There is a strong interest to develop a method to estimate mean annual ungaged streamflow with varying precipitation. A method was developed utilizing GIS and other statistical analysis to estimate ungaged mean annual streamflow. This method utilizes a normalized streamflow fraction (NSF) method previously developed which relies on drainage basin area, coupled with mean annual local precipitation, to estimate the ungaged streamflow variability. This method has been applied to west central Florida.The test of the method yielded an R squared value of 0.9894, proceeded by a verification that yielded an R squared value of 0.998. This method is believed to be generally applicable to other areas and the particular results should be useful in and around west central Florida and perhaps, other coastal plain environments.

Prediction of flow is necessary for planning and management of water resources. The objective of this study is to estimate mean annual flows for the USA and mean monthly flows for the rivers of central Texas based on the precipitation and their watershed characteristics. Flow varies largely with topographic and climatic parameters and hence generalization of runoff models is difficult. This model aims at providing a prediction at ungaged locations with very few parameters that are easily available and measurable. Scatter in predicted data will be seen at the annual and monthly time scale in the range selected for each data. This model will work on annual and monthly means to reduce the scatter and produce better estimates.

Technological and methodological advances of the past few decades have provided hydrologists with advanced and increasingly complex hydrological models. These models improve our ability to simulate hydrological systems, but they also require a lot of detailed input data and, therefore, have a limited applicability in locations with poor data availability. From a case study of Big Creek watershed, a 186.4 km2 urbanizing watershed in Atlanta, GA, for which continuous flow data are available since 1960, this project investigates the relationship between model complexity, data availability and predictive performance in order to provide reliability factors for the use of reduced complexity models in areas with limited data availability, such as small ungaged watersheds in similar environments. My hope is to identify ways to increase model efficiency without sacrificing significant model reliability that will be transferable to ungaged watersheds.

More than a billion dollars are spent annually on stream restoration in the United States (Bernhardt et al., 2005), but the science remains immature. A promising technique for estimating a single or range of design discharges is the generalization of a parsimonious conceptual continuous simulation model. In this study the Probability Distributed Model (PDM), was generalized for the Maryland and Virginia Piedmont. Two hundred and sixty years of daily average flow data from fifteen watersheds were used to calibrate PDM. Because the application of the study is to stream restoration, the model was calibrated to discharges greater than two times baseflow and less than flows with a return period of ten years. The hydrologic calibration parameters were related to watershed characteristics through regression analysis, and these equations were used to calculate regional model parameters based on watershed characteristics for a single "ungaged" independent evaluation watershed in the region. Simulated flow was compared to observed flow; the model simulated discharges of lower return periods moderately well (e.g., within 13% of observed for a flow with a five year return period). These results indicate this technique may be useful for stream restoration and wetland design. / Master of Science

Deriving accurate streamflow estimates for ungaged watersheds provides a challenging task for water resource engineers. Traditional methods include correlation to the nearest USGS streamflow station or numeric simulation of watershed rainfall-runoff processes. Mean annual flow, ten percent exceedance and other streamflow indices can be normalized and non-dimensionalized by dividing by the watershed drainage area and the mean annual precipitation rate. Obtaining non-dimensional parameters can be especially useful for extrapolation of flows to downstream, ungaged, coastal fringe regions. Florida and other states along the Gulf Coast exhibit strong variability in the magnitude of streamflow fraction of precipitation. The irregular patterns created by the variance in magnitude do not correlate well with traditional statistical methods of parameter estimation. Using spatial and hydrologic factors, this study, through parameter sensitivity analysis, correlates land-use, slope, soil type, precipitation, and watershed area to a non-dimensional fraction that is to be applied to ungaged regions to determine the streamflow scaling. The study domain for the land-use correlation method is West-Central Florida. Strong trends in correlation to land-use were found but underlying geology must also be considered when defining the study domain. Urbanization, depth-to-water-table and grassland were the dominant parameters in the northern study domain yielding an 80 percent correlation to streamflow fraction for the combined factors. While in the southern domain, wetlands and depth-to-water-table combined to be an indicator with a 75 percent correlation.