42 citations as recorded by crossref.

1. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
2. Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al. 10.1371/journal.pwat.0000059
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4. 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
5. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
6. 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
7. A hydrological knowledge-informed LSTM model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the Tibetan plateau S. Hou et al. 10.1016/j.jhydrol.2024.132409
8. Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv 10.1016/j.jhydrol.2025.133033
9. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
10. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
11. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
12. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
13. Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models S. Khorram & N. Jehbez 10.1080/19942060.2024.2387051
14. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al. 10.1016/j.envsoft.2023.105831
15. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al. 10.5194/hess-29-1061-2025
16. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al. 10.1016/j.jhydrol.2025.133594
17. Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. 10.3390/w16223335
18. Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V. Shankar et al. 10.1063/5.0122115
19. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
20. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al. 10.18307/2025.0342
21. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. 10.5194/hess-29-1277-2025
22. 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
23. Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil G. Costa et al. 10.3390/hydrology10110208
24. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al. 10.1016/j.watres.2022.118972
25. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
26. 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. 10.5194/gmd-17-7181-2024
27. Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales A. Pandit et al. 10.1016/j.watres.2025.123156
28. Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method T. Amnuaylojaroen et al. 10.3390/w16223296
29. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
30. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al. 10.5194/hess-28-4521-2024
31. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al. 10.1016/j.jhydrol.2025.132895
32. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
33. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish 10.2166/wst.2023.330
34. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
35. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
36. Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration D. Mingo et al. 10.5194/gmd-18-1709-2025
37. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. 10.3390/w17060907
38. Neural ordinary differential equations-based approach for enhanced building energy modeling on small datasets Z. Ma et al. 10.1007/s12273-025-1269-z
39. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
40. Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana J. Katsekpor et al. 10.1016/j.envc.2025.101181
41. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
42. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh 10.1029/2022WR034188