63 citations as recorded by crossref.

1. Climate change rather than vegetation greening dominates runoff change in China Z. Song et al. https://doi.org/10.1016/j.jhydrol.2023.129519
2. Ecohydrological processes can predict biocrust cover at regional scale but not global scale N. Chen et al. https://doi.org/10.1007/s11104-024-07079-7
3. Machine learning-based peak flow estimation for Nebraska streams S. Pokharel et al. https://doi.org/10.1080/15715124.2025.2469901
4. Enhanced evapotranspiration prediction by incorporating plant stomatal-hydraulic co-regulation into hydrological model Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134586
5. Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2022.128848
6. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach https://doi.org/10.1007/s12145-024-01390-8
7. Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri https://doi.org/10.2166/wcc.2023.467
8. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim https://doi.org/10.1016/j.ejrh.2025.102854
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. https://doi.org/10.1016/j.ejrh.2024.102114
10. Optimizing deep neural networks for high-resolution land cover classification through data augmentation S. Sierra et al. https://doi.org/10.1007/s10661-025-13870-5
11. Hybrid Deep Neural Architectures with Evolutionary Optimization and Explainable AI for Drought Susceptibility Assessment J. Liu et al. https://doi.org/10.3390/rs17173122
12. Improving the streamflow simulations of a data-poor mountainous watershed using parameter transfer based on similar land use and land cover M. Kavya et al. https://doi.org/10.1080/02626667.2026.2621070
13. Deep learning reveals the relationship between vegetation and runoff in the Weihe River Basin on the Loess Plateau Q. Ju et al. https://doi.org/10.1080/02626667.2025.2496281
14. Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. https://doi.org/10.3390/w15142572
15. Сопоставление библиотек для создания моделей машинного обучения на основе методов градиентного бустинга Д. Пономарев https://doi.org/10.47813/2782-2818-2025-5-2-3001-3006
16. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al. https://doi.org/10.3390/w17182722
17. 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 https://doi.org/10.1007/s44290-024-00084-w
18. Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al. https://doi.org/10.1016/j.ejrh.2026.103158
19. Regionalizing hydrologic information for runoff predictions beyond continental boundaries using machine learning M. Fathi & A. Awadallah https://doi.org/10.1016/j.advwatres.2025.105162
20. Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. https://doi.org/10.1016/j.jhydrol.2024.130948
21. Unveiling climate-driven water surface dynamics in the largest tropical lake in Borneo: A machine learning approach using multi-source satellite imagery M. Rifai & . Harintaka https://doi.org/10.1016/j.aiig.2025.100166
22. Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha https://doi.org/10.1088/2752-5295/ae2a60
23. Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future A. Bhadauria et al. https://doi.org/10.1051/e3sconf/202455201074
24. Vegetation greening promotes the conversion of blue water to green water by enhancing transpiration Z. Song et al. https://doi.org/10.1016/j.jhydrol.2025.133181
25. Ensemble Boosting Methods for Surface Water Quality Modeling: A Review M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1007/s11831-025-10422-5
26. Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang https://doi.org/10.1016/j.ejrh.2026.103349
27. Training a hidden Markov model with PMDI and temperature to create climate informed scenarios B. Tezcan & M. Garcia https://doi.org/10.3389/frwa.2025.1472695
28. Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features Z. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131627
29. Data-driven parameterization of SWAT+ reservoir module without access to operation rules S. Sreeraj et al. https://doi.org/10.1016/j.envsoft.2025.106852
30. Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya S. Ghosh et al. https://doi.org/10.1007/s00382-023-06893-6
31. Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds N. Karki et al. https://doi.org/10.1016/j.ejrh.2023.101359
32. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. https://doi.org/10.1080/02626667.2023.2273402
33. Regional Flood Frequency Analysis of Northern Iran M. Adhami https://doi.org/10.21205/deufmd.2024267711
34. 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. https://doi.org/10.3390/app15031242
35. Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. https://doi.org/10.1016/j.jhydrol.2024.131472
36. 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. https://doi.org/10.1080/10256016.2025.2508811
37. Hybrid machine learning model for sediment transport forecast in the cheliff Basin, Northern Algeria R. Mokhtari et al. https://doi.org/10.1007/s41207-025-01052-1
38. A Daily Runoff Prediction Model for the Yangtze River Basin Based on an Improved Generative Adversarial Network T. Liu et al. https://doi.org/10.3390/su17072990
39. Comparative evaluation of RF and GBT models for dead fuel moisture estimation and fuel-type-specific drivers under drought conditions in Central Yunnan, China Y. Liu et al. https://doi.org/10.1016/j.foreco.2025.123458
40. Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. https://doi.org/10.1080/02626667.2023.2248112
41. 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. https://doi.org/10.2166/nh.2025.184
42. 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. https://doi.org/10.1016/j.jhydrol.2024.131175
43. Response of hydrological drought to vegetation change in China X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135283
44. 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. https://doi.org/10.1016/j.jhydrol.2024.131061
45. An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method B. Mohammadi et al. https://doi.org/10.1016/j.jhydrol.2024.132130
46. 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. https://doi.org/10.3389/frwa.2024.1332888
47. A regionalization based machine learning framework for bias correction and downscaling of ESACCI soil moisture in data limited region: A case study over India I. Dasari & V. Vema https://doi.org/10.1016/j.jhydrol.2025.134657
48. Streamflow prediction across Canada’s southern provinces using gradient boosting M. Alipour https://doi.org/10.1080/23249676.2025.2606416
49. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131598
50. Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. https://doi.org/10.1016/j.jhydrol.2023.130057
51. Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China Q. Wang et al. https://doi.org/10.3390/w16121729
52. Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City Y. Yang et al. https://doi.org/10.3390/su15043804
53. Assessing the role of gridded evapotranspiration products in improving streamflow simulation and reducing hydrological modeling uncertainty M. Li et al. https://doi.org/10.1016/j.ecoinf.2026.103698
54. Multi-Machine Learning Ensemble Regionalization of Hydrological Parameters for Enhancing Flood Prediction in Ungauged Mountainous Catchments K. Li et al. https://doi.org/10.5194/hess-30-205-2026
55. Large-scale modeling of solar water pumps using machine learning G. Zuffinetti et al. https://doi.org/10.1016/j.apenergy.2025.127268
56. Machine Learning-Based Residual Bias Correction of SWAN Significant Wave Height Using Satellite Altimeter Observations as Ground Truth in the Black Sea E. Anık et al. https://doi.org/10.1016/j.oceaneng.2026.125991
57. A physics-guided machine learning framework for accurate reconstruction of missing discharge records: case study of the Gandak River at Hajipur, India R. Prakash & J. Tripura https://doi.org/10.1007/s00477-026-03264-5
58. A Proposal for a New Python Library Implementing Stepwise Procedure L. Fávero et al. https://doi.org/10.3390/a17110502
59. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. https://doi.org/10.18307/2025.0342
60. DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment H. Wang et al. https://doi.org/10.5194/hess-30-1543-2026
61. Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach I. Hani et al. https://doi.org/10.1016/j.ejrh.2025.102373
62. Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. https://doi.org/10.3390/rs16203756
63. Model parameterization scheme for a distributed hydrological model, BTOPMC S. Bastola https://doi.org/10.1016/j.hydres.2025.06.001