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.
Highlights
During the past few decades, global climate change and human disturbance have meant that land degradation has become one of the most serious environmental problems of the 21st century [1]
The research consists of four main steps: First, based on a time-series decomposition model, the wind speed variability of ground measurement data and reanalysis data was explored; second, by using multi-source geospatial data, the monthly Soil Wind Erosion Potential (SWEP) across Central Asia (CA) was generated based on Google Earth Engine (GEE)-Revised Wind Erosion Equation (RWEQ), and we explored the spatiotemporal variation of SWEP between 2000 and 2019; third, based on dust storm index (DSI) and satellite-based Aerosol Optical Depth (AOD), validation was conducted to test the reliability of annual SWEP; was explored; second, by using multi-source geospatial data, the monthly SWEP across CA was generated based on GEE-RWEQ, and we explored the spatiotemporal variation of SWEP between
We developed a fully automated algorithm for quantitatively mapping wind erosion based on the Google Earth Engine, processed terabytes of geo-spatial data, and retrieved spatial and temporal patterns of monthly SWEP in CA, over 20 years (2000 to 2019)
Summary
During the past few decades, global climate change and human disturbance have meant that land degradation has become one of the most serious environmental problems of the 21st century [1]. The purposes of this study are (1) to evaluate the near-surface wind speed trend in CA from 2000–2019, based on multiple source climate data; (2) to quantify mapping the soil wind erosion potential (SWEP) in CA based on the RWEQ model by using the GEE platform; and (3) to analyze the monthly and seasonally change of soil wind erosion and the response of soil wind erosion dynamics to land cover change (LCC). This is the first study to execute the wind erosion model on the GEE platform. Sdabltyawndinddusetrsotsoiromnso, cwchuircrhinagreincaAursaedlkum Desebryt,wrienpdreersoesniot nonocecuorfrtihnge imn oAsrtalskeurmiouDsesperrot,brleepmressefnotrohnuemofatnhehmeaolstthsearnioduasgprriocbulletmursaflorachtuivmiatines in CA [h1e6a]l.th and agricultural activities in CA [16]
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