<p> This work quantifies the impacts of urban development on groundwater storage and groundwater-surface water interactions using intensive data analysis and mathematical modeling. The monthly water balance for the period 2000-2009 for 65 Baltimore area watersheds was calculated using remote sensing data and the dense network of instrumented sites in this region. This analysis included estimation of spatially-distributed anthropogenic fluxes (water supply pipe leakage, lawn irrigation, and infiltration and inflow (I&amp;I) of groundwater and stormwater into wastewater pipes) as well as natural fluxes of precipitation, streamflow, and evapotranspiration. Inflow fluxes of water supply pipe leakage and lawn irrigation were significant but small compared to precipitation, but I&amp;I was approximately equal to gaged streamflow. Building on knowledge of the altered water balance, an integrated hydrologic model of the Baltimore metropolitan region was developed to quantify the impact of urban development on groundwater storage. The three-dimensional groundwater-surface water-land surface model ParFlow.CLM was implemented and a methodology to incorporate urban and hydrogeologic input datasets was developed. Using the model, the impacts of reduced vegetative cover, impervious surfaces, I&amp;I, and other anthropogenic discharge and recharge fluxes were isolated. Removal of I&amp;I led to the largest change in storage, and removal of impervious surface cover had the smallest effect. To investigate the relationship between pre-event water proportion, storage, and streamflow at small watershed scales spanning a gradient of urbanization, chemical hydrograph separation, hillslope numerical experiments, and simple dynamical systems analysis were utilized. From analysis of high-frequency specific conductance data, the pre-event water proportion of stormflow was found to be greatest for storms with higher total precipitation. Using the simple dynamical systems approach, watersheds with larger percentages of impervious surfaces were found to have the largest sensitivity of streamflow to changes in storage. HydroGeoSphere, a three-dimensional groundwater-surface water flow and transport model, was implemented in an idealized hillslope and showed that the relationship between streamflow and storage was clockwise hysteretic. Overall this work demonstrates the importance of infrastructure leakage on urban hydrologic systems and shows that pre-event water contributions of stormflow are primarily related to precipitation and not initial storage in urban watersheds.</p>

<p>The detection of trace explosives in the subsurface is an active area of research for landmine detection. Understanding the air-water flow and heat transport phenomena in the subsurface plays an important role in improving chemical vapor detection. Implementing a finite element method that accurately captures water vapor transport in the vadose zone is still an open question. A non-equilibrium, pressure-pressure formulation has been implemented based on Smits, et al [22]. This implementation consists of four equations: a wetting phase (water) mass balance equation, a non-wetting phase (air) mass balance equation, a water vapor transport equation, and a heat transport equation. This work will compare two implementations, a fully coupled approach and an operator splitting approach for the water vapor and heat transport equations. The formulation of the methods will be presented and the methods will be tested using collected data from physical experiments.

<p> The Radar-based Quantitative Precipitation Estimates (QPE) is one of the NEXRAD products that are available in a high temporal and spatial resolution compared with gauges. Radar-based QPEs have been widely used in many hydrological and meteorological applications; however, a few studies have focused on using radar QPE products in deriving of Precipitation Frequency Estimates (PFE). Accurate and regionally specific information on PFE is critically needed for various water resources engineering planning and design purposes. This study focused first on examining the data quality of two main radar products, the near real-time Stage IV QPE product, and the post real-time RFC/MPE product. Assessment of the Stage IV product showed some alarming data artifacts that contaminate the identification of rainfall maxima. Based on the inter-comparison analysis of the two products, Stage IV and RFC/MPE, the latter was selected for the frequency analysis carried out throughout the study. The precipitation frequency analysis approach used in this study is based on fitting Generalized Extreme Value (GEV) distribution as a statistical model for the hydrologic extreme rainfall data that based on Annual Maximum Series (AMS) extracted from 11 years (2002-2012) over a domain covering Louisiana. The parameters of the GEV model are estimated using method of linear moments (L-moments). Two different approaches are suggested for estimating the precipitation frequencies; Pixel-Based approach, in which PFEs are estimated at each individual pixel and Region-Based approach in which a synthetic sample is generated at each pixel by using observations from surrounding pixels. The region-based technique outperforms the pixel based estimation when compared with results obtained by NOAA Atlas 14; however, the availability of only short record of observations and the underestimation of radar QPE for some extremes causes considerable reduction in precipitation frequencies in pixel-based and region-based approaches. </p>

<p> Forecasts of hydrological information are vital for many of society's functions. Availability of water is a requirement for any civilization, and this necessitates quantitative estimates of water for effective resource management. The research in this dissertation will focus on the forecasting of hydrological quantities, with emphasis on times of anomalously low water availability, commonly referred to as droughts. Of particular focus is the quantification of uncertainty in hydrological forecasts, and the factors that affect that uncertainty. With this focus, Bayesian methods, including ensemble data assimilation and multi-model combinations, are utilized to develop a probabilistic forecasting system. This system is applied to the upper Colorado River Basin for water supply and drought forecast analysis. </p><p> This dissertation examines further advancements related to the identification of drought intensity. Due to the reliance of drought forecasting on measures of the magnitude of a drought event, it is imperative that these measures be highly accurate. In order to quantify drought intensity, hydrologists typically use statistical indices, which place observed hydrological deficiencies within the context of historical climate. Although such indices are a convenient framework for understanding the intensity of a drought event, they have obstacles related to non-stationary climate, and non-uniformly distributed input variables. This dissertation discusses these shortcomings, demonstrates some errors that conventional indices may lead to, and then proposes a movement towards physically-based indices to overcome these issues. </p><p> A final advancement in this dissertation is an examination of the sensitivity of hydrological forecasts to initial conditions. Although this has been performed in many recent studies, the experiment here takes a more detailed approach. Rather than determining the lead time at which meteorological forcing becomes dominant with respect to initial conditions, this study quantifies the lead time at which the forecast becomes entirely insensitive to initial conditions, and estimating the rate at which the forecast loses sensitivity to initial conditions. A primary goal with this study is to examine the recovery of drought, which is related to the loss of sensitivity to below average initial moisture conditions over time. Through this analysis, it is found that forecasts are sensitive to initial conditions at greater lead times than previously thought, which has repercussions for development of forecast systems.</p>

<p> This dissertation presents the development and application of a computational model called BioChemFOAM developed using the computation fluid dynamic software OpenFOAM (Open source Field Operation And Manipulation). BioChemFOAM is a three dimensional incompressible unsteady-flow model that is coupled with a water-quality model via the Reynolds Average Navier-Stokes (RANS) equations. BioChemFOAM was developed to model nutrient dynamics in inland riverine aquatic ecosystems. BioChemFOAM solves the RANS equations for the hydrodynamics with an available library in OpenFOAM and implements a new library to include coupled systems of species transport equations with reactions. Simulation of the flow and multicomponent reactive transport are studied in detail for fundamental numerical experiments as well as for a real application in a backwater area of the Mississippi River. BioChemFOAM is a robust model that enables the flexible parameterization of processes for the nitrogen cycle. The processes studied include the following main components: algae, organic carbon, phosphorus, nitrogen, and dissolved oxygen. In particular, the research presented has three phases. The first phase involves the identification of the common processes that influence the nitrogen removal. The second phase covers the development and validation of the model that uses common parameterization to simulate the main features of an aquatic ecosystem. The main processes considered in the model and implemented in BioChemFOAM are: fully resolved hydraulic parameters (velocity and pressure), temperature variation, light's influence on the ecosystem, nutrients dynamics, algae growth and death, advection and diffusion of species, and isotropic turbulence (using a two-equation k-epsilon model). The final phase covers the application and analysis of the model and is divided in two sub stages: 1) a qualitative comparison of the main processes involved in the model (validation with the exact solution of different components of the model under different degrees of complexity) and 2) the quantification of main processes affecting nitrate removal in a backwater floodplain lake (Round Lake) in Pool 8 of the Mississippi River near La Crosse, WI. </p><p> The BioChemFOAM model was able to reproduce different levels of complexity in an aquatic ecosystem and expose several main features that may help understand nutrient dynamics. The validation process with fabricated numerical experiments, discussed in Chapter 4, not only presents a detailed evaluation of the equations and processes but also introduces a step-by-step method of validating the model, given a level of complexity and parameterization when modeling nutrient dynamics in aquatic ecosystems. The study cases maintain fixed coefficients and characteristic values of the concentration in order to compare the influences that increasing or decreasing complexity has on the model, BioChemFOAM. Chapter 4, which focuses on model validation with numerical experiments, demonstrates that, with characteristic concentration and coefficients, some processes do not greatly influence the nutrient dynamics for algae. </p><p> Chapters 5 and 6 discuss how BioChemFOAM was subsequently applied to an actual field case in the Mississippi River to show the model's ability to reproduce real world conditions when nitrate samples are available and other concentrations are used from typical monitored values. The model was able to reproduce the main processes affecting nutrient dynamics in the proposed scenarios and for previous studies in the literature. First, the model was adapted to simulate one species, nitrate, and its concentration was comparable to measured data. Second, the model was tested under different initial conditions. The model shows independence on initial conditions when reaching a steady mass flow rate for nitrate. Finally, a sensitivity analysis was performed using all eleven species in the model. The sensitivity takes as its basis the influence of processes on nitrate fate and transport and it defines eight scenarios. It was found in the present parameterization that green algae as modeled does not have a significant influence on improving nitrate spatial distributions and percentage of nitrate removal (PNR). On the other hand, reaction rates for denitrification at the bed and nitrification in the water shows an important influence on the nitrate spatial distribution and the PNR. One physical solution, from the broad range of scenarios defined in the sensitivity analysis, was selected as most closely reproducing the backwater natural system. The selection was based on published values of the percentage of nitrate removal (PNR), nitrate spatial concentrations, total nitrogen spatial concentrations and mass loading rate balances. The scenario identified as a physically valid solution has a reaction rate of nitrification and denitrification at the bed of 2.37x10^-5 s^-1. The PNR was found to be 39% when reaching a steady solution for the species transport. The denitrification at the bed process was about 6.7% of the input nitrate mass loading rate and the nitrification was about 7.7% of the input nitrate mass loading rate. </p><p> The present research and model development highlight the need for additional detailed field measurements to reduce the uncertainty of common processes included in advanced models (see Chapter 2 for a review of models and Chapter 3 for the proposed model). The application presented in Chapter 6 utilizes only spatial variations of nitrate and total nitrogen to validate the model, which limits the validation of the remaining species. Despite the fact that some species are not known a priori, numerical experiments serve as a guide that helps explain how the aquatic ecosystem responds under different initial and boundary conditions. In addition, the PNR curves presented in this research were useful when defining realistic removal rates in a backwater area. BioChemFOAM's ability to formulate scenarios under different driving forces makes the model invaluable in terms of understanding the potential connections between species concentration and flow variables. In general, the case study presents trends in spatial and temporal distributions of non-sampled species that were comparable to measured data.</p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3154. Adviser: Marcelo H. Garcia. Includes bibliographical references (leaves 118-130) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3662. Adviser: Praveen Kumar. Includes bibliographical references (leaves 133-139) Available on microfilm from Pro Quest Information and Learning.