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Abstract
Scientists have shown that climate change is speeding up parts of the global water cycle, and that the water cycle intensity (WCI) is a tool to quantify this acceleration. Given that groundwater is a component of the global water cycle, this dissertation aims to understand how WCI changes (attributable to climate change) affect groundwater availability, specifically for arid regions. A previous effort to quantify the WCI over any landscape made use of ground-based datasets and focused on historical trends (1945 – 2014). This dissertation: (1) Validates a remote sensing approach for quantifying the WCI over the contiguous United States (CONUS) for a more recent period (2001 – 2019) – to capture current climate change trends. (2) Employs data analytics to predict groundwater level anomalies (GWLAs) across a select arid region based on the results of the WCI analysis, using observations from existing groundwater monitoring wells and remotely sensed predictor variables, including precipitation, soil moisture, evapotranspiration, and vegetation cover. (3) Evaluates the dynamic relationship between the results of the first two objectives for the arid region of interest, based on an innovative approach to statistical correlation and causation analyses. The water cycle is speeding up over about half of the CONUS particularly the west, and the state of Arizona might be experiencing much higher WCI rates on average compared to other arid regions of the CONUS. A multi-model data analytics approach to predict monthly GWLAs across multiple aquifers in Arizona between January 2010 and December 2019 demonstrated satisfactory performance, and the predictive accuracy was much higher for the unconsolidated sand and gravel aquifers. Finally, a moderate to strong negative lead-lag relationship between groundwater and WCI anomalies (GWLAs leading WCI anomalies) was revealed for various sites across the study area. Some of these locations were contained within Active Management Areas (AMAs) – areas characterized by high groundwater reliance and the enforcement of the strictest groundwater regulations. This study underscores the importance of groundwater monitoring and strategic management in vulnerable areas, and the exclusive use of remotely sensed variables ensures that data scarce and vulnerable regions are well represented and the study’s objectives can be replicability globally.