Determining Impervious Surfaces for Watershed Modeling Applications

Transcription

1 Determining Surfaces for Watershed Modeling Applications Michael (Sandy) Prisloe, GIS Educator University of Connecticut, Cooperative Extension System, Haddam, CT Laurie Giannotti, Natural Resource Management Educator University of Connecticut, Cooperative Extension System, Haddam, CT William Sleavin, GIS Manager 3001, Inc., New Orleans, LA Abstract The Nonpoint Education for Municipal Officials (NEMO) Project uses a simple GIS-based model to estimate existing nonpoint source pollution (NPS) impacts on water quality within the state s watersheds. The model requires only two geographic information system (GIS) data sets, watershed boundaries and satellite derived land use and land cover (LULC), and a set of LULCspecific impervious surface (IS) coefficients. The model estimates overall watershed imperviousness and uses this value to generate maps that depict estimated watershed water quality. The maps help to educate municipal land-use officials about how their land-use practices and policies can and do affect water resources. To improve the application of the model to Connecticut, research was conducted to generate Connecticut-specific LULC IS coefficients. Accurate, large-scale and up-to-date planimetric data were used from four Connecticut municipalities to create a digital GIS database of impervious surface features that included buildings, roads, driveways, sidewalks and other constructed landscape features. These data were analyzed with a digital 1995 satellite derived 28-category Connecticut LULC database to produce LULC-specific IS coefficients. There was a strong correlation among the LULC IS coefficients for the three suburban towns, however; the IS coefficients for one urban town were significantly higher. The results suggest that there may be a need to develop two sets of IS coefficients, one for rural/suburban towns and the other for urban cities. 1.0 INTRODUCTION Land use and water resource planners increasingly must deal with impervious surfaces; those landscape features that include buildings, roads, sidewalks and other built surfaces that prevent 1

2 the natural infiltration of water into the ground. surfaces increase runoff and reduce recharge, both of which can alter a watershed s hydraulics; they reduce water quality by collecting and transporting pollutants to waterbodies; they increase the temperature of runoff and receiving waters; and they reduce aquatic biodiversity. A large body of research consistently has found that impacts begin to occur even at very low levels of overall watershed imperviousness (Schueler, 1994; Arnold and Gibbons, 1996). Runoff from impervious surfaces, which typically contains a variety of chemicals, is a form of nonpoint source pollution. Unlike point sources, such as treated wastewater from an industrial process, non-point source pollution generally is unregulated. The U.S. Environmental Protection Agency has defined runoff from impervious surfaces as NPS and has identified it as the number one threat to the nation s water quality (EPA, 1994). surfaces are created with most types of development, from single-family homes to urbanized cities. Understanding, controlling and mitigating the NPS impacts from impervious surfaces essentially is a land use planning and management issue; one that is largely decided at the local level of government often by volunteer or elected officials untrained in land use planning and water quality. 1.1 The NEMO Program The University of Connecticut s Nonpoint Education for Municipal Officials (NEMO) Program was started in 1991 to help local officials and land-use commissioners understand the links between land use and water quality. The central tenets of the program are that water quality is a function of land use, that nearly all land-use decisions are local, and that local land-use decision makers need understandable and useful tools to help them make informed decisions and to develop environmentally sound land-use plans. In a review of numerous watershed studies, Schueler (1994) concluded that the percent area of a watershed covered by impervious surfaces could be used to estimate existing NPS related water 50 Percent Watershed Cover Degraded Impacted Protected Decreasing Water Quality 0 Figure 1 As the area of imperviousness in a watershed increases, water quality decreases. 2

3 quality conditions. When less than ten percent of a watershed is impervious, impacts are measurable but slight, at between ten and twenty-five percent water quality is impacted and at above twenty-five percent water quality is degraded. One way the NEMO educational program uses this imperviousness-to-water-quality relationship is to prepare watershed maps that simply but graphically depict estimates of existing water quality. The maps are produced by applying literature-derived LULC-specific impervious surface coefficients to Connecticut s land-use land-cover GIS database (Giannotti, Prisloe and Stocker, 1999). surface build-out analyses that show future increases to imperviousness and decreases to water quality are used to drive home the point that many plans of development do not adequately protect water resources. The maps are simple, easy to understand and often serve as wake-up calls to local officials. Figure 2 The town of Old Saybrook, CT under existing and hypothetical fully built out conditions. The colors of the watersheds correspond to the percent of their area that is covered by impervious surfaces and to the likely water quality conditions as a result (see figure 1). 1.2 The Need for Better Data One problem, however, was that the impervious surface coefficients used to calculate watershed imperviousness were based on research conducted in other parts of the country where land use patterns often are very different from Connecticut. To address the problem, a study was initiated in 1999 to research and develop a set of impervious surface coefficients based on large-scale digital planimetric data from four Connecticut towns. The study was funded by the Connecticut Department of Environmental Protection through a U. S. Environmental Protection Agency NPS grant under Section 319 of the Clean Water Act. 3

4 The study s goal was to prepare coefficients that could be used to estimate existing levels of watershed imperviousness from Connecticut s digital LULC data, and predict impervious surface increases from future development. It was assumed that impervious surface coefficients developed through this study could be applied elsewhere in the northeast where land use patterns are similar to those in Connecticut. The methods and results reported below represent the status of research completed as of the writing of this paper. Research is continuing and the authors expect that additional results will be reported in the future. 2.0 METHODS Four Connecticut towns, West Hartford, Waterford, Marlborough, and Woodbridge, were used for this study. The towns ranged from rural/suburban to urbanized. Table 1 provides some basic statistics to characterize the four study towns. Based on population density, Marlborough, Waterford and Woodbridge are similar and could be considered to be rural/suburban whereas West Hartford is far more densely populated and falls into an urban category. However, within each municipality population density exhibited considerable spatial variability. Each town had accurate digital planimetric data circa 1995 that had been prepared by private companies from 1:2,400 scale aerial photographs. surface landscape features (including building footprints, sidewalks, driveways, parking lots, roads and recreational features such as pools, tennis courts, and patios) were extracted from these data. West Hartford, Marlborough and Woodbridge also had parcel outlines. Table 1 Characteristics of Study Towns Town Area 1995 Population Pop. density / sq. mile 1 Total parcels Developed Parcels Mean parcel size Marlborough 14,964 5, ,463 2, Woodbridge 12,351 8, ,478 3, Waterford 21,523 17, , West Hartford 14,316 58,810 2,685 20,085 19, estimated population / land area, exclusive of water, in square miles 2 Parcel data for Waterford were unavailable For the study, no distinction was made between impervious surfaces that deliver runoff to storm drainage systems or directly to surface waters (sometimes referred to as effective imperviousness) vs. those that drain to natural or constructed areas that permit infiltration. Also, 4

5 impervious surfaces other than constructed surfaces were not included. These could be areas such as compacted soil, bedrock outcrops and naturally occurring soils with a low infiltration rate. Digital GIS databases were prepared, edited and coded for the project and included: impervious surface features (all four towns) parcel boundaries and road rights-of-way (Marlborough, West Hartford and Woodbridge) municipal zone boundaries (West Hartford and Woodbridge) All GIS analyses were done using Environmental Systems Research Institute s Arc/Info version software or ArcView GIS versions 3.1 and 3.2 software running on an NT 4.0 PC. Database analyses were done using Microsoft s Access97 and/or Excel2000 programs. 2.1 Methods - Surface Coefficients for LULC Data Connecticut s land-use land-cover (LULC) GIS data are a general interpretation of 1995 conditions. They are based on a classification of several dates of 1995 Landsat Thematic Mapper data that have a ground resolution of 30 x 30 meters, or about a quarter acre, and SPOT panchromatic data with a ground resolution of 10 x 10 meters (Civco and Hurd, 1999). Within each 30 x 30 meter area, there often is considerable heterogeneity among land uses and land covers, yet the entire area is classified into one of 28 LULC categories. Given the resolution of these data, impervious landscape features cannot be detected and a method to estimate the amount of impervious surface was developed. To produce the LULC-specific impervious surface coefficients, the LULC GIS data for each town were overlaid with the impervious surface GIS data for the town. Summary statistics were prepared of the total area of each LULC category in each town and the total area of impervious surfaces within each of the LULC categories. Also calculated, although not reported here, was the area of impervious surface contributed by each impervious surface category (i.e. buildings, roads, etc.) within each LULC class. The following calculation was performed for each LULC category to calculate an impervious surface coefficient. LULC ISarea /LULC Area * 100 = LULC coefficient Where LULC ISarea is the total area of impervious surface for a LULC class, and LULC Area is the total area for the same LULC class 2.2 Methods - Surface Coefficients for Parcels by Zoning Category The other set of impervious surface coefficients was based on parcel size and zoning classification. This approach has been the focus of many studies (Alley and Veenhuls, 1983; City of Olympia, 1994; U.S. Soil Conservation Service, 1986) and is one of the more common methods used to estimate impervious cover. surface coefficients based on parcel size and zone class are particularly useful for conducting zoning-based build-out analyses that 5

6 calculate how much new impervious surface will be created under various development scenarios. Because zoning categories and definitions vary among the study towns, it was necessary to reclassify each town s zone categories into a simple classification scheme that included only residential, commercial and industrial categories. Parcels were coded with the simplified zoning classes and road rights-of-way were kept as a separate category. Based on acreage (1 acre = 43,560 ft. 2 ), parcels were categorized into parcel-size groups commonly used by municipal zoning commissions. It should be noted that some municipalities for zoning purposes define an acre to be 40,000 ft. 2. Digital GIS databases of parcels and road rights-of-way, simplified zoning and impervious surface features were combined and the percent area of impervious surface for each parcel and road rights-of-way polygon was calculated. The resulting data were analyzed and impervious surface coefficients for each parcel size group in the various zoning categories in each town were calculated. 3.0 RESULTS Results of the research for the two sets of impervious surface coefficients are summarized below. It should be noted that these results are preliminary. We are currently incorporating additional data into our research that will lead to an update of the impervious surface coefficients. 3.1 Results - Surface Coefficients for LULC Data There was considerable similarity among the existing percent imperviousness per LULC category in the rural/suburban towns of Marlborough, Waterford, and Woodbridge. However, percent imperviousness generally was greater for corresponding LULC categories in West Hartford. The data suggest that at least two sets of impervious surface coefficients may be necessary, one set for rural/suburban towns and another for urban towns. With only one urban town in the study, the sample size was inadequate to test this hypothesis. Table 2 summarizes the study findings for the four municipalities. The percent imperviousness, which is the impervious surface coefficient, for each LULC category is listed in the left column labeled Coeff. beneath the town name. To the right of each value in the column labeled ΣIS is the percent of total imperviousness within the town from that LULC category. In all four municipalities, the majority of impervious surface area could be attributed to seven LULC categories that included commercial/industrial/paved, residential and commercial, turf and tree complex, rural residential, pasture and hay and grass, deciduous forest, and coniferous forest. These LULC categories contributed one percent or greater to total imperviousness for at least three of the four municipalities. They are listed as the first seven LULC categories in Table 2. The percent of total imperviousness for each town from these categories is reported in last row of the table. 6

7 Table 2 Surface Coefficients by Town and LULC Category LULC Category Marlborough Woodbridge Waterford West Hartford Coeff. ΣIS Coeff. ΣIS Coeff. ΣIS Coeff. ΣIS Commercial/Industrial/Paved Residential & Commercial Turf & Tree Complex Rural Residential Pasture & Hay & Grass Deciduous Forest Coniferous Forest Exposed Soil Pasture & Hay / Exposed Soil Forest / Clear Cut Deciduous Shrub Wetland Exposed Soil / Cropland Turf & Grass Nursery Stock NA NA NA NA NA NA Exposed Ground & Sand NA NA Shallow Water & Mud Flats Coniferous Forested Wetland NA NA NA NA Deciduous Forested Wetland Non-forested Wetland Scrub & Shrub Mixed Forest Deciduous Forest & Mt Laurel Dead & Dying Hemlock NA NA NA NA High Coastal Marsh NA NA NA NA NA NA Deep Water Low Coastal Marsh NA NA NA NA NA NA NA NA Shade Grown Tobacco NA NA NA NA NA NA NA NA Pasture & Hay / Cropland NA NA NA NA NA NA NA NA Percent of total IS from the first seven LULC categories 97.61% 94.68% 95.95% 95.87% Rural/suburban impervious surface coefficients were calculated for each LULC type with data from the towns of Marlborough and Woodbridge. The town of Waterford was used to test the accuracy of the rural/suburban impervious surface coefficients. Table 3 lists for each LULC category its impervious surface coefficient, the total acres and the calculated impervious acres. Based on this analysis, the predicted percent imperviousness for Waterford was 8.40% whereas the actual percent imperviousness based on the measured planimetric GIS data was 7.02%. 7

8 Table 3 Estimated Surface Data for Waterford LULC Category surface coefficient Total acres acres Commercial/Industrial/Paved Residential & Commercial Turf & Tree Complex Rural Residential Pasture & Hay & Grass Deciduous Forest Coniferous Forest Exposed Soil Pasture & Hay / Exposed Soil Forest / Clear Cut Deciduous Shrub Wetland Exposed Soil / Cropland Turf & Grass Nursery Stock NA Exposed Ground & Sand NA Shallow Water & Mud Flats Coniferous Forested Wetland NA Deciduous Forested Wetland Non-forested Wetland Scrub & Shrub Mixed Forest Deciduous Forest & Mt Laurel Dead & Dying Hemlock 0.00 High Coastal Marsh Deep Water Low Coastal Marsh NA Shade Grown Tobacco NA Pasture & Hay / Cropland NA TOTAL ACRES % OF TOWN 100% 8.40% 3.2 Results - Surface Coefficients for Parcels by Size and Zoning Category Summary statistics were calculated to quantify the average percent imperviousness for developed parcels within each parcel-size group for each of the three study towns for which parcel data were available. Data, summarized for parcel-size group by town and for parcel-size group and simplified zone category by town are included as an appendix to this paper. 8

9 The data were further analyzed to produce a set of averaged impervious surface coefficients as listed in Table 4 below. These coefficients are based on averages of all developed parcels within each standardized zone class for the three towns of Marlborough, Woodbridge and West Hartford. Parcel imperviousness within residential parcel-size groups was normally distributed, allowing us to produce the residential coefficients in Table 4. However, parcel imperviousness within industrial and commercial parcel-size groups was uniformly distributed (amount of imperviousness did not vary based upon parcel size). Table 4 Calculated Surface Coefficients for Parcel Data and General Zone Classes General Zone Category Parcel Size in Acres Surface Coefficient Residential < 1/8 39 Residential 1/8-1/4 28 Residential 1/4-1/2 21 Residential 1/2-3/4 16 Residential 3/ Residential 1-1 1/2 10 Residential 1 1/2-2 9 Residential Residential Residential > 5 8 Industrial all 53 Commercial all 54 ROW Parcel Figure 3 Only the impervious surfaces within each parcel were used to develop coefficients. features within road rights-of-way were excluded. It also should be noted that the impervious surface coefficients do not include the area of impervious surface from local and state roads located within road rights-of-way, see Figure 3. Two methods were investigated to develop a roadway impervious surface coefficient: 1) calculating a value based on all road surfaces within all road rights-of-way and 2) using an average road frontage per parcel (see tables in Appendix) to calculate that amount of pavement associated with lots for each parcel-size zone class. Neither method worked well. Additional research into how to deal with this problem is needed. An accuracy assessment was conducted to assess the validity of the parcel-zoning specific impervious surface coefficients. Predicted impervious area, based on the use of coefficients, was compared to the impervious area calculated using the impervious surface planimetric data. The residential coefficients appear to be the most precise with the margin of error ranging from 2.2% to 6.3%. The industrial zone coefficient was effective in predicting percent imperviousness in West Hartford and Woodbridge (±2.3%), but was highly inaccurate in Marlborough where imperviousness was overestimated by 26%. The commercial zone coefficient proved inaccurate for all three towns. ness was overestimated in Marlborough (+34.5%), and Woodbridge (+25.5%) and underestimated in West Hartford (-10.5%). These data are summarized in Table 5. 9

10 Table 5 Actual vs. Parcel-Zone Predicted Percent ness Marlborough (%) Woodbridge (%) West Hartford (%) Zone Predicted Actual Predicted Actual Predicted Actual Residential Industrial Commercial APPLICATIONS The IS coefficients developed through this research can be used to estimate the existing percent area of a watershed that is impervious. Thay also can be used to estimate increases in watershed imperviousness due to future development and land use changes. 4.1 LULC-based IS coefficients The LULC IS coefficients can be applied in Connecticut at the local, subregional, or regional drainage basin level (or any other geographic unit) to estimate the percent area covered with impervious surfaces. The total watershed area for each of the twenty-eight LULC categories must be calculated and then multiplied by the appropriate LULC IS coefficient. The area of impervious surface for each LULC category can then be totaled for the watershed and the watershed s percent imperviousness can be calculated. For areas where LULC patterns and population densities are similar to the study towns, estimated results should be fairly accurate. However, in locations where there are significant differences either in LULC patterns and/or population densities, caution is advised in interpreting and using the estimates. A prototype interactive GIS-based application to calculate and display information on watershed imperviousness has been created by the NAUTILUS Project, a NASA funded Regional Earth Science Applications Center at the University of Connecticut. The application was built around Enrivonmental Systems Research Institute s (ESRI) ArcViewGIS software, version 3.2 and ESRI s Spatial Analyst Extension to ArcView. The model in its present state uses a modified rural/suburban impervious surface coefficient and a generalized ten-category land use land cover data set to estimate watershed imperviousness. Through a graphical user interface, the model allows a user to select a watershed by clicking on it. A bar chart showing the area of each land cover category in the watershed is displayed. A menu then can be opened and used to calculate impervious surface area for each land cover category within the selected watershed and for the entire watershed. Adjustments, up or down, can be made to the default impervious surface coefficients and the area of impervious surfaces can be recalculated. Increases to the existing urban land cover categories also can be made and imperviousness can be recalculated to estimate how such land cover changes would affect overall watershed imperviousness. Figure 4 is a screen capture showing these features. 10

11 Figure 4 Screen capture of the NAUTILUS Project s interactive model to calculate watershed imperviousness. By clicking on a watershed, the model calculates the area of each land cover and displays the information in a bar chart. The user can then modify impervious surface coefficients and/or the area of urban land uses to determine the change to overall watershed imperviousness. 4.2 Parcel size and zone-based IS coefficients At the detailed municipal zone or parcel level, parcel/zone coefficients developed through this research can be used to estimate existing and future levels of residential imperviousness. Unlike the use of LULC IS coefficients, however, use of parcel size and zone-based IS coefficients requires automated parcel and zoning maps with are not available for all locations in the state. Where digital maps exist, however, they can be overlaid with watershed boundary data and the number, size and zone of existing parcels can be detemined. The IS coefficients reported in Table 4 can be used to estimate existing watershed imperviousness. Build out analyses also can be conducted by determining what areas are available and suitable for development, how many additional lots could be added to the watershed, applying the IS coefficients to the new parcels and adding this to the existing watershed imperviousness. 11

12 5.0 CONCLUSIONS In general, the amount of impervious surface in each LULC category in each town increased as population density increased. Based on population density, three of the towns, Marlborough, Woodbridge and Waterford, fell into a rural/suburban category. Therefore, a set of LULC impervious surface coefficients was developed for them. The authors believe that these coefficients can be used to estimate water quality impacts from impervious surfaces in other areas of the state that have levels of development similar to these three towns. West Hartford had significantly higher levels of imperviousness associated with its LULC data. However, based on data for just one urban town, the researchers do not know if these values are representative of other similarly developed towns in Connecticut. Unanticipated differences were found among the towns for which we analyzed zoning, parcel size, and imperviousness. The authors suspect that at least some differences may be attributed to inconsistencies among municipal zones. Residential parcel/zone coefficients can be used to assess existing and future impacts from impervious surfaces within Connecticut s rural/suburban municipalities. Industrial and commercial parcel/zone coefficients similarly can be used but with the understanding that they will produce less accurate estimates. LITERATURE CITED: Alley, William M., and Andrew J. Veenhuls Effective Cover in Urban Runoff Modeling. Journal of Hydraulic Engineering, American Society of Civil Engineers. New York, New York. Arnold, Chester L., and C. James Gibbons Surface Coverage: The Emergence of a Key Environmental Indicator. Journal of the American Planning Association, vol. 62(2): pp City of Olympia, Washington Surface Reduction Study: Surface Coverage Evaluation: A Basin and Site Coverage Assessment. City of Olympia Public Works Department. Civco, D. L., and J. D. Hurd A Hierarchical Approach to Land Use and Land Cover Mapping Using Multiple Image Types. Proc ASPRS Annual Convention, Portland, OR. pp Environmental Protection Agency The Quality of Our Nation s Water: United States Environmental Protection Agency EPA-841-S Washington, DC: USEPA Office of Water. Giannotti, L., S. Prisloe and J. Stocker Do It Yourself!: Surface Buildout Analysis. NEMO Technical Paper Number 4. University of Connecticut, Cooperative Extension System, Haddam, CT Schueler, T. R., The Importance of ness. Watershed Protection Techniques, vol. 1(3): pp Sleavin, W. J., Measuring Surface in Connecticut Using Planimetric GIS Data. Thesis, University of Connecticut, Storrs, CT. 126 p. 12

13 Appendix Appendix - 1

14 Average percent impervious for different parcel size groups in each of the three study towns are summarized below. Averages are based only on the developed parcels and do not include impervious surface area from public and private roads. Developed Total Marlborough % < 1/ /8-1/ /4-1/ /2-3/ / / < ½ < < < > < Right of Ways 37.2 Developed Total Woodbridge % < 1/ /8-1/ < /4-1/ /2-3/ / / < / < < < > < Right of Ways 44.3 Developed Total West Hartford % < 1/ < /8-1/ < /4-1/ < /2-3/ < / < / < / < < < > < Right of Ways 62.6 Appendix - 2

15 Average percent impervious for different parcel size groups in the generalized zone classes in each of the three study towns are summarized below. Averages are based only on the developed parcels and do not include impervious surface area from public and private roads. Marlborough Residential Zone Developed Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > Woodbridge Residential Zone Developed Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > West Hartford Residential Zone Developed Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > Appendix - 3

16 Developed Marlborough Industrial Zone Total % < 1/8 1/8-1/4 1/4-1/ /2-3/4 3/ / / > Developed Woodbridge Industrial Zone Total % < 1/ /8-1/ /4-1/ /2-3/4 3/ /2 1 1/ > Developed West Hartford Industrial Zone Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > Appendix - 4

17 Developed Marlborough Commercial Zone Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > Developed Woodbridge Commercial Zone Total % < 1/8 1/8-1/ /4-1/ /2-3/ / / / > Developed West Hartford Commercial Zone Total % < 1/ /8-1/ /4-1/ /2-3/ / / / > Appendix - 5