# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
## 5 Conclusions  
With the ongoing satellite missions, more and more earth observation data become available to the environmental studies. In this study, multi-source data including variables in surface water, vegetation, meteorology, the Palmer Drought Severity Index (PDSI) and reservoirs data were used to analyze the responses of seasonal indicators to extreme droughts in Southwest China from 2000 to 2017. Compared to the traditional drought indicators (e.g., PDSI), the variations of seasonal water area (SWA) and vegetation phenology could sensitively and rapidly response to the development and recovery of extreme droughts, while permanent water area (PWA) kept a rising pattern in these two periods. The variations of these indicators showed that there were two obvious drought periods during 2000-2017. In the first period, SWA decreased by 11.81% and length of season (LOS) shortenedby 5 days; in the second period, they reduced by 21.04% and 9 days, respectively. Start of season (SOS) usually delayed by 3-6 days in spring of drought years, while end of season (EOS) advanced 1-3 days in autumn of drought periods. Moreover, both seasonal indicators and meteorological factors showed that the second drought period was more serious than the first one. All of PDSI, SWA and LOS could reflect the periods of droughts in Southwest China. The LOS and PDSI were sensitive to the meteorological events, for example, precipitation and temperature, while SWA performed more stable to drought periodicity. Short events of precipitation during drought periods could increase the LOS and PDSI, but the SWA was not influenced by the short-term rainfall and showed the continuous drought trend. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. These results are helpful for monitoring droughts in the ecologically fragile southwest China.  
Conceptualization, X.H.; data curation, P.L. and B.H.; formal analysis, P.L. and J.H.; funding acquisition, M.M. and X.H.; investigation, M.M.; methodology, Z.G., H.Y. and X.H.; project administration, M.M.; resources, P.L. and M.M.; software, P.L. and Z.S.; supervision, M.M. and X.H.; validation, M.Z., P.L. and Z.G.; visualization, P.L. and H.Y.; writing--original draft, P.L.; writing--review and editing, H.Y., M.Z. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number: 41830648, 41801095, 41771361; the Fundamental Research Funds for the Central Universities, grant number: GK201903117 and the Southwest University research funding, grant number: SWU117035. The APC was funded by the National Natural Science Foundation of China, grant number: 41771361.  
In this study, multi-source data are download from different data centers. These data include LAI data from Global LAnd Surface Satellite (GLASS), rainfall, air temperature and solar radiation data from the Global Land Data Assimilation System, evapotranspiration data, high-resolution surface water dataset data from Google Earth Engine and several products from MODIS. We sincerely appreciate the anonymous reviewers' helpful comments and the editor's efforts in improving this manuscript.  
The authors declare no conflict of interest.