Impacts of Delineation Methods on Modeled Runoff in Watersheds Containing Non-Connected Internally Drained Depressions
University of Wisconsin-Stevens Point, College of Natural Resources
MetadataShow full item record
In order to accurately model runoff with GIS software, areas that are capable of contributing runoff to surface waters must be correctly identified. Identifying areas capable of contributing runoff to streams is problematic in regions containing many topographically-closed, internally-drained depressions and low relief over large distances. Both of these complicating factors are present in the Upper Midwest of the United States. Traditional delineation methods fill sinks in the terrain under the assumption that most are results of data errors. This assumption can cause inaccuracies when modeling runoff in extensively internally-drained watersheds. Three delineation methods were compared for ten Wisconsin and Minnesota watersheds: filled digital elevation models, removing filled sinks from modeled area, and delineated potential contributing source areas from an unfilled digital elevation model outward from an initial contributing area. The delineations were used to model runoff in ArcMap using the Curve Number equation. Runoff producing rain events were modeled from summers with total precipitation within one standard deviation of the mean. Modeled runoff was compared to USGS discharge data. Results from watersheds of varied sizes were compared using normalized error, defined as the model error divided by the filled watershed area. Normalized error was compared to drainage density, percent internally drained area, and land cover types. Results from ten watersheds indicate that models perform similarly for all delineations. Within watersheds models perform differently for large storms and small storms, with large storms defined as over 0.2 feet precipitation. Modeled storms under 0.2 feet tend to underestimate runoff with increasing drainage density and with increasing area. Large storms show no discernible differences when comparing normalized error to other watershed statistics.