Regional-scale hydrologic drought is investigated using water storage data from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites and terrestrial moisture output fields from the Global Land Data Assimilation System driving the community Noah land surface model (GLDAS/Noah). The objective is to utilize probability theory, statistics, and principle component analysis to track the spatio-temporal propagation of hydrologic drought for the purposes of detailing the degree, time frame, and extent of drought within the terrestrial hydrologic system, while exploring the drought monitoring potential of the GRACE satellites. The probability and magnitude of a drought event based on probability distribution functions allows for a simpler expression of the temporal aspect of drought severity, while principal component analysis allows for exploration of the spatial facets of drought. Hydrologic drought is defined as the persistence of negative water storage anomalies ranging between 30% (abnormally dry) and <2% (exceptional drought) probability frequency, based on the region's long term (61 years), GLDAS/Noah terrestrial water storage anomaly (TWSA) cumulative distribution function. Once this region-specific threshold is defined, drought magnitude, intensity, and duration are calculated to form a general picture of the temporal variation of drought specifically in land water stores. Combining probability theory and principle components allows us to demarcate clusters of dry spells within a region over a given timeframe. Water deficiency will be addressed by using TWSA data to calculate the volume of water entering and leaving a region. The focus here is to statistically assess monthly and seasonal water volume changes in surface and subsurface stores to determine the “total water deficit”, an attribute that is difficult to characterize on a global scale. Correlation analyses will relate the severity and duration of various droughts to subsequent water deficits, which helps advance drought prediction capabilities. This work will lead to the recognition of causalities associated with drought magnitude and duration in addition to the identification of commonalities between hydrologic droughts from varying river basins around the world by way of correlation and regression analyses with TWSA, regional climatology, and land-atmospheric patterns (i.e., El Niño Southern Oscillation).