One of the most rapid transformations forced by recent global climate change is the recession of glaciers worldwide. The study of the fate of glacier meltwater lies at the intersection of the cryosphere and hydrologic cycle, making it a unique interdisciplinary study of the Earth system. A substantial body of research has focused on the how the changing volume of glaciers is contributing to sea level rise. However, comparatively fewer studies have investigated how glacier meltwater moves over the land surface to the oceans, impacting runoff and freshwater resources. Using models, in situ data, and remote sensing observations, this work seeks to quantify glacier contributions to runoff at large scales.

First, a temperature index melt model is developed to estimate mass balance for the Gulkana Glacier, Alaska, US. The model is forced with meteorological conditions taken near the surface of the glacier. Results from the model are compared with 40 years of glacier stake data recorded by the US Geological Survey.

Next, glacier contributions to streamflow are estimated for eight highly glaciated river basins in high mountain Asia (HMA). This work utilizes a new, globally complete glacier inventory along with remotely sensed estimates of glacier recession to quantify glacier storage change for 2003 - 2009. Results are integrated with the HydroSHEDS (Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales) river network to simulate how glacier melt in this region moves downstream. Runoff data from previous literature and simulations from the Global Land Data Assimilation System (GLDAS) are used throughout the study region to quantify the fraction of streamflow due to glacier melt.

Finally, the combined land-atmosphere water balance is used to estimate basin runoff for the HMA region. Terrestrial water storage changes observed by the Gravity Recovery and Climate Experiment (GRACE) satellites are combined with atmospheric reanalysis from the ERA-Interim Project to provide a remote sensing estimate of runoff. These are compared with runoff simulations from the various GLDAS models.

Groundwater resources are an important supplier of fresh water to East Africa’s communities. Resource degradation, resulting from climate variability and population growth, places many livelihoods at risk of water shortages. Monitoring of regional groundwater behaviors and associated climate and anthropogenic controls remains a limited practice over the region, a hindrance to effective resource management. In this thesis, I apply remote sensing and land surface modeling approaches to increase the state of knowledge of groundwater behaviors and resource monitoring over East Africa. In Chapter 1, a methodology is developed aimed at improving the estimation of groundwater storage changes from the Gravity Recovery and Climate Experiment (GRACE) satellites over East Africa (EA), a region characterized by significant surface water variability. Results document strong correlation between GRACE groundwater estimates and in-situ groundwater observations (Spearman’s ρ = 0.6), after systematic data processing that limits the effect of spatial leakage due to surface water bodies. Additional analysis, comparing groundwater storage behaviors with satellite precipitation, permits a regional view of groundwater characteristics as well as regional recharge behaviors, which can enhance effective resource management. In Chapter 2, a multi-objective calibration approach (using GRACE total water storage anomalies and streamflow) is applied to improve land surface model parameterization and simulation of baseflow and groundwater depth within the Community Land Model (CLM)). Model simulation of groundwater depth and baseflow is shown to significantly improve, formulating a basis for further hydrologic experiments, including anthropogenic impacts. In Chapter 3, I use the calibrated model to examine the hydrologic impacts of groundwater abstraction. Specifically, the CLM groundwater generation scheme is modified to include ‘human’ abstraction fluxes. Results show a decline in baseflow and groundwater depth with increased groundwater abstraction, while evaporation, especially during dry periods, increases. The results suggest that a deeper groundwater table reduces capillary influences, limiting the potential to affect soil moisture and Hortonian runoff, while causing induced recharge from soil layers. Results show that induced recharge does not exceed abstractions with increased groundwater withdrawals, suggesting unsustainable use of the aquifers.

The ability to accurately quantify water storages and fluxes in water management systems through observations or models is of increasing importance due to the expected impacts from climate change and population growth worldwide. Here, I describe three innovative techniques developed to better understand this problem. First, a model was created to represent reservoir storage and outflow with the objective of integration into a Land Surface Model (LSM) to simulate the impacts of reservoir management on the climate system. Given this goal, storage capacity represented the lone model input required that is not already available to an LSM user. Model parameterization was linked to air temperature to allow future simulations to adapt to a changing climate, making it the first such model to mimic the potential response of a reservoir operator to climate change. Second, spatial and temporal error properties of future NASA Surface Water and Ocean Topography (SWOT) satellite reservoir operations were quantified. This work invoked the use of the SWOTsim instrument simulator, which was run over a number of synthetic and actual reservoirs so the resulting error properties could be extrapolated to the global scale. The results provide eventual users of SWOT data with a blueprint of expected reservoir error properties so such characteristics can be determined a priori for a reservoir given knowledge about its topology and anticipated repeat orbit pass over its location. Finally, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission was used in conjunction with in-situ water use records to evaluate sustainable water use at the two-digit HUC basin scale over the contiguous United States. Results indicate that the least sustainable water management region is centered in the southwest, where consumptive water use exceeded water availability by over 100% on average for some of these basins. This work represents the first attempt at evaluating sustainable water use using exclusively remote sensing and in-situ observations at the continental scale, and offers a framework for future similar studies attempting to evaluate water use sustainability at large scales.

Global continental discharge is an important component of global water cycle, but it is difficult to measure by traditional methods alone. In this dissertation, we provide an overview of current methods of estimating global continental discharge, and provide mass-balance estimates of global discharge using remote sensing and reanalysis products. Evaluating against observation-based estimates, we find that discharge computed from land mass balance using reanalysis-based moisture convergences and GRACE can provide a viable alternative to declining in situ observations’ records for major river basins. Then we conduct a series of sensitivity experiments on an ocean-ice model forced with varying discharge to understand ocean’s response to changes and uncertainties in discharge. We find that sea surface salinity (SSS), mixed layer depth, potential temperature, net primary production, and stratification show notable changes in the coastal and river plume regions, and that for certain river plume regions, interannual variability accounts to up to half of the total variance. Lastly, we introduce another novel method of estimating discharge from space. We isolate discharge and transport signal in SSS after removing atmospheric freshwater flux signal from SSS using joint EOF analysis, and then use discharge-SSS correlation to infer discharge for the Orinoco-Amazon, and Congo-Niger river basins.

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.