Assessing characterization of large-scale groundwater quality with remote sensing
- Author(s): Gibbons, Aimee Christine
- Advisor(s): Famiglietti, James S
- et al.
NASA’s Gravity Recovery and Climate Experiment (GRACE) mission measures global gravity variability, which are converted into monthly groundwater storage variations in the world's largest watersheds. Large-scale groundwater storage variability is determined from GRACE and supplementary data at monthly and longer timescales for regions that are 150,000 km2 and greater. Estimates of groundwater availability focus on quantity, but methods to infer groundwater quality have not yet been developed, in part due to a lack of spatially representative quality data. Natural contaminants dissolved in groundwater generally increase with depth due to continued dissolution of rock and soil material along flow paths. Anthropogenic contaminants are generally concentrated near the water table due to changes in frequency and location of recharge. These basic relationships between groundwater quality and depth provide the conceptual framework for the project research. This work aims to characterize relationships between observed total dissolved solid (TDS) concentrations and GRACE-derived subsurface storage anomalies for the High Plains aquifer in the central United States and the Central Valley aquifer in California. The relationship between observed water levels and contaminant concentrations are expected to vary based on physical parameters influencing spatial and/or temporal patterns of infiltration including dominant land use type, principle rock and soil types, and constituent. In this work, a database of publicly available in situ TDS concentrations in groundwater and groundwater levels is compiled for each of the study areas and assessed for simplistic preliminary relationships, and methods of scaling point observations and large-scale gridded data are explored. Models estimating average TDS concentrations through time as a function of in situ groundwater levels and season are constructed and explore potential improvements by classifying models in terms of dominant lithology and land use, and by including GRACE-derived subsurface storage anomalies as a potential predictor. Finally, two spatial analysis approaches explore methods of TDS characterization on a subbasin scale and TDS variability in time and space on regional scales. Results of this work have implications on improving groundwater management practices by exploring potential methods of estimating groundwater quality on regional to global scales using remote sensing.