11 citations as recorded by crossref.

1. Deep learning-constrained projection of global fluvial floods and their socioeconomic implications under global warming X. Huang et al. https://doi.org/10.1088/1748-9326/ae19fe
2. A Century of Data: Machine Learning Approaches to Drought Prediction and Trend Analysis in Arid Regions M. Bouaziz et al. https://doi.org/10.3390/w17243567
3. Central Asian vegetation is more sensitive to soil moisture drought than to heat and meteorological drought L. Liu et al. https://doi.org/10.1016/j.jenvman.2026.130220
4. 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
5. Hydropower vulnerability to drought-flood abrupt alternation under climate change Y. Huang et al. https://doi.org/10.1016/j.energy.2025.139212
6. Evolution of nonstationary hydrological drought characteristics in the UK under warming S. Jha et al. https://doi.org/10.5194/hess-30-2685-2026
7. An extension of the logistic function to account for nonstationary drought losses T. Zhao et al. https://doi.org/10.5194/hess-29-2429-2025
8. Effect of terrestrial water storage deficit on economic growth for China Y. Ren et al. https://doi.org/10.1080/07900627.2025.2537402
9. Monitoring and forecasting agricultural drought in Golestan Province, Iran (2001–2028): an integrated approach using remote sensing and machine learning M. Jahanbakhsh & M. Akhoondzadeh https://doi.org/10.1016/j.asr.2025.11.113
10. Research Advances in Projections of Regional Climate Change over China B. Zhou et al. https://doi.org/10.1007/s13351-025-4913-8
11. Modelling the Dependence Between Drought and Heatwave Extremes in Kenya Using an Integrated EVT-Copula-XGBoost Framework C. Mulwa et al. https://doi.org/10.11648/j.ajmcm.20261101.11