Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Mahmood Nachabe, Ph.D.


Regression, Time series, Autocorrelation, Flood, Drought


Many water resources throughout the world are demonstrating changes in historic water levels. Potential reasons for these changes include climate shifts, anthropogenic alterations or basin urbanization. The focus of this research was threefold: 1) to determine the extent of spatio-temporal changes in regional precipitation patterns 2) to determine the statistical changes that occur in lakes with urbanizing watersheds and 3) to develop accurate prediction of trends and lake level return frequencies. To investigate rainfall patterns regionally, appropriate distributions, either gamma or generalized extreme value (GEV), were fitted to variables at a number of rainfall gages utilizing maximum likelihood estimation. The spatial distribution of rainfall variables was found to be quite homogenous within the region in terms of an average annual expectation.

Furthermore, the temporal distribution of rainfall variables was found to be stationary with only one gage evidencing a significant trend. In order to study statistical changes of lake water surface levels in urbanizing watersheds, serial changes in time series parameters, autocorrelation and variance were evaluated and a regression model to estimate weekly lake level fluctuations was developed. The following general conclusions about lakes in urbanizing watersheds were reached: 1) The statistical structure of lake level time series is systematically altered and is related to the extent of urbanization 2) in the absence of other forcing mechanisms, autocorrelation and baseflow appear to decrease and 3) the presence of wetlands adjacent to lakes can offset the reduction in baseflow.

In regards to the third objective, the direction and magnitude of trends in flood and drought stages were estimated and both long-term and short-term flood and drought stage return frequencies were predicted utilizing the generalized extreme value (GEV) distribution with time and starting stage covariates. All of the lakes researched evidenced either no trend or very small trends unlikely to significantly alter prediction of future flood or drought return levels. However, for all of the lakes, significant improvement in the prediction of extremes was obtained with the inclusion of starting lake stage as a covariate.