Utilizing Grace TWS, NDVI, and Precipitation for Drought Identification and Classification in Texas

Tuesday, 16 December 2014
Sarah Elizabeth McCandless, Srinivas V Bettadpur, Teresa Howard and Gordon L Wells, University of Texas at Austin, Center for Space Research, Austin, TX, United States
Drought is one of the most widespread natural phenomena in the world and many indices exist today to monitor drought progression. The “Merged-dataset Drought Index” (MDI) is a new quantitative index calculated using the US/German Gravity Recovery and Climate Experiment total water storage (GRACE TWS), NASA MODIS-derived normalized difference vegetation index (NDVI), and NOAA/NWS precipitation data. These particular datasets constitute MDI because each correlates with a different drought type. Dataset deviations from established climatology are used, where negative deviations indicate deficits. MDI is objectively and transparently calculated based on dataset z-scores. GRACE TWS is the least mature dataset used in these calculations, but TWS solution variance does not negatively impact MDI. A new classification scheme to categorize drought severity is also proposed. MDI is studied in Texas and its smaller sub-regions. Within these sub-regions, MDI identifies multiple droughts during 2002 – 2014, with the most severe beginning in 2011. Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low overall signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales.