26 citations as recorded by crossref.

1. BiasCast: learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions O. Konold et al. https://doi.org/10.5194/hess-30-5067-2026
2. An argument for parsimony in differentiable hydrologic models S. Poudel & S. Steinschneider https://doi.org/10.5194/hess-30-5173-2026
3. A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135319
4. Process-Driven Cross-Factor Integration of SWAT and Interpretable Machine Learning for Enhancing Riverine Total Nitrogen Prediction X. Xi et al. https://doi.org/10.1021/acsestwater.5c01391
5. Joint prediction of river discharge and suspended sediment load through a physics-informed capacity-supply decomposition framework C. Ding et al. https://doi.org/10.1016/j.jhydrol.2026.136501
6. Only a matter of time? Evaluating modeling strategies while building a national operational flood forecast system for Denmark C. Brendel et al. https://doi.org/10.1016/j.ijdrr.2026.106393
7. A comparative study of deep learning and process-based models for hourly flood simulation under a unified input and optimization framework S. Gao et al. https://doi.org/10.1016/j.envsoft.2026.107101
8. A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al. https://doi.org/10.1016/j.jconhyd.2026.104952
9. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
10. Machine learning algorithms in sediment transport modeling: a review H. Hamidifar et al. https://doi.org/10.2166/aqua.2026.216
11. Development and implementation of a passive surveillance system for Aedes albopictus in Emilia-Romagna, Italy M. Blaha et al. https://doi.org/10.1016/j.ecoinf.2026.103718
12. A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling M. Álvarez Chaves et al. https://doi.org/10.1088/3049-4753/ae89ba
13. Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery L. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134749
14. Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al. https://doi.org/10.1016/j.aei.2025.104152
15. Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models S. Poudel et al. https://doi.org/10.1016/j.jhydrol.2025.134427
16. Benchmark-guided insights into hybrid process-based-deep learning models for continental-scale streamflow prediction across the United States D. Sourya et al. https://doi.org/10.1016/j.ejrh.2026.103636
17. Riverine heatwaves are an emergent climate change risk A. van Hamel et al. https://doi.org/10.1038/s44221-025-00541-5
18. Hydrologically constrained genetic programming for interpretable rainfall–runoff model discovery: A process–informed machine learning approach N. Matin et al. https://doi.org/10.1016/j.jhydrol.2026.136493
19. A physics-informed geospatial machine-learning downscaling framework for improving extreme rainfall K. He et al. https://doi.org/10.1016/j.geosus.2026.100513
20. River temperature response to atmospheric heatwaves is modulated by discharge and meltwater A. van Hamel et al. https://doi.org/10.1038/s43247-026-03269-6
21. Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
22. Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins Z. Wang et al. https://doi.org/10.5194/hess-30-5521-2026
23. How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al. https://doi.org/10.5194/hess-30-825-2026
24. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
25. Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
26. Benchmarking a bounded-coordinate DeepONet for unsaturated flow and solute transport under time-varying infiltration Z. Ma et al. https://doi.org/10.1016/j.jhydrol.2026.136388