80 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 https://doi.org/10.1002/hyp.13394
2. River Classification as a Geographic Tool in the Age of Big Data and Global Change S. Praskievicz https://doi.org/10.1111/gere.12251
3. River runoff evaluation for ungauged watersheds by SWAP model. 2. Application of methods of physiographic similarity and spatial geostatistics G. Ayzel et al. https://doi.org/10.1134/S0097807817040029
4. A generalised ecohydrological landscape classification for assessing ecosystem risk in Australia due to an altering water regime A. Herr et al. https://doi.org/10.5194/hess-28-1957-2024
5. Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison K. Sink & T. Brikowski https://doi.org/10.5194/hess-29-4015-2025
6. On the non-stationarity of hydrological response in anthropogenically unaffected catchments: an Australian perspective H. Ajami et al. https://doi.org/10.5194/hess-21-281-2017
7. Landscape and climate conditions influence the hydrological sensitivity to climate change in eastern Canada O. Aygün et al. https://doi.org/10.1016/j.jhydrol.2022.128595
8. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists C. Shen https://doi.org/10.1029/2018WR022643
9. Towards catchment classification in data‐scarce regions D. Auerbach et al. https://doi.org/10.1002/eco.1721
10. 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
11. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar https://doi.org/10.5194/hess-26-2019-2022
12. Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors Z. Li & S. Quiring https://doi.org/10.1016/j.jhydrol.2021.126621
13. Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests M. Yang & F. Olivera https://doi.org/10.1016/j.jhydrol.2023.129409
14. Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions A. Hövel et al. https://doi.org/10.1016/j.jhydrol.2023.130585
15. Spatial and Temporal Changes in Hydrological Regionalization of Lowland Rivers V. Akstinas et al. https://doi.org/10.1007/s41742-021-00380-8
16. Dominant flood types in Europe and their role in flood statistics S. Fischer & A. Schumann https://doi.org/10.1080/02626667.2025.2450369
17. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. https://doi.org/10.1016/j.envsoft.2021.104983
18. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. https://doi.org/10.1029/2021WR029583
19. Correlation and causation in tree‐ring‐based reconstruction of paleohydrology in cold semiarid regions A. Elshorbagy et al. https://doi.org/10.1002/2016WR018985
20. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. https://doi.org/10.1007/s40808-021-01298-y
21. From river flow regime diversity to proxies for hydrologic homogeneity a Canada-wide case study S. Ariano & G. Ali https://doi.org/10.1038/s41598-025-00244-7
22. Event-based prediction of stream turbidity using a combined cluster analysis and classification tree approach A. Mather & R. Johnson https://doi.org/10.1016/j.jhydrol.2015.10.032
23. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. https://doi.org/10.5194/hess-22-6257-2018
24. 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
25. Homogeneous region determination using linear and nonlinear techniques J. Swain et al. https://doi.org/10.1080/02723646.2016.1211460
26. Catchment coevolution: A useful framework for improving predictions of hydrological change? P. Troch et al. https://doi.org/10.1002/2015WR017032
27. Intra-catchment comparison and classification of long-term streamflow variability in the Alps using wavelet analysis T. Pérez Ciria & G. Chiogna https://doi.org/10.1016/j.jhydrol.2020.124927
28. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. https://doi.org/10.1016/j.jhydrol.2022.127969
29. Assessing hydrologic similarity in the semi-arid region: a case study in Piranhas-Açu basin P. Bernardo Costa et al. https://doi.org/10.1007/s40899-025-01196-6
30. 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 https://doi.org/10.1002/hyp.11488
31. Integrating scientific knowledge into machine learning using interactive decision trees G. Sarailidis et al. https://doi.org/10.1016/j.cageo.2022.105248
32. Assessment of Hydrologic Alteration Metrics for Detecting Urbanization Impacts R. McDaniel & F. O’Donnell https://doi.org/10.3390/w11051017
33. Which Potential Evapotranspiration Formula to Use in Hydrological Modeling World‐Wide? R. Pimentel et al. https://doi.org/10.1029/2022WR033447
34. Simulation of river flow in the Thames over 120 years: Evidence of change in rainfall-runoff response? S. Crooks & A. Kay https://doi.org/10.1016/j.ejrh.2015.05.014
35. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. https://doi.org/10.1016/j.jhydrol.2024.131638
36. A scale-based framework to understand the promises, pitfalls and paradoxes of irrigation efficiency to meet major water challenges B. Lankford et al. https://doi.org/10.1016/j.gloenvcha.2020.102182
37. Assessing baseflow index vulnerability to variation in dry spell length for a range of catchment and climate properties A. Longobardi & A. Van Loon https://doi.org/10.1002/hyp.13147
38. Linking the Budyko framework and the Dunne diagram R. Trancoso et al. https://doi.org/10.1016/j.jhydrol.2016.02.017
39. A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution X. Zeng et al. https://doi.org/10.1016/j.advwatres.2020.103799
40. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. https://doi.org/10.1029/2018WR022913
41. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani https://doi.org/10.1038/s41597-025-04389-y
42. Long‐Term Coevolution of an Urban Human‐Water System Under Climate Change: Critical Role of Human Adaptive Actions B. Li & M. Sivapalan https://doi.org/10.1029/2020WR027931
43. 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
44. 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. https://doi.org/10.7868/S0321059617040022
45. 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. https://doi.org/10.1029/2018WR022587
46. Characterising baseflow signature variability in the Yellow River Basin S. Lyu et al. https://doi.org/10.1016/j.jenvman.2023.118565
47. Tracking rainfall contribution in a conceptual runoff model T. Nguyen et al. https://doi.org/10.1016/j.jhydrol.2026.135366
48. 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. https://doi.org/10.3389/fenvs.2023.1207508
49. Catchment clustering in Taiwan through comparative assessment of baseflow separation methods H. Chen & H. Yeh https://doi.org/10.1016/j.ejrh.2026.103438
50. A Gaussian sliding windows regression model for hydrological inference S. Schrunner et al. https://doi.org/10.1093/jrsssc/qlaf009
51. Regionalization of runoff models derived by genetic programming M. Heřmanovský et al. https://doi.org/10.1016/j.jhydrol.2017.02.018
52. Exploring hydrologically similar catchments in terms of the physical characteristics of upstream regions Y. Jin et al. https://doi.org/10.2166/nh.2017.191
53. Catchment Morphing (CM): A Novel Approach for Runoff Modeling in Ungauged Catchments J. Zhang & D. Han https://doi.org/10.1002/2017WR021403
54. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment L. Gudmundsson et al. https://doi.org/10.5194/essd-10-787-2018
55. Understanding hydrologic variability across Europe through catchment classification A. Kuentz et al. https://doi.org/10.5194/hess-21-2863-2017
56. Future evolution and uncertainty of river flow regime change in a deglaciating river basin J. Mackay et al. https://doi.org/10.5194/hess-23-1833-2019
57. Hysteresis in streamflow‐water table relation provides a new classification system of rainfall‐runoff events Y. Gelmini et al. https://doi.org/10.1002/hyp.14685
58. Using spatially explicit indicators to investigate watershed characteristics and stream temperature relationships Z. Grabowski et al. https://doi.org/10.1016/j.scitotenv.2016.02.042
59. Assessing Hydrograph Similarity and Rare Runoff Dynamics by Cross Recurrence Plots D. Wendi et al. https://doi.org/10.1029/2018WR024111
60. Uncertainty in hydrological signatures for gauged and ungauged catchments I. Westerberg et al. https://doi.org/10.1002/2015WR017635
61. A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany L. Tarasova et al. https://doi.org/10.1029/2019WR026951
62. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues https://doi.org/10.1016/j.jhydrol.2021.126674
63. Understanding process controls on groundwater recharge variability across Africa through recharge landscapes C. West et al. https://doi.org/10.1016/j.jhydrol.2022.127967
64. Groundwater Vulnerability in a Megacity Under Climate and Economic Changes: A Coupled Sociohydrological Analysis B. Li et al. https://doi.org/10.1029/2022WR033943
65. Improving Budyko curve‐based estimates of long‐term water partitioning using hydrologic signatures from GRACE K. Fang et al. https://doi.org/10.1002/2016WR018748
66. A review of hydrologic signatures and their applications H. McMillan https://doi.org/10.1002/wat2.1499
67. Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US K. Fang & C. Shen https://doi.org/10.1002/2016WR020283
68. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation B. Arheimer et al. https://doi.org/10.5194/hess-24-535-2020
69. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al. https://doi.org/10.1016/j.jhydrol.2025.132766
70. Identifying most relevant controls on catchment hydrological similarity using model transferability – A comprehensive study in Iran A. Jahanshahi et al. https://doi.org/10.1016/j.jhydrol.2022.128193
71. Delineation of Environmental Units by Multivariate Techniques in the Duero River Watershed, Michoacán, Mexico G. Cruz-Cárdenas et al. https://doi.org/10.1007/s10666-016-9534-2
72. Five guidelines for selecting hydrological signatures H. McMillan et al. https://doi.org/10.1002/hyp.11300
73. Classifying annual daily hydrographs in Western North America using t‐distributed stochastic neighbour embedding W. Tang & S. Carey https://doi.org/10.1002/hyp.14473
74. A Multi-Dimensional Hydro-Climatic Similarity and Classification Framework Based on Budyko Theory for Continental-Scale Applications in China J. Liu et al. https://doi.org/10.3390/w11020319
75. Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments – A comparative hydrology approach R. Singh et al. https://doi.org/10.1016/j.jhydrol.2014.06.030
76. Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning C. Klingler et al. https://doi.org/10.1007/s00506-022-00891-4
77. Assessing Streamflow Sensitivity to Precipitation Variability in Karst‐Influenced Catchments With Unclosed Water Balances Y. Liu et al. https://doi.org/10.1029/2020WR028598
78. A large-sample investigation into uncertain climate change impacts on high flows across Great Britain R. Lane et al. https://doi.org/10.5194/hess-26-5535-2022
79. Current Progress in and Future Visions of Key Technologies of UAV-Borne Multi-Modal Geophysical Exploration for Mineral Exploration: A Scoping Review X. Wu et al. https://doi.org/10.3390/rs17152689
80. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang https://doi.org/10.1002/wat2.1533