Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

Abstract

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

Impacts of climate change on wind erosion in Southern Africa between 1991 and 2015

“…In addition, previous studies on large‐scale spatial patterns for dust emissions also showed that there was significant soil wind erosion in Southern Africa (Luo, Mahowald, & del Corral, 2003; Shao et al, 2011). Due to the lack of long time‐series and wide range of ground‐measured wind erosion data in Southern Africa, the MODIS MCD19A2 dataset was used in this study, along with the parameter Optical_Depth_047, to validate the results (Wang et al, 2020). It was found that the average AOD was positively correlated (R 2 = 0.37, p < 0.05) to the average soil wind erosion modulus, which demonstrated the reliability of our assessment of soil wind erosion in Southern Africa.…”

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

“…The eastern section of the south Aral Sea disappeared in 2014. As the Aral Sea shrank, the salt desert landscape continued to expand within the dry lakebed and a new desert formed on the dry lakebed of the Aral Sea, namely the Aralkum Desert, which became an important dust source in arid central Asia [56,57]. Dust storms from the dry lakebed contain a special saline-alkali dust because of the special composition of the lake's sediments, which contains high concentrations of salt, heavy metals, pesticides, and other substances (sulfates (SO 4 2− ), nitrates (NO 3 − ), and ammonium (NH 4 + )).…”

Potential erodibility of semi‐arid steppe soils derived from aggregate stability tests

“…This indicates limited wind speed events for potential wind erosion on bare fallow in the study area. In Central Asia, the wind speed increased significantly (+0.6 m s −1 decade −1 , p < 0.001) from 2011 to 2019, and moderate and heterogeneous changes are expected in future (Li et al, 2020; Wang et al, 2020). However, projections for northern Kazakhstan include particularly strong warmings and increasing precipitation that will also affect wind erosion severely, leading to complex spatiotemporal patterns (Li et al, 2020).…”