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.

Bioretention basins are a common stormwater best management practice (BMP) used to mitigate the hydrologic consequences of urbanization. Dry wells, also known as vadose-zone wells, have been used extensively in bioretention basins in Maricopa County, Arizona to decrease total drain time and recharge groundwater. A mixed integer nonlinear programming (MINLP) model has been developed for the minimum cost design of bioretention basins with dry wells.

The model developed simultaneously determines the peak stormwater inflow from watershed parameters and optimizes the size of the basin and the number and depth of dry wells based on infiltration, evapotranspiration (ET), and dry well characteristics and cost inputs. The modified rational method is used for the design storm hydrograph, and the Green-Ampt method is used for infiltration. ET rates are calculated using the Penman Monteith method or the Hargreaves-Samani method. The dry well flow rate is determined using an equation developed for reverse auger-hole flow.

The first phase of development of the model is to expand a nonlinear programming (NLP) for the optimal design of infiltration basins for use with bioretention basins. Next a single dry well is added to the NLP bioretention basin optimization model. Finally the number of dry wells in the basin is modeled as an integer variable creating a MINLP problem. The NLP models and MINLP model are solved using the General Algebraic Modeling System (GAMS). Two example applications demonstrate the efficiency and practicality of the model.

The increasingly recurrent extraordinary flood events in the metropolitan area of Monterrey, Mexico have led to significant stakeholder interest in understanding the hydrologic response of the Santa Catarina watershed to extreme events. This study analyzes a flood mitigation strategy proposed by stakeholders through a participatory workshop and are assessed using two hydrological models: The Hydrological Modeling System (HEC-HMS) and the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS).

The stakeholder-derived flood mitigation strategy consists of placing new hydraulic infrastructure in addition to the current flood controls in the basin. This is done by simulating three scenarios: (1) evaluate the impact of the current structure, (2) implementing a large dam similar to the Rompepicos dam and (3) the inclusion of three small detention dams. These mitigation strategies are assessed in the context of a major flood event caused by the landfall of Hurricane Alex in July 2010 through a consistent application of the two modeling tools. To do so, spatial information on topography, soil, land cover and meteorological forcing were assembled, quality-controlled and input into each model. Calibration was performed for each model based on streamflow observations and maximum observed reservoir levels from the National Water Commission in Mexico.

Simulation analyses focuses on the differential capability of the two models in capturing the spatial variability in rainfall, topographic conditions, soil hydraulic properties and its effect on the flood response in the presence of the different flood mitigation structures. The implementation of new hydraulic infrastructure is shown to have a positive impact on mitigating the flood peak with a more favorable reduction in the peak at the outlet from the larger dam (16.5% in tRIBS and 23% in HEC-HMS) than the collective effect from the small structures (12% in tRIBS and 10% in HEC-HMS). Furthermore, flood peak mitigation depends strongly on the number and locations of the new dam sites in relation to the spatial distribution of rainfall and flood generation. Comparison of the two modeling approaches complements the analysis of available observations for the flood event and provides a framework within which to derive a multi-model approach for stakeholder-driven solutions.

Vegetative filter strips (VFS) are an effective methodology used for storm water management particularly for large urban parking lots. An optimization model for the design of vegetative filter strips that minimizes the amount of land required for stormwater management using the VFS is developed in this study. The resulting optimization model is based upon the kinematic wave equation for overland sheet flow along with equations defining the cumulative infiltration and infiltration rate.

In addition to the stormwater management function, Vegetative filter strips (VFS) are effective mechanisms for control of sediment flow and soil erosion from agricultural and urban lands. Erosion is a major problem associated with areas subjected to high runoffs or steep slopes across the globe. In order to effect economy in the design of grass filter strips as a mechanism for sediment control & stormwater management, an optimization model is required that minimizes the land requirements for the VFS. The optimization model presented in this study includes an intricate system of equations including the equations defining the sheet flow on the paved and grassed area combined with the equations defining the sediment transport over the vegetative filter strip using a non-linear programming optimization model. In this study, the optimization model has been applied using a sensitivity analysis of parameters such as different soil types, rainfall characteristics etc., performed to validate the model

A model is presented for real-time, river-reservoir operation systems. It epitomizes forward-thinking and efficient approaches to reservoir operations during flooding events. The optimization/simulation includes five major components. The components are a mix of hydrologic and hydraulic modeling, short-term rainfall forecasting, and optimization and reservoir operation models. The optimization/simulation model is designed for ultimate accessibility and efficiency. The optimization model uses the meta-heuristic approach, which has the capability to simultaneously search for multiple optimal solutions. The dynamics of the river are simulated by applying an unsteady flow-routing method. The rainfall-runoff simulation uses the National Weather Service NexRad gridded rainfall data, since it provides critical information regarding real storm events. The short-term rainfall-forecasting model utilizes a stochastic method. The reservoir-operation is simulated by a mass-balance approach. The optimization/simulation model offers more possible optimal solutions by using the Genetic Algorithm approach as opposed to traditional gradient methods that can only compute one optimal solution at a time. The optimization/simulation was developed for the 2010 flood event that occurred in the Cumberland River basin in Nashville, Tennessee. It revealed that the reservoir upstream of Nashville was more contained and that an optimal gate release schedule could have significantly decreased the floodwater levels in downtown Nashville. The model is for demonstrative purposes only but is perfectly suitable for real-world application.

Land-atmosphere interactions of semiarid shrublands have garnered significant scientific interest. One of the main tools used for this research is the eddy covariance (EC) method, which measures fluxes of energy, water vapor, and carbon dioxide. EC fluxes can be difficult to interpret due to complexities within the EC footprint (i.e. the surface conditions that contribute to the flux measurements). Most EC studies use a small number of soil probes to estimate the land surface states underlying the measured fluxes, which likely undersamples the footprint-scale conditions, especially in semiarid shrublands which are characterized by high spatial and temporal variability. In this study, I installed a dense network of soil moisture and temperature probe profiles in the footprint region of an EC tower at two semiarid sites: a woody savanna in southern Arizona and a mixed shrubland in southern New Mexico. For data from May to September 2013, I link land surface states to EC fluxes through daily footprints estimated using an analytical model. Novel approaches are utilized to partition evapotranspiration, estimate EC footprint soil states, connect differences in fluxes to footprint composition, and assess key drivers behind soil state variability. I verify the hypothesis that a small number of soil probes poorly estimates the footprint conditions for soil moisture, due to its high spatial variability. Soil temperature, however, behaves more consistently in time and space. As such, distributed surface measurements within the EC footprint allow for stronger ties between evapotranspiration and moisture, but demonstrate no significant improvement in connecting sensible heat flux and temperature. I also find that in these systems vegetation cover appears to have stronger controls on soil moisture and temperature than does soil texture. Further, I explore the influence of footprint vegetation composition on the measured fluxes, which reveals that during the monsoon season evaporative fraction tends to increase with footprint bare soil coverage for the New Mexico site and that the ratio of daily transpiration to evapotranspiration increases with grass coverage at the Arizona site. The thesis results are useful for understanding the land-atmosphere interactions of these ecosystems and for guiding future EC studies in heterogeneous landscapes.

Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.