%0 Generic
%9 Master's Thesis
%A Swensson, Matthew T
%D 2004
%F pittir:9795
%K  distributed; GIS; kinematic wave; watershed; watershed model; calibrated radar rainfall; little pine creek; runoff
%T Refinements on a GIS-Based, Spatially Distributed Rainfall-Runoff Model for a Small Watershed
%U http://d-scholarship-dev.library.pitt.edu/9795/
%X This research addresses the problem of predicting the direct runoff generated by precipitation falling over a small watershed using an existing rainfall-runoff model. The model consists of several separate modules, which process data describing the spatial variation of watershed properties, and compute the runoff time series at the watershed outlet. Data processing and visualization is handled primarily by GIS software, while computer programs perform the bulk of the computations. The model is designed to operate a simply and generally as possible, requiring only four external data sets as input (Digital Elevation Model (DEM), land coverage, soil coverage, and incremental precipitation depths) to create all other data needed to compute the direct runoff for the watershed under study. Our goal is to improve the model by refining the existing programs and increasing the level of spatial distribution.To achieve this goal, we focus our research on the following three objectives: testing and refining the existing model; developing a hydraulic flow routing module using the Kinematic Wave model; and exploring the possibility of calibrating the model results to the actual soil moisture conditions present in the watershed during the time of interest by modifying the SCS curve number values across the watershed. We consider Little Pine Creek watershed located in Allegheny County as our case study, and compare the model's predictions for two separate precipitation events with the direct runoff hydrographs given by a USGS stream gauge located at the outlet of the Little Pine Creek watershed. Based on the results of our investigation we identify and correct several problems with the existing model, and provide a better understanding of how the model responds under varying conditions and assumptions. We show that the Kinematic Wave flow routing model does not work well with our model because is cannot account for storage within the watershed. We also develop an effective method for calibrating the volume of runoff predicted by the model to that given by the USGS stream gauge by adjusting the SCS curve number values in the watershed to reflect that actual soil moisture conditions present at the time of interest, guaranteeing that fluid mass is conserved between our model predictions and reality.