24 citations as recorded by crossref.

1. Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models H. Meresa et al. 10.1007/s11069-021-05152-3
2. Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models J. Yang et al. 10.1016/j.ejrh.2024.102034
3. Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
4. Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data H. Haas et al. 10.3390/w13212947
5. Quantifying changes and drivers of runoff in the Kaidu River Basin associated with plausible climate scenarios B. Zhao et al. 10.1016/j.ejrh.2021.100968
6. The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration C. Nickles et al. 10.3390/rs12193241
7. Performance assessment of six bias correction methods using observed and RCM data at upper Awash basin, Oromia, Ethiopia B. Tumsa 10.2166/wcc.2021.181
8. Compound flood impact of water level and rainfall during tropical cyclone periods in a coastal city: the case of Shanghai H. Xu et al. 10.5194/nhess-22-2347-2022
9. Importance of Parameter and Climate Data Uncertainty for Future Changes in Boreal Hydrology A. Marshall et al. 10.1029/2021WR029911
10. Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin C. Nickles & E. Beighley 10.3390/rs13081590
11. Multi-Model Assessment of Streamflow Simulations under Climate and Anthropogenic Changes Exemplified in Two Indian River Basins A. Somisetty et al. 10.3390/w14020194
12. Quantifying the contribution of SWAT modeling and CMIP6 inputting to streamflow prediction uncertainty under climate change C. Chen et al. 10.1016/j.jclepro.2022.132675
13. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning R. Maity et al. 10.1016/j.acags.2024.100206
14. Aridity shifts in the MENA region under the Paris Agreement climate change scenarios M. Hamed et al. 10.1016/j.gloplacha.2024.104483
15. Relative impacts of climate change and land cover change on streamflow using SWAT in the Clackamas River Watershed, USA J. Chen & H. Chang 10.2166/wcc.2020.123
16. Adapting cities to the surge: A comprehensive review of climate-induced urban flooding G. Dharmarathne et al. 10.1016/j.rineng.2024.102123
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18. Assessment of Basin‐Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios S. Swain et al. 10.1002/joc.8677
19. Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India S. Naha et al. 10.5194/hess-25-6339-2021
20. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
21. Uncertainty in high‐resolution hydrological projections: Partitioning the influence of climate models and natural climate variability J. Moraga et al. 10.1002/hyp.14695
22. Future climate impacts on the hydrology of headwater streams in the Amazon River Basin: Implications for migratory goliath catfishes D. Feng et al. 10.1002/hyp.13952
23. Application of a Fractional Instantaneous Unit Hydrograph in the TOPMODEL: A Case Study in Chengcun Basin, China X. Xiang et al. 10.3390/app13042245
24. Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods Z. Ul Hassan et al. 10.1016/j.jhydrol.2024.131546