14 citations as recorded by crossref.

1. A Hydroclimatic Forecast System to Support Decision-Making and Improve Water Security in an Agricultural Frontier of the Brazilian Cerrado L. Sant’Anna Commar et al. https://doi.org/10.1061/JWRMD5.WRENG-6261
2. A multi-catchment evaluation of hydrological model transferability and ensemble averaging under contrasting hydroclimatic conditions in Iran A. Jahanshahi & M. Booij https://doi.org/10.1016/j.jhydrol.2026.135878
3. BiGRU-dXAJ: A hybrid hydrologic model enabling differentiable parameter learning for runoff simulation M. Sun et al. https://doi.org/10.1016/j.envsoft.2026.107136
4. Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework L. Wang et al. https://doi.org/10.5194/hess-29-361-2025
5. Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
6. Time-varying parameters from the same period in previous years to improve runoff forecasting X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.130685
7. A hybrid modelling framework for zero-inflated and skewed streamflow: enhancing prediction of extreme flows in tropical rain fed catchments D. Chandran & N. Chithra https://doi.org/10.1016/j.jhydrol.2025.134377
8. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. https://doi.org/10.3390/w16060896
9. Catchment Attributes Influencing Performance of Global Streamflow Reanalysis X. Ding https://doi.org/10.3390/w16243582
10. Incremental learning–based Kolmogorov–Arnold Networks for adaptive hydrological parameter optimization of flood forecasting X. Chi et al. https://doi.org/10.1016/j.eswa.2026.132098
11. Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. https://doi.org/10.3390/w16223335
12. Leveraging GCM-based forecasts for enhanced seasonal streamflow prediction in diverse hydrological regimes M. Girons Lopez et al. https://doi.org/10.1016/j.jhydrol.2024.132504
13. Understanding data sufficiency and temporal informativeness for hydrological model calibration in data scarce regions V. Manikanta et al. https://doi.org/10.1016/j.jhydrol.2026.136424
14. A calibration framework for distributed hydrological models considering spatiotemporal parameter variations Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132273