Advancing the use of remote sensing data and models to understand hydrologic processes in California
Keywords: Geophysics.

Satellite remote sensing has emerged as a powerful tool in water resources management. However, the extent to which remote sensing data and models can be used to derive novel insights about groundwater systems remains unclear, and the full spectrum of applications for remote sensing in water resources management remains unrealized. In this thesis, we use remote sensing data and models, integrating on-the-ground datasets where appropriate, to recover hydrologic properties related to groundwater systems -- (1) the change in groundwater storage, estimated across three spatial orders of magnitude through a mass balance approach and compared to independent estimates for each spatial scale, and (2) the source areas where rainfall and snowmelt strongly influence downstream baseflow, determined through application of baseflow separation, baseflow recession, signal processing and information theoretic methods. Remote sensing data describing precipitation, evapotranspiration, soil moisture, and snow-water-equivalent within California are used, for the first time, in a mass balance approach to estimate changes in stored groundwater for study regions spanning ~1,000 km2 to > 100,000 km2. Results of the remotely sensed mass balance agree across scales with independent estimates of changes in groundwater storage derived from (1) the Gravity Recovery and Climate Experiment satellites, (2) well-based measurements of the water table, and (3) regional groundwater flow models. The method is an appealing supplementary tool to estimate changes in groundwater storage relative to traditional methods for a number of key factors: (a) the ability to produce low-latency estimates -- well and model-based methods lag years behind the present due to extensive on-the-ground data requirements and model calibration, (b) quantification of uncertainty, both for estimates of changes in groundwater storage and among water balance components -- traditional methods produce only a single estimate of changes in storage, and (c) a growing number of satellite-based datasets which can be used to accurately estimate the required parameters, as well as capture the uncertainty in water balance components and mass balance results. Promising results were obtained for three out of four study areas, but mass balance results obtained at the finest spatial scale do not agree well with independent estimates, suggesting there are important scale-dependent limitations associated with the remotely sensed mass balance approach. Baseflow, the persistent component of streamflow fed by groundwater discharge to stream channels, is critical for water supply, hydropower generation, and habitat for ecosystems. For these reasons, it is of great interest to identify the areas which strongly influence baseflow through the processes of rainfall and snowmelt. To accomplish this, we combined remotely sensed data describing rainfall and snowmelt with ground-based streamflow estimates in a physics-guided statistical analysis in order to identify the areas in California's Sierra Nevada which have a prevailing influence on baseflow. An important finding suggests that the areas with the highest annual rates of rainfall and snowmelt do not necessarily exhibit the greatest influence on downstream baseflow, and that snowmelt occurring in the 3000-meter to 3700-meter elevation range has the strongest overall influence on baseflow. Our findings provide novel ways to utilize remote sensing data and models to recover essential properties of groundwater systems, and generally support the combined use of remote sensing data and models with on the ground measurements in order to address problems in groundwater hydrology and water resources management. As new sensors are launched into orbit, such as the Surface Water Ocean Topography satellite in late 2022, the spectrum of possible hydrologic applications widens, and the potential for remote sensing in water resources management will broaden