41 citations as recorded by crossref.

1. Climate change rather than vegetation greening dominates runoff change in China Z. Song et al. 10.1016/j.jhydrol.2023.129519
2. Exploring stable isotope patterns in monthly precipitation across Southeast Asia using contemporary deep learning models and SHapley Additive exPlanations (SHAP) techniques M. Heydarizad et al. 10.1080/10256016.2025.2508811
3. Ecohydrological processes can predict biocrust cover at regional scale but not global scale N. Chen et al. 10.1007/s11104-024-07079-7
4. Machine learning-based peak flow estimation for Nebraska streams S. Pokharel et al. 10.1080/15715124.2025.2469901
5. Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture Y. Yang et al. 10.1016/j.jhydrol.2022.128848
6. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
7. A Daily Runoff Prediction Model for the Yangtze River Basin Based on an Improved Generative Adversarial Network T. Liu et al. 10.3390/su17072990
8. Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri 10.2166/wcc.2023.467
9. Evaluating the performance of five global gridded potential evapotranspiration products in hydrological simulation: Application in the upper Han River Basin M. Li et al. 10.1016/j.ejrh.2024.102114
10. Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. 10.1080/02626667.2023.2248112
11. Optimizing deep neural networks for high-resolution land cover classification through data augmentation S. Sierra et al. 10.1007/s10661-025-13870-5
12. A simple and effective approach to enhance the representation of spatial heterogeneity in lumped conceptual hydrological models: Application in the time-variant gain model Y. Zhu et al. 10.2166/nh.2025.184
13. Characterizing future changes in compound flood risk by capturing the dependence between rainfall and river flow: An application to the Yangtze River Basin, China J. Yu et al. 10.1016/j.jhydrol.2024.131175
14. Hybrid Deep Neural Architectures with Evolutionary Optimization and Explainable AI for Drought Susceptibility Assessment J. Liu et al. 10.3390/rs17173122
15. Deep learning reveals the relationship between vegetation and runoff in the Weihe River Basin on the Loess Plateau Q. Ju et al. 10.1080/02626667.2025.2496281
16. Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
17. Сопоставление библиотек для создания моделей машинного обучения на основе методов градиентного бустинга Д. Пономарев 10.47813/2782-2818-2025-5-2-3001-3006
18. Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel 10.1007/s44290-024-00084-w
19. Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project X. Li et al. 10.1016/j.jhydrol.2024.131061
20. An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method B. Mohammadi et al. 10.1016/j.jhydrol.2024.132130
21. Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates A. DelSanto et al. 10.3389/frwa.2024.1332888
22. Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. 10.1016/j.jhydrol.2024.130948
23. Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future A. Bhadauria et al. 10.1051/e3sconf/202455201074
24. Vegetation greening promotes the conversion of blue water to green water by enhancing transpiration Z. Song et al. 10.1016/j.jhydrol.2025.133181
25. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
26. Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. 10.1016/j.jhydrol.2023.130057
27. Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China Q. Wang et al. 10.3390/w16121729
28. Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City Y. Yang et al. 10.3390/su15043804
29. Training a hidden Markov model with PMDI and temperature to create climate informed scenarios B. Tezcan & M. Garcia 10.3389/frwa.2025.1472695
30. Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features Z. Xu et al. 10.1016/j.jhydrol.2024.131627
31. Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya S. Ghosh et al. 10.1007/s00382-023-06893-6
32. Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds N. Karki et al. 10.1016/j.ejrh.2023.101359
33. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
34. A Proposal for a New Python Library Implementing Stepwise Procedure L. Fávero et al. 10.3390/a17110502
35. Regional Flood Frequency Analysis of Northern Iran M. Adhami 10.21205/deufmd.2024267711
36. Experimental Comparative Study on Self-Imputation Methods and Their Quality Assessment for Monthly River Flow Data with Gaps: Case Study to Mures River Z. Magyari-Sáska et al. 10.3390/app15031242
37. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. 10.18307/2025.0342
38. Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach I. Hani et al. 10.1016/j.ejrh.2025.102373
39. Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. 10.3390/rs16203756
40. Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. 10.1016/j.jhydrol.2024.131472
41. Model parameterization scheme for a distributed hydrological model, BTOPMC S. Bastola 10.1016/j.hydres.2025.06.001