84 citations as recorded by crossref.

1. Probabilistic multi-step ahead streamflow forecast based on deep learning D. Karimanzira et al. https://doi.org/10.1515/auto-2024-0033
2. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu https://doi.org/10.1109/ACCESS.2024.3384066
3. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al. https://doi.org/10.3390/rs14102488
4. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. https://doi.org/10.5194/hess-27-1865-2023
5. Evaluating Earth observation products for Catchment-Scale operational flood monitoring and risk management in a sparsely gauged to ungauged river basin in Nigeria D. Idowu et al. https://doi.org/10.1016/j.jag.2025.104445
6. 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
7. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al. https://doi.org/10.3390/w16050777
8. Diagnosing the Added Value of Remote Sensing and Gridded Precipitation for Daily Runoff Forecasting Under Strong Antecedent Runoff Control R. Song et al. https://doi.org/10.3390/su18147494
9. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability C. Song https://doi.org/10.1016/j.jhydrol.2021.127324
10. A comprehensive assessment and comparison of streamflow simulations performance between the national water model versions 3.0 and 2.1 across the continental United States A. Ojha et al. https://doi.org/10.1016/j.jhydrol.2026.136046
11. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider https://doi.org/10.1029/2022WR032123
12. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. https://doi.org/10.2166/hydro.2024.268
13. 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
14. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. https://doi.org/10.1029/2022WR034352
15. Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality M. Jeung et al. https://doi.org/10.5194/hess-30-1077-2026
16. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. https://doi.org/10.5194/hess-28-4187-2024
17. A novel feature attention mechanism for improving the accuracy and robustness of runoff forecasting H. Wang et al. https://doi.org/10.1016/j.jhydrol.2023.129200
18. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al. https://doi.org/10.5194/hess-27-2357-2023
19. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al. https://doi.org/10.1016/j.ejrh.2025.102748
20. Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach M. Fan et al. https://doi.org/10.3390/geosciences15080279
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. https://doi.org/10.1016/j.jhydrol.2025.133764
22. Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation P. Li et al. https://doi.org/10.5194/hess-29-6829-2025
23. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. https://doi.org/10.21105/joss.04237
24. Enhancing groundwater level prediction accuracy at a daily scale through combined machine learning and physics-based modeling K. Sun et al. https://doi.org/10.1016/j.ejrh.2023.101577
25. How to deal w___ missing input data M. Gauch et al. https://doi.org/10.5194/hess-29-6221-2025
26. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. https://doi.org/10.5194/hess-26-3377-2022
27. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al. https://doi.org/10.1016/j.jhydrol.2023.129199
28. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. https://doi.org/10.1002/hyp.14847
29. Pre-processing satellite rainfall products improves hydrological simulations with machine learning T. Boulmaiz et al. https://doi.org/10.1080/02626667.2024.2378108
30. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
31. Hybrid Deep Learning Time-Lagged Precipitation-Runoff Model T. Calvette et al. https://doi.org/10.1061/JHYEFF.HEENG-6222
32. Riverine heatwaves are an emergent climate change risk A. van Hamel et al. https://doi.org/10.1038/s44221-025-00541-5
33. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
34. Heterogeneous impacts of climate change on streamflow in typical watersheds of three mountain systems in Xinjiang, Northwest China C. Zan et al. https://doi.org/10.1016/j.jag.2025.104585
35. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.128835
36. Urban flood risk analysis using presence-only machine learning approach: an integrated MaxEnt-cloud model framework in Harbin, China J. Hu et al. https://doi.org/10.1007/s11069-025-07452-4
37. 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. https://doi.org/10.5194/hess-27-4529-2023
38. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. https://doi.org/10.5194/hess-26-5085-2022
39. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. https://doi.org/10.3389/frwa.2023.1126310
40. The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses P. Shuai et al. https://doi.org/10.5194/hess-26-2245-2022
41. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
42. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. https://doi.org/10.1016/j.jhydrol.2024.130771
43. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. https://doi.org/10.1016/j.jhydrol.2023.129269
44. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. https://doi.org/10.5194/hess-26-5493-2022
45. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
46. Enhancing the performance of machine learning models for simulation of river flows in data-scarce catchments using bootstrapping and physics-based data augmentation J. Murungi et al. https://doi.org/10.1016/j.engappai.2026.115603
47. 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 https://doi.org/10.5194/hess-28-479-2024
48. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. https://doi.org/10.5194/hess-28-4099-2024
49. A hybrid Capsule-Transformer Network for daily runoff forecasting Z. Wu & H. Yan https://doi.org/10.1016/j.jhydrol.2025.134125
50. Use of regional sensitivity analysis for diagnosing parsimony of models: A water model case study R. Srikanthan et al. https://doi.org/10.1016/j.envsoft.2025.106727
51. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy https://doi.org/10.1016/j.jhydrol.2024.131301
52. 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. https://doi.org/10.5194/hess-28-2871-2024
53. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. https://doi.org/10.1016/j.jhydrol.2025.133320
54. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. https://doi.org/10.1016/j.jhydrol.2024.130861
55. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. https://doi.org/10.1007/s11442-024-2235-x
56. Adjusting rainfall inputs to hydrological models in a data-scarce area of Southern Africa D. Hughes https://doi.org/10.1080/02626667.2024.2360971
57. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
58. CAMELS-GB v2: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. https://doi.org/10.5194/essd-18-4345-2026
59. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. https://doi.org/10.5194/hess-26-1673-2022
60. Daily Runoff Series Prediction Using GWO Optimization and Secondary Decomposition: A Case Study of the Xujiang River Basin Q. Li et al. https://doi.org/10.3390/w18080946
61. A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al. https://doi.org/10.5194/hess-26-5163-2022
62. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130107
63. Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
64. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn https://doi.org/10.1016/j.jhydrol.2024.130862
65. Ensemble streamflow forecasting with diverse loss functions K. Dahal et al. https://doi.org/10.1016/j.asoc.2026.115276
66. 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
67. 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
68. Deep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation R. Vazirian https://doi.org/10.1061/JHYEFF.HEENG-6651
69. Unveiling the Role of Weighted Loss Functions in Deep Learning-Based Nowcasting of Extreme Rainfall Events H. Choi et al. https://doi.org/10.1109/TGRS.2025.3592853
70. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al. https://doi.org/10.5194/hess-29-4951-2025
71. Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.131389
72. Global Tropical Cyclone Precipitation Estimation via a Multitask Convolutional Neural Network Based on HURSAT-B1 Data M. Xue et al. https://doi.org/10.1109/TGRS.2021.3126419
73. Explainable global wildfire prediction model using graph neural networks D. Chen et al. https://doi.org/10.1016/j.geoai.2026.100052
74. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. https://doi.org/10.1016/j.jhydrol.2024.131867
75. Impact of Three Gorges Reservoir Operation on Water Level at Jiujiang Station and Poyang Lake in the Yangtze River Y. Wang et al. https://doi.org/10.3390/hydrology12030052
76. Deep learning-based prediction of flood hazard and future streamflow changes in the Brahmaputra River Basin under CMIP6 climate change scenarios S. Shahariar et al. https://doi.org/10.1016/j.ejrh.2026.103357
77. Uncertainty Reduction in Streamflow Prediction Using Mixture Density Networks N. Kumar & T. Roy https://doi.org/10.1061/JHYEFF.HEENG-6781
78. Learning landscape features from streamflow with autoencoders A. Bassi et al. https://doi.org/10.5194/hess-28-4971-2024
79. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. https://doi.org/10.1016/j.ejrh.2023.101584
80. 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. https://doi.org/10.5194/hess-29-841-2025
81. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
82. Regionalization of a Distributed Hydrology Model Using Random Forest S. Markhali et al. https://doi.org/10.1002/hyp.70437
83. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. https://doi.org/10.3389/frwa.2023.1166124
84. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al. https://doi.org/10.1016/j.ejrh.2025.102998