For surface-water resource management studies, it is important to quantify all of the mechanisms that contribute to water quantity and influence water quality. In this regard, various methods have been used to ground-water fluxes in lake systems. These have included physical measurements (e.g., seepage meters), flow-net analyses, water budgets, chemical tracers, ground-water flow models, and statistical analyses. The method developed for this study for calculating ground-water inflow uses a simplified, 1-layer (surficial aquifer) ground-water flow model. The test area was on a set of lakes known as the Winter Haven Chain of Lakes in Polk County, Florida. The technique combines the use of a numerical model (MODFLOW) with an inverse prediction technique (PEST) to determine net surficial recharge rates. Within the model, the lakes were represented as constant-head boundaries. A general, surficial ground water no-flow boundary was delineated around the entire lake system based on the topographic boundaries. The model used annual average lake elevations to create a constant-head boundary for each lake for each year. Annual average elevations of surficial well heads were used as target well data. Model results generally support previous studies in the region, concluding that the lake chain receives significant inflow from the surficial aquifer and leaks to the Floridan aquifer. As a consequence, ground-water quality constituency was found to be of critical importance. One of the most important observations from this study is the need for accurate ground-water concentrations for ridge lake water quality management. The initial measured values used in this study were highly variable, uncertain, and likely underestimated the effect that ground water has on nutrient loading to the Winter Haven Chain of Lakes.

[Truncated abstract] Generally hydrologic and ecologic models operate on arbitrary time and space scales, selected by the model developer or user based on the availability of field data. In reality rainfall is highly variable not only annually, seasonally and monthly but also the intensities within a rainfall event and infiltration properties on semi-arid hillslopes can also be highly variable as a result of discontinuous vegetation cover that form mosaics of areas with vegetation and areas of bare soil. This thesis is directed at improving our understanding of the impacts of the temporal representation of rainfall and spatial heterogeneity on model predictions of hydrologic thresholds and surface runoff coefficients on semi-arid landscapes at the point and hillslope scales. We firstly quantified within storm rainfall variability across a climate gradient in Western Australia by parameterizing the bounded random cascade rainfall model with one minute rainfall from 15 locations across Western Australia. This study revealed that rainfall activity generated in the tropics had more within storm variability and a larger proportion of the storm events received the majority of rain in the first half of the event. Rainfall generated from fontal activity in the south was less variable and more evenly distributed throughout the event. Parameters from the rainfall analysis were then used as inputs into a conceptual point scale surface runoff model to investigate the sensitivity of point scale surface runoff thresholds to the resolution of rainfall inputs. This study related maximum infiltration capacities to average storm intensities (k*) and showed where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k* = 0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k* > 2). For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g* <2). The sensitivity of surface runoff predictions and the influence of specific within storm properties were then analysed on the hillslope scale. '...' It was found that using the flow model we still get threshold behaviour in surface runoff. Where conditions produce slow surface runoff velocities, spatial heterogeneity and temporal heterogeneity influences hillslope surface runoff amounts. Where conditions create higher surface runoff velocities, the temporal structure of within storm intensities has a larger influence on runoff amounts than spatial heterogeneity. Our results show that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions. The results of this study can be considered a contribution to understanding the way within storm properties effect the processes on the hillslope under a range of overall storm, slope and infiltration conditions as well as an improved understanding of how different vegetation patterns function to trap runoff at different total vegetation covers and rainfall intensities.

Hydrological observations are characterised by wet and dry cycles, a characteristic that is termed hydrological persistence. Interactions between global climate phenomena and the hydrological cycle result in rainfall and streamflow data clustering into wetter and drier states. These states have implications for the management and planning of water resources. Statistical tests constructed from the theory of wet and dry spells indicate that evidence for persistence in monthly observations is more compelling than at an annual scale. This thesis demonstrates that examination of monthly data yields spatially - consistent patterns of persistence across a range of hydrological variables. It is imperative that time series models for rainfall and streamflow replicate the observed fluctuations between the climate regimes. Monthly time series are generally represented with linear models such as ARMA variants ; however simulations from such models may underestimate the magnitude and frequency of persistence. A different approach to modelling these data is to incorporate shifting levels in the broader climate with a tendency to persist within these regimes. Hidden Markov models ( HMMs ) provide a strong conceptual basis for describing hydrological persistence, and are shown to provide accurate descriptions of fluctuating climate states. These models are calibrated here with a full Bayesian approach to quantify parameter uncertainty. A range of novel variations to standard HMMs are introduced, in particular Autoregressive HMMs and hidden semi - Markov models which have rarely been used to model monthly rainfall totals. The former model combines temporal persistence within observations with fluctuations between persistent climate states, and is particularly appropriate for modelling streamflow time series. The latter model extends the modelling capability of HMMs by fitting explicit probability distributions for state durations. These models have received little attention for modelling persistence at monthly scale. A non - parametric ( NP ) HMM, which overcomes the major shortcomings of standard parametric HMMs, is also described. Through removing the requirement to assume parametric forms of conditional distributions prior to model calibration, the innovative NP HMM framework provides an improved estimation of persistence in discrete and continuous data that remains unaffected by incorrect parametric assumptions about the state distributions. Spatially - consistent persistence is identified across Australia with the NP HMM, showing a tendency toward stronger persistence in low-rainfall regions. Coherent signatures of persistence are also identified across time series of total monthly rainfall, numbers of rain - days each month, and the intensities of the most extreme rain events recorded each month over various short durations, illustrating that persistent climate states modulate both the numbers of rain events and the amount of moisture contained within these events. These results provide a new interpretation of the climatic interactions that underlie hydrological persistence. The value of HMMs to water resource management is illustrated with the accurate simulation of a range of hydrologic data, which in each case preserves statistics and spell properties over a range of aggregations. Catchment - scale rainfall for the Warragamba Reservoir is simulated accurately with HMMs, and rainfall - runoff transformations from these simulations provide reservoir inflows of lower drought risk than provided from ARMA models. / Thesis (Ph.D.)--School of Civil and Environmental Engineering, 2006.

The effect of landuse change on the hydrologic, biogeochemical and ecological response of watersheds is a concern throughout the world. To help characterize the potential magnitude of such changes, and of the potential to remediate or avoid undesirable features, studies focused on the cumulative watershed effects of site level change are necessary. The current state of the art model for water quality in agricultural lands, Soil Water Assessment Tool (SWAT), was used to estimate the effects of a set of future landscape scenarios on water quality in the Corn Belt region of the United States. These results indicated that changes to the current water quality management strategies will be necessary to significantly improve water quality in the Corn Belt region. In addition, the experience of implementing SWAT suggested a variety of changes to the model structure and study design with potential to improve the quality of the results. These changes include improved treatment of hydrologic process, full integration of input data and model code, different methods of distributing data across space, the use of fewer parameters, more sophisticated numerical techniques, and improved methods for generating potential landscape scenarios. A new model structure (WET_Hydro) was developed to address these issues. The hydrologic components of the model focus on a conceptual physically based characterization of the movement of water in soils, as overland flow, and in channels. Tests using a variety of input data sets, including both synthetic inflows and real watershed data were developed to verify the hydrologic components of the model. Additional model analyses evaluate how model scale interacts with parameters and with measurements. These analyses point toward additional criteria that may prove useful to the determination of correct model scales and to the utility of the flexible model structure which provides automatic changes to model scale. In addition to the scale analysis, a method of estimating the average new water contribution to storm discharge was developed.. This additional model criterion was shown to provide further understanding of model utility under different hydrologic regimes. The hydrologic model was extended to produce estimates of erosion and sediment export. Sensitivity to various restoration options were developed focusing on simple descriptions of remediation potential, and a minimum of parameters. In addition, the water quality model was coupled with a Decision Support System (DSS). Example applications demonstrate the potential of the combination to improve the process of restoration planning at the watershed scale. / Graduation date: 2003 / Best scan available. Figures in original are very light.

Based on the determination of the zero-flux plane, a water balance procedure for large-scale experimental databases was automated to estimate the soil water balance based on soil water content distribution with depth through time. The automated procedure was verified using data from the BOREAS project obtained in three Intensive Field Campaigns during the spring and summer of 1994. The data used correspond to four tower sites measuring atmospheric fluxes above the forest canopy from the Northern and Southern Study Areas and are designated according to the predominant vegetation in the area as Old Jack Pine and Young Jack Pine. The total hydraulic head through time at these sites is determined to identify the position of the zero-flux plane, which separates that part of the soil profile in which water flow is upward from the region in which the water flow is downward. In conjunction with precipitation and soil water content data, the procedure allows estimation of the actual soil water balance, the water used from the region above the zero-flux plane being evapotranspiration, and the change in soil water content below the mean zero-flux plane being drainage. Prior to this study, no published attempt had been made to automate a water balance procedure for large-scale experimental databases based on the position of the zero-flux plane and soil water content distribution through time. / Graduation date: 1997

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 113-121).

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 66-69). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (p. 199-216). Also available in print.

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains vii, 73 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 71-73).

published_or_final_version / Civil Engineering / Master / Master of Philosophy