FundaÃÃo de Amparo à Pesquisa do Estado do Cearà / Este trabalho trata da aplicaÃÃo da Teoria Fuzzy em balanÃo hidrolÃgico de um reservatÃrio, para avaliar sua capacidade de previsÃo, na determinaÃÃo do cÃlculo do risco de falha de sistemas compostos por este tipo de corpo hÃdrico. No desenvolvimento da pesquisa, uma metodologia, transformando as equaÃÃes do balanÃo hidrolÃgico em equaÃÃes fuzzys, foi aplicada. ParÃmetros como tempo de esvaziamento e evaporaÃÃo foram considerados como funÃÃes de pertinÃncias dando, assim, ao modelo um carÃter fuzzy em suas formulaÃÃes. A vazÃo e o rendimento foram determinados, para diferentes cenÃrios, em forma de funÃÃes de pertinÃncias, onde essas variÃveis de controle, com maior grau de pertinÃncia foram analisadas. Os resultados mostraram que a metodologia fuzzy pode se apresentar como uma importante alternativa no cÃlculo do risco de colapso de sistemas hidrolÃgicos, como tambÃm, pode, igualmente, se apresentar como uma boa alternativa na determinaÃÃo da sustentabilidade hÃdrica, em regiÃes com alto grau de vulnerabilidade como ocorre em regiÃes semi-Ãridas. / This work concerns with the application of Fuzzy Theory in the hydrologic system as a reservoir, to evaluate its forecast capacity, in the calculation of the risk of collapse of systems composed by this type of body of water. In the development of the research, a methodology, transforming the equations of the hydrologic balance, in fuzzy equations, was applied. Parameters as time of emptying and evaporation were considered as membership functions, giving, like this, to the model the fuzzy characteristic in their formulations. The flow and the income net were calculated, for different sceneries, as membership functions, where those control variables, with larger pertinence degree, were analyzed. The results showed that the methodology fuzzy could come as an important alternative in the calculation of the risk of collapse of hydrologic systems, as well as, it can, equally, come as a good alternative in the determination of the sustainability of water, in areas with high vulnerability degree, as it happens in semi-arid regions.

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The Occoquan Reservoir is a public water supply in northern Virginia. The Occoquan Watershed has developed over the years from rural land uses to metropolitan suburbs within easy commuting distance from Washington, DC. Due to this urbanization, the Occoquan Reservoir is especially vulnerable to hypereutrophication, which results in problems such as algal blooms (including cyanobacteria), periodic fish kills, and taste and odor problems. In the 1970's, a new management plan for the Occoquan Reservoir called for the construction of the Upper Occoquan Sewage Authority (UOSA), an advanced wastewater treatment plant that would take extraordinary measures for highly reliable and highly efficient removal of particulates, organics, nutrients, and pathogens. Eliminating most of the water quality problems associated with point source discharges in the watershed, this state-of-the-art treatment is the foundation for the successful indirect surface water reuse system in the Occoquan Reservoir today. A limnological analysis of thirty years of water quality monitoring data from the reservoir and its two primary tributaries shows that the majority of the nutrient and sediment load to the reservoir comes from nonpoint sources, which are closely tied to hydrometeorologic conditions. Reservoir water quality trends are very similar to trends in stream water quality, and the tributary in the most urbanized part of the watershed, Bull Run, has been identified as the main contributor of sediment and nutrients to the reservoir. Despite significant achievements in maintaining the reservoir as a source of high quality drinking water, the reservoir remains a phosphorus-limited eutrophic waterbody. / Master of Science

In a hydro-dominated system, such as New Zealand, the continual improvement and development of effective optimization and simulation software to inform decision making is necessary for effective resource management. Stochastic Constructive Dual Dynamic Programming (SCDDP) is a technique which has been effectively applied to the New Zealand system for optimization and simulation. This variant of Dynamic Programming (DP) allows optimization to occur in the dual space reducing the computational complexity and allows solutions from a single run to be formed as price signal surfaces and trajectories. However, any application of this method suffers from issues with computational tractability for higher reservoir numbers. Furthermore, New Zealand specific applications currently provide limited information on the system as they all use the same two-reservoir approximation of the New Zealand system. This limitation is of increasing importance with the decentralization of the New Zealand electricity sector. In this thesis we develop this theory with respect to two key goals: • To advance the theory surrounding SCDDP to be generalizable to higher reservoir numbers through the application of the point-wise algorithm explored in R. A. Read, Dye, S. & Read, E.G. (2012) to the stochastic case. • To develop at least two new and distinct two-reservoir SCDDP representations of the New Zealand system to provide a theoretical basis for greater flexibility in simulation and optimization of hydro-thermal scheduling in the New Zealand context.

The objective of reservoir management is to maximize a key performance indicator (net present value in this study) at a minimum cost. A typical approach includes engineering analysis, followed by the economic value of the technical study. In general, operators are inclined to spend more effort on the engineering side to the detriment of the economic area, leading to unbalanced and occasionally suboptimal results. Moreover, most of the optimization methods used for production scheduling focus on a given recovery phase, or medium-term strategy, as opposed to an integrated solution that allocates resources from discovery to field abandonment. This thesis addresses the optimization of a reservoir under both technical and economic constraints. In particular, the method presented introduces a life cycle maximization approach to establish the best exploitation strategy throughout the life of the project. Deterministic studies are combined with stochastic modeling and risk analysis to assess decision making under uncertainty. To demonstrate the validity of the model, this document offers two case studies and the optimal times associated with each recovery phase. In contrast with traditional depletion strategies, where the optimization is done myopically by maximizing the net present value at each recovery phase, our results suggest that time is dramatically reduced when the net present value is optimized globally by maximizing the NPV for the life of the project. Furthermore, the sensitivity analysis proves that the original oil in place and non-engineering parameters such as the price of oil are the most influential variables. The case studies clearly show the greater economic efficiency of this life cycle approach, confirming the potential of this optimization technique for practical reservoir management. / text

Freshwater reservoirs store, process, and emit to the atmosphere large quantities of carbon (C). Despite the important role of reservoirs in the global carbon cycle, it remains unknown how human activities are altering their carbon cycling. Climate change and land use are resulting in lower dissolved oxygen (DO) concentrations in freshwater ecosystems, yet more frequent, powerful storms are occurring that temporarily increase DO availability. The net effect of these opposing forces results in anoxia (DO < 0.5 mg L-1) punctuated by short-term increases in DO. The availability of DO controls alternate redox reactions in freshwaters, thereby determining the rate and end products of organic C mineralization, which include two greenhouse gases, carbon dioxide (CO2) and methane (CH4). I performed ecosystem-level DO manipulations and evaluated how changing DO conditions affected redox reactions and the production and emission of CO2 and CH4. I also explored how the magnitude and drivers of CH4 emissions changed spatio-temporarily in a eutrophic reservoir using time series models. Finally, I used a coupled data-modeling approach to forecast future emissions of CH4 from the same reservoir. I found that the depletion of DO results in the rapid onset of alternate redox reactions in freshwater reservoirs for organic C mineralization and greater production of CH4. When the anoxia occurred in the water column (vs. at the sediments), diffusive CO2 and CH4 efflux phenology was affected, and resulted in degassing occurring during storms before fall turnover. I observed that the magnitude of CH4 emissions varied along a longitudinal gradient of a small reservoir and that the environmental drivers of ebullition and diffusion can change substantially both over space (within one hundred meters) and time (within a few weeks). Finally, I developed a forecasting workflow that successfully predicted future CH4 ebullition rates during one summer season. My research provides insight to how changing DO conditions will alter redox reactions in the water column and greenhouse gas emissions, as well as provides a new technique for improving future predictions of CH4 emissions from freshwater reservoirs. Althogether, this work improves our understanding of how freshwater lake and reservoir carbon cycling will change in the future. / Doctor of Philosophy / Freshwater reservoirs store a lot of carbon in their sediments and emit a lot of carbon as greenhouse gases (carbon dioxide and methane) to the atmosphere. However, climate change, land use, and water quality management can change the chemical reactions that are responsible for the production of carbon dioxide and methane, which could have substantial effects on the global carbon budget. Here, I manipulated the oxygen conditions of a freshwater reservoir and monitored the chemistry and greenhouse gas emissions in the experimental reservoir relative to an upstream reference reservoir. I then estimated the methane emissions from the reservoir to understand how the chemistry and greenhouse gas emissions in freshwater reservoirs may change in the future. I found that reservoir oxygen availability controls the magnitude and timing of the chemical reactions that produce carbon dioxide and methane, which in turn alters greenhouse gas emissions. Additionally, I developed models that showed how the magnitude and drivers of methane emissions changed within a small reservoir over time. Finally, I was able to predict the timing and magnitude of methane bubbling from the sediments. Altogether, this work provides evidence how climate change, land use change, and water quality management will affect future water chemistry and greenhouse gas emissions from reservoirs.

Historical water quality databases from two South Australian drinking water reservoirs were used, in conjunction with various computational modelling methods for the ordination, clustering and forecasting of complex ecological data. Techniques used throughout the study were: Kohonen artificial neural networks (KANN) for data categorisation and the discovery of patterns and relationships, recurrent supervised artificial neural networks (RANN) for knowledge discovery and forecasting of algal dynamics and hybrid evolutionary algorithms (HEA) for rule-set discovery and optimisation for forecasting algal dynamics. These methods were combined to provide an integrated approach to the analysis of algal populations including interactions within the algal community and with other water quality factors, which results in improved understanding and forecasting of algal dynamics. The project initially focussed on KANN for the patternising and classification of the historical data to reveal links between the physical, chemical and biological components of the reservoirs. This offered some understanding of the system and relationships being considered for the construction of the forecasting models. Specific investigations were performed to examine past conditions and the impacts of different management regimes, as well as to discover sets of conditions that correspond with specific algal functional groups. RANN was then used to build models for forecasting both Chl-a and the main nuisance species, Anabaena, up to 7 days in advance. This method also provided sensitivity analyses to demonstrate the relationship between input and output variables by plotting the reaction of the output to variations in the inputs. Initially one year from the data set was selected for the testing of a model, as per the split-sample technique. To further test the models, it was later decided to select several years for testing to ensure the models were useful under changed conditions, and that test results were not misleading regarding the models true capabilities. RANN were firstly used to create reservoir specific or ad-hoc models. Later, the models were trained with the merged data sets of both reservoirs to create one model that could be applied to either reservoir. Another method of forecasting was trialled and compared to RANN. HEA was found to be equal or superior to RANN in predictive power, also allowed sensitivity analysis and provided an explicit, portable rule set. The HEA rule sets were initially tested on selected years of data, however to fully demonstrate the models potential, a process for k-fold cross-validation was developed to test the rule-set on all years of data. To further extend the applicability of the HEA rule-set; the idea of rule-based agents for specific lake ecosystem categories was examined. The generality of a rule-based agent means that, after successful validation on several lakes from one category, the agent could then be applied to other water bodies from within that category that had not been involved in the training process. The ultimate test of the rule-based agent for the warm monomictic and eutrophic lake ecosystem category was to be applied to a real-time monitoring and forecasting situation. The agent was fed with online, real-time data from a reservoir that belonged to the same ecosystem category but was not used in the training process. These preliminary experiments showed promising results. It can be concluded that the concept of rulebased agents will facilitate real-time forecasting of algal blooms in drinking water reservoirs provided on-line monitoring of relevant variables has been implemented. Contributions of this research include: (1) to offer insight into the capabilities of 3 kinds of computational modelling techniques applied to complex water quality data, (2) novel applications of KANN including the division of data into separate management periods for comparison of management efficiency, (3) to both qualitatively and quantitatively elucidate relationships between water quality parameters, (4) research toward the development of a forecasting tool for algal abundance 7 days in advance that could be generic for a particular lake ecosystem category and implemented in real-time, and (5) to suggest a thorough testing method for such models (k-fold cross validation). / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1331584 / Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2008

Limited freshwater supply is the most important challenge in water resources management, particularly in arid and semi-arid basins. However, other variations in a basin, including climate change, population growth, and economic development intensify this threat to water security. The Oldman River Basin (OMRB), located in southern Alberta, Canada, is a semi-arid basin and encompasses several water challenges, including uncertain water supply as well as increasing, uncertain water demands (consumptive irrigation, municipal, and industrial demands, and non-consumptive hydropower generation, and environmental demands). Reservoirs, of which the Oldman River Reservoir is the largest in the basin, are responsible for meeting most of demands, and, protecting the basin’s economy. The OMRB has also faced extreme natural events, floods and droughts, in the past, which reservoir management plays a critical role to adapt to. The complexity of the climate, hydrology, and water resource system and water governance escalates the challenges in the basin. These factors are highly interconnected and establish dynamic, non-linear behavior, which requires an integrated, feedback-based tool to investigate. Integrated water resources (IWRM) modelling using system dynamics (SD) is such an approach to tackle the different water challenges and understand their non-linear, dynamic pattern. In this research study the Sustainability-oriented Water Allocation, Management, and Planning (SWAMPOM) model for the Oldman River Basin is developed. SWAMPOM comprises a water allocation model, dynamic irrigation demand, instream flow needs (IFN), and economic evaluation sub-models. The water allocation model allocates water to all the above-mentioned demands at a weekly time step from 1928 to 2001, and under different water availability scenarios. Meeting irrigation demands relies on the crop water requirement (CWR), which is calculated under different climatic conditions by the dynamic irrigation demand sub-model. This sub-model estimates the weekly irrigation demand for main crops planted in the basin. SWAMPOM also computes environmental demands or instream flow need (IFN) for the Oldman River, and allocates water to rivers to meet IFN under different policy scenarios and uncertain water supply. Finally, the major water-related economic benefit in the basin, earned by agriculture and hydropower generation, is computed by the economic evaluation sub-model. The results show that SWAMPOM could reasonably satisfy the demands at a weekly time step and provide an adequate estimation of the crop water requirement under different hydrometeorological conditions. Based on the SWAMPOM’s results, the average annual irrigation demand is 306 mm over the historical time period from 1928 to 2001 in the main irrigation districts. The average weekly instream flow need of the Oldman River is calculated to be approximately 20.5 m3/s, which can be met in more than 97% of weeks in the historical time period. Average annual water-related economic benefit was computed to be 192.5 M$ in the OMRB. It decreased to 82.8 M$ in very dry years, and increased up to 328.6 M$ in very wet years. This research also developed different sets of Oldman Reservoir’s operation zones, resulting in trade-offs between the optimal economic benefit, water allocated to the ecosystem, minimum floodwater and minimum flood frequency. This helps decision makers to decide how much water should be stored in the reservoir to meet a specific objective while not sacrificing others. A multi-objective performance assessment, Pareto curve approach, is applied to identify the optimal trade-offs between the four objective functions (OFs), and 18 different optimal, or close to optimal sets of operating zones are provided. The decision regarding the operating zones depends on decision makers’ preference for higher economic benefit, water allocated to IFN, or flood security. However, the set of operating zones with minimum floodwater causes 11 less flood events; the operating zones with maximum economic benefits result in 4.1% more financial gain; and the zones with maximum water allocated to IFN lead to 10.1% more ecosystem protection in the whole 74 years, compared to current zones.

Increasing of the oil recovery is an essential task of the reservoir engineers. Many highly fractured carbonate reservoirs with low matrix porosity and low matrix permeability in the world have been depleted with low ultimate recovery due to the improper management. To obtain higher oil recovery and an optimum oil production, a three dimensional geological model for the reservoir is needed. The reservoir model should be calibrated via the history matching process which makes the model reliable for forecasting and development planning. An investigation has been carried out to find the most sensitive parameter which affects the matching between the real production data and the simulated production data. After the calibration of the model, some prediction scenarios have been run to realize the future performance of the reservoir. Three wells have been suggested and included in the simulation as producers in some prediction cases and as gas injectors in another other case. The results show that the three producers with low production rate will assist in increasing the recovery and by converting those to gas injectors will assist more especially when the oil production rate is higher. An oil field in Kurdistan region has been selected for this research.

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