GIS and Stormwater Management

However, hydrology is as much an art as it is a science, and small changes in any of the variables in either method can result in a wide variety of answers. In a recent paper, David Knipe P.E. of INDNR’s Division of Water pointed out inaccuracies with the SCS Curve Number Method. In a study of gauged streams in Indiana, the SCS Method did not reproduce the observed hydrographs from known storm events. Mr. Knipe’s discussion pointed out the inability of the Curve Number to accurately reflect the infiltration of rain water. Furthermore, the paper discusses how Holtan’s Method for determining infiltration through the ground produced more accurate hydrographs and peak discharges.

Whether it is a Rational Method runoff coefficient, an SCS Curve Number or one of the Holtan’s infiltration variables, careful area measurements are necessary to reduce the input errors and improve the final results. Ordinarily, this means initially defining a drainage area, then delineating the permeable and impermeable regions within the drainage area, and finally breaking up the permeable areas into different soil types. Determining all these variables by hand can take roughly an hour per basin. To reduce this time, engineers reduce the number of basins, averaging the land characteristics over the entire basin, and assuming certain types of coverings have certain characteristics. Unfortunately, this leads to generalized results with limited applications. Determining the land characteristics for a complex, multiple basin hydrologic model in an urban area can take months, while compiling and running the model only takes hours. Often models are run several times using different methods and different variables to verify results. While there exist many off-the-shelf programs to perform many of these tasks, they are expensive, limited in their applications and sometimes not compatible with existing data. A simple internal Geographical Information System program, that performs the overlay, cut and paste functions necessary to determine the land characteristics of a given basin, is a useful tool that could increase the number of basins analyzed, reduce calculation time, eliminate errors, and improve results.

GIS Applications
The City of Bloomington, Indiana implemented a citywide GIS in 1996 using aerial photography to generate base map data and manual digitizing to create data layers for the City owned water and wastewater utility. Base map layers included buildings, streets, parking lots, elevation lines/points, drainage basins and water features. Cultural features such as political boundaries, planning zones and properties (maintained and shared by the County in the same GIS software) are also a pert of the system. The GIS data has been kept up to date through periodic import of AutoCAD drawing files showing details of new building, road and subdivision projects. Bloomington uses Genasys GIS software and has distributed a general user interface, called GENIUS, on the desktop of most of the city’s office-based employees.

In the spring of 1998, the first work began using GIS for Stormwater analysis. This initial work was focused on modifying the extents of a drainage basin layer in the City’s GIS. Using two-foot contour intervals and spot elevations, derived from 1996-air photos, the assistant city engineer re-digitized the boundaries of these basins to more accurately reflect their extents. Smaller subsets of original basins were also digitized to generate additional analysis points and isolate areas such as sinkholes. These refined drainage basins were then used to calculate their total area so that more specific calculations for runoff could be generated. These calculations were all done by hand initially, which were not only tedious but also time consuming and inaccurate. With the city exploring using these basins for long-term stormwater inventory planning and the use of runoff-based stormwater user-fees, a more automated process was needed.

The City’s GIS contained other relevant base map data for calculating runoff such as the permeable soil types and impermeable streets, buildings and parking lots. Since the features found within these basin areas were not all closed polygons with stored area values, they were being measured manually and placed into the runoff calculations. The first step on the way to automating this process was to take the existing soil type layer, with hundreds of soil type polygons and generalize it down to the four SCS Hydrological Soil types. Once these soil polygons were reclassified, the like polygons were dissolved into one another to produce a master layer of hydrologic soil types.

The impermeable streets and buildings existed as closed polygons within the City’s GIS and thus could be queried for area measurements. Parking lots, however, were not closed area features and modifications were made to this layer to close off the parking lots using coincident line features shared with roads and buildings (Figure 1). The nature of a traditional map layer based GIS requires true polygon (closed area) features to be entirely contained within one file. Area features that might “share” a boundary with an area on another map layer, required the same edge to be maintained in two files. Ensuring that these edges were indeed coincident, allowed for precise measurement and accurate representation. Bloomington’s parking lots were never digitized or maintained as polygons. Copying these coincident line features from building and street layers proved to be the largest single effort in the use of GIS for stormwater water analysis.

Figure 1

Generating the topology of closed, impermeable surfaces would not have been possible without the aid of aerial photography and ground truthing. Careful analysis of base map data compared with photographs and field surveys was necessary to eliminate ambiguity found in the GIS. Further refinement of these layers required that holes existing in the impermeable layers, such as courtyards in buildings and landscaping within parking lots, were considered permeable. This classification allowed for a conservative estimate of impermeable surfaces, erring on the side of the stormwater water customer. These holes in impermeable surfaces were tagged as “islands”, allowing for reclassification in the future. Sidewalks were not consistently maintained in the GIS and were therefore not considered in this analysis.

With all the necessary base map layers cleaned up and forming closed polygons, the first application, a series of basic overlay commands, were scripted for calculating runoff at the drainage basin level. The basin polygon was used to clip the soil type layer, and the building, parking lot and street layers. With these subsets of base map data, the building, parking lot and street layers were merged together to form one homogeneous, impermeable feature. Areas outside of these features as well as within the “island” holes were not part of the impermeable data. This impermeable layer was then overlaid on top of the soil types. The resulting basin polygon could be queried for total impervious surface and total open permeable soil types given in both percentage and area measurements. These values were used for general basin planning and could be regenerated as new development filled in the basin.

When the City Stormwater Utility was formed and the decision to use a runoff-based user-fee was made, these layers were considered on the smaller scale of permeable and impermeable surface within a given property.