Enhancing drought monitoring using an Evolutionary satellite data assimilation

Abstract:

The aim of this study is to use the recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into the Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~5cm) over the entire Continental United States (CONUS). The EPFM outperformed the Ensemble Kalman filter (EnKF) when the assimilated soil moisture values using both approaches are compared with those obtained from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) stations scattered throughout the nation. Also, we used a multivariate probability distribution and a Copula function to integrate the posterior soil moisture, precipitation and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. The new integrated drought index is then compared with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM from April 2015 to June 2018. We also noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI is better correlated with the yield loss of spring and winter wheat in the United States than SPEI and SSMI. This novel drought monitoring framework could serve as a potentially complementary drought monitoring system relative to USDM.