29 citations as recorded by crossref.

1. Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al. https://doi.org/10.1016/j.jhydrol.2026.135098
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3. Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106648
4. Performance of multiple tree-based models for estimating daily streamflow in the Cau River Basin T. Tuan Thach https://doi.org/10.2166/wpt.2025.077
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6. Long-term forecasting of monthly reservoir inflow using deep and machine-learning-based algorithms B. Ghenaati et al. https://doi.org/10.1016/j.engappai.2025.112175
7. A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133683
8. Streamflow prediction in the Danube River Basin using a multi-source graph-integrated GCN-LSTM model M. Sun et al. https://doi.org/10.1016/j.ejrh.2026.103275
9. The development and validation of a national-scale process-based hydrological model for Finland V. Kolhinen et al. https://doi.org/10.1016/j.jhydrol.2025.134650
10. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. https://doi.org/10.1016/j.jhydrol.2025.134270
11. Evaluation of a socio-hydrological water resource model for drought management in groundwater-rich areas D. Wendt et al. https://doi.org/10.5194/hess-30-2837-2026
12. Drought dynamics across the hydrological cycle – an extensive validation of the National Hydrological Model of Denmark R. Schneider et al. https://doi.org/10.5194/hess-30-4019-2026
13. Climate Change Impacts on River Hydraulics: A Global Synthesis of Hydrological Shifts, Ecological Consequences, and Adaptive Strategies B. Nile et al. https://doi.org/10.1007/s41101-025-00375-y
14. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
15. Optimizing runoff simulation in three mid-high latitude catchments by integrating terrestrial ecosystem modelling, hybrid machine learning, and causal inference H. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103085
16. Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach R. Li et al. https://doi.org/10.1080/02626667.2025.2456211
17. A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models K. Robles et al. https://doi.org/10.3390/w18010119
18. Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. https://doi.org/10.5194/hess-29-5099-2025
19. Effects of meteorological reconstruction on hydrological and deep learning streamflow models in Lanyang Watershed, Taiwan N. Tran & Y. Su https://doi.org/10.1016/j.ejrh.2026.103673
20. Enhancing water management in data-scarce watersheds using satellite and reanalysis precipitation: a combined SWAT+ and SMOGN machine learning approach M. Almeida et al. https://doi.org/10.1016/j.jhydrol.2026.135895
21. Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention L. Zhao et al. https://doi.org/10.3390/w17142043
22. Data-driven model as a post-process for daily streamflow prediction in ungauged basins J. Choi & S. Kim https://doi.org/10.1016/j.heliyon.2025.e42512
23. Hydrological Predictive Modeling for Indian River: Leveraging LSTM and GRU Attention Mechanisms S. Lachure & A. Tiwari https://doi.org/10.1007/s42979-025-04289-3
24. A comparative analysis of machine learning models for predicting groundwater and surface water in a stressed semi-arid watershed: The Khanmirza case study Z. Ebrahimzadeh et al. https://doi.org/10.1007/s12665-025-12801-4
25. An Enhanced Hybrid LSTM–Linear Regression Framework for 90-Day Rainfall Forecasting in Rainfed Agricultural Regions A. Kunlerd et al. https://doi.org/10.48084/etasr.15622
26. Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model H. Shen et al. https://doi.org/10.3390/w17243507
27. A novel hybrid framework for combining process-based models with machine learning for streamflow prediction X. Jiang et al. https://doi.org/10.1016/j.advwatres.2025.105177
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29. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025