Trade-offs and synergistic relationships in wind erosion in Central Asia over the last 40 years: A Bayesian Network analysis

Abstract

Wind erosion may have significant effects on both the environment and human activities. Central Asia, a prominently affected region by wind erosion on a global scale, has been the focus of a significant number of studies employing fundamental linear equations to establish causal relationships among the driving factors of wind erosion. However, such approaches neglect the imperative need for comprehensive analysis accounting for the intricate non-linear interactions among multiple factors, thereby potentially leading to distorted assessment outcomes. This study evaluated the multidimensional gradients (temporal, spatial, and vertical) of wind erosion in Central Asia over the last four decades using an enhanced version of the Revised Wind Erosion Equation (RWEQ) model. The Wind Erosion Modulus – Bayesian Network (WIEM-BN) model was developed by incorporating the Wind Erosion Modulus (WIEM) calculated by the RWEQ model as the parent node of the Bayesian Network (BN) and integrating multiple factors that influence the occurrence of wind erosion. The findings revealed a synergistic relationship between precipitation and the Normalized Difference Vegetation Index (NDVI), snowy days, and the Human Influence Index (HII), with trade-off correlations between temperature and precipitation and NDVI and soil moisture. The lowest overall WIEM level in Central Asia was associated with high HII and temperature (10–19 °C) as well as moderate precipitation (180–280 mm) and NDVI (0.18–0.24). Wind erosion in Central Asia varied substantially with Land Use Cover Change (LUCC), with the following drivers receiving precedence: wind speed > windy days > elevation > surface roughness (K') > HII > snowy days. Wind erosion was primarily caused by climate conditions (R = 0.741), with human activities (R = 0.629) acting as a suppressor. This study offered strategies to reduce wind erosion by up to 647.06 t km2 yr−1 in Central Asia, including a greater emphasis on ecological conservation programs and increased plant cover.