Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Abstract

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020

“…This might be related to the location of Southwestern China, which is the subtropical monsoon climate zone, with adequate precipitation. Therefore, small changes of precipitation may not cause seriously change on vegetation growth (Lai et al 2020;Hou et al 2015;Zhao et al 2017). In addition, the dual hydrological structures of surface and underground in karst region in Southwest China lead to drainage of precipitation through leakage and runoff (Tong et al 2016), indicating the precipitation has limited effect of vegetation growth.…”

Responses of Climate Indicators to Droughts in SF Bay Area

“…Figures 2 and 3 shows the actual data as blue lines and the calculated average as the orange line. Month as number (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) is also used as input to take the seasonal change into account.…”

“…There are a lot of researches to analyze and predict droughts in different regions and using different methods including machine learning. Peiyu Lai et al [2] analyzed seasonal indicators related to droughts in Southwest China. Their work performed comprehensive survey on changes of variety of variables including surface water area, vegetation, meteorological factors and human activities, and whether these changes match the start and end of drought period on record without providing a mathematical modeling for predicting drought.…”

Estimating Macro-Fiscal Effects of Climate Shocks From Billions of Geospatial Weather Observations

“…Wetness and drought variables. Finally, we use the Palmer Drought Severity Index (PDSI) (Palmer, 1965) to introduce a measure of dry and wet periods that combines temperature and precipitation data to estimate cumulative deviations in soil moisture from normal conditions (Dai et al, 2004;Abatzoglou et al, 2018;Lai et al, 2020). 36 The PDSI ranges from -10 to +10, but values below -4 and above +4 are rare.…”