18 citations as recorded by crossref.

1. AMAformer: Dynamic and accurate forecasting of water quality in aquaculture with adaptive multi-kernel decomposition and attention Y. Li et al. https://doi.org/10.1016/j.envsoft.2026.107023
2. Flash flood forecasting in North East England through weak label-guided mixture of experts with multi-scale explainability J. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103402
3. Impact of climate change on hydrological processes in data-scarce regions: A case study in Tianshan Mountain, Central Asia H. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103553
4. Data-driven estimation of the hydrologic response using generalized additive models Q. Duchemin et al. https://doi.org/10.5194/gmd-18-8663-2025
5. Catchment hydrological response and transport are affected differently by precipitation intensity and antecedent wetness J. Knapp et al. https://doi.org/10.5194/hess-29-3673-2025
6. Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique R. Din et al. https://doi.org/10.3390/hydrology13040106
7. A runoff prediction method for arid regions integrating physics-guided signal extraction and temporally adaptive feature selection Z. Li et al. https://doi.org/10.1016/j.ejrh.2025.103034
8. Tree planting and soil conservation measures have strongly attenuated storm runoff responses on the Chinese Loess Plateau S. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134039
9. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
10. Annual memory in the terrestrial water cycle W. Berghuijs et al. https://doi.org/10.5194/hess-29-1319-2025
11. Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set Z. Eslami et al. https://doi.org/10.5194/hess-29-5121-2025
12. The Elusive Runoff Generation: Understanding Thresholds and Pathways in a Dry Sedimentary Plain M. Niborski et al. https://doi.org/10.1002/hyp.70399
13. Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al. https://doi.org/10.1088/1748-9326/ae49a2
14. Enhanced Spatiotemporal Relationship-Guided Deep Learning for Water Quality Prediction R. Chen et al. https://doi.org/10.3390/w18020185
15. Investigating the Dry–Wet Differentiation of the Yellow River Basin Driven by Climate Change and Anthropogenic Activities Q. Yu et al. https://doi.org/10.3390/rs18070974
16. Quantifying controls on rapid and delayed runoff response in double-peak hydrographs using ensemble rainfall-runoff analysis (ERRA) H. Gao et al. https://doi.org/10.5194/hess-29-6529-2025
17. Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al. https://doi.org/10.5194/hess-29-4761-2025
18. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025