There is a considerable need for improved understanding of the outcome and amounts of water used to manage urban landscapes in arid and semiarid cities. Outdoor irrigation in urban parks consists of a large fraction of water demands in Phoenix, Arizona. Hence, ecohydrological processes need to be considered to improve outdoor irrigation management. With the goal of reducing outdoor water use and advancing the general knowledge of water fluxes in urban parks, this study explores water conservation opportunities in an arid city through observations and modeling.Most urban parks in Phoenix consist of a mosaic of turfgrass and trees which receive scheduled maintenance, fertilization and watering through sprinkler or flood irrigation. In this study, the effects that different watering practices, turfgrass management and soil conditions have on soil moisture observations in urban parks are evaluated. Soil moisture stations were deployed at three parks with stations at control plots with no compost application and compost treated sites with either a once or twice per year application instead of traditional fertilizer. An eddy covariance system was installed at a park to help quantify water losses and water, energy and carbon fluxes between the turfgrass and atmosphere. Additional meteorological observations are provided through a network of weather stations. The assessment covers over one year of observations, including the period of turfgrass growth in the warm season, and a period of dormancy during the cool season. The observations were used to setup and test a plot-scale soil water balance model to simulate changes in daily soil moisture in response to irrigation, precipitation and evapotranspiration demand for each park. Combining modeling and observations of climate-soil-vegetation processes, I provide guidance on irrigation schedules and management that could help minimize water losses while supporting turfgrass health in desert urban parks. The irrigation scenarios suggest that water savings of at least 18% can be achieved at the three sites. While the application of compost treatment to study plots did not show clear improvements in soil water retention when compared to the control plots, this study shows that water conservation can be promoted while maintaining low plant water stress.

The National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) will provide the next generation of operational streamflow forecasts at different lead times across United States using the Weather Research and Forecasting (WRF)-Hydro hydrologic system. These forecasts are crucial for flood protection agencies and water utilities, including the Salt River Project (SRP). The main goal of this study is to calibrate WRF-Hydro in the Oak Creek Basin (OCB; ~820 km2), an unregulated mountain sub-watershed of the Salt and Verde River basins in Central Arizona, whose water resources are managed by SRP and crucial for the Phoenix Metropolitan area. As in the NWM, WRF-Hydro was set up at 1-km (250-m) resolution for the computation of the rainfall-runoff (routing) processes. Model forcings were obtained by bias correcting meteorological data from the North American Land Data Assimilation System-2 (NLDAS-2). A manual calibration approach was designed that targets, in sequence, the sets of model parameters controlling four main processes responsible for streamflow and flood generation in the OCB. After a first calibration effort, it was found that WRF-Hydro is able to simulate runoff generated after snowmelt and baseflow, as well as magnitude and timing of flood peaks due to winter storms. However, the model underestimates the magnitude of flood peaks caused by summer thunderstorms, likely because these storms are not captured by NLDAS-2. To circumvent this, a seasonal modification of soil parameters was adopted. When doing so, acceptable model performances were obtained during calibration (2008-2011) and validation (2012-2017) periods (NSE > 0.62 and RMSE = ~2.5 m3/s at the daily time scale).

The process-based calibration strategy utilized in this work provides a new approach to identify areas of structural improvement for WRF-Hydro and the NWM.

The Phoenix Metropolitan region is subject to intense summer monsoon thunderstorms that cause highly localized flooding. Due to the challenges in predicting these meteorological phenomena and modeling rainfall-runoff transformations in urban areas, the ability of the current operational forecasting system to predict the exact occurrence in space and time of floods in the urban region is still very limited. This thesis contributes to addressing this limitation in two ways. First, the existing 4-km, 1-h Stage IV and the new 1-km, 2-min Multi-Radar Multi-Sensor (MRMS) radar products are compared using a network of 365 gages as reference. It is found that MRMS products consistently overestimate rainfall during both monsoonal and tropical storms compared to Stage IV and local rain gauge measurements, although once bias-corrected offer a reasonable estimate for true rainfall at a higher spatial and temporal resolution than rain gauges can offer. Second, a model that quantifies the uncertainty of the radar products is applied and used to assess the propagation of rainfall errors through a hydrologic-hydraulic model of a small urban catchment in Downtown Phoenix using a Monte Carlo simulation. The results of these simulations suggest that for this catchment, the magnitude of variability in the distribution of runoff values is proportional to that of the input rainfall values.

Agriculture is the second largest water consumer in the Phoenix Metropolitan region, after the municipal sector. A significant portion of the cultivated land and agricultural water demand is from the production of animal feed, including alfalfa (~69% of total cropland area), corn (~8), and sorghum (-3%), which are both exported and needed to support local dairy industry. The goal of this thesis is to evaluate the impacts on water demand and crop production of four different crop portfolios using alfalfa, corn, sorghum, and feed barley. For this aim, the Water Evaluation And Planning (WEAP) platform and the embedded MABIA agronomic module are applied to the Phoenix Active Management Area (AMA), a political/hydrological region including most of Phoenix Metro. The simulations indicate that the most efficient solution is a portfolio where all study crop production is made up by sorghum, with an increase of 153% in crop yield and a reduction of 60% of water consumption compared to current conditions. In contrast, a portfolio where all study crop production is made up by alfalfa, which is primary crop grown in current conditions, decreased crop yield by 77% and increases water demand by 105%. Solutions where all study crop production is achieved with corn or feed barley lead to a reduction of 77% and 65% of each respective water demand, with a portfolio of all corn for study crop production increasing crop yield by 245% and a portfolio of all feed barley for study crop production reducing crop yield by 29%.

Uncertainty is inherent in predictive decision-making, both with respect to forecasting plausible future conditions based on a historic record, and with respect to backcasting likely upstream states from downstream observations. In the first chapter, I evaluated the status of current water resources management policy in the United States (U.S.) with respect to its integration of projective uncertainty into state-level flooding, drought, supply and demand, and climate guidance. I found uncertainty largely absent and discussed only qualitatively rather than quantitatively. In the second chapter, I turned to uncertainty in the interpretation of downstream observations as indicators of upstream behaviors in the field of Wastewater-Based Epidemiology (WBE), which has made possible the near real-time, yet anonymous, monitoring of public health via measurements of biomarkers excreted to wastewater. I found globally, seasonality of air and soil temperature causes biomarker degradation to vary up to 13-fold over the course of a year, constituting part of the background processes WBE must address, or detrend, prior to decision-making. To determine whether the seasonal change in degradation rates was introducing previously unaccounted for uncertainty with respect to differences in observed summertime and winter-time populations, I evaluated demographic indicators recorded by the Census Bureau for correlation with their distance from all major wastewater treatment plants across the U.S. The analysis identified statistically significant correlation for household income, education attainment, unemployment, military service, and the absence of health insurance. Finally, the model was applied to a city-wide case study to test whether temperature could explain some of the trends observed in monthly observations of two opiate compounds. Modeling suggests some of the monthly changes were attributed to natural temperature fluctuation rather than to trends in the substances’ consumption, and that uncertainty regarding discharge location can dominate even relative observed differences in opiate detections. In summary, my work has found temperature an important modulator of WBE results, influencing both the type of populations observed and the likelihood of upstream behaviors disproportionally magnified or obscured, particularly for the more labile biomarkers. There exists significant potential for improving the understanding of empirical observations via numerical modeling and the application of spatial analysis tools.

The world has been continuously urbanized and is currently accommodating more than half of the human population. Despite that cities cover only less than 3% of the Earth’s land surface area, they emerged as hotspots of anthropogenic activities. The drastic land use changes, complex three-dimensional urban terrain, and anthropogenic heat emissions alter the transport of mass, heat, and momentum, especially within the urban canopy layer. As a result, cities are confronting numerous environmental challenges such as exacerbated heat stress, frequent air pollution episodes, degraded water quality, increased energy consumption and water use, etc. Green infrastructure, in particular, the use of trees, has been proved as an effective means to improve urban environmental quality in existing research. However, quantitative evaluations of the efficacy of urban trees in regulating air quality and thermal environment are impeded by the limited temporal and spatial scales in field measurements and the deficiency in numerical models.

This dissertation aims to advance the simulation of realistic functions of urban trees in both microscale and mesoscale numerical models, and to systematically evaluate the cooling capacity of urban trees under thermal extremes. A coupled large-eddy simulation–Lagrangian stochastic modeling framework is developed for the complex urban environment and is used to evaluate the impact of urban trees on traffic-emitted pollutants. Results show that the model is robust for capturing the dispersion of urban air pollutants and how strategically implemented urban trees can reduce vehicle-emitted pollution. To evaluate the impact of urban trees on the thermal environment, the radiative shading effect of trees are incorporated into the integrated Weather Research and Forecasting model. The mesoscale model is used to simulate shade trees over the contiguous United States, suggesting how the efficacy of urban trees depends on geographical and climatic conditions. The cooling capacity of urban trees and its response to thermal extremes are then quantified for major metropolitans in the United States based on remotely sensed data. It is found the nonlinear temperature dependence of the cooling capacity remarkably resembles the thermodynamic liquid-water–vapor equilibrium. The findings in this dissertation are informative to evaluating and implementing urban trees, and green infrastructure in large, as an important urban planning strategy to cope with emergent global environmental changes.

Imagine you live in a place without any storm water or wastewater systems!

Wastewater and storm water systems are two of the most crucial systems for urban infrastructure. Water resources have become more limited and expensive in arid and semi-arid regions. According to the fourth World Water Development Report, over 80% of global wastewater is released into the environment without adequate treatment. Wastewater collection and treatment systems in the Kingdom of Saudi Arabia (KSA) covers about 49% of urban areas; about 25% of treated wastewater is used for landscape and crop irrigation (Ministry of Environment Water and Agriculture [MEWA], 2017). According to Guizani (2016), during each event of flooding, there are fatalities. In 2009, the most deadly flood occurred in Jeddah, KSA within more than 160 lives lost. As a consequence, KSA has set a goal to provide 100% sewage collection and treatment services to every city with a population above 5000 by 2025, where all treated wastewater will be used.

This research explores several optimization models of planning and designing collection systems, such as regional wastewater and stormwater systems, in order to understand and overcome major performance-related disadvantages and high capital costs. The first model (M-1) was developed for planning regional wastewater system, considering minimum costs of location, type, and size sewer network and wastewater treatment plants (WWTPs). The second model (M-2) was developed for designing a regional wastewater system, considering minimum hydraulic design costs, such as pump stations, commercial diameters, excavation costs, and WWTPs. Both models were applied to the Jizan region, KSA.

The third model (M-3) was developed to solve layout and pipe design for storm water systems simultaneously. This model was applied to four different case scenarios, using two approaches for commercial diameters. The fourth model (M-4) was developed to solve the optimum pipe design of a storm sewer system for given layouts. However, M-4 was applied to a storm sewer network published in the literature.

M-1, M-2, and M-3 were developed in the general algebraic modeling system (GAMS) program, which was formulated as a mixed integer nonlinear programming (MINLP) solver, while M-4 was formulated as a nonlinear programming (NLP) procedure.

Flooding is a critical issue around the world, and the absence of comprehension of watershed hydrologic reaction results in lack of lead-time for flood forecasting and expensive harm to property and life. It happens when water flows due to extreme rainfall storm, dam breach or snowmelt exceeds the capacity of river system reservoirs and channels. The objective of this research was to develop a methodology for determining a time series operation for releases through control gates of river-reservoir systems during flooding events in a real-time using one- and/or two-dimensional modeling of flows through river-reservoir systems.

The optimization-simulation methodology interfaces several simulation-software coupled together with an optimization model solved by genetic algorithm coded in MATLAB. These software include the U.S. Army Corps of Engineers HEC-RAS linked the genetic algorithm in MATLAB to come up with an optimization-simulation model for time series gate openings to control downstream elevations. The model involves using the one- and two-dimensional ability in HEC-RAS to perform hydrodynamic routing with high-resolution raster Digital Elevation Models. Also, the model uses both real-time gridded- and gaged-rainfall data in addition to a model for forecasting future rainfall-data.

This new model has been developed to manage reservoir release schedules before, during, and after an extraordinary rainfall event that could cause extreme flooding. Further to observe and control downstream water surface elevations to avoid exceedance of threshold of flood levels in target cells in the downstream area of study, and to minimize the damage and direct effects in both the up and downstream.

The application of the complete optimization-simulation model was applied to a portion of the Cumberland River System in Nashville, Tennessee for the flooding event of May 2010. The objective of this application is to demonstrate the applicability of the model for minimizing flood damages for an actual flood event in real-time on an actual river basin. The purpose of the application in a real-time framework would be to minimize the flood damages at Nashville, Tennessee by keeping the flood stages under the 100-year flood stage. This application also compared the three unsteady flow simulation scenarios: one-dimensional, two-dimensional and combined one- and two-dimensional unsteady flow.

In the recent past, Iraq was considered relatively rich considering its water resources compared to its surroundings. Currently, the magnitude of water resource shortages in Iraq represents an important factor in the stability of the country and in protecting sustained economic development. The need for a practical, applicable, and sustainable river basin management for the Tigris and Euphrates Rivers in Iraq is essential. Applicable water resources allocation scenarios are important to minimize the potential future water crises in connection with water quality and quantity. The allocation of the available fresh water resources in addition to reclaimed water to different users in a sustainable manner is of the urgent necessities to maintain good water quantity and quality.

In this dissertation, predictive water allocation optimization models were developed which can be used to easily identify good alternatives for water management that can then be discussed, debated, adjusted, and simulated in greater detail. This study provides guidance for decision makers in Iraq for potential future conditions, where water supplies are reduced, and demonstrates how it is feasible to adopt an efficient water allocation strategy with flexibility in providing equitable water resource allocation considering alternative resource. Using reclaimed water will help in reducing the potential negative environmental impacts of treated or/and partially treated wastewater discharges while increasing the potential uses of reclaimed water for agriculture and other applications. Using reclaimed water for irrigation is logical and efficient to enhance the economy of farmers and the environment while providing a diversity of crops, especially since most of Iraq’s built or under construction wastewater treatment plants are located in or adjacent to agricultural lands. Adopting an optimization modelling approach can assist decision makers, ensuring their decisions will benefit the economy by incorporating global experiences to control water allocations in Iraq especially considering diminished water supplies.

The phrase water-energy nexus is commonly used to describe the inherent and critical interdependencies between the electric power system and the water supply systems (WSS). The key interdependencies between the two systems are the power plant’s requirement of water for the cooling cycle and the water system’s need of electricity for pumping for water supply. While previous work has considered the dependency of WSS on the electrical power, this work incorporates into an optimization-simulation framework, consideration of the impact of short and long-term limited availability of water and/or electrical energy.

This research focuses on the water supply system (WSS) facet of the multi-faceted optimization and control mechanism developed for an integrated water – energy nexus system under U.S. National Science Foundation (NSF) project 029013-0010 CRISP Type 2 – Resilient cyber-enabled electric energy and water infrastructures modeling and control under extreme mega drought scenarios. A water supply system (WSS) conveys water from sources (such as lakes, rivers, dams etc.) to the treatment plants and then to users via the water distribution systems (WDS) and/or water supply canal systems (WSCS). Optimization-simulation methodologies are developed for the real-time operation of water supply systems (WSS) under critical conditions of limited electrical energy and/or water availability due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions including natural and manmade disasters. The coupling between WSS and the power system was done through alternatively exchanging data between the power system and WSS simulations via a program control overlay developed in python.

A new methodology for WDS infrastructural-operational resilience (IOR) computation was developed as a part of this research to assess the real-time performance of the WDS under emergency conditions. The methodology combines operational resilience and component level infrastructural robustness to provide a comprehensive performance assessment tool.

The optimization-simulation and resilience computation methodologies developed were tested for both hypothetical and real example WDS and WSCS, with results depicting improved resilience for operations of the WSS under normal and emergency conditions.