Climate change is currently threatening the livelihoods of farmers in developing countries. Psychological models have been developed to identify factors associated with adapting to climate change; however, little work has investigated the role of farmers’ risk attitudes in these models. We assessed perceptions of adaptation cost and adaptation intentions for five drought- specific adaptive behaviors among 550 farmers from 12 villages in the dry zone of Sri Lanka, as well as their attitudes toward risk. Results suggest that perceived adaptation cost and risk attitude are negatively associated with adaptation intentions. The conditional effect of adaptation cost on adaptation intention as a function of risk attitude was also investigated. Results showed that only farmers with risk averse attitudes were impacted by their perceptions of adaptation costs. These findings have implications for those interested in increasing adaptive practices of farmers in developing countries who face increasingly scarce water supplies.

Abstract The Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus, Mitchell) is an anadromous species that spawns in tidal freshwater rivers from Canada to Florida. Overfishing, river sedimentation and alteration of the river bottom have decreased Atlantic Sturgeon populations, and NOAA lists the species as endangered. Ecologists sometimes find it difficult to locate individuals of a species that is rare, endangered or invasive. The need for methods less invasive that can create more resolution of cryptic species presence is necessary. Environmental DNA (eDNA) is a non-invasive means of detecting rare, endangered, or invasive species by isolating nuclear or mitochondrial DNA (mtDNA) from the water column. We evaluated the potential of eDNA to document the presence of Atlantic Sturgeon in the James River, Virginia. Genetic primers targeted the mitochondrial Cytochrome Oxydase I gene, and a restriction enzyme assay (DraIII) was developed. Positive control mesocosm and James River samples revealed a nonspecific sequence—mostly bacteria commonly seen in environmental waters. Methods more stringent to a single species was necessary. Novel qPCR primers were derived from a second region of Cytochrome Oxydase II, and subject to quantitative PCR. This technique correctly identified Atlantic Sturgeon DNA and differentiated among other fish taxa commonly occurring in the lower James River, Virginia. Quantitative PCR had a biomass detection limit of 32.3 ug/L and subsequent analysis of catchment of Atlantic Sturgeon from the Lower James River, Virginia from the fall of 2013 provided estimates of 264.2 ug/L Atlantic Sturgeon biomass. Quantitative PCR sensitivity analysis and incorporation of studies of the hydrology of the James River should be done to further define habitat utilization by local Atlantic Sturgeon populations. IACUC: AD20127

Increased demands on water supplies caused by population expansion, saltwater intrusion, and drought have led to water shortages which may be addressed by use of recycled water as recycled water products. Study I investigated recycled water production in Florida and California during 2009 to detect gaps in distribution and identify areas for expansion. Gaps were detected along the panhandle and Miami, Florida, as well as the northern and southwestern regions in California. Study II examined gaps in distribution, identified temporal change, and located areas for expansion for Florida in 2009 and 2015. Production increased in the northern and southern regions of Florida but decreased in Southwest Florida. Recycled water is an essential component water management a broader adoption of recycled water will increase water conservation in water-stressed coastal communities by allocating recycled water for purposes that once used potable freshwater.

Light Detection and Ranging (Lidar) data are used to report the temporal and spatial patterns of subsidence as well as the potential contributors to subsidence within the Barataria and Terrebonne Bays. In recent decades, subsidence in southeast Louisiana has become a topic of substantial and growing concern to the scientific community, the local residents, and all those invested in the region. Lidar data were acquired from the United States Geological Survey (USGS) and the LSU Center for Geoinformatics. The data has been manipulated to map the differenced Lidar, complete an instantaneous slope analysis, and determine the thickness of the Holocene sediments. The goal was to gain a more comprehensive understanding of the subsidence patterns and the dynamic processes driving subsidence within the study area. These efforts provide a better ability to plan for the future of the Louisiana working coast and mitigate against relative sea level rise and coastal land loss.

In general, server rooms have restricted access requiring that staff possess access codes, keys, etc. Normally, only administrators are provided access to protect the physical hardware and the data stored in the servers. Servers also have firewalls to restrict outsiders from accessing them via the Internet. Servers also cost a lot of money. For this reason, server rooms also need to be protected against overheating. This will prolong the lifecycle of the units and can prevent data loss from hardware failure. The California State University San Bernardino (CSUSB), Specifically the College of Arts and Letters server room has faced power failures that affected the Air Conditioning Unit (AC) and as a result the room became overheated for a long time, causing hardware failure to server units. This is why this project is important for the College and needs to be implemented as soon as possible. The administrator’s old method of controlling server room temperature was by manually adjusting the temperature box inside of the server room. Now it can be controlled and monitored using remote access. The purpose of A Web-Based Temperature Monitoring System for the College of Arts and Letters proposed in this project is to allow users to monitor the server room temperature through a website by using any computer or mobile device that has Internet access. Also, this system notifies users when the room attains a critical temperature by sending an email/text to the server room administrator. A Web-Based Temperature Monitoring System for the College of Arts and Letters project is for the exclusive use of the College of Arts & Letters (CAL) server room. The administrator is the only person that can grant access to others by creating a proper account. For this project three prototypes will be implemented, first to measure the current server room temperature, the second to show the temperature history of the room, and third to use the built-in search system to locate times that given temperatures were attained.

New Zealand Agricultural Engineering Institute / Also published as: Agricultural Engineering Thesis no. 4 / For large areas of New Zealand that suffer from agricultural drought, the only practicable way of providing irrigation is through the use of water harvesting schemes that divert winter flood water in nearby streams into off-stream storages for irrigation use in the summer. A community water harvesting scheme is presently under construction in the Glenmark area of North Canterbury which was designed using traditional methods. The objectives of this thesis were to assess the limitations of traditional design methods for water harvesting schemes using the Glenmark Scheme as a case study and to develop an improved method based on a systems modelling approach. A daily simulation model was developed that incorporated in a realistic way the engineering, hydrologic, agronomic and economic features of importance to the design of water harvesting schemes in New Zealand. The model was used to study the adequacy of the traditional methods used for the design of the Glenmark Scheme; to arrive at alternative design solutions that achieved higher levels of engineering, agronomic and economic efficiency; and to develop a better understanding of the nature of complex water harvesting systems. It was demonstrated that compounding conservatism inherent in traditional design methods resulted in scheme overdesign and that the ability of the systems model to capture the essential dynamics of the system allowed higher levels of design performance to be achieved. The experience gained in the use of the systems model led to the development of a formalised design procedure for water harvesting schemes that represents an advance on methods hitherto available.

Oregon is a state with great social and ecological diversity. Unfortunately however, Oregon's water-rich reputation is more rumor than reality. As with many Western states, Oregon struggles with water scarcity, especially during dry summer months. Recent efforts by the state to develop an integrated water resource strategy (IWRS) to manage present and future water demand in Oregon signifies the very real concern that water is no longer as abundant and available as it once was. With the predicted impacts of climate change and population growth, the already-strained water supply will unlikely sustain current water needs. Using a statewide mail survey of 1,537 Oregon residents (2010), a second survey of 390 water stakeholders (2011), and 12 semi-structured interviews conducted in 2010 and 2011 of stakeholders and elected officials this dissertation examined the role of sociodemographic attributes and environmental values pertaining to concern about Oregon’s water supply, climate change, water conservation behaviors, and prioritization of water use. Data analysis (regression analysis) revealed that to varying degrees gender, age, education, income, concern about water scarcity and belief in the New Ecological Paradigm (NEP) proved reliable predicators of concern about that water quantity is a problem, that Oregonians will be personally affected by water scarcity, and personal water conservation behaviors. The dissertation further applies the Institutional and Analysis Development (IAD) framework to the current efforts by the state to create and IWRS. Recommendations for successful application of the IWRS are discussed, specifically use of adaptive governance in basin and sub-basin planning efforts. / Graduation date: 2012

The Walla Walla basin lies in an arid region of Eastern Washington and Oregon. A large portion of the area is devoted to agricultural production, relying on irrigation water diverted from the Walla Walla River and underlying aquifers occurring within Quaternary and Mio-pliocene era gravel deposits, as well as a supplemental source from the Columbia River Basalt formation. Heavy water demand over summer months has resulted in a fully allocated surface water supply and significant drawdown in groundwater levels. The Walla Walla River also hosts two salmonid species listed as threatened under the endangered species act and entitled to federal protection. Specific questions have emerged regarding regional water supply as stakeholders work towards management strategies that meet water user demands, well also addressing concerns such as groundwater depletion and fish habitat. Currently, there are proposals aimed at increasing water use efficiency such as the lining of permeable canal beds and the expansion of a shallow aquifer recharge program. Effective implementation of such strategies, in part, relies on understanding the interactions between surface water and groundwater within this region. This project used the distributed hydrologic model, Integrated Water Flow Model (IWFM), for simulating surface and subsurface flows over a portion of the Walla Walla River basin spanning from Milton Freewater, Oregon to west of Touchet, Washington. This application of IWFM uses a grid with an average spacing of 100 x 100 meters over the 230 square kilometer model area. The model was developed and calibrated using data from 2007 through 2009, with 2010 data to be used as a data set for validation. Data collection has been a collaborative effort between a research team from Oregon State University and the Walla Walla Basin Watershed Council (WWBWC). This thesis provides explanation and documentation of model development. This includes details of data collection and processing for groundwater and surface water conditions, estimation of initial and boundary conditions, parameter calibration, model validation, and error analysis. Data sources include federal and state agencies, a gauge network managed by the WWBWC, and geologic research primarily performed by Kevin Lindsey of GSI Water Solutions with support of the WWBWC. Parameters have been independently determined from field measurements whenever possible. Otherwise they were estimated using established methods of hydrologic analysis, values drawn from previous regional studies, or the process of model calibration. Outputs include detailed hydrological budgets and hydrographs for groundwater and surface water gauges. The calibrated model has an overall correlation coefficient of 0.59 for groundwater and 0.63 for surface water. The standard deviation for groundwater is 3.2 meters at 62 well locations and surface water has a mean relative error of 22.3 percent at 34 gauges. This model intended as a tool for formulating water budgets for the basin under present conditions and making predictions of systemic responses to hypothetical water management scenarios. Scenarios of increased inputs into the Locher Road aquifer recharge site and conversion of irrigation district canals into pipelines are presented. / Graduation date: 2012

Although arsenic is naturally present in the environment, 99% of human exposure to arsenic is through ingestion. Throughout history, arsenic is known as “the king of poisons”; it is mutagenic, carcinogenic, and teratogenic. Even in smaller concentrations, it accumulates in the body and takes decades before any physical symptoms of arsenic poisoning shows. According to the World Health Organization (WHO), the safe concentration of arsenic in drinking water is 10 µg/L. However, this limit is often times ignored until it is decades too late and people begin showing symptoms of having been poisoned. This is the current situation for Vietnam, whose legal arsenic concentration limit is 50 µg/L, five times higher than the WHO guidelines. Groundwater in Vietnam was already naturally high in arsenic due to arsenic-rich soils releasing arsenic into groundwater. Then, in the past half century, with the use of arsenic-laden herbicides dispersed during the Vietnam War and subsequent industrial developments, the levels of bio-available arsenicals has dangerously spiked. With the proliferation of government-subsidized shallow tube-wells in the past two decades, shallow groundwater has become the primary source for drinking and irrigation water in Vietnam. This is a frightening trend, because this groundwater has arsenic concentrations up to 3050 µg/L, primarily in the +3 and +5 oxidation states, the most readily available oxidation states for bioaccumulation. This thesis argues that measures must be taken immediately to remedy the high concentration of arsenic in groundwater, which in Vietnam is the primary and, in some cases, the sole source of water for domestic consumption and agricultural production. Although there are numerous technologies available for treating arsenic in groundwater, not all of them are suited for Vietnam. By analyzing the historical, cultural, economic, and political parameters of Vietnam, several optimal treatments of groundwater for drinking water emerged as most recommended, a classification that is based on their local suitability, social acceptability, financial feasibility, and governmental support. Further research on irrigation water treatment is proposed due to the need for sustainable crop production, the safe ingestion of rice and vegetables, and the continued growth of Vietnam’s economy, which is heavily dependent on agriculture.

Most of the real world problems in water resources involve nonlinear formulations in their solution construction. Obtaining optimal solutions for large scale nonlinear optimization problems is always a challenging task. The conventional methods, such as linear programming (LP), dynamic programming (DP) and nonlinear programming (NLP) may often face problems in solving them. Recently, there has been an increasing interest in biologically motivated adaptive systems for solving real world optimization problems. The multi-member, stochastic approach followed in Evolutionary Algorithms (EA) makes them less susceptible to getting trapped at local optimal solutions, and they can search easier for global optimal solutions. In this thesis, efficient optimization techniques based on swarm intelligence and evolutionary computation principles have been proposed for single and multi-objective optimization in water resource systems. To overcome the inherent limitations of conventional optimization techniques, meta-heuristic techniques like ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution (DE) approaches are developed for single and multi-objective optimization. These methods are then applied to few case studies in planning and operation of reservoir systems in India. First a methodology based on ant colony optimization (ACO) principles is investigated for reservoir operation. The utility of the ACO technique for obtaining optimal solutions is explored for large scale nonlinear optimization problems, by solving a reservoir operation problem for monthly operation over a long-time horizon of 36 years. It is found that this methodology relaxes the over-year storage constraints and provides efficient operating policy that can be implemented over a long period of time. By using ACO technique for reservoir operation problems, some of the limitations of traditional nonlinear optimization methods are surmounted and thus the performance of the reservoir system is improved. To achieve faster optimization in water resource systems, a novel technique based on swarm intelligence, namely particle swarm optimization (PSO) has been proposed. In general, PSO has distinctly faster convergence towards global optimal solutions for numerical optimization. However, it is found that the technique has the problem of getting trapped to local optima while solving real world complex problems. To overcome such drawbacks, the standard particle swarm optimization technique has been further improved by incorporating a novel elitist-mutation (EM) mechanism into the algorithm. This strategy provides proper exploration and exploitation throughout the iterations. The improvement is demonstrated by applying it to a multi-purpose single reservoir problem and also to a multi reservoir system. The results showed robust performance of the EM-PSO approach in yielding global optimal solutions. Most of the practical problems in water resources are not only nonlinear in their formulations but are also multi-objective in nature. For multi-objective optimization, generating feasible efficient Pareto-optimal solutions is always a complicated task. In the past, many attempts with various conventional approaches were made to solve water resources problems and some of them are reported as successful. However, in using the conventional linear programming (LP) and nonlinear programming (NLP) methods, they usually involve essential approximations, especially while dealing withdiscontinuous, non-differentiable, non-convex and multi-objective functions. Most of these methods consider multiple objective functions using weighted approach or constrained approach without considering all the objectives simultaneously. Also, the conventional approaches use a point-by-point search approach, in which the outcome of these methods is a single optimal solution. So they may require a large number of simulation runs to arrive at a good Pareto optimal front. One of the major goals in multi-objective optimization is to find a set of well distributed optimal solutions along the true Pareto optimal front. The classical optimization methods often fail to attain a good and true Pareto optimal front due to accretion of the above problems. To overcome such drawbacks of the classical methods, there has recently been an increasing interest in evolutionary computation methods for solving real world multi-objective problems. In this thesis, some novel approaches for multi-objective optimization are developed based on swarm intelligence and evolutionary computation principles. By incorporating Pareto optimality principles into particle swarm optimization algorithm, a novel approach for multi-objective optimization has been developed. To obtain efficient Pareto-frontiers, along with proper selection scheme and diversity preserving mechanisms, an efficient elitist mutation strategy is proposed. The developed elitist-mutated multi-objective particle swarm optimization (EM-MOPSO) technique is tested for various numerical test problems and engineering design problems. It is found that the EM-MOPSO algorithm resulting in improved performance over a state-of-the-art multi-objective evolutionary algorithm (MOEA). The utility of EM-MOPSO technique for water resources optimization is demonstrated through application to a case study, to obtain optimal trade-off solutions to a reservoir operation problem. Through multi-objective analysis for reservoir operation policies, it is found that the technique can offer wide range of efficient alternatives along with flexibility to the decision maker. In general, most of the water resources optimization problems involve interdependence relations among the various decision variables. By using differential evolution (DE) scheme, which has a proven ability of effective handling of this kind of interdependence relationships, an efficient multi-objective solver, namely multi-objective differential evolution (MODE) is proposed. The single objective differential evolution algorithm is extended to multi-objective optimization by integrating various operators like, Pareto-optimality, non-dominated sorting, an efficient selection strategy, crowding distance operator for maintaining diversity, an external elite archive for storing non- dominated solutions and an effective constraint handling scheme. First, different variations of DE approaches for multi-objective optimization are evaluated through several benchmark test problems for numerical optimization. The developed MODE algorithm showed improved performance over a standard MOEA, namely non-dominated sorting genetic algorithm–II (NSGA-II). Then MODE is applied to a case study of Hirakud reservoir operation problem to derive operational tradeoffs in the reservoir system optimization. It is found that MODE is achieving robust performance in evaluation for the water resources problem, and that the interdependence relationships among the decision variables can be effectively modeled using differential evolution operators. For optimal utilization of scarce water resources, an integrated operational model is developed for reservoir operation for irrigation of multiple crops. The model integrates the dynamics associated with the water released from a reservoir to the actual water utilized by the crops at farm level. It also takes into account the non-linear relationship of root growth, soil heterogeneity, soil moisture dynamics for multiple crops and yield response to water deficit at various growth stages of the crops. Two types of objective functions are evaluated for the model by applying to a case study of Malaprabha reservoir project. It is found that both the cropping area and economic benefits from the crops need to be accounted for in the objective function. In this connection, a multi-objective frame work is developed and solved using the MODE algorithm to derive simultaneous policies for irrigation cropping pattern and reservoir operation. It is found that the proposed frame work can provide effective and flexible policies for decision maker aiming at maximization of overall benefits from the irrigation system. For efficient management of water resources projects, there is always a great necessity to accurately forecast the hydrologic variables. To handle uncertain behavior of hydrologic variables, soft computing based artificial neural networks (ANNs) and fuzzy inference system (FIS) models are proposed for reservoir inflow forecasting. The forecast models are developed using large scale climate inputs like indices of El-Nino Southern Oscialltion (ENSO), past information on rainfall in the catchment area and inflows into the reservoir. In this purpose, back propagation neural network (BPNN), hybrid particle swarm optimization trained neural network (PSONN) and adaptive network fuzzy inference system (ANFIS) models have been developed. The developed models are applied for forecasting inflows into the Malaprabha reservoir. The performances of these models are evaluated using standard performance measures and it is found that the hybrid PSONN model is performing better than BPNN and ANFIS models. Finally by adopting PSONN model for inflow forecasting and EMPSO technique for solving the reservoir operation model, the practical utility of the different models developed in the thesis are demonstrated through application to a real time reservoir operation problem. The developed methodologies can certainly help in better planning and operation of the scarce water resources.