Spatiotemporal variations of dust emission in Qaidam basin during the past 40 years

The Qaidam Basin, situated on the Qinghai-Tibetan Plateau, ranks among the highest deserts in the world. Dust derived from this basin are carried to neighboring regions by atmospheric circulation, resulting in accelerated glacier melting, earlier onset and prolonged duration monsoon, and modification of atmospheric temperature structure, thereby influencing climate patterns. Therefore, it is crucial to understand the characteristics of dust emissions in the Qaidam Basin. However, previous studies have often relied on satellite remote sensing data or models that lack validations. Hence, we use a dust emission model validated with extensive measured data from the Qinghai-Tibetan Plateau to simulate the spatiotemporal distribution of dust emissions in the Qaidam Basin from 1982 to 2020. And, the Classification and Regression Trees (CART) machine learning method is used for multi-factor comprehensive analysis to identify the dominant factors affecting dust emissions and to determine the configuration of factors that have the most significant impact. We found that the dust emissions in the Qaidam Basin showed a slightly increasing trend over the past 40 years. The internal variability of dust emissions and external factors such as meteorological conditions and surface characteristics contributed to a notable increase in dust emissions during the early 2000s. Meanwhile, the annual cyclic variation in meteorological conditions is the reason for the higher dust emissions in spring and early summer and lower ones in the other seasons in this region. The spatial distribution of dust emissions exhibits significant variation with altitude, and in the piedmont alluvial fans that locate the transition zones between the mountains and the basins the dust emission rates generally higher than the other places. The surface soil moisture (SM) and the wind speed at 10-m height (WS) are the dominant factors influencing dust emission, and their mechanisms of affecting dust emission are different. SM plays a crucial role in the initial stage of dust lifting, while WS has a continuous and more significant influence throughout the entire dust emission process. Among the combinations of multiple influencing factors, the one where SM is less than or equal to 0.113 mm 3 /mm 3 , WS is greater than 5.159 m/s, and air temperature (AT) is greater than −12.488 °C is the most conducive to dust emission. We provide new insights into dust emission in the Qaidam region, offering directions for future research to focus on the different mechanisms of various influencing factors in the dust emission process, as well as on the changes in dust emissions under different configurations of multiple factors.

Authors

Publication Details

Journal
Research in Cold and Arid Regions
Published
2024-12-01
DOI
https://doi.org/10.1016/j.rcar.2024.12.007
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal variations of dust emission in Qaidam basin during the past 40 years

Yawei Fan, Heqiang Du, S. Yang
Research in Cold and Arid Regions
Atmospheric and Environmental Gas Dynamics
article

Spatiotemporal variations of dust emission in Qaidam basin during the past 40 years

Yawei Fan, Heqiang Du, S. Yang
article en

Abstract

The Qaidam Basin, situated on the Qinghai-Tibetan Plateau, ranks among the highest deserts in the world. Dust derived from this basin are carried to neighboring regions by atmospheric circulation, resulting in accelerated glacier melting, earlier onset and prolonged duration monsoon, and modification of atmospheric temperature structure, thereby influencing climate patterns. Therefore, it is crucial to understand the characteristics of dust emissions in the Qaidam Basin. However, previous studies have often relied on satellite remote sensing data or models that lack validations. Hence, we use a dust emission model validated with extensive measured data from the Qinghai-Tibetan Plateau to simulate the spatiotemporal distribution of dust emissions in the Qaidam Basin from 1982 to 2020. And, the Classification and Regression Trees (CART) machine learning method is used for multi-factor comprehensive analysis to identify the dominant factors affecting dust emissions and to determine the configuration of factors that have the most significant impact. We found that the dust emissions in the Qaidam Basin showed a slightly increasing trend over the past 40 years. The internal variability of dust emissions and external factors such as meteorological conditions and surface characteristics contributed to a notable increase in dust emissions during the early 2000s. Meanwhile, the annual cyclic variation in meteorological conditions is the reason for the higher dust emissions in spring and early summer and lower ones in the other seasons in this region. The spatial distribution of dust emissions exhibits significant variation with altitude, and in the piedmont alluvial fans that locate the transition zones between the mountains and the basins the dust emission rates generally higher than the other places. The surface soil moisture (SM) and the wind speed at 10-m height (WS) are the dominant factors influencing dust emission, and their mechanisms of affecting dust emission are different. SM plays a crucial role in the initial stage of dust lifting, while WS has a continuous and more significant influence throughout the entire dust emission process. Among the combinations of multiple influencing factors, the one where SM is less than or equal to 0.113 mm 3 /mm 3 , WS is greater than 5.159 m/s, and air temperature (AT) is greater than −12.488 °C is the most conducive to dust emission. We provide new insights into dust emission in the Qaidam region, offering directions for future research to focus on the different mechanisms of various influencing factors in the dust emission process, as well as on the changes in dust emissions under different configurations of multiple factors.

Research in Cold and Arid Regions
National Natural Science Foundation of China
Openalex Percentile: Top 79%
Atmospheric and Environmental Gas Dynamics
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