Dominant role of grazing and snow cover variability on vegetation shifts in the drylands of Kazakhstan
Venkatesh Kolluru, Ranjeet John, Jiquan Chen, Preethi Konkathi, Srinivas Kolluru, Sakshi Saraf, Geoffrey M. Henebry, Jingfeng Xiao, Khushboo Jain, Maira Kussainova
Abstract
Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km2), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km2). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ. A detection, contribution, and attribution framework analysis suggests that almost 46% of the land area in Kazakhstan has experienced vegetation degradation, with grazing and snow cover variability identified as the principal drivers of degradation.