The electric power system (EPS) is an extremely complex system that has operational interdependencies with the water delivery and treatment system (WDTS). The term water-energy nexus is commonly used to describe the critical interdependencies that naturally exist between the EPS and water distribution systems (WDS). Presented in this work is a framework for simulating interactions between these two critical infrastructure systems in short-term and long-term time-scales. This includes appropriate mathematical models for system modeling and for optimizing control of power system operation with consideration of conditions in the WDS. Also presented is a complete methodology for quantifying the resilience of the two interdependent systems.

The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain.

The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated.

Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.

ABSTRACT Water and energy resources are intrinsically linked, yet they are managed separately even in the water scarce America southwest. This study develops a spatially explicit model of water energy inter-dependencies in Arizona and assesses the potential for co beneficial conservation programs. The interdependent benefits of investments in 8 conservation strategies are assessed within the context of legislated renewable energy portfolio and energy efficiency standards. The co- benefits of conservation are found to be significant. Water conservation policies have the potential to reduce statewide electricity demand by 1.0 - 3.0 %, satisfying 3.3 -10 % of the state's mandated energy-efficiency-standard. Adoption of energy -efficiency measures and renewable generation portfolios can reduce non - agricultural water demand by 2.3 - 12 %. The conservation co- benefits are typically not included in conservation plans or benefit cost analyses. Many co-benefits offer negative costs of saved water and energy, indicating that these measures provide water and energy savings at no net cost. Because ranges of costs and savings for water energy conservation measures are somewhat uncertain, future studies should investigate the co-benefits of individual conservation strategies in detail. Although this study focuses on Arizona, the analysis can be extended elsewhere as renewable portfolio and energy efficiency standards become more common nationally and internationally.

This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning horizon. The model distinguishes between short and long-term planning horizons and goals using a short-term modeling component (STM) and a long term modeling component (LTM) respectively. An STM optimizes a monthly allocation schedule on an annual basis in terms of maximum net economic benefit. A cost of depletion based upon Hotelling’s exhaustible resource theory is included in the STM net benefit calculation to address the non-use value of groundwater. An LTM consists of an STM for every year of the long-term planning horizon. Net economic benefits for both use and non-use values are generated by the series of STMs. In addition output from the STMs is measured in terms of sustainability which is quantified using a sustainability index (SI) with two groups of performance criteria. The first group measures risk to supply and is based on demand-supply deficits. The second group measures deviations from a target flow regime and uses a modified Hydrologic Alteration (HA) factor in the Range of Variability Approach (RVA). The STM is a linear programming (LP) model formulated in the General Algebraic Modeling System (GAMS) and the LTM is a nonlinear programming problem (NLP) solved using a genetic algorithm. The model is applied to the Prescott Active Management Area in north-central Arizona. Results suggest that the maximum sustainable net benefit is realized with a residential population and consumption rate increase in some areas, and a reduction in others.

The partitioning of available solar energy into different fluxes at the Earth's surface is important in determining different physical processes, such as turbulent transport, subsurface hydrology, land-atmospheric interactions, etc. Direct measurements of these turbulent fluxes were carried out using eddy-covariance (EC) towers. However, the distribution of EC towers is sparse due to relatively high cost and practical difficulties in logistics and deployment. As a result, data is temporally and spatially limited and is inadequate to be used for researches at large scales, such as regional and global climate modeling. Besides field measurements, an alternative way is to estimate turbulent fluxes based on the intrinsic relations between surface energy budget components, largely through thermodynamic equilibrium. These relations, referred as relative efficiency, have been included in several models to estimate the magnitude of turbulent fluxes in surface energy budgets such as latent heat and sensible heat. In this study, three theoretical models based on the lumped heat transfer model, the linear stability analysis and the maximum entropy principle respectively, were investigated. Model predictions of relative efficiencies were compared with turbulent flux data over different land covers, viz. lake, grassland and suburban surfaces. Similar results were observed over lake and suburban surface but significant deviation is found over vegetation surface. The relative efficiency of outgoing longwave radiation is found to be orders of magnitude deviated from theoretic predictions. Meanwhile, results show that energy partitioning process is influenced by the surface water availability to a great extent. The study provides insight into what property is determining energy partitioning process over different land covers and gives suggestion for future models.

The North American Monsoon System (NAMS) contributes ~55% of the annual rainfall in the Chihuahuan Desert during the summer months. Relatively frequent, intense storms during the NAMS increase soil moisture, reduce surface temperature and lead to runoff in ephemeral channels. Quantifying these processes, however, is difficult due to the sparse nature of coordinated observations. In this study, I present results from a field network of rain gauges (n = 5), soil probes (n = 48), channel flumes (n = 4), and meteorological equipment in a small desert shrubland watershed (~0.05 km2) in the Jornada Experimental. Using this high-resolution network, I characterize the temporal and spatial variability of rainfall, soil conditions and channel runoff within the watershed from June 2010 to September 2011, covering two NAMS periods. In addition, CO2, water and energy measurements at an eddy covariance tower quantify seasonal, monthly and event-scale changes in land-atmosphere states and fluxes. Results from this study indicate a strong seasonality in water and energy fluxes, with a reduction in Bowen ratio (B, the ratio of sensible to latent heat fluxes) from winter (B = 14) to summer (B = 3.3). This reduction is tied to shallow soil moisture availability during the summer (s = 0.040 m3/m3) as compared to the winter (s = 0.004 m3/m3). During the NAMS, I analyzed four consecutive rainfall-runoff events to quantify the soil moisture and channel flow responses and how water availability impacted the land-atmosphere fluxes. Spatial hydrologic variations during events occur over distances as short as ~15 m. The field network also allowed comparisons of several approaches to estimate evapotranspiration (ET). I found a more accurate ET estimate (a reduction of mean absolute error by 38%) when using distributed soil moisture data, as compared to a standard water balance approach based on the tower site. In addition, use of spatially-varied soil moisture data yielded a more reasonable relationship between ET and soil moisture, an important parameterization in many hydrologic models. The analyses illustrates the value of high-resolution sampling for quantifying seasonal fluxes in desert shrublands and their improvements in closing the water balance in small watersheds.

Fluctuating flow releases on regulated rivers destabilize downstream riverbanks, causing unintended, unnatural, and uncontrolled geomorphologic changes. These flow releases, usually a result of upstream hydroelectric dam operations, create manmade tidal effects that cause significant environmental damage; harm fish, vegetation, mammal, and avian habitats; and destroy riverbank camping and boating areas. This work focuses on rivers regulated by hydroelectric dams and have banks formed by sediment processes. For these systems, bank failures can be reduced, but not eliminated, by modifying flow release schedules. Unfortunately, comprehensive mitigation can only be accomplished with expensive rebuilding floods which release trapped sediment back into the river. The contribution of this research is to optimize weekly hydroelectric dam releases to minimize the cost of annually mitigating downstream bank failures. Physical process modeling of dynamic seepage effects is achieved through a new analytical unsaturated porewater response model that allows arbitrary periodic stage loading by Fourier series. This model is incorporated into a derived bank failure risk model that utilizes stochastic parameters identified through a meta-analysis of more than 150 documented slope failures. The risk model is then expanded to the river reach level by a Monte Carlos simulation and nonlinear regression of measured attenuation effects. Finally, the comprehensive risk model is subjected to a simulated annealing (SA) optimization scheme that accounts for physical, environmental, mechanical, operations, and flow constraints. The complete risk model is used to optimize the weekly flow release schedule of the Glen Canyon Dam, which regulates flow in the Colorado River within the Grand Canyon. A solution was obtained that reduces downstream failure risk, allows annual rebuilding floods, and predicts a hydroelectric revenue increase of more than 2%.