129 citations as recorded by crossref.

1. Rainfall–Surface Runoff Estimation Using SCS-CN Model and Geospatial Techniques: A Case Study of the Shatt Al-Arab Region, Iraq–Iran H. Allafta et al. https://doi.org/10.3390/earth7010032
2. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia https://doi.org/10.1016/j.jhydrol.2023.130421
3. Enhancing runoff prediction accuracy of deep learning model using baseflow separation method and timestamp information C. Jin et al. https://doi.org/10.1016/j.jhydrol.2025.134044
4. 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
5. A hybrid modelling framework for zero-inflated and skewed streamflow: enhancing prediction of extreme flows in tropical rain fed catchments D. Chandran & N. Chithra https://doi.org/10.1016/j.jhydrol.2025.134377
6. Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression B. Bates & A. Dowdy https://doi.org/10.1002/joc.8368
7. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. https://doi.org/10.3390/su132011485
8. Integrated learning model for water intake capacity of Tyrolean weirs under supercritical flow G. Shen et al. https://doi.org/10.2166/hydro.2024.192
9. The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT L. Kadiyala et al. https://doi.org/10.3390/hydrology11090148
10. Enhancing daily runoff prediction: A hybrid model combining GR6J-CemaNeige with wavelet-based gradient boosting technique B. Mohammadi et al. https://doi.org/10.1016/j.jhydrol.2025.133114
11. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. https://doi.org/10.5194/hess-26-3537-2022
12. Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed R. Wang et al. https://doi.org/10.1111/jfr3.70022
13. Mountainous Flood Resilience: A Comprehensive Systematic Review of Flood Analysis Methods M. Rijal et al. https://doi.org/10.1007/s11831-025-10439-w
14. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. https://doi.org/10.1029/2021WR029583
15. A study on the runoff prediction mechanism of “water-soil-heat” in cold alpine regions with complex spatial distribution Q. Yu et al. https://doi.org/10.1016/j.scitotenv.2024.178059
16. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. https://doi.org/10.1111/jfr3.12880
17. A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN) J. Sha et al. https://doi.org/10.1007/s00382-023-06971-9
18. The Great Catchment Hysteresis Challenge K. Beven et al. https://doi.org/10.1002/hyp.70438
19. Leveraging advanced deep learning models for large-scale hydrological forecasting in Florida S. Shaghaghi et al. https://doi.org/10.1007/s00477-026-03259-2
20. Streamflow prediction using machine learning models in selected rivers of Southern India R. Sharma et al. https://doi.org/10.1080/15715124.2023.2196635
21. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. https://doi.org/10.3390/su16041376
22. Enhancing Multi-Step Runoff Forecasts Through Machine Learning and Climate-Informed Rainfall Prediction N. Salaeh et al. https://doi.org/10.1007/s41748-025-00785-x
23. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. https://doi.org/10.5194/hess-25-5517-2021
24. Prediction of climate change on surface water using NARX neural network model: a case study on Ghezel Ozan River, Northwest, Iran S. Mohammadi et al. https://doi.org/10.5004/dwt.2023.29802
25. The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment A. Tarpanelli et al. https://doi.org/10.1007/s10712-026-09935-w
26. Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM J. Guo et al. https://doi.org/10.1016/j.jhydrol.2023.129969
27. Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand P. Tulla et al. https://doi.org/10.1007/s00704-024-04862-5
28. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques J. Dharpure et al. https://doi.org/10.1007/s12145-024-01322-6
29. Trends and variability of rainfall characteristics influencing annual streamflow: A case study of southeast Australia G. Fu et al. https://doi.org/10.1002/joc.7923
30. Realising smarter stormwater management: A review of the barriers and a roadmap for real world application C. Sweetapple et al. https://doi.org/10.1016/j.watres.2023.120505
31. Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms J. Jang et al. https://doi.org/10.1016/j.ecoinf.2023.102370
32. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
33. Review of machine learning and WEAP models for water allocation under climate change D. Hirko et al. https://doi.org/10.1007/s12145-025-01820-1
34. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area D. Wang et al. https://doi.org/10.3390/rs15174153
35. A Deep Learning Framework for Flash-Flood-Runoff Prediction: Integrating CNN-RNN with Neural Ordinary Differential Equations (ODEs) K. Alkaabi et al. https://doi.org/10.3390/w17091283
36. Enhancing interpretability of AI models in reservoir operation simulation: Exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks A. Mahmoud et al. https://doi.org/10.1016/j.jhydrol.2024.131618
37. A two-phase physics-informed hybrid deep learning model for flood forecasting: improving process learning and interpretability C. Chen et al. https://doi.org/10.1016/j.advwatres.2026.105330
38. Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations Y. Zhan et al. https://doi.org/10.1016/j.jhydrol.2024.131504
39. Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data D. Singh et al. https://doi.org/10.5194/hess-27-1047-2023
40. Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis N. Chanda et al. https://doi.org/10.2166/wcc.2024.150
41. A review of current trends, challenges, and future perspectives in machine learning applications to water resources in Nepal S. Chaulagain et al. https://doi.org/10.1016/j.hazadv.2025.100678
42. Distributed simulation of fully coupled hydrological-hydrodynamic model for predicting rainfall-induced runoff/flood in small watersheds Y. Zhu et al. https://doi.org/10.1016/j.ejrh.2025.102450
43. Physics-informed machine learning in geotechnical engineering: a direction paper B. Yuan et al. https://doi.org/10.1080/17486025.2025.2502029
44. Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting K. Lin et al. https://doi.org/10.1016/j.scitotenv.2023.164494
45. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. https://doi.org/10.3390/w16131904
46. Development of a variable instantaneous unit hydrograph based on quantified rainfall spatial distribution W. Dong et al. https://doi.org/10.1016/j.jhydrol.2025.134497
47. Editorial: Spatiotemporal modelling and assessment of water-related multi-hazards P. Ganguli et al. https://doi.org/10.3389/frwa.2025.1579106
48. Data and knowledge-driven model for flood peak runoff forecasting H. Malik et al. https://doi.org/10.1016/j.jher.2026.100695
49. Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka N. Rathnayake et al. https://doi.org/10.1109/ACCESS.2023.3238717
50. Concentration Field Estimation on Effluent Mixing and Transport With a Parameter‐Based Field Reconstruction Convolutional Neural Network and Random Forests X. Yan et al. https://doi.org/10.1029/2022WR033375
51. Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study S. Wang et al. https://doi.org/10.1038/s41598-024-74269-9
52. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability A. El Bilali et al. https://doi.org/10.1007/s11356-024-34245-2
53. Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios Y. Song et al. https://doi.org/10.1016/j.scitotenv.2022.156162
54. Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review A. Chew et al. https://doi.org/10.1007/s11831-024-10145-z
55. Physical information-fused deep learning model ensembled with a subregion-specific sampling method for predicting flood dynamics C. Li et al. https://doi.org/10.1016/j.jhydrol.2023.129465
56. On the future of hydroecological models of everywhere K. Beven https://doi.org/10.1016/j.envsoft.2025.106431
57. Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin X. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130510
58. Model predictive control based on an integrator delay coupled model applied to open canal irrigation systems X. Li et al. https://doi.org/10.2166/hydro.2025.037
59. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal https://doi.org/10.1016/j.jhydrol.2024.130907
60. Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling S. Sammen et al. https://doi.org/10.1111/jfr3.70013
61. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez https://doi.org/10.1016/j.advwatres.2024.104796
62. A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings A. Mohanty et al. https://doi.org/10.1016/j.jhydrol.2024.131225
63. Unravelling spatiotemporal patterns of event-based surface rainfall-runoff response using a cellular automata approach L. Zhang et al. https://doi.org/10.1016/j.envsoft.2025.106623
64. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. https://doi.org/10.5194/gmd-15-6359-2022
65. Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization H. Mao et al. https://doi.org/10.3390/w16213030
66. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al. https://doi.org/10.1016/j.jhydrol.2022.128323
67. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. https://doi.org/10.1029/2023WR035676
68. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra https://doi.org/10.1007/s11269-024-04029-x
69. 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
70. Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al. https://doi.org/10.1007/s11356-024-35482-1
71. Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series A. Parsaie et al. https://doi.org/10.1016/j.jhydrol.2024.131041
72. Advanced artificial intelligence and remote sensing for earth observation–based tree crop monitoring: A global review with african perspectives A. Moussaid et al. https://doi.org/10.1016/j.neucom.2026.134165
73. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. https://doi.org/10.5194/essd-15-5597-2023
74. Bridging process-based simulation and deep learning: A location-aware framework for quantifying water diversion impacts on groundwater recovery Y. Song et al. https://doi.org/10.1016/j.ejrh.2026.103480
75. Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria M. Achite et al. https://doi.org/10.1007/s12665-024-12051-w
76. Integrated Random Forest and APEX-MODFLOW model for predicting and mapping river salinity in the Animas Watershed, Colorado River Basin H. Han et al. https://doi.org/10.1016/j.ejrh.2026.103302
77. Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test D. Vishwakarma et al. https://doi.org/10.1016/j.heliyon.2023.e16290
78. Global streamflow modelling using process-informed machine learning M. Magni et al. https://doi.org/10.2166/hydro.2023.217
79. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. https://doi.org/10.1016/j.advwatres.2024.104694
80. Using supervised machine learning for regional hydrological hazard estimation in metropolitan France Q. Ding & P. Arnaud https://doi.org/10.1016/j.ejrh.2024.101872
81. Unraveling threshold effects and feature contributions in hydrological-soil-vegetation response to Budyko parameters: A data-driven perspective Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134612
82. Developing an explainable deep learning module based on the LSTM framework for flood prediction Z. Zhang et al. https://doi.org/10.3389/frwa.2025.1562842
83. Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling G. Cheng et al. https://doi.org/10.1029/2021WR031370
84. Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin Y. Wang et al. https://doi.org/10.3390/w15223928
85. Water surface profile prediction in compound channels with vegetated floodplains M. Mohseni & A. Naseri https://doi.org/10.1680/jwama.21.00005
86. Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach R. Li et al. https://doi.org/10.1080/02626667.2025.2456211
87. An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP T. Mo et al. https://doi.org/10.2166/hydro.2023.050
88. Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting M. Asif et al. https://doi.org/10.1007/s11269-025-04093-x
89. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. https://doi.org/10.1016/j.jhydrol.2023.130038
90. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. https://doi.org/10.1016/j.jhydrol.2023.129990
91. Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions B. Mohammadi et al. https://doi.org/10.1016/j.jhydrol.2025.132963
92. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. https://doi.org/10.1016/j.envsoft.2022.105326
93. Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau K. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102331
94. Multi-step ahead probabilistic runoff forecasting with SHAP interpretability: a GPR-enhanced deep learning ensemble approach integrating teleconnection factors F. Zhu et al. https://doi.org/10.1016/j.envsoft.2025.106647
95. Bridging process-based hydrology and deep learning: exploring hybrid MIKE NAM–LSTM techniques for accurate streamflow prediction in a humid tropical river basin in India D. Chandran et al. https://doi.org/10.1007/s12040-026-02798-0
96. 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
97. Revolutionizing flood forecasting by integrating rainfall-runoff correlation analysis with advanced deep learning techniques D. Xu et al. https://doi.org/10.1016/j.rineng.2025.107804
98. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. https://doi.org/10.1017/eds.2024.14
99. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. https://doi.org/10.1016/j.ejrh.2022.101119
100. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. https://doi.org/10.1016/j.jhydrol.2022.127781
101. Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics J. Yang et al. https://doi.org/10.1016/j.watres.2026.125613
102. A Novel Approach for Predicting Friction Factor in Steep Mountain Channels: An Investigation into Machine Learning Methodologies for Complex Phenomena A. Mir & M. Patel https://doi.org/10.1007/s11269-026-04606-2
103. A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models B. Mohammadi et al. https://doi.org/10.1007/s10668-025-06743-x
104. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. https://doi.org/10.1016/j.scitotenv.2023.163328
105. Hybrid data-driven and process-based modeling for streamflow forecasting: comparative uncertainty analysis in the upper Baro watershed Y. Belina et al. https://doi.org/10.1007/s00477-025-03117-7
106. Subgrid informed neural networks for high-resolution flood mapping H. Herath et al. https://doi.org/10.1016/j.jhydrol.2025.133329
107. Multi-station Joint Runoff Forecasting Using Graph Neural Network Coupled with Spatial Connectivity of Hydrological Stations Q. Wang & S. Zhu https://doi.org/10.1007/s11004-024-10167-0
108. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta https://doi.org/10.1016/j.ejrh.2023.101607
109. Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al. https://doi.org/10.1016/j.ecoinf.2025.103526
110. Hybrid-cryo: a modular data-physics hybrid framework for cryospheric runoff simulation Z. Tang et al. https://doi.org/10.1016/j.jhydrol.2026.135575
111. A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. https://doi.org/10.2166/nh.2024.016
112. A new stable and interpretable flood forecasting model combining multi-head attention mechanism and multiple linear regression Y. Wang et al. https://doi.org/10.2166/hydro.2023.160
113. Reconstruction of Daily Runoff Series in Data-Scarce Areas Based on Physically Enhanced Seq-to-Seq-Attention-LSTM Model Z. Yin et al. https://doi.org/10.3390/w17233396
114. Performance comparison of a variable modification-based finite difference neural network and physics-informed neural networks in open channel modelling S. Cedillo et al. https://doi.org/10.2166/hydro.2026.096
115. Real-time water quality detection based on fluctuation feature analysis with the LSTM model L. Wang et al. https://doi.org/10.2166/hydro.2023.127
116. Quantifying the Effects of National Water Model Freshwater Flux Predictions on Estuarine Hydrodynamic Forecasts N. Chin et al. https://doi.org/10.1111/1752-1688.70033
117. Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al. https://doi.org/10.1016/j.rineng.2025.106345
118. A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models S. Yoon et al. https://doi.org/10.1016/j.jhydrol.2026.135097
119. Enhancing Conceptual Rainfall-Runoff Modeling in Data-Scarce Catchments using Machine Learning: Kolmogorov-Arnold Networks Compared to LSTMs S. Houénafa & M. Sylla https://doi.org/10.1007/s41748-025-01010-5
120. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. https://doi.org/10.1038/s41586-024-07145-1
121. Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China H. Xue et al. https://doi.org/10.1016/j.ejrh.2024.102100
122. QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification A. Kapoor & R. Chandra https://doi.org/10.1016/j.jhydrol.2025.134434
123. A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels A. Bassi et al. https://doi.org/10.2166/hydro.2023.246
124. Towards real-time water level and discharge measurements using imagery, machine learning, and edge computing R. Issa et al. https://doi.org/10.2166/hydro.2026.147
125. Exploring the influence of training sampling strategies on time-series deep learning model in hydrology S. Yoon & K. Ahn https://doi.org/10.1016/j.jhydrol.2025.132774
126. Smooth Spatial Modeling of Extreme Mediterranean Precipitation H. Hammami et al. https://doi.org/10.3390/w14223782
127. 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
128. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. https://doi.org/10.5194/hess-26-3377-2022
129. A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones Y. Wang et al. https://doi.org/10.1029/2023WR035194