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Aeolian particle transport inferred using a ~150-year sediment record from Sayram Lake, arid northwest China

Long Ma, Jinglu Wu, Jilili Abuduwaili, Wen Liu
  • Long Ma
    Chinese Academy of Sciences, China
  • Jilili Abuduwaili
    Chinese Academy of Sciences, China
  • Wen Liu
    Chinese Academy of Sciences, China

Abstract

We studied sediment cores from Sayram Lake in the Tianshan Mountains of northwest China to evaluate variations in aeolian transport processes over the past ~150 years. Using an end-member modeling algorithm of particle size data, we interpreted end members with a strong bimodal distribution as having been transported by aeolian processes, whereas other end members were interpreted to have been transported by fluvial processes. The aeolian fraction accounted for an average of 27% of the terrigenous components in the core. We used the ratio of aeolian to fluvial content in the Sayram Lake sediments as an index of past intensity of aeolian transport in the Tianshan Mountains. During the interval 1910-1930, the index was high, reflecting the fact that dry climate provided optimal conditions for aeolian dust transport. From 1930-1980, the intensity of aeolian transport was weak. From the 1980s to the 2000s, aeolian transport to Sayram Lake increased. Although climate in northwest China became more humid in the mid-1980s, human activity had by that time altered the impact of climate on the landscape, leading to enhanced surface erosion, which provided more transportable material for dust storms. Comparison of the Lake Sayram sediment record with sediment records from other lakes in the region indicates synchronous intervals of enhanced aeolian transport from 1910 to 1930 and 1980 to 2000.

Keywords

Grain-size; aeolian; lake sediment; Tianshan Mountains; China.

Supporting Agencies

National Natural Science Foundation of China; National Basic Research Program of China;Youth Innovation Promotion Association (CAS).

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Submitted: 2015-03-17 08:51:55
Published: 2015-05-28 00:00:00
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Copyright (c) 2015 Long Ma, Jinglu Wu, Jilili Abuduwaili, Wen Liu

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