16 citations as recorded by crossref.

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3. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. https://doi.org/10.1029/2022WR032183
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7. Rivers in transition: 21st-century hydroclimate projections for the Thaya River Basin, Central Europe J. Bernsteinová et al. https://doi.org/10.1016/j.ejrh.2026.103375
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9. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. https://doi.org/10.1016/j.envsoft.2022.105326
10. Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept F. Cappelli et al. https://doi.org/10.1007/s00477-022-02332-w
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12. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. https://doi.org/10.3390/su132011485
13. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al. https://doi.org/10.2166/nh.2025.075
14. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. https://doi.org/10.5194/hess-26-5449-2022
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