48 citations as recorded by crossref.

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2. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
3. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
4. Molecular links between whitesand ecosystems and blackwater formation in the Rio Negro watershed C. Simon et al. 10.1016/j.gca.2021.06.036
5. 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. 10.1016/j.jhydrol.2023.129503
6. The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology A. de Lavenne et al. 10.1016/j.envsoft.2022.105562
7. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
8. Features of the Earth’s seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents G. Papacharalampous et al. 10.1186/s40645-023-00574-y
9. Interpreting and modelling the daily extreme sediment events in karst mountain watersheds J. Jing et al. 10.1016/j.scitotenv.2024.171956
10. Evaluation of Catchments’ Similarity by Penalization in the Context of Engineering Tasks—A Case Study of Four Slovakian Catchments M. Cisty et al. 10.3390/w13202894
11. How extreme are flood peak distributions? A quasi-global analysis of daily discharge records M. Zaerpour et al. 10.1016/j.jhydrol.2024.130849
12. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
13. A Nonlinear Local Approximation Approach for Catchment Classification S. Khan & B. Sivakumar 10.3390/e26030218
14. Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner 10.1111/jfr3.12772
15. Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection J. Johnson et al. 10.1029/2023JD038534
16. Mixed statistical and data mining analysis of river flow and catchment properties at regional scale M. Giełczewski et al. 10.1007/s00477-022-02169-3
17. Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling G. Cheng et al. 10.1029/2021WR031370
18. Transfer performance of gated recurrent unit model for runoff prediction based on the comprehensive spatiotemporal similarity of catchments X. Min et al. 10.1016/j.jenvman.2022.117182
19. Understanding the hierarchical controls of geographical features on hydrological responses in humid mountainous areas through a stepwise clustering scheme Y. Yang et al. 10.1007/s00477-021-02080-3
20. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
21. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al. 10.3389/feart.2021.730631
22. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. 10.1007/s40808-021-01298-y
23. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al. 10.1029/2021WR030186
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. 10.1016/j.jhydrol.2022.128555
25. Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions A. Hövel et al. 10.1016/j.jhydrol.2023.130585
26. A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
27. Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region L. Johnston et al. 10.1061/(ASCE)HE.1943-5584.0002200
28. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
29. On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. 10.5194/hess-25-5805-2021
30. 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. 10.1029/2020WR028300
31. Optimised power generation system by the SWAT and artificial neural network based on improved farmland fertility optimisation (IFFO) algorithm C. Zhang et al. 10.1080/01430750.2021.1918244
32. A global algorithm for identifying changing streamflow regimes: application to Canadian natural streams (1966–2010) M. Zaerpour et al. 10.5194/hess-25-5193-2021
33. 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
34. Pattern recognition describing spatio-temporal drivers of catchment classification for water quality C. O’Sullivan et al. 10.1016/j.scitotenv.2022.160240
35. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
36. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
37. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli 10.1029/2021WR030263
38. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
39. Simple Catchments and Where to Find Them: The Storage-Discharge Relationship as a Proxy for Catchment Complexity F. Jehn et al. 10.3389/frwa.2021.631651
40. Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS M. Shen & T. Chui 10.1016/j.jhydrol.2023.130255
41. Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data C. O'Sullivan et al. 10.1016/j.scitotenv.2021.151139
42. Design and Implementation of Community Pension Service Quality Evaluation System Based on Clustering Algorithm W. Liu & T. Zhou 10.1155/2022/5832853
43. Clustering of causal graphs to explore drivers of river discharge W. Günther et al. 10.1017/eds.2023.17
44. Causal effects of dams and land cover changes on flood changes in mainland China W. Yang et al. 10.5194/hess-25-2705-2021
45. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. 10.1038/s41597-023-02828-2
46. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
47. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
48. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. 10.1007/s40808-021-01298-y