Leibniz Institute of Agricultural Development in Transition Economies (IAMO) (Germany)

Central Asia heavily relies on mountain snowpacks for its water resources, which are crucial for hydropower and irrigated agriculture. Unfortunately, the scarcity of comprehensive snow observations in the area, compounded by the discontinuation of systematic surveys in the 1990s, has created a significant knowledge gap. Current observations are limited to a few stations monitoring only snow depth, with essential variables like snow water equivalent (SWE) notably absent. The lack of snow data impedes hydrological and climate studies in the region. How climate change affects snow accumulation and melt at present and in the future is hence uncertain, and ultimately downstream water supply for irrigation and other uses also remains unknown.

SWECA addresses these challenges by reconstructing high-resolution SWE data for the period 1979-2016 in Central Asia, focusing specifically on the mountainous zones. The approach is based a novel machine-learning snow mass model (GEMS), 1 km resolution daily climate forcing data (CHELSA-W5E5), and extensive evaluation with historical SWE records and satellite snow cover data. In addition, the project team conducted a mission to Central Asia, engaging with various geoscientific organizations in Kazakhstan, Kyrgyzstan, and Uzbekistan. The team also hosted two early-career researchers (ECRs) from the region, providing hands-on training on geospatial analysis of gridded snow data using the R programming language. 


Main outputs: 

The main output of the project is the daily, 1 km SWE dataset for mountainous Central Asia (1979–2016). A paper describing the dataset is under development, and this page will be updated once that is published. In addition, a paper describing the model applied was published during the project with support from the GEO Mountains grant. 

Contact: Prof. Daniel Müller, Department "Structural Change", IAMO: mueller [at] iamo.de

Image by Daniel Müller & Atabek Umirbekov

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