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Abstract

Snow is an important hydrological component in Central Asia. The snowmelt contributes to more than 50 % of total water formation in the region. Many hydro-meteorological phenomena such as floods or droughts can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow in order to improve the quality of hydrological forecasts and early warning of natural hazards such as flash-floods. In this study, we present an approach of using spatially distributed snow cover and snow water equivalent data as predictors for hydrological forecasts at different time scales in selected river basins in Central Asia. Our analysis are based on limited data and mostly on remote sensing products as well as modeling techniques that allow us to apply the proposed approach in data-scarce regions such as Central Asia. The proposed methodologies are introduced to the National Meteorological and Hydrological Services (NMHS) of Central Asian countries and are operational currently to improve their services. Our validation in 10 selected river basins across Central Asia in the period from 2000 to 2020 showed an agreement of hydrological forecasts with actual river flow of 75 % or more.