Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply.

Lake Mead, the largest reservoir in the United States along the Colorado River on the border between the states of Nevada and Arizona, is one of the nation's most important sources of freshwater. As reported by the U.S. drought monitor (USDM), the entire region has been experiencing recurring severe to extreme droughts since the early 2000s, which have further intensified during the past two years. The drought-driven water deficit caused Lake Mead's water volume to decrease to approximately one-third of its capacity, creating a water crisis and negatively affecting soil and groundwater storage across the region. Water deficits have further reduced the mass of water loading on the Earth's crust, causing it to elastically deform. I observe this process from the ground by recording the vertical land motion occurring at Global Navigation Satellite System (GNSS) stations, or from space via Interferometric Synthetic Aperture Radar (InSAR) technology. In this study, I analyze vertical deformation observations from GNSS sites and multi-temporal InSAR analysis of Sentinel-1A/B to investigate the contribution of water mass changes in lake, soil, and groundwater to the deformation signal. To achieve this, I remove the effects of glacial isostatic adjustment and non-tidal mass loads from GNSS/InSAR observations. Our findings indicate that recent drought periods led to a notable uplift near Lake Mead, averaging 7.3 mm/year from 2012 to 2015 and an even larger rate of 8.6 mm/year from 2020 to 2023. Further, I provide an estimate of the expected vertical crustal deformation in response to well-known changes in lake and soil moisture storage. For that, I quantify hydrological loads through two different loading models. These include the application of Green's functions for an elastic, layered, self-gravitating, spherical Earth, and the Love load numbers from the Preliminary Reference Earth Models (PREMs), as well as elastic linearly homogeneous half-space Earth models. I further test various load models against the GNSS observations. Our research further investigates the impact of local crustal properties and evaluates the output of several elastic loading models using crustal properties and different model types under non-drought and drought conditions. For future studies, I suggest a comprehensive analysis of the deformation field InSAR data. Also, rigorous monitoring of groundwater levels is essential to accurately predict changes in water masses based on deformation. In addition, for each data set, I suggest implementing an uncertainty analysis to assess the predictability of groundwater level changes based on vertical loading deformation observed by INSAR/GNSS data around the region. Obtaining such estimates will provide valuable insight into the dynamic interactions of the local aquifers with Lake Mead. / Master of Science / The drought has led to a decline of approximately 40 meters in Lake Mead since 1999. During the process of water mass loss from a lake, the crust lifts and extends from the center. However, the water mass loss seen on the lake is not sufficient to explain the movement seen at nearby GPS sites. Hence, the uplift loading of water loss in the form of other hydrological components surrounding Lake Mead needs to be estimated. Here, I analyze several models that best fit the geodetic displacements and try to fill in the gap in deformation observations.

To slow climate change, humans should take immediate and widespread action. One way to slow climate change is by switching to switch to renewable power plants such as solar fields. Recently, pioneering companies have built solar fields on water bodies. This study found that such a pairing of water and solar could increase production efficiency by 8-10% through panel cooling, save millions of liters of water from evaporation, and produce energy with under-utilized space.

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