73 citations as recorded by crossref.

1. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani
2. Zero-shot forecasting of streamflow using time series foundation models: are we there yet? A. Sun & A. Sun
3. Derivation and analysis of spatial variability in master recession curves with mixed effects model B. Chen et al.
4. JAMES BUTTLE REVIEW: Interflow, subsurface stormflow and throughflow: A synthesis of field work and modelling K. McGuire et al.
5. Integrated Multi-Source Data Fusion Framework Incorporating Surface Deformation, Seismicity, and Hydrological Indicators for Geohazard Risk Mapping in Oil and Gas Fields M. Al Sulaimani et al.
6. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al.
7. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al.
8. Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al.
9. Investigating the spatial–temporal changes of flood events across the Yangtze River Basin, China: Identification, spatial heterogeneity, and dominant impact factors J. Yu et al.
10. Features of the Earth’s seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents G. Papacharalampous et al.
11. Interpreting and modelling the daily extreme sediment events in karst mountain watersheds J. Jing et al.
12. A Nonlinear Local Approximation Approach for Catchment Classification S. Khan & B. Sivakumar
13. Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al.
14. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al.
15. Mixed statistical and data mining analysis of river flow and catchment properties at regional scale M. Giełczewski et al.
16. Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling G. Cheng et al.
17. Transfer performance of gated recurrent unit model for runoff prediction based on the comprehensive spatiotemporal similarity of catchments X. Min et al.
18. Examining changes in daily rainfall patterns attributable to urbanization: a study of watershed hydrology transformation F. Rahmani & M. Fattahi
19. Comparative assessment of hydrological and deep learning models for runoff simulation and water storage in irrigated basins A. Razeghi Haghighi et al.
20. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al.
21. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al.
22. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al.
23. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al.
24. Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al.
25. Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions A. Hövel et al.
26. Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region L. Johnston et al.
27. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli
28. Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al.
29. Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al.
30. On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al.
31. Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos
32. Integrated catchment classification across China based on hydroclimatological and geomorphological similarities using self-organizing map and fuzzy c-means clustering for hydrological modeling J. Niu et al.
33. Identification of robust catchment classification methods for Sahelian watersheds P. Darbandsari et al.
34. Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al.
35. Simple Catchments and Where to Find Them: The Storage-Discharge Relationship as a Proxy for Catchment Complexity F. Jehn et al.
36. Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS M. Shen & T. Chui
37. Identifying control factors of hydrological behavior through catchment classification in Mainland of China H. Xu et al.
38. Clustering of causal graphs to explore drivers of river discharge W. Günther et al.
39. Causal effects of dams and land cover changes on flood changes in mainland China W. Yang et al.
40. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al.
41. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan
42. Changes in snowmelt flooding and driver factors in the China-Pakistan Economic Corridor H. Zhang et al.
43. Analysis of the Intermittent Characteristics of Streamflow in Taiwan X. Fang et al.
44. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al.
45. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al.
46. Molecular links between whitesand ecosystems and blackwater formation in the Rio Negro watershed C. Simon et al.
47. The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology A. de Lavenne et al.
48. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al.
49. Evaluation of Catchments’ Similarity by Penalization in the Context of Engineering Tasks—A Case Study of Four Slovakian Catchments M. Cisty et al.
50. How extreme are flood peak distributions? A quasi-global analysis of daily discharge records M. Zaerpour et al.
51. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey
52. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
53. Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner
54. Understanding the hierarchical controls of geographical features on hydrological responses in humid mountainous areas through a stepwise clustering scheme Y. Yang et al.
55. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al.
56. A review of hydrologic signatures and their applications H. McMillan
57. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al.
58. Beyond the Flow: Multifractal Clustering of River Discharge Across Canada Using Near-Century Data A. Olusola et al.
59. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al.
60. From seasonal variability to long-term trends: a comprehensive analysis of reservoir-induced flow regime alterations K. Ren et al.
61. A global algorithm for identifying changing streamflow regimes: application to Canadian natural streams (1966–2010) M. Zaerpour et al.
62. Catchment characteristics dominate the hydrological behavior of closed lakes across the Tibetan Plateau M. Hou et al.
63. Catchment clustering in Taiwan through comparative assessment of baseflow separation methods H. Chen & H. Yeh
64. Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G. Papacharalampous et al.
65. Pattern recognition describing spatio-temporal drivers of catchment classification for water quality C. O’Sullivan et al.
66. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar
67. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al.
68. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli
69. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al.
70. Using a Hydro-Morphic Classification of Catchments to Characterise and Explain High Flow and Overbank Flood Behaviour A. Arash et al.
71. Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data C. O'Sullivan et al.
72. Design and Implementation of Community Pension Service Quality Evaluation System Based on Clustering Algorithm W. Liu & T. Zhou
73. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al.