Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia

The arid and semi-arid areas of Central Asia are especially susceptible and vulnerable to climatic change, thus making understanding the relationship between extreme climate and vegetation dynamics in recent decades very important. Based on the maximum value composite, trend analysis, Mann-Kendall test, correlation analysis, and cross-correlation analysis, this study investigated the variations of extreme climatic indices and their influences on vegetation dynamics using the Unified Gauged-Based Analysis data from the NOAA Climate Prediction Center (CPC) and the Global Inventory Monitoring and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) series of 1982-2015. In general terms, it is suggested that Central Asia has experienced more extreme precipitation and high temperature events, especially in the mountainous regions. The vegetation in Central Asia has significantly increased during the past 34 years at a rate of 0.0006.year(-1). The NDVI is significantly and positively related to extreme precipitation and temperature intensity indices on a monthly scale, with strong spatiotemporal heterogeneity. The influences of extreme precipitation indices mainly occurred in May and June, without time lag on a monthly scale, while extreme temperatures exhibited significant relationship with maximum NDVI in April, with a time lag of at least 1 month. Analyzing the relationship between extreme climatic factors and vegetation dynamics on a monthly scale can help us to better understand the main limiting factors for vegetation growth in different growth periods compared with those on an annual scale. In the most severely affected regions, adaptation methods must be initiated, especially for improving the speed of disaster relief and reducing socioeconomic losses. The findings of this study will provide vital information for the ecological protection and sustainable development of Central Asia. (C) 2020 Elsevier Ltd. All rights reserved.