13 citations as recorded by crossref.

1. Riverine heatwaves are an emergent climate change risk A. van Hamel et al. https://doi.org/10.1038/s44221-025-00541-5
2. 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
3. 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
4. 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
5. 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
6. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
7. 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
8. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
9. 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
10. 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
11. 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
12. 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
13. 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