Terrestrial water storage across scales: Applications of the GRACE satellite mission for global hydrology
Terrestrial water storage is difficult to observe over large areas, and as a result, studies of the global water cycle under changing climate tend to focus on a few key methodologies: monitoring of the large-scale fluxes of precipitation, evaporation and discharge; modeling the relationship between runoff response and precipitation forcing; or applying limited in-situ data sets to generalize and scale behavior. With NASA's Gravity Recovery And Climate Experiment (GRACE) mission, hydrologists are finally able to study terrestrial water storage for large river basins (>200,000 km2) with monthly time resolution, opening what was previously an unobservable 'black box' in land-surface water dynamics. GRACE data are ideally suited for monitoring global water storage variability and classifying differences in regional water storage behavior that are relevant for global climate studies. In this research, I explore global to regional scale applications of GRACE data that highlight the novelty, functionality and importance of these groundbreaking observations.
First, I present a new metric for the monitoring of global water cycle health and energy expenditure --– the time-series of Total Global Ocean Mass Anomaly and Total Global Land Mass Anomaly from GRACE --– and show that this metric is heavily influenced by highly variable regional water cycle dynamics in a few global wet spots. Second, I provide results of a statistical model of basin-averaged GRACE terrestrial water storage anomaly and Global Precipitation Climatology Project (GPCP) precipitation for the world's largest basins, by calculating frequency-domain transfer functions of storage response to precipitation forcing, and then parameterizing these transfer functions based on large-scale basin characteristics, such as percent forest cover and basin temperature. Finally, I highlight two 1-degree applications of GRACE water storage: estimation of a global effective soil active depth, with applications for global land surface modeling and monitoring of high latitude changes in soil storage; and a remotely-sensed, storage-based flood potential method, validated with Dartmouth flood observatory global flood maps.