13 citations as recorded by crossref.

1. Prediction of Snowmelt Days Using Binary Logistic Regression in the Umbria-Marche Apennines (Central Italy) M. Gentilucci & G. Pambianchi 10.3390/w14091495
2. Impacts of hydroclimate change on climate-resilient agriculture at the river basin management C. Singha et al. 10.2166/wcc.2023.656
3. Nonstationary Runoff Responses Can Interact With Climate Change to Increase Severe Outcomes for Freshwater Ecology A. John et al. 10.1029/2021WR030192
4. Smallholder farmers’ preference for climate change adaptation for lowland rain‑fed rice production in Lao PDR . Jong Ahn CHUN et al. 10.2480/agrmet.D-21-00001
5. Assessment of Future Water Security under Climate Change: Practical Water Allocation Scenarios in a Drought-Prone Watershed in South Korea W. Kim et al. 10.3390/w16202933
6. Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria K. Adiat et al. 10.1007/s10661-020-08532-7
7. How to incorporate climate change into modelling environmental water outcomes: a review A. John et al. 10.2166/wcc.2020.263
8. Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply T. Lachaut et al. 10.1016/j.advwatres.2022.104311
9. Development of Climate Impact Response Functions for highly regulated water resource systems P. Marcos-Garcia et al. 10.1016/j.jhydrol.2020.125251
10. Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis T. Lachaut & A. Tilmant 10.5194/hess-25-6421-2021
11. Coupled annual and daily multivariate and multisite stochastic weather generator to preserve low- and high-frequency variability to assess climate vulnerability K. Ahn 10.1016/j.jhydrol.2019.124443
12. Climate stress impacts on the reservoir inflows: a decision-scaling and IHACRES modeling approach in South Korean basins T. Kim & B. Kang 10.2166/wcc.2023.156
13. Hydrogeochemical characterization of the groundwater of Lahore region using supervised machine learning technique S. Ismail & M. Ahmed 10.1007/s10661-022-10648-x