13 citations as recorded by crossref.

1. Riverine heatwaves are an emergent climate change risk A. van Hamel et al.
2. A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al.
3. Process-Driven Cross-Factor Integration of SWAT and Interpretable Machine Learning for Enhancing Riverine Total Nitrogen Prediction X. Xi et al.
4. River temperature response to atmospheric heatwaves is modulated by discharge and meltwater A. van Hamel et al.
5. A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al.
6. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
7. How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al.
8. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
9. Development and implementation of a passive surveillance system for Aedes albopictus in Emilia-Romagna, Italy M. Blaha et al.
10. Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch
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.
12. Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al.
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.