Ph.D. Dissertation Abstract

Building a Drought Characterization Framework with Satellite Remote Sensing and Computer Modeling

Prolonged hydrologic drought disturbs the natural state of ecosystems, stresses regional water supplies, and can adversely affect agricultural production. Determining the severity of hydrologic drought traditionally lies in evaluations of historical rainfall, stream flow, and soil moisture; yet, a comprehensive measure of the magnitude of a drought’s impact on all components of the terrestrial hydrologic system, including surface, soil, and groundwater storage, remains absent from standard drought analyses. NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite mission fills a gap by providing monthly measures of terrestrial water storage anomalies based on timevariable gravitational fields. This dissertation details an in-depth investigation of regional hydrological drought using both satellite data and outputs from computer atmospheric-land models.

In Chapter 1, I present a new quantitative, GRACE-based framework for measuring the severity of hydrologic drought. GRACE observations are used for drought characterization by calculating the deviation of monthly-average terrestrial water storage anomalies from the regional climatological reference, where negative deviations represent storage deficits. Each deficit conveys the volume of water that would be required to recover from a drought. Moreover, this finite deficit observation allows for the calculation of a likely time for recovery based on statistical percentiles of storage change distributions, for every month through the end of the event. To quantify event severity, we combine storage deficits with event duration. Substantial drought events are investigated for four study regions during the GRACE record: the Amazon and Zambezi basins, the Southern Plains, and the Southeastern United States.

In Chapter 2, I investigate the usability of GRACE observations for use in drought characterization and monitoring, we utilize land surface model outputs, assimilated with satellite observations, for the purposes of: gaining near-real time analysis, downscaling GRACE spatial resolution, and disaggregating the total water storage signal into surface and subsurface storage components. In Chapter 3, I combine the results from previous work with additional climate datasets and discuss the benefits of doing so; essentially, providing a more accurate and comprehensive measure of regional scale drought. This GRACE-based framework has implications for improving drought early warning lead times together with drought preparation and management efforts.