Variations in terrestrial water storage caused by drought: Near real-time monitoring using GPS vertical position data
Abstract:There is no operational system to monitor drought-induced changes in terrestrial water storage (TWS) -- the amount of water stored as soil moisture, groundwater, snow, and in surface water bodies. Existing drought indices, such as the USDM, rely primarily on meteorological and shallow soil moisture data. Measuring changes in TWS is difficult because in situ networks do not provide collocated measurements of all TWS components. GRACE satellite data can be used to retrospectively analyze TWS variations caused by drought, but cannot be used for real-time drought monitoring. Global Positioning System (GPS) instruments can be used to estimate TWS variations because the solid Earth responds elastically to changes in hydrologic loading. A decrease (increase) in TWS yields upward (downward) motion, which is apparent in GPS vertical position anomalies, u. GPS vertical position anomalies have been compared to the seasonal cycle of hydrologic loading. However, these data have not been used to monitor TWS fluctuations caused by drought, even though they have attributes that make them well-suited for this application: (1) availability in near real-time (2) widespread spatial distribution; and (3) sensitivity to both local (~10 km) and regional loading.
Here, GPS vertical position data, u, are used to assess the timing and duration of TWS anomalies in the High Plains caused by the 2012 drought. u indicates an initial TWS minimum in 2012 consistent with GRACE TWS, several months after the most severe meteorological forcing. Standard drought indices show recovery from drought during spring 2013. In contrast, u indicates that the TWS anomaly intensified by approximately 15% during summer 2013, an interval when no GRACE data are available. Hydrologic observations indicate that depletion of groundwater, not soil moisture, is the source of the persistent TWS anomaly. Our results show how GPS vertical position records can be used to monitor TWS in near real-time during drought. TWS based on u complements GRACE data, given their differences in latency, sensing footprint, and error sources.