Assessment of the potential impacts of climate changes on Syr Darya watershed: A hybrid ensemble analysis method

Abstract

Study region: Syr Darya watershed, Central Asia. Study focus: Climate change has the potential to significantly impact the precipitation patterns, available water resources, food security, and ecosystem balance of a watershed. However, limited understanding exists of the temperature, precipitation, and streamflow trends in the Syr Darya watershed under the influence of climate change due to the uncertainties associated with climate change and the incompleteness of observational data. In this study, a multi-GCMs based statistical ensemble analysis (MGSEA) method is developed to effectively reflect the uncertainty of climate predictions and comprehensively assess the impact of climate change on the Syr Darya watershed during the period from 2021 to 2100. New hydrological insights for the region: The variations in temperature, precipitation, and streamflow were assessed by analyzing the projection results of various scenario combinations. Compared to the baseline (1960–2005), the findings reveal a rise in the annual average temperature ranging from 0.2 to 3.8 ℃ for RCP 4.5 and from 1.4 to 5.5 ℃ for RCP 8.5 in the 2080s. Additionally, the research confirms a downward trend in annual precipitation, with decreases of 3.7–27.8% for RCP 4.5 and 5.1–47.7% for RCP 8.5. The results of streamflow analysis exhibit an increasing trend during winter and autumn and a decreasing trend during summer and spring. The research outcomes obtained from MGSEA can be utilized for supporting water resources planning and management.

Keywords

• Multi-GCMs
• Climate change
• Machine learning
• Syr Darya
• Watershed