Statistical Analysis between Soil Wetness Variational Index and Precipitation

Floods are one of the major causes for damages and loss of life around the world. In the United States alone the average of fatalities from 1977 to 2006 is 99 people per year. The ability to globally monitor flood events as they unfold, makes it possible to assess their impacts more accurately, even if the floods occur in remote regions. Our case study is a flood event in Argentina, which occurred during April 2013. It caused about 530.4 million pesos ($104 million) in damages and at least 51 deaths. The severity of flood events mainly depends on precipitation and soil moisture. Scientists at NOAA-CREST have recently developed a daily global flood observation system in an effort to monitor global floods accurately. The developed flood observation system is based on the concept of Soil Wetness Variation Index (SWVI). The lag correlation between precipitation and SWVI is studied. Precipitation data was obtained from Tropical Rainfall Measuring Mission (TRMM) 3B42v7, TRMM-adjusted merged-infrared(IR) precipitation data set. The SWVIs data used for flooding observation are calculated from data (Brightness Temperature) collected by Advanced Technology Microwave Sounder (ATMS), on board of Soumi-NPP. Results indicate a lag of 4 days to be the highest correlation.