Impervious surfaces negatively affect urban hydrology by altering the depth, frequency and seasonal distribution of stormwater runoff. To assess the imperviousness of green roofs, a mathematical model was developed to simulate the stormwater response of a hypothetical green roof. The model is based on the physical processes that affect the green roof stormwater response and uses historic climate data. The results show that green roof imperviousness fluctuated according to climate conditions and precipitation sequence. Only 29% of the total precipitation received by the green roof resulted in runoff, however, the response varied substantially when evaluated at a daily interval. Runoff was eliminated during 82% of days with rain and a higher proportion of runoff disturbances were eliminated during the spring and summer compared to the fall. In comparison to an impervious surface, the green roof showed a reduction in the depth and frequency of runoff thereby improving urban hydrology.

Northwest Montana has seen a significant increase in its population in the past twenty years. The increase in population, and associated development, is thought to be associated with "amenity migration"; people moving to an area to exploit the recreational opportunities that are unique to that area. Impervious surfaces can serve as a suitable proxy for tracking the spread of various anthropogenic influences on an ecosystem; it impacts groundwater recharge, increases overall surface runoff as well as pollution and sediment load, and fragments landscapes. In this study, an Artificial Neural Network model was developed to update NLCD impervious surface product (2011) in Flathead County, Montana. Four Landsat 8 images from 2015 and 2016 were used to characterize imperviousness. This multi-temporal analytical method was designed to reduce the spectral confusion between impervious surface and soil/agricultural lands. We compared the neural network-predicted impervious surface maps with 2011 NLCD. When all four neural network prediction images agreed with a change of 50% or more from the 2011 NLCD map, the average of those four images replaced that pixel from the 2011 imperviousness map. Compared to the ground truth, the method used showed significant promise, with an R2 of 0.73 and RMSE of 0.123. A comparison of the artificial neural network model results and the 2011 NLCD data showed a continuation of urbanization trends; the urban cores of towns in the study remain static while the majority of impervious surface development takes place along the perimeter of urban areas. / Master of Science / Remotely sensed Landsat data can be used to rapidly detect and estimate changes in impervious surface cover. This study used artificial neural networks in conjunction with the National Landcover Database’s 2011 Percent Developed Imperviousness layer and Landsat 8 data from four dates between the summer of 2015 and fall of 2016 to predict impervious surface cover in 2016, by deriving spectral relationships between Landsat data and impervious surfaces. We found that by requiring agreement between the four dates’ neural networks outputs, we eliminated many of the false positives that arose from exposed soil. Using this method, we achieved an R2 of 0.73 and RMSE of .123, sampling only the areas along a rural-urban gradient, in an area with significant seasonal spectral variability.

Stormwater management is an essential aspect of urban hydrology. Urbanized areas have large amounts of impervious surface cover (ISC) and well developed sewer and drainage networks which rapidly channel water and pollutants off of streets and into local streams. This research evaluates the use of vegetated roofs as mechanisms to reduce ISC and stormwater runoff in downtown Atlanta. A 3-D model of the study site was created so that runoff rates could be measured for various rooftop scenarios under different size storm events. The results revealed a reduction in peak runoff and an increase in both the lag time and duration of response time. The results were most significant for the smallest storm event with 2/3 of the rooftops vegetated. As these experiments use a scale model for a section of downtown Atlanta, results are likely to be applicable to similar urban environments and may provide guidance for stormwater engineers.

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