# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 1 Introduction  
Global warming caused by rising atmospheric CO\({}_{2}\) concentrations has become one of the most serious issues threatening the sustainable development of human society [1; 2]. Associated with global warming, weather events such as floods, heat waves, extreme drophts, and ice storms occur more frequently, especially extreme droughts, which have been widely observed in various parts of the world [3]. Extreme drought will reduce vegetation growth and weaken the ability to sequester carbon and may even cause a net loss of ecosystem carbon stocks [4; 5]. Therefore, analyzing the characteristics of extreme drought and exploring the responses of vegetation to extreme drought are of great importance for promoting vegetation stability and reducing eco-economic losses in the future. The occurrence of extreme drought disrupts the growth of vegetation due to temperature increases and a lack of moisture [6], and when climate conditions improve, vegetation resumes growth. The process of the response of vegetation to extreme drought can be quantified using the concept of resilience. Resilience represents the capacity of vegetation to remain in a stable state in the face of extreme drought [7] and can be characterized in terms of resistance, which refers to the ability of vegetation to persist during extreme drought and recovery, which quantifies the ability of vegetation to return to the original state after extreme drought [8; 9]. Resilience analysis provides a better understanding of the impact of extreme drought on vegetation and is a prerequisite for vegetation management to adapt to climate change [10]. At present, observation data sourced from tree-ring sampling, flux towers, remote sensing, etc., are used to study the resilience of vegetation. In Spain [11], the Tibetan Plateau [12], Central Asia [13], and even on a global scale [14], researchers have found that the resilience is related to vegetation types, and resistance and recovery generally have displayed an opposite pattern. Previous studies were mostly focused on trees, using tree-ring data. However, radial growth is further reduced due to trees allocating more photosynthate to their roots during extreme drought events, which means that tree-ring data are not a good indicator of primary productivity [15]. However, the resilience of different vegetation types to extreme drought has rarely been evaluated using remote sensing data, which can reliably reflect vegetation growth and productivity variations with spatial continuity [16]. Southwest China (SWC) is the main carbon storage area and one of the most ecologically vulnerable areas in China [3]. In recent decades, extreme drought events have frequently occurred and the intensity and duration of droughs have increased greatly [17; 18]. During 2009/2010, SWC experienced a \"once-in-a-century\" extreme drought event [19]. This extreme drought event caused a tremendous decrease in vegetation growth and productivity [18; 20; 21]. However, the resilience of different vegetation types to the extreme drought in SWC has seldom been systematically considered in previous studies. Analyzing how different vegetation types resist extreme droughs and measuring the recovery time from extreme droughs are crucial in order to reduce drought impacts and maintain the stability of the ecosystem in SWC. Hence, we used the self-calibrating Palmer drought severity index (scPDSI), water indices (precipitation, surface soil moisture, and root-zone soil moisture) and temperature indices (moderate resolution imaging spectroradiometer (MODIS), land surface temperature (LST), and mean air temperature) to describe the characteristics of the extreme drought and variations of water and temperature in SWC during 2009/2010. Vegetation growth and productivity indices (the normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)) were applied to assess the resilience of vegetation to the extreme drought event in SWC.