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Eutrophication is the accelerated aging process of a lake which generally decreases the water quality in terms of aesthetic and recreational uses. At Liberty Lake, near Spokane, Washington, eutrophication has become a problem. Many individuals and agencies are interested in the benefits that occur when the lake's water quality has improved. In this research the benefits to private property owners were measured at Liberty Lake. Three methods — present sales, appraised value and personal interview — were compared and empirically tested to determine the economic benefits to private property owners as well as the overall community impact. For this study property was defined as land, buildings and other improvements. Current market sales and appraised values at six different lakes, each with different water quality levels, were used as the dependent variables for the present sales and appraised value methods. The dependent variables were regressed against several factors including physical (.housing, neighborhood, accessibility, and environmental characteristics) to estimate the effect water quality had on property values. A quadratic and double logarithmic function were examined. In the results a positive relationship was found between water quality and the dependent variables. A 100 percent increase in the water quality ranking resulted in a $3,800 increase in the sales price per lot and $884 increase in the appraised value per lot for the quadratic function. Separate equations were determined for vacant lots. The dependent variables were the same, but only neighborhood, accessibility and environmental characteristics were used to estimate the effect water quality had on property values. In the quadratic form with a 100 percent increase in the water quality ranking, sales price increased $556 per lot and appraised value increased $782 per lot. In the personal interview method home owners at Liberty Lake were interviewed and asked for how much they would be willing to buy and sell a particular home at different water quality levels. The differences between the two buying and selling prices were the estimated impact of a change in water quality and totaled $4,795 and $5,679, respectively. Both differences were significant at the one percent level. The buying price difference was used when comparing the personal interview method to the other methods, since it was less likely to overestimate the water quality effect. This research used the best water quality index available, but there is a definite need for future research to develop a uniform water quality index. / Graduation date: 1980

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This study looks at spatial variation of groundwater nitrate in Texas and its fluctuations at 10 year increments using data from the Texas Water Development Board. While groundwater nitrate increased in the Ogallala and Seymour aquifers across the time period, the overall rate in Texas appears to be declining as time progresses. However, the available data is limited. Findings show that a much more targeted, knowledge based strategy for sampling would not only reduce the cost of water quality analysis but also reduce the risk of error in these analyses by providing a more realistic picture of the spatial variation of problem contaminants, thereby giving decision-makers a clearer picture on how best to handle the reduction and elimination of problem contaminants.

A dissertation submitted to the Faculty of Science in fulfilment of the requirements for the degree of Master of Science November 2015, Johannesburg / South Africa is faced with water quantity and quality issues in most catchments. Intensification of coal-based industrial activity in the Waterberg, Limpopo and the concomitant emissions of oxides of nitrogen (NOX) and sulphur (SOX) pose potential ecological impacts to regional freshwater systems. Some research indicates that a significant proportion of minerals in the Waterberg parent rock may be prone to acid generation with catchment soils being potentially susceptible to acidification via NOX and SOX deposition. Cultural and recurrent nutrient loading of freshwater bodies also impacts on primary production and can ultimately alter the natural structure and functioning of these ecosystems. Trend analyses on historical hydrological data from 1982 to 2013 were carried out for several response water quality variables from six quaternary Waterberg catchments. Results were assessed for possible changes attributable to increased NOX and SOX loading post commencement of large-scale coal combustion. Historical inorganic N:P ratios were calculated in conjunction with a series of nutrient (N and P) bioassay experiments to predict which nutrient may be limiting growth of stream periphyton. Although trends were identified in most catchments for several of the water quality variables, the notion that the onset of large scale coal combustion has led to noticeable downward trends in pH and upward trends in either inorganic N or sulphate is not unequivocal. Patterns in trends were not distinct for catchments situated in close proximity to the primary emission source and those further away. Nor were there any distinct differences in trends between upwind and downwind catchments. Climate and geo-hydrological factors are likely to still function as the primary drivers of spatial and temporal variation in past and present catchment water quality. Contrary to the view that stream primary production is limited largely by the availability of P, predictions based on N:P ratios calculated in this study suggest N to be the limiting nutrient. This was shown to be the case in four of the five study-site rivers. N-limitation increased by 18% (67% to 85%) in the Matlabas River post-commencement of large-scale coal combustion. A greater increase of 24% (60% to 84%) was observed in the Middle Mokolo. Although cultural eutrophication levels in the Waterberg do not yet exceed management-set targets, the cumulative effect of industrial-derived nutrient inputs remains a threat to the nearshore marine ecosystem and human communities living downstream.

Thesis (doctoral)--University of Tartu, 2007. / Vita. Includes bibliographical references. Also available on the Internet.

This dissertation utilizes data from four sub-watersheds in the Little River Experimental Watershed, GA to develop models to improve forecast predictions related to the management of surface-water pollution due to non-point source runoff. Non-point source pollution is the primary cause of US surface-water quality impairment and a main transport mechanism for pathogens and other pollutants into receiving surface water bodies (US EPA 2008). In response to pollution reduction and watershed remediation mandates under the Federal Clean Water Act (1972)-particularly the Total Maximum Daily Load (TMDL) program-the role of water quality modeling in effectively rehabilitating impaired waters has taken on greater importance. Consequently, the significance of this study is that it is the first of its kind to incorporate a multi-model approach to address limitations in using single water quality models. In this regard, it builds on water quality engineering research by presenting methods to estimate contaminant concentrations and reduce uncertainty in overall model predictions in impaired water-bodies. Methodologically, the key point of departure in this dissertation is centered on the fact that water quality modeling is the cornerstone of TMDL analyses but the associated prediction uncertainty affects their adequacy in providing reliable contaminant loadings estimates in an impaired water body. As such, utilizing hydrological and water-quality process equations embedded in the two most widely used watershed-scale models, the Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-Fortran (HSPF), and observed data from the sub-watersheds mentioned above, the dissertation addresses this limitation by combining results from the two competing models to reduce uncertainty and enhance accuracy of predictions. The study was conducted in two phases. First, HSPF and SWAT-two extensively-used, scientifically-rigorous, US EPA-approved watershed-scale codes-were used to build models of the four study catchments. The models were individually calibrated and shown (based on Nash-Sutcliffe Efficiency (NSE) ratios) to produce reliable simulations of the hydrologic and water quality conditions in the watershed. The second phase of the analysis involved using a multi-model approach to combine model forecasts. Model combination, introduced by Bates and Granger in 1969, has emerged as a viable analytical technique (Claesken and Hjort, 2008; Ajami et al., 2006) and widely-used across disciplines to improve model-forecasting results (Kim et al., 2006; Shamseldin et al., 1997; Granger, 2001; Clemens, 1989; Thompson, 1976; Newbold and Granger, 1974; Dickinson, 1973). After calibration, the model predictions were combined for each catchment using three different methods: the Weighted Average Method (WAM), the Nash-Sutcliffe Efficiency Maximization Method (NSE-max) and an Artificial Neural Network Method (ANN). Comparison of the results of the multi-model formulation with original individual model results showed improved estimates with all three combination methods. The improvement in model accuracy (based on NSE ratios) varied from modest to significant in both hydrologic and water quality variables. These improvements were attributed to a reduction in model structural uncertainty resulting from the ability to capture aspects of some of the more complex watershed interactions from exogenous information provided by the contributing models. It should be noted here, however, that as model availability increases, if additional models (beyond those utilized here) are used with this approach, care should be taken to ensure the credibility of each individual model for simulating the watershed scale processes under review. Limitations of this study include possible bias introduced by the use of deterministic models to estimate probabilistic contaminant distributions, limitations in available data, and the use of a seven-year study period that did not account for possible impacts of shorter periods of extreme hydrologic conditions on the individual model performances and model combination weightings. Recommendations for future research include (a) improving watershed-scale codes to better describe the probability distribution functions characteristic of contaminant distributions and data collection on wildlife species and populations; and investigating the fate and transport processes of pathogenic indicator bacteria deposited in forested areas and the impact of extreme hydrologic conditions on model performance and weighting. Overall, the findings from this dissertation suggest that water quality modeling incorporating a multi-model approach has the potential to significantly improve predictions compared to the predictions obtained when only one model is used. Clearly, the findings reported here have significant implications in improving TMDL analyses and remediation plans by presenting an approach that exploits the strengths of two of the most complete and well-accepted watershed-scale water quality models in the United States. Moreover, the findings of this dissertation auger well for the future of TMDL management in that it provides a more robust and cost effective basis for policy makers to decide on effective management strategies that incorporate acceptable risk, allowable loading and land use.