86 citations as recorded by crossref.

1. SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism Z. Cui et al. https://doi.org/10.1029/2023WR035932
2. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
3. Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model S. Fang et al. https://doi.org/10.1029/2023WR034557
4. Spatially-varying parametrization of the Total Runoff Integrating Pathways (TRIP) scheme for improved river routing at the global scale A. Tsilimigkras et al. https://doi.org/10.1016/j.jhydrol.2025.133477
5. Image-based classification of stream stage to support ephemeral stream monitoring S. Ogle et al. https://doi.org/10.5194/hess-30-709-2026
6. 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
7. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. https://doi.org/10.1016/j.jhydrol.2024.131638
8. Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al. https://doi.org/10.1016/j.jhydrol.2025.134137
9. Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. https://doi.org/10.1016/j.jhydrol.2024.130841
10. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. https://doi.org/10.1016/j.jhydrol.2025.133320
11. 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
12. Coupling strategies of snowmelt runoff model and machine learning in the Lhasa River Basin T. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103400
13. A physically based neural network for flood routing: The Muskingum-Recurrent neural network Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135403
14. Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges C. Ye et al. https://doi.org/10.3390/rs17132280
15. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
16. An explainable deep learning model based on hydrological principles for flood simulation and forecasting X. Xiang et al. https://doi.org/10.5194/hess-29-7217-2025
17. 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 https://doi.org/10.3390/w15234194
18. Trends in Rainfall-Temperature Projections in Upper Bernam River Basin Using CMIP6 Scenarios in Malaysia M. Zakari et al. https://doi.org/10.32604/rig.2025.065835
19. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al. https://doi.org/10.1016/j.ecoinf.2025.103565
20. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. https://doi.org/10.5194/hess-28-3051-2024
21. TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections P. Parasar et al. https://doi.org/10.3390/su17094230
22. 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
23. 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
24. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. https://doi.org/10.3390/su16041376
25. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al. https://doi.org/10.5194/essd-17-461-2025
26. 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
27. Understanding the relationship between streamflow forecast skill and value across the western US P. Modi et al. https://doi.org/10.5194/hess-29-5593-2025
28. Machine learning model for stage-discharge curve calculation J. Langhammer et al. https://doi.org/10.14712/23361980.2025.25
29. Benchmarking structured state space models for differentiable parameter learning: A large-scale evaluation on accuracy and physical consistency X. Jing et al. https://doi.org/10.1016/j.eswa.2026.133040
30. Advances in coupling machine learning with hydrological simulation: A review Y. Yan et al. https://doi.org/10.1016/j.wse.2026.01.002
31. ESCAPE: An ensemble-based self-calibrated autoencoder with physics-informed estimation of high-resolution soil moisture and surface roughness from ALOS-2/PALSAR-2 polarimetric observations J. Lee et al. https://doi.org/10.1016/j.jag.2026.105206
32. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
33. Ice-covered lakes, hidden threats: Seasonal fate and risks of antibiotics and PAHs J. li et al. https://doi.org/10.1016/j.watres.2026.125477
34. Physics-informed machine learning with embedded sediment rating curve constraints for high-fidelity multi-lead-time forecast of suspended sediment concentration Y. Hemmatzadeh et al. https://doi.org/10.1016/j.jhydrol.2026.135287
35. Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins V. Budamala et al. https://doi.org/10.1016/j.jhydrol.2026.135672
36. Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. https://doi.org/10.1029/2023WR034420
37. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al. https://doi.org/10.1016/j.jhydrol.2025.132895
38. Simulación del caudal en España utilizando redes neuronales Long Short-Term Memory J. Casado-Rodríguez et al. https://doi.org/10.4995/ia.25084
39. Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. https://doi.org/10.1029/2023WR035337
40. Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al. https://doi.org/10.1111/gcb.70742
41. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. https://doi.org/10.1016/j.jhydrol.2025.134270
42. Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. https://doi.org/10.1016/j.jhydrol.2024.131573
43. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
44. Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. https://doi.org/10.1038/s41467-025-64367-1
45. DMFS: differentiable modeling for frozen soil thermodynamic characteristics Y. Ren et al. https://doi.org/10.1139/cgj-2025-0364
46. Runoff prediction in gauged and ungauged basins using Transformer-XAJ model H. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.133954
47. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. https://doi.org/10.3390/w16060896
48. 无资料流域日径流预测<bold>:</bold> 模型迁移优化及参考流域决策分析 昕. 宋 et al. https://doi.org/10.1360/SSTe-2024-0299
49. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al. https://doi.org/10.1016/j.hydres.2023.11.001
50. Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects D. Dalla Torre et al. https://doi.org/10.1007/s42452-024-05819-z
51. Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al. https://doi.org/10.1016/j.compag.2026.111480
52. From Gauged to Ungauged: Using Catchment Morphology to Guide the Transfer of Physics‐Informed Neural Network Discharge Predictions M. Khan et al. https://doi.org/10.1002/hyp.70579
53. Predicting annual peak daily streamflow in natural basins using quantile regression forests K. Kim et al. https://doi.org/10.1016/j.jhydrol.2025.133233
54. A Surrogate Model for Shallow Water Equations Solvers with Deep Learning Y. Song et al. https://doi.org/10.1061/JHEND8.HYENG-13190
55. Utilizing CMIP6-SSP scenarios with the VIC model to enhance agricultural and ecological water consumption predictions and deficit assessments in arid regions Q. Bao et al. https://doi.org/10.1016/j.compag.2025.110083
56. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. https://doi.org/10.5194/gmd-17-7181-2024
57. 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
58. Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. https://doi.org/10.1016/j.jhydrol.2024.132165
59. Incorporating artificial intelligence into the future of stormwater management M. Rahman et al. https://doi.org/10.1007/s42452-026-08488-2
60. AI-enabled predictive pipelines for early warning of agricultural pests, plant diseases, and drought M. Sarker https://doi.org/10.1016/j.atech.2026.101968
61. 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
62. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al. https://doi.org/10.1016/j.jhydrol.2026.135479
63. Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133086
64. High-resolution remote sensing-driven water management in semi-arid basins: A CNN-Attention-SWAT fusion framework for the Fen River J. Liu et al. https://doi.org/10.1016/j.srs.2025.100333
65. A data-driven framework for monthly hydrological modelling in regulated basins: application to the Jucar Hydrographic Confederation A. Garcia-Monteagudo et al. https://doi.org/10.1007/s40899-026-01362-4
66. Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.131389
67. Physics-informed neural networks for predicting sediment transport in pressurized pipe flows R. Tipu et al. https://doi.org/10.1007/s12665-025-12295-0
68. Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning Z. Zhang et al. https://doi.org/10.1371/journal.pone.0346237
69. Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks J. Martel et al. https://doi.org/10.5194/hess-29-2811-2025
70. δHT4P: A differentiable physical modeling framework for thermal evolution of permafrost L. Gou & W. Likos https://doi.org/10.1016/j.compgeo.2026.108132
71. Evaluating the Hydrological Applicability of Satellite Precipitation Products Using a Differentiable, Physics-Based Hydrological Model in the Xiangjiang River Basin, China S. Yan et al. https://doi.org/10.3390/rs18010137
72. 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
73. Reconstruction of the Vertical Distribution of Suspended Sediment Using Support Vector Machines F. Zhang et al. https://doi.org/10.3390/jmse14080695
74. Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning S. Chen et al. https://doi.org/10.1016/j.resenv.2024.100177
75. A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106802
76. Assessing Climate and Watershed Controls on Rain-on-Snow Runoff Using XGBoost-SHAP Explainable AI (XAI) Y. Aryal https://doi.org/10.3390/geosciences15120467
77. Machine learning paradigms in natural and engineered water systems: From proof-of-concept to trustworthy deployment R. Ma et al. https://doi.org/10.1016/j.watres.2026.125932
78. 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
79. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies M. Habib et al. https://doi.org/10.1515/geo-2025-0853
80. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. https://doi.org/10.5194/hess-28-3665-2024
81. Various Indices of Meteorological and Hydrological Drought in the Warta Basin in Poland J. Wicher-Dysarz et al. https://doi.org/10.3390/w17213035
82. 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
83. Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain S. Asadi et al. https://doi.org/10.1016/j.envsoft.2025.106360
84. Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins X. Song et al. https://doi.org/10.1007/s11430-024-1630-7
85. Deep Learning-Based Monthly Runoff Simulation in Changing Environments: Enhancing Accuracy by Reducing Data Redundancy S. Liu et al. https://doi.org/10.1007/s11269-026-04680-6
86. HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights Y. Zhu et al. https://doi.org/10.1016/j.jhydrol.2026.135153