[M-IS01] Environmental, Socio-Economic and Climatic Changes in Northern Eurasia

[MIS01-P08] Non-linear and non-stationary interactions between vegetation dynamics and potential driving factors in Kazakhstan

*Venkatesh Kolluru¹, Ranjeet John^2,1, Jiquan Chen^3,4, Reza Goljani Amirkhiz², Vincenzo Giannico⁵, Raffaele Lafortezza^4,5 (1.Department of Sustainability and Environment, University of South Dakota, 2.Department of Biology, University of South Dakota, 3.Department of Geography, Environment, and Spatial Sciences, Michigan State University, 4.Center for Global Change and Earth Observations, Michigan State University, 5.Department of Agricultural and Environmental Sciences, University of Bari A. Moro)

Keywords:Vegetation, Socioeconomic, Climate, Livestock, Snow, Kazakhstan

The Kazakh steppe, within Central Asian grasslands, was recently identified as a land cover/use and climate change 'hotspot' and thus is considered a vulnerable ecosystem susceptible to intensified grazing with considerable interannual variations. Studies examining vegetation dynamics due to the combined impacts of moisture, climatic and anthropogenic drivers are scarce in Central Asia. A comprehensive analysis of the spatial distribution of vegetation greenness and its driving factors will therefore improve understanding of the causes of grassland degradation. Hence, we seek to investigate the individual and combined influences of various drivers on greenness dynamics in Kazakhstan. We seek to understand whether there is a relationship between peak season greenness and spring precipitation, spring drought, preceding winter freeze-thaw cycles, snow (percent snow cover and snow depth) for Kazakhstan. As hypothesized, snow depth and spring precipitation accounted for 60% and 30% variations in Normalized Difference Vegetation Index (NDVI). Our results showed that freeze-thaw accounts for 50% of changes in NDVI and continuous thawing in winter contribute to improved greenness. We also found that moisture and topographic factors were the primary driving factors for NDVI dynamics rather than socio-economic drivers. However, the impacts of socio-economic drivers were magnified when interacting with the physical environment affecting vegetation growth. Our results showed that slope and SM have the highest q-value, accounting for approximately 70% of changes in greenness. Socio-economic drivers also exhibited significant impacts on vegetation dynamics, with the majority explained by crop production (59%) followed by population (48%) and livestock (26%) densities. We found that most of the pair-wise driver interactions exhibited bi-factor enhancement when interacting with NDVI, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of socio-ecological system variables that provide an in-depth understanding of the impacts exerted by various driving factors on NDVI. These findings help us understand vegetation dynamics, the factors causing degradation and thereby provide a theoretical and scientific basis for ecological protection in semi-arid grassland regions.