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published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

Multiple aquifer systems similar to those that exist at coal gasification sites are complicated groundwater situations. In these types of systems, the aquifers are separated by aquitards through which interaction between aquifers can occur. The movement of the products of combustion into the coal seam and adjacent aquifers is a serious problem of interest. This dissertation presents two-dimensional finite element models for water flow and water quality in multiple aquifer systems. These models can be applied for general problems as well as the problems associated with the burned cavities in coal gasification sites. The Galerkin weightedresidual method is used in both models. Eight-noded isoparametric elements are used. Spatial numerical integration is performed using Gaussian quadrature. A weighted finite difference scheme is used, in both of them, for time integration. The two models are written in FORTRAN V for the CDC CYBER 175. They are applicable to one- or two-dimensional problems involving steady-state or transient flow. Each aquifer can have different initial conditions and boundary conditions. Boundary conditions, pumping rates, and the recharge can be specified as a function of time. The output of the flow program-nodal heads and velocity components is used as an input to the quality program. The numerical models were validated for simple problems that have available analytical solutions.

Numerical modeling has become a major tool for managing water quality in surface waterbodies such as rivers, lakes, reservoirs, and estuaries. Since the two-dimensional longitudinal/vertical model CE-QUAL-W2 is a well-known model and it has been applied to thousands of waterbodies around the world successfully, its numerical scheme was adapted to develop a new three-dimensional numerical model for simulating hydrodynamics, temperature, and water quality in surface waterbodies. Finite difference approximations were used to solve the fluid dynamic governing equations of continuity, free water surface, momentums, and mass transport. No coordinate transformations were performed and the z-coordinate system has been used. Higher-order schemes (QUICK, QUICKEST, and ULTIMATE QUICKEST) in addition to the UPWIND scheme were used for the advective temperature and mass transport. A novel numerical approach was used for the numerical formulation of the three-dimensional scheme. This approach forced the numerical solution of the free surface equation to be a tri-diagonal matrix form rather than a more computationally intensive penta-diagonal matrix solution. This new approach was done by linking a method called line-by-line with the free water surface numerical solution. Another new approach was that the three-dimensional numerical scheme involved a simultaneous solution of hydrodynamics, temperature, and water quality at every model time level instead of saving the hydrodynamic results to be used later for water quality simulation. Hence, this scheme allowed feedback between the hydrodynamics and water quality every time step. In addition, various unique numerical algorithms were employed from CE-QUAL-W2 such as the W2 turbulence model, selective withdrawal theory, surface heat fluxes, and water quality sources and sinks, making the three-dimensional model built on well-tested algorithms. To test the model structure and assumptions, an analytical verification was performed by comparing model predictions to known analytical exact solutions test cases. Good agreement was showed by the model for all of these tests. A computation of the volume balance over the simulation period was also incorporated within the model to assess how well the code performed. Sensitivity tests were also made varying bed and wind shear. The model was also applied to three reservoirs in the USA as field case studies: Lake Chaplain in WA, Laurance Lake in OR, and Cooper Creek Reservoir in OR. The model was validated by comparing the model predictions of water levels, velocities, vertical temperature profiles, and dissolved oxygen with field data. Through these real applications, the numerical predictions of the 3D model showed good agreement with field data based on error statistics. The model results of each field case study were discussed separately. In the Lake Chaplain model application, the study was focused on the importance of the higher-order schemes compared to the first-order UPWIND scheme. The model predictions of temperature were determined by using the UPWIND, QUICK, and QUICKEST scheme and compared with field data. The Error statistics of the model predictions compared to field data were an absolute mean error (AME) of 0.065 m for the water level predictions and an overall AME of 1.62 °C, 1.09 °C, and 1.23 °C for the temperature predictions by using the UPWIND, QUICK, and QUICKEST scheme, respectively. In the Laurance Lake model application, a comparison was performed between the present 3D model and the 2D CE-QUAL-W2. Since the 3D model was build based on CE-QUAL-W2, differences between the two models were evaluated. Error statistics between the model predictions of water level and temperature compared to field data showed that both models were in good agreement with field data. However, the 3D model AME (0.30 m for the water level predictions and 0.48 °C for the temperature predictions) was higher than the 2D model (0.03 m for the water level predictions and 0.42 °C for the temperature predictions). Finally, the Cooper Creek Reservoir case study was done to show the model predictions of temperature and dissolved oxygen. In this application, vertical temperature profiles were covered the entire simulation period in order to show how the model transfer heat between stratification and non- stratification conditions. The model showed good agreement with field data (0.12 m AME for the water level predictions, 1.00 °C overall AME for the temperature predictions, and 1.32 g/m3 overall AME for the dissolved oxygen predictions). Finally, comparisons were made between CE-QUAL-W2 and the 3D model. The 2D model generally performed better in the tests cases if the model user is unconcerned about lateral impacts. The 3D model is important to use when lateral currents and variation in the lateral dimension are important.

CE-QUAL-W2 is a 2-D hydrodynamic and water quality model that has been applied to reservoirs, lakes, river systems, and estuaries throughout the world. However, when this model is applied for shallow systems, this model requires a long calculation time to maintain numerical stability, compared to applications of reservoirs or deeper river systems. To solve this problem, a new hydrodynamic and temperature model was built based on the framework of CE-QUAL-W2 but that allows for steady-state hydrodynamic computations. By calculating the hydrodynamics at steady-state, the time step for stability is relaxed and simulations can proceed at much higher time steps. The rest of the model framework is still used for water quality state variables, in this case, temperature. The algorithm used for computing the water surface elevation is Manning's equation. This thesis study is one part of the Willamette Water 2100 project (Santelmann et al., 2012), which examines hydrological, ecological, and human factors affecting water scarcity in the Willamette River Basin. This study included three stages: (1) Convert six existing CE-QUAL-W2 V3.1 models into a newer version: CE-QUAL-W2 V3.7. (2) Develop the steady-state model code in FORTRAN. (3) Test the steady-state model on three river systems in the Willamette River Basin at Year 2001 and 2002. The result proved that the steady-state model could reduce the computing time by 90% for river applications, while predicting dynamic river temperature with high accuracy at a two-minute time scale. This new model will be employed to simulate the future of the Willamette River System at a decadal or centennial timescales, addressing river temperature concerns and fish habitat issues.

Corrigenda pasted onto front end paper. / One folded col. map in pocket on back endpaper. / Bibliography: leaves 348-368. / xx, 368 leaves : ill. (some col.), maps (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Develops a framework balancing water quality costs resulting from waterborne disease, disinfection by-product exposure and aesthetic concerns, against hydraulic costs, which include pipes, pumps and tanks. The genetic algorithms developed, successfully obtained the current optimal hydraulic solution, before adapting the model to incorporate water quality issues. / Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2000

The National Water Act (Act 36 of 1998) provides for an ecological Reserve as the quantity (flow) and quality of water needed to protect aquatic ecosystems. While there are methods available to quantify the ecological Reserve in terms of flow, methods of linking flow to water quality are lacking. Therefore, the research presented in this thesis investigated various modelling techniques to estimate the effect of flow on water quality. The aims of the research presented in this thesis were: Aim 1: Can the relationship between flow and water quality be accurately represented by simple statistical models? Aim 2: Can relatively simple models accurately represent the relationship between flow and water quality? Aim 3: Can the effect of diffuse sources be omitted from a water quality model and still obtain realistic simulations, and if so under what conditions? Aim 4: Can models that solely use historical monitoring data, accurately represent the relationships between flow and water quality? In Chapter 3, simple Q-C regressions of flow and water quality were investigated using Department of Water Affairs (DWA) historical monitoring data. It was found that while flow versus salinity regressions gave good regression fits in many cases, the Q-C regression approach is limited. A mechanistic/statistical model that attempted to estimate the point and diffuse signatures of nutrients in response to flow was developed in Chapter 4 using DWA historical monitoring data. The model was verified as accurate in certain case studies using observed point loading information. In Chapter 5, statistical models that link land cover information to diffuse nutrient signatures in response to flow using DWA historical data were developed. While the model estimations are uncertain due to a lack of data, they do provide an estimation of the diffuse signature within catchments where there is flow and land cover information available. Chapter 6 investigates the extension of an existing mass-balance salinity model to estimate the effect of saline irrigation return flow on in-stream salinity. The model gave accurate salinity estimates for a low order stream with little or no irrigation within its catchment, and for a permanently flowing river within a catchment used extensively for irrigation. Chapter 7 investigated a modelling method to estimate the reaction coefficients involved in nitrification using only DWA historical monitoring data. Here, the model used flow information to estimate the residence time of nutrients within the studied river reaches. While the model obtained good estimations of nitrification for the data it was applied to, very few DWA data sets were suitable for the model. Chapter 8 investigated the ability of the in-stream model QUAL2K to estimate nutrient concentrations downstream of point and diffuse inputs of nutrients. It was found that the QUAL2K model can give accurate results in cases where point sources dominate the total nutrient inputs into a river. However, the QUAL2K simulations are too uncertain in cases where there are large diffuse source inputs of nutrients as the load of the diffuse inputs is difficult to measure in the field. This research highlights the problem of data scarcity in terms of temporal resolution as well as the range of constituents measured within DWA historical monitoring data for water quality. This thesis in addition argues that the approach of applying a number of models is preferable to applying one model to investigate the research aims, as particular models would be suited to particular circumstances, and the development of new models allowed the research aims of this thesis to be explored more thoroughly. It is also argued that simpler models that simulate a few key processes that explain the variation in observed data, are more suitable for implementing Integrated Water Resource Management (IWRM) than large comprehensive water quality models. From this research, it is clear that simple statistical models are not adequate for modelling the relationship between flow and water quality, however, relatively simple mechanistic models that simulate a limited number of processes and water quality variables, can provide accurate representations of this relationship. Under conditions where diffuse sources are not a major factor within a catchment, models that omit diffuse sources can obtain realistic simulations of the relationship between flow and water quality. Most of the models investigated in this thesis demonstrate that accurate simulations of the relationships between flow and water quality can be obtained using solely historical monitoring data.

One of the most important purposes of surface water resource management is to develop predictive models to assist in identifying and evaluating operational and structural measures for improving water quality. To better understand the effects of external and internal nutrient and organic loading and the effects of reservoir operation, a model is often developed, calibrated, and used for sensitivity and management simulations. The importance of modeling and simulation in the scientific community has drawn interest towards methods for automated calibration. This study addresses using an automatic technique to calibrate the water quality model CE-QUAL-W2 (Cole and Wells, 2013). CE-QUAL-W2 is a two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model for surface water bodies, modeling eutrophication processes such as temperature-nutrient-algae-dissolved oxygen-organic matter and sediment relationships. The numerical method used for calibration in this study is the particle swarm optimization method developed by Kennedy and Eberhart (1995) and inspired by the paradigm of birds flocking. The objective of this calibration procedure is to choose model parameters and coefficients affecting temperature, chlorophyll a, dissolved oxygen, and nutrients (such as NH4, NO3, and PO4). A case study is presented for the Karkheh Reservoir in Iran with a capacity of more than 5 billion cubic meters that is the largest dam in Iran with both agricultural and drinking water usages. This algorithm is shown to perform very well for determining model parameters for the reservoir water quality and hydrodynamic model. Implications of the use of this procedure for other water quality models are also shown.

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

The Chehalis River Basin is located in the southwest region of Washington State, originating in the Olympic Mountains and flowing to Grays Harbor and the Pacific Ocean. The Chehalis River is over 125 miles, exists within five counties, and flows through agricultural, residential, industrial, and forest land areas. Four major rivers discharge to the Chehalis River, as well as many smaller creeks, five wastewater treatment plants, and groundwater flows. Flooding is a major problem in the relatively flat areas surrounding the cities of Chehalis and Centralia, with severe consequences for property, safety and transportation. As a result, construction of a flood-control dam in the upper basin has been proposed. One major concern of constructing a dam is the potentially severe impacts to fish health and habitat. The Chehalis River has routinely violated water quality standards for primarily temperature and dissolved oxygen, and has had multiple water quality and Total Maximum Daily Load studies beginning in 1990. CE-QUAL-W2, a two-dimensional (longitudinal and vertical) hydrodynamic and water quality model, was used to simulate the Chehalis River, including free flowing river stretches and stratified (in summer) lake-like stretches. The goals of this research were to assess the flood retention structure's impacts to water quality, as well as river responses to potential climate change scenarios. In order to use the model to achieve these goals, calibration to field data for flow, temperature, and water quality constituents was performed. This involved developing meteorological data, riparian shading data, and flow, temperature, water quality records for all tributaries during the calibration period of January 1, 2013 to December 31, 2014. System cross-sectional geometry data were also required for the model grid. Because of the short travel time in the river, the model was sensitive to boundary condition data, wind speed, bathymetry, nutrient kinetics, and algae, epiphyton, and zooplankton kinetics. Future conditions showed predictions of warmer water temperatures and slight changes to water quality conditions on the river. As fish in the area prefer cooler water temperatures, this could pose a threat to fish health and habitat. Flood retention structures also showed impacts to river temperature and water quality. Structures with the purpose of flood retention only (only operating during times of flooding) gave model predictions for daily maximum temperature higher than structures that employed flood retention and flow augmentation (operating during all times of the year). This suggested the management of flow passage or retention by the dam is important for water quality on the river. As this research continues improvements will be made, particularly to temperature and water quality constituents. Additional data for the system would be beneficial to this process. Model predictions of temperature were sensitive to meteorological data, including cloud cover, which were largely estimated based on solar radiation. Additional meteorological data throughout the basin would be useful to temperature results. Temperature results were also sensitive to the model bathymetry, and additional investigations into segments widths and water depths may improve temperature predictions. Water quality constituent data were largely lacking for the system. Many estimation techniques and approximations were used for input water quality constituents for the model upstream boundary and tributaries when little or no data were available, introducing uncertainty to the model. It was not possible to calibrate pH to field data because alkalinity data were essentially unavailable. However, other constituents had good agreement between model predictions and field data, including dissolved oxygen, nitrates, total phosphorus, and total suspended solids.