6 citations as recorded by crossref.

1. Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment Z. Dong et al. 10.1016/j.ejrh.2025.102228
2. Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. 10.1016/j.advwatres.2025.104955
3. On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al. 10.1080/02626667.2025.2452357
4. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al. 10.1007/s11269-025-04231-5
5. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
6. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024