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Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

A.J. DietzGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Straße 20, 82234 Wessling, GermanyChristopher ConradDepartment of Remote Sensing, Institute of Geography, University of Wuerzburg, 97074 Wuerzburg, GermanyClaudia KuenzerGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Straße 20, 82234 Wessling, GermanyGerhard GesellGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Straße 20, 82234 Wessling, GermanyStefan DechGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Straße 20, 82234 Wessling, Germany
2014en
ABI

Аннотация

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

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