Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Qiong Zhang, Ph.D.

Committee Member

Joni Downs, Ph.D.

Committee Member

Mahmoud Nachabe, Ph.D.

Committee Member

Lingling Fan, Ph.D.

Committee Member

Kalanithy Vairavamoorthy, Ph.D.

Keywords

Demand and Supply, Greenhouse Gas Emission, Management Options, System Archetypes, Water Quality

Abstract

Water and energy are two of the most important resources for societal prosperity and economic development. It is clear that water and energy are intrinsically linked together and depend on one another in modern society. To date, however, efforts on water-energy nexus concentrate on quantifying the energy use in water cycle or the water use in energy production. From management perspective, water and energy are still managed separately. Little work has been done to investigate the impacts of the management options associated with one resource on the other and examine the integrated water and energy management options. Accordingly, the overall goal of this study is to examine the integrated management options for long-term regional water and energy resources management with consideration of their interactions through a system dynamics approach.

System dynamics is based on systems thinking, which focuses on the system structure and offers a deeper insight into problems. It can link ecological, human, and social elements of water and energy systems in one modeling platform to investigate their interactions A four-step system dynamics modeling process was used in this study, which includes problem articulation, model formulation, model testing, and scenario design and simulation. Tampa Bay region was chosen as the study area, which is located on the west central coast of Florida and estuary along the Gulf of Mexico. This study considered a 100-year time scale with monthly interval, the first 30 years of which are used for model validation and the rest of which are for simulation.

In order to investigate the interrelationship between water and energy systems, two sub-models (i.e., water sub-model and energy sub-model) were developed first. The water sub-model is composed of sectoral water demand (agriculture, industry, municipality, and energy sector), water supply (surface water, groundwater, reclaimed water, and water imports), and water quality and energy consumption associated with water supply. The result shows that surface water level increases by 1.32~1.39% when considering water quality and 1.10~1.30% considering both water quality and energy consumption. There is a slight decrease in groundwater storage (0.02~0.08%) compared with the reference behavior. The result also reveals that water conservation education is the most effective option to reduce the freshwater withdrawals (~17.3%), followed by rebates on indoor water-efficient appliances (~15.4%). Water loss control has a high potential to reduce freshwater withdrawals but it is not effective currently due to limited budget. The implementation of minimum surface water level reduces the surface water withdrawal by 26 MGD (million gallons per day) and requires alternative water supply sources to meet the water demands.

The energy sub-model consists of sectoral energy demand (agriculture, industry, municipality, and water sector), energy supply (coal, natural gas, oil, and electricity), and greenhouse gas (GHG) emissions and water pollution associated with energy supply. The result finds that cost of fuels is the primary concern of determining the energy mix for power generation. The current electricity mix in the study area consists of 35.4% fuels from coal, 44.6% from natural gas, and 20% from oil. When considering the environmental impacts associated with energy supply, this percentage of coal reduces to 10.6%, and GHG emissions and water pollution can be reduced by 22% and 43% accordingly. The result also shows that energy price is most effect of reducing the demand (~16.3%), followed by energy conservation education (~10.6%). Rebates on household appliances are the least effective option (~3.6%) due to consumers' low willingness to pay. Combining the supply decision incorporating environmental impacts and the demand option of energy price increase, the reductions of GHG emissions and water pollution can reach 37% and 55%, respectively.

The integrated model is developed by linking the water and energy models through the interactions between water and energy systems identified by the system archetypes. The result shows that water demand is reinforced by energy demand, and vice versa. This growth, however, is limited by water and energy availability. The result also reveals that some decisions to solve the problems of one resource result in the problems of the other resource. The increase of water price is one of these, which decreases the water demand by 24.3% but leads to increase of the energy demand by 1.53% due to the use of reclaimed water. Rebates on indoor water-efficient appliances are effective to reduce both water and energy demands largely due to the household energy use in water heating. In addition, this study demonstrates that integrated management options can improve the uses of water and energy, but decisions without considering each other may lead to more issues. For example, reclaimed water, a supply management option considering the energy, can increase the water balance index by 27.3% and the energy balance index by 0.14%; it can also reduce the water pollution by 11.76% and the GHG emissions by 13.16%. Seawater desalination, a supply management option without integrated consideration, intends to decrease the water shortage but eventually increases the water balance index by 29.7%. It also causes the increases in water pollution and GHG emissions by 89.79% and 14.53%, respectively. Similarly, solar energy presents the advantage in increasing the balance indices and reducing the environmental impacts.

This study is an initial attempt to link water and energy systems to explore integrated management options. It is limited by the data availability, assumptions for model simplification, and lack of consideration of climate change. The recommendations for future study include (a) employing a more accurate projection or representation of precipitation, (b) testing the energy model with local data, (c) considering water and energy allocation between different users under shortages, (d) examining the environmental impacts associated with bay water withdrawal for power generation, (e) investigating the water and energy use under climate change, and (f) involving stakeholders early in model development and continuous participation in policy analysis.

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