Influence of Land Use, Precipitation, and Mean Monthly Temperature on High Flow Trends in an Urban Watershed

Abstract

Recently, few Lake Michigan Watersheds in Northwest Indiana are showing increased trends in high flows. This study examines the influence of different factors over high flows. Low and high flow regime can affect the watershed ecosystem and its sustainability, as well as flooding issues and flood mitigation. For planning purposes, it is important to understand, and segregate the influence of land use changes and climate variables such as precipitation and temperature. This research work examines the data quantitatively and qualitatively in the high flow regime of the northwest Indiana region. Further, the research investigates the use of an artificial neural network model (ANN) in identifying the influences of climate parameters and land use changes over the high flow regime. This research includes three watersheds in the northwest Indiana region. These watersheds include Hart Ditch, Little Calumet East Arm, and Deep River. High flow trends in these watersheds were examined initially using a qualitative and quantitative analyses using Mann Kendall Test. Using 15 years of land use data, monthly rainfall, and mean monthly temperature data, artificial neural network models were trained to predict the number of high flow days having flow more than 15 % probability of exceedance. An index, called relative strength effect, was calculated for each input after the successful training of the ANN model. This index which is defined as the rate of change of output to that of a considered input indicates the influence of each input. Based on this index, the effect of different land use categories, temperature, and precipitation over high flow events was computed and documented. For the high flow regime, this study indicates almost equal influence of climate variables and the land use factors.