This project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.

The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

As population increases and global climate changes, policy makers are challenged with protecting human and environmental health in the face of extreme precipitation events. Ecosystem based adaptation strategies are increasingly recommended. If ecosystems are relied upon for adaptation, it will be critical to maintain ecosystem health through landscape-level management and planning. To support ecosystem management, Surrey recently mapped its `Green Infrastructure Network' (GIN). This study assessed the value of the GIN for climate change adaptation by quantifying its flood mitigation services under 11 different land management scenarios. ArcGIS was used to show the effect of land management policies on land cover. A hydrologic modelling tool, Win TR-55, was used to simulate the effect of land cover change on peak flows. The study found that different management strategies in the GIN can substantially increase or decrease peak flows. This provides government with information needed to support community resilience to climate change.

The indirect effect of aerosols expresses how changes in aerosols would influence clouds and cause impacts on Earth's climate and hydrological cycle. The current assessment of the interactions between aerosols and clouds is uncertain and parameterizations used to represent cloud processes are not well constrained. This thesis first evaluates a cloud activation parameterization by investigating cloud droplet number concentration closure for stratocumulus clouds sampled during the 2005 MArine Stratus Experiment (MASE). Further analysis of the droplet size distribution characteristics using the extended parameterization is performed by comparing the predicted droplet spectra with the observed ones. The effect of dynamical variability on the droplet size distribution evolution is also investigated by considering a probability density function for updraft velocity. The cumulus and stratocumulus cloud datasets from in-situ field measurements of NASA's Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) and Coastal STRatocumulus Imposed Perturbation Experiment (CSTRIPE) campaigns are used for this task. Using the same datasets, the autoconversion rate is calculated based on direct integration of kinematic collection equation (KCE). Six autoconversion parameterizations are evaluated and the effect of turbulence on magnifying collection process is also considered. Finally, a general circulation model (GCM) is used for studying the effect of different autoconversion parameterizations on indirect forcing estimates. The autoconversion rate given by direct KCE integration is also included by implementing a look-up table for collection kernels. Although these studies add more variability to the current estimate of aerosol indirect forcing, they also provide direction towards a more accurate assessment for climate prediction.

Thesis (M.S. - Renewable Natural Resources)--University of Arizona, 1980. / Includes bibliographical references.

Thesis (M.S. - Hydrology and Water Resources Administration)--University of Arizona, 1982. / Includes bibliographical references (leaves 121-124).

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona, 1983. / Includes bibliographical references (leaves 87-88).

Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2002. / "May 2002" Includes list of papers published during this study. Errata slip inserted inside back cover of v. 1. Includes bibliographical references (leaves 331-357).

Thesis (Ph.D.)--University of Western Australia, 2006.

Thesis (M.Sc.). / Written for the Dept. of Bioresource Engineering. Title from title page of PDF (viewed 2008/05/14). Includes bibliographical references.