic programming M. Heřmanovský et al. 10.1016/j.jhydrol.2017.02.018
48. Exploring hydrologically similar catchments in terms of the physical characteristics of upstream regions Y. Jin et al. 10.2166/nh.2017.191
49. Catchment Morphing (CM): A Novel Approach for Runoff Modeling in Ungauged Catchments J. Zhang & D. Han 10.1002/2017WR021403
50. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment L. Gudmundsson et al. 10.5194/essd-10-787-2018
51. Understanding hydrologic variability across Europe through catchment classification A. Kuentz et al. 10.5194/hess-21-2863-2017
52. Future evolution and uncertainty of river flow regime change in a deglaciating river basin J. Mackay et al. 10.5194/hess-23-1833-2019
53. Hysteresis in streamflow‐water table relation provides a new classification system of rainfall‐runoff events Y. Gelmini et al. 10.1002/hyp.14685
54. Using spatially explicit indicators to investigate watershed characteristics and stream temperature relationships Z. Grabowski et al. 10.1016/j.scitotenv.2016.02.042
55. Assessing Hydrograph Similarity and Rare Runoff Dynamics by Cross Recurrence Plots D. Wendi et al. 10.1029/2018WR024111
56. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. 10.1002/2015WR017635
57. A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany L. Tarasova et al. 10.1029/2019WR026951
58. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues 10.1016/j.jhydrol.2021.126674
59. Understanding process controls on groundwater recharge variability across Africa through recharge landscapes C. West et al. 10.1016/j.jhydrol.2022.127967
60. Groundwater Vulnerability in a Megacity Under Climate and Economic Changes: A Coupled Sociohydrological Analysis B. Li et al. 10.1029/2022WR033943
61. Improving Budyko curve‐based estimates of long‐term water partitioning using hydrologic signatures from GRACE K. Fang et al. 10.1002/2016WR018748
62. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
63. Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US K. Fang & C. Shen 10.1002/2016WR020283
64. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation B. Arheimer et al. 10.5194/hess-24-535-2020
65. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al. 10.1016/j.jhydrol.2025.132766
66. Identifying most relevant controls on catchment hydrological similarity using model transferability – A comprehensive study in Iran A. Jahanshahi et al. 10.1016/j.jhydrol.2022.128193
67. Delineation of Environmental Units by Multivariate Techniques in the Duero River Watershed, Michoacán, Mexico G. Cruz-Cárdenas et al. 10.1007/s10666-016-9534-2
68. Five guidelines for selecting hydrological signatures H. McMillan et al. 10.1002/hyp.11300
69. Classifying annual daily hydrographs in Western North America using t‐distributed stochastic neighbour embedding W. Tang & S. Carey 10.1002/hyp.14473
70. A Multi-Dimensional Hydro-Climatic Similarity and Classification Framework Based on Budyko Theory for Continental-Scale Applications in China J. Liu et al. 10.3390/w11020319
71. Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments – A comparative hydrology approach R. Singh et al. 10.1016/j.jhydrol.2014.06.030
72. Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning C. Klingler et al. 10.1007/s00506-022-00891-4
73. Assessing Streamflow Sensitivity to Precipitation Variability in Karst‐Influenced Catchments With Unclosed Water Balances Y. Liu et al. 10.1029/2020WR028598
74. A large-sample investigation into uncertain climate change impacts on high flows across Great Britain R. Lane et al. 10.5194/hess-26-5535-2022
75. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
76. Large-sample hydrology: a need to balance depth with breadth H. Gupta et al. 10.5194/hess-18-463-2014