11 citations as recorded by crossref.

1. Applications of artificial intelligence models in seawater intrusion: progress, challenges, and future directions X. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.136086
2. Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention P. Yang & D. Chen https://doi.org/10.1007/s11269-026-04502-9
3. Explaining spatial variation and catchment characteristics thresholds in streamflow response A. Amorim Brandão et al. https://doi.org/10.1016/j.jhydrol.2026.135492
4. Prediction of monthly precipitation and maximum 24 h precipitation using Random Forest, Decision Tree and XGBoost models M. Kashefi et al. https://doi.org/10.1007/s40808-025-02714-3
5. Multi-scale machine learning model for long-term blue and green water consumption scenarios for crop production Z. Li et al. https://doi.org/10.1016/j.jia.2026.04.015
6. Localized Browning in Thermokarst-Dominated Landscapes Reverses Regional Greening Trends Under a Warming Climate in Northeastern Siberia R. Wang et al. https://doi.org/10.3390/rs18020308
7. Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135310
8. Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al. https://doi.org/10.3389/frwa.2025.1709138
9. 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
10. Continental-scale prediction of hydrologic signatures and processes R. Araki et al. https://doi.org/10.5194/hess-30-3647-2026
11. 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