Reduction of and adaptation to environmental risks
12.07.2023 - 12.02.2024
Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan
Snow is an important component of regional hydrological cycles in Central Asia, where the largest share of the water resources depends on snowpack accumulated during winter and early spring in the mountains of Tian-Shan and Pamir. The snowpack provides essential water for irrigated agriculture in both mountain and downstream communities. However, understanding of annual and seasonal evolution of snowpack in the mountainous regions of Central Asia is to date inadequate due to lack of in-situ snow observations in the region. The lack of long-term data on snowpack impedes hydrological and climate studies in the region. Besides, it remains elusive how climate change affects snow accumulation and snowmelt at present and in the future. Therefore, the supply of water for downstream irrigation and other uses also remains unknown. This is why the project aims to reconstruct long-term and high-resolution snow water equivalent data for Tian Shan and Pamir mountains.
High resolution (1km) snow water equivalent estimates will be generated for the mountain domain in Central Asia from 1979 to 2016 using the novel Generalizable Empirical Model of Snow accumulation and melt (GEMS). GEMS leverages the capabilities of machine learning methods and incorporates empirical relationships between daily changes in snow water equivalent and precipitation, temperature, and topographic factors to produce accurate estimates in complex terrain.