Abstract
The Agro-Pastoral Ecotone of Northern China (APEC) is a transitional area suffering from severe wind erosion. The wind data used in wind erosion modeling generally have different temporal resolutions and spatial station distributions. Previous studies have suggested that the temporal wind speed resolution influences the prediction of wind erosion events at the field scale. To date, no studies have been conducted to assess the impact of the type of wind data on regional wind erosion estimation. In this study, the Revised Wind Erosion Equation (RWEQ) and the Integrated Wind Erosion Modeling System (IWEMS) were used to evaluate the regional potential wind erosion in the Agro-Pastoral Ecotone of Northern China (APEC) during 2000 and 2012 based on four wind data type scenarios, including basic weather stations with daily wind statistics, basic weather stations with four wind speed measurements per day, reference climatological stations with daily wind statistics, and reference climatological stations with four wind speed measurements per day. The principal results reveal that the potential wind erosion estimates evaluated using the two models are closely correlated with the measured wind erosion data reported in the published literature, but the predicted values are generally lower than the observed values for the different scenarios. The magnitudes of the mean potential wind erosion ranged from 15.73 to 27.33 t ha−1 a−1 by RWEQ and changed between 61.77 and 98.54 t ha−1 a−1 by IWEMS for different scenarios. The spatial distribution and temporal trends of the annual or seasonal potential wind erosion obtained using the two models were similar for the different scenarios. This study revealed that wind speed is the most sensitive input, and hourly wind speed generated by the different temporal interpolation can significantly affect regional wind erosion estimation. Some studies involving precise regional wind erosion estimation, such as the impacts of landscape changes (land use/cover) on wind erosion, ecosystem service evaluation of reducing soil erosion, soil carbon sequestration and emissions through wind erosion, and wind erosion induced surface soil nutrient loss (e.g., nitrogen and phosphorus), may have been influenced by conducting regional wind erosion modeling based on different types of wind data. The users need to calibrate and validate the selected models for precise wind erosion prediction.
Highlights
Land degradation due to wind-induced soil erosion is an important surface process in arid and semi-arid regions (Dong and wang, 2000; Song et al, 2005; Guo et al, 2014; Webb et al, 2020; Borrelli et al, 2021)
Our study indicates that the type of wind data has a significant influence on the potential wind erosion estimation obtained using the Revised Wind Erosion Equation (RWEQ) and Integrated Wind-Erosion Modeling System (IWEMS)
1) The potential wind erosion evaluated using the two models are closely correlated with the measured wind erosion documented in the literature, but the observed values were generally lower than the predicted values for all four scenarios
Summary
Land degradation due to wind-induced soil erosion is an important surface process in arid and semi-arid regions (Dong and wang, 2000; Song et al, 2005; Guo et al, 2014; Webb et al, 2020; Borrelli et al, 2021). Many wind erosion models have been developed to quantify wind erosion since the 1960s These models mainly include the Wind Erosion Equation (WEQ) (Woodruff and Siddoway, 1965), the Revised Wind Erosion Equation (RWEQ) (Fryrear et al, 2000), the Wind Erosion Prediction System (WEPS) (Hagen, 1991), the Texas Tech Erosion Analysis Model (TEAM) (Gregory et al, 2004), the Wind Erosion on European Light Soil (WEELS) (Böhner et al, 2003), and the Wind Erosion Stochastic Simulator (WESS) (Potter et al, 1998) at the field scale, and the Integrated Wind-Erosion Modeling System (IWEMS) (Lu and Shao, 2001; Shao, 2001) and the AUStralian Land Erodibility Model (AUSLEM) (Webb et al, 2009) at a regional scale. Wind data (speed, direction, and turbulence) are generally considered to be the most sensitive parameters in wind erosion modeling (Lin et al, 2020; Webb et al, 2020)
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