Evaluation of Climatic and Anthropogenic Impacts on Dust Erodibility: A Case Study in Xilingol Grassland, China

Abstract

Aeolian dust is dependent on erosivity (i.e., wind speed) and erodibility (i.e., land surface conditions). The effect of erodibility on dust occurrence remains poorly understood. In this study, we proposed a composite erodibility index (dust occurrence ratio, DOR) and examined its interannual variation at a typical steppe site (Abaga-Qi) in Xilingol Grassland, China, during spring of 1974–2018. Variation in DOR is mainly responsible for dust occurrence (R2 = 0.80, p-value < 0.001). During 2001–2018, DOR values were notably higher than those during 1974–2000. There was also a general declining trend with fluctuations. This indicates that the land surface conditions became vulnerable to wind erosion but was gradually reversed with the implementation of projects to combat desertification in recent years. To understand the relative climatic and anthropogenic impacts on erodibility, multiple regression was conducted between DOR and influencing factors for the period of 2001–2018. Precipitation (spring, summer, and winter) and temperature (summer, autumn, and winter), together with livestock population (June) explained 82% of the variation in DOR. Sheep and goat population made the greatest contribution. Therefore, reducing the number of sheep and goat could be an effective measure to prevent dust occurrence in Xilingol Grassland.

Impacts of Underlying Surface on the Dusty Weather in Central Inner Mongolian Steppe, China

“…(2011), and Wu et al. (2020) used the same definition for DOR. In this study, the threshold wind speed for dust emissions in a certain area when the dust emission frequency was 5% was used as the standard for strong wind.…”

“…The formula is as follows: The DOR expresses the vulnerability of the land surface conditions to wind erosion. Kimura and Shinoda (2010), Kurosaki et al (2011), andWu et al (2020) used the same definition for DOR. In this study, the threshold wind speed for dust emissions in a certain area when the dust emission frequency was 5% was used as the standard for strong wind.…”

“…In recent years, extreme weather phenomena driven by global warming have increased sharply, and El Nino and La Nina events occur frequently, which leads to more drought, less precipitation, less soil moisture, low vegetation coverage, and frequent dust weather in East Asia (Hara et al., 2006; J. Li et al., 2021; Ma et al., 2011; Shi et al., 2020; Zong et al., 2021). Dust Strom was one of the extreme weather events, although dust events were decreased in most parts of East Asia, the threat of strong dust storms is still increasing in the Mongolian plateau steppe (Jugder et al., 2011; Lee & Shon, 2011; Tana et al., 2021; Wu et al., 2020). Therefore, it is necessary to understand the influence factors of dusty weather in the Xilingol league, an ecological barrier in northern China.…”

Prediction and assessment model of the impact of different grazing strategies on Xilingole grassland

“…Therefore, researchers use emerging technological tools and algorithms to study grassland ecosystems to give scientific predictions and evaluations for sustainable resource development and ecological balance. To study the effect of wind and dust on soil desertification, Wu, Jing et al used multiple regression algorithms to analyze a time series dataset from 2001-2018 obtained from the Xilinguole grassland, China, and concluded that an increase in livestock load enhances the effect of wind and dust [2]. For vegetation and precipitation, Chi, Dengkai et al combined vegetation net primary production (NPP) and precipitation variability analysis using residual trend method and fixed-size moving window method to conclude that grazing and precipitation are the main drivers of grassland vegetation change [3].…”

Impacts of Land Surface Conditions and Land Use on Dust Events in the Inner Mongolian Grasslands, China

“…This dead leaf hypothesis cannot be widely observed or monitored by satellite observation, however, Nandintsetseg and Shinoda (2015) recently combined land surface memory and modeling to give support to this theory. In addition, Yang et al (2016) and Wu et al (2020) analyzed the quantitative contributions of human activity and climate change to the recovery of grassland vegetation net primary productivity. However, a limited number of studies have focused on longterm land-use policies and quantitatively evaluated the impact of grazing and mowing on dust in Inner Mongolian TGs.…”