Thursday, 11 January 2018: 11:45 AM
Room 18A (ACC) (Austin, Texas)
Xing Yuan, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
The European Space Agency Climate Change Initiative (ESA CCI) has released a 36-year (1979-2014) soil moisture retrieval dataset by merging passive and active microwave products, which enables the application of satellite soil moisture in climate studies including drought analysis. By comparing the satellite products with in-situ soil moisture observations at 312 stations in China, as well as ERA Interim, GLDASv1 and GLDASv2 soil moisture modeling datasets, it is found that both the land surface modeling (reanalysis) and remote sensing products can only detect less than 60% of drought months at in-situ station scale. However, ESA CCI products capture the responses of inter-annual drought area variations to ENSO at river basin scales quite well, and show an added value that are spatially complementary to the land surface modeling products for the drought analysis.
Given its promising performance in drought analysis, the ESA CCI products are being used for identifying drought onset and recovery over global major river basins. Given the high ratios of missing values of satellite products at daily time scale, a cumulative distribution function (CDF) matching approach is used to fill the missing values by matching soil moisture CDF of satellite data with the CDF of ERA Interim/LAND reanalysis, the filled passive and active microwave remote sensing datasets are then merged by an optimization procedure, and a new dataset is therefore created. As verified against the International Soil Moisture Network (ISMN) station observations, the new dataset shows a smaller unbiased root mean square error (unRMSE) and a higher correlation than those for the original ESA CCI products. During crop growing seasons, the starting and ending dates of short-term droughts defined by the drought area percentage for a given river basin will be determined, and their interannual variations and possible connections with large-scale climate variability will be investigated.
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