Comparison of the power spectra of TDS time series from different locations on the Lower Colorado River is useful in showing changes in salinity and for indicating physical factors influencing salinity. Similarities between the power spectra of the Lee Ferry and Grand Canyon tine series indicated that lateral inputs and evaporation are not greatly influencing the salinity cycle. The salinity change within this reach was approximated by a constant concentration change of 66.6 ppm. A similar model form was used for the Hoover Dam to Parker Dam reach. Dissimilarities between power spectra indicated that additional inputs are significant and must be accounted for in any model of such reaches. The model for Lake Mead required compensation for evaporation and for the inputs of the Virgin River and Las Vegas Wash. The modeled salinity increase between Parker Dam and Yuma contained a trend factor to allow for the effect of irrigation return flows and seepage. The crosscovariance function was used to approximate the time lag between data stations. Time series statistics, including coherence, response function spectra, and overall unit response, were used and are of utility in estimating salinity in a river system.

<p> Groundwater samples from seven boreholes at the Atikokan research area, northwestern Ontario, have been analysed to determine their Sr content and isotopic composition. Whole rock and mineral separates have also been analysed. The groundwaters can be broadly divided into two groups. The 'shallow' waters have low Sr content and high and variable isotopic ratios (.705 - .728) while the 'deep' Sr-rich saline groundwaters have low and constant 87Sr/86Sr ratios (.706 - .707). The saline waters are in isotopic equilibrium with plagioclase and the role of other major rock forming minerals in controlling the isotopic and chemical composition of these waters is negligible. Gypsum is in isotopic equilibrium with the saline waters and appears to be 'young'.</p> <p> The degree of water-plagioclase interaction appears to have been extensive implying low water/rock ratios (ie., 'closed' system. The chemistry of the Atikokan groundwaters is similar to several Shield mine waters. If seawater was a precursor of these waters, there is no evidence for it today. Some degree of mixing with surface waters is indicated in all samples. </p> <p> The granites of the Eye-Dashwa lakes pluton are very Sr-rich. This is reflected in the low isotopic ratios of plagioclase and other minerals and in turn in the low ratios for the saline groundwaters, which are some of the lowest values yet reported.</p> / Thesis / Master of Science (MSc)

"February 2003." Corrigenda for, inserted at back Includes bibliographical references (leaves 475-524 ) A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia.

"February 2003." / Corrigenda for, inserted at back / Includes bibliographical references (leaves 475-524 ) / xxx, 524 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia. / Thesis (Ph.D.)--University of Adelaide, School of Civil and Environmental Engineering, 2003

Of all the strategies to reduce carbon emissions, carbon dioxide (CO₂) geological sequestration is an immediately available option for removing large amounts of the gas from the atmosphere. However, our understanding of the transition behavior between Forchheimer and Darcy flow through porous media during CO₂ injection is currently very limited. In addition, the kinetic mass transfer of SO₂ and CO₂ from CO₂ stream to the saline and the fully coupling between the changes of porosity and permeability and multiphase flow are two significant dimensions to investigate the brine acidification and the induced porosity and permeability changes due to SO₂ co-injection with CO₂. Therefore, this dissertation develops a multiphase flow, contaminant transport and geochemical model which includes the kinetic mass transfer of SO₂ into deep saline aquifers and obtains the critical Forchheimer number for both water and CO₂ by using the experimental data in the literature. The critical Forchheimer numbers and the multiphase flow model are first applied to analyze the application problem involving the injection of CO₂ into deep saline aquifers. The results show that the Forchheimer effect would result in higher displacement efficiency with a magnitude of more than 50% in the Forchheimer regime than that for Darcy flow, which could increase the storage capacity for the same injection rate and volume of a site. Another merit for the incorporation of Forchheimer effect is that more CO₂ would be accumulated in the lower half of the domain and lower pressure would be imposed on the lower boundary of the cap-rock. However, as a price for the advantages mentioned above, the injection pressure required in Forchheimer flow would be higher than that for Darcy flow. The fluid flow and contaminant transport and geochemical model is then applied to analyze the brine acidification and induced porosity and permeability changes due to SO₂ co-injection. The results show that the co-injection of SO₂ with CO₂ would lead to a substantially acid zone near the injecting well and it is important to include the kinetic dissolution of SO₂ from the CO₂ stream to the water phase into the simulation models, otherwise considerable errors would be introduced for the equilibrium assumption. This study provides a useful tool for future analysis and comprehension of multiphase Darcy-Forchheimer flow and brine acidification of CO₂ injection into deep saline aquifers. Results from this dissertation have practical use for scientists and engineers concerned with the description of flow behavior, and transport and fate of SO₂ during SO₂ co-injection with CO₂ in deep saline aquifers.