95 citations as recorded by crossref.

1. Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS R. Lazin et al. 10.1038/s41598-025-97610-2
2. Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches A. Husic et al. 10.1016/j.hydroa.2022.100134
3. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification M. Fan et al. 10.1016/j.envsoft.2023.105849
4. A deep learning-based probabilistic approach to flash flood warnings in mountainous catchments Y. Zhao et al. 10.1016/j.jhydrol.2025.132677
5. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. 10.1098/rsta.2024.0287
6. Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al. 10.1016/j.rineng.2025.106345
7. Deep Learning zur Optimierung der Trinkwasserversorgung aus Talsperren G. Johnen et al. 10.1007/s35152-025-1982-z
8. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
9. WATER LEVEL PREDICTIONS THAT VISUALIZE UNCERTAINTY USING LSTM COUPLED WITH ALTERNATIVE METHODS IN BAYESIAN INFERENCE AT A RESERVOIR NEAR A DRAINAGE PUMPING STATION N. KIMURA et al. 10.2208/jscejj.23-22011
10. Adapting cities to the surge: A comprehensive review of climate-induced urban flooding G. Dharmarathne et al. 10.1016/j.rineng.2024.102123
11. Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al. 10.5194/hess-29-3405-2025
12. Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling S. Chen et al. 10.1038/s41598-024-77678-y
13. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
14. Unstructured mesh-based graph neural networks for estimating the spatiotemporal distribution of a human-induced chemical in freshwater S. Kim et al. 10.1016/j.wroa.2025.100367
15. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity D. Li et al. 10.1016/j.jhydrol.2024.131737
16. 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. 10.3390/w16131904
17. Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S. Liu et al. 10.3389/frwa.2023.1150126
18. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
19. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al. 10.1007/s00267-024-01989-z
20. Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation Y. Wang et al. 10.1016/j.advwatres.2025.105024
21. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. 10.1016/j.jhydrol.2025.133764
22. Hydropower flexibility valuation tool for flow requirement evaluation M. Roni et al. 10.1016/j.egyr.2022.11.172
23. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
24. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan 10.5194/hess-29-785-2025
25. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
26. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
27. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al. 10.5194/hess-28-4331-2024
28. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. 10.1016/j.eswa.2024.126004
29. Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang 10.3390/w14223672
30. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
31. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. 10.5194/hess-29-2361-2025
32. A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea K. Kareem et al. 10.3390/w14142172
33. Estimating uncertainty in flood model outputs using machine learning informed by Monte Carlo analysis M. Nguyen et al. 10.1016/j.jhydrol.2025.133928
34. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
35. Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method S. Zhan et al. 10.1016/j.jhydrol.2025.133484
36. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. 10.3390/w14111745
37. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
38. Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting A. Ferchichi et al. 10.1007/s00521-025-11026-7
39. Machine learning in stream and river water temperature modeling: a review and metrics for evaluation C. Corona & T. Hogue 10.5194/hess-29-2521-2025
40. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al. 10.3390/w15081635
41. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
42. Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling S. Sammen et al. 10.1111/jfr3.70013
43. Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain J. Farfán-Durán & L. Cea 10.1007/s12145-024-01454-9
44. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
45. Uncertainties in different leak localization methods for water distribution networks: a review P. Mohan Doss et al. 10.1080/1573062X.2023.2229301
46. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
47. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
48. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
49. Hybrid modeling for streamflow prediction in ungauged rivers with uncertainty quantification: a comparative analysis Y. Zeng et al. 10.2166/hydro.2025.011
50. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. 10.5194/hess-29-841-2025
51. Response to hydrometeorological changes and evaluation of habitat quality in the Dongting Lake basin, China H. Wang et al. 10.2166/wcc.2024.179
52. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al. 10.1016/j.jenvman.2024.120931
53. Evaluation of daily stream temperature predictions (1979–2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm J. Diaz et al. 10.1016/j.envsoft.2025.106655
54. Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang 10.3390/w15234194
55. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
56. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
57. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
58. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
59. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
60. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
61. ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye Ö. Çoban et al. 10.1007/s11069-023-06233-1
62. How confident and reliable are deep learning models for streamflow prediction under flood conditions M. Albared et al. 10.1007/s11069-025-07527-2
63. EEG-Based Seizure Prediction via Model Uncertainty Learning C. Li et al. 10.1109/TNSRE.2022.3217929
64. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
65. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al. 10.1061/JHYEFF.HEENG-6246
66. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
67. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
68. Comparing Robust Optimization Approaches for Addressing Hydrologic Model Uncertainty in Infrastructure Planning: A Green Infrastructure Example J. Smith et al. 10.1061/JWRMD5.WRENG-6729
69. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
70. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
71. MC-ANN: A Mixture Clustering-Based Attention Neural Network for Time Series Forecasting Y. Li & D. Anastasiu 10.1109/TPAMI.2025.3565224
72. Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique X. Yang et al. 10.1007/s11269-023-03668-w
73. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
74. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al. 10.5194/hess-29-1749-2025
75. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
76. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
77. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
78. A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs V. Ngoc Tran et al. 10.1016/j.jhydrol.2024.130608
79. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
80. Multi-step ahead probabilistic runoff forecasting with SHAP interpretability: a GPR-enhanced deep learning ensemble approach integrating teleconnection factors F. Zhu et al. 10.1016/j.envsoft.2025.106647
81. Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique X. Le et al. 10.1016/j.ejrh.2025.102668
82. Multi-step-ahead forecasting of daily reference evapotranspiration using hybrid deep learning models for the Taklamakan Desert oasis M. Tang et al. 10.1016/j.ejrh.2025.102663
83. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al. 10.1002/hyp.14936
84. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
85. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
86. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
87. Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea J. Kwak et al. 10.1007/s00477-021-02094-x
88. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
89. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
90. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
91. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. 10.1029/2021GL096847
92. Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al. 10.1111/1752-1688.13093
93. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
94. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
95. A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning A. Abbaszadeh Shahri et al. 10.1007/s11053-022-10051-w