1

A Method to Estimate Effective Impervious Surface and Its

Application in Pollutant Loading Computation

UEP 232 Introduction to GIS Yuan (Rose) Wang Final Paper

[Project Description]

Impervious surface in urban landscape is often thought to be correlated with non-point source

pollutant loadings, which is the number one cause of water quality impairments in the United States. To

measure and use impervious coverage as a tool for protecting water resources, it is necessary to know

how imperviousness is distributed about land use.

Recent studies suggest that effective impervious surface, which is a subset of total impervious

surface, is better correlated with water quality variables. Effective impervious surfaces are defined as

areas directly connected to urban drainage system. The rainwater falls on them will be routed to water

bodies through the drains. On the other hand, the rainwater falls on rooftops with gutters not

connected to the stormwater pipes will probably infiltrate through lawns and soil. These rooftop areas

are considered as ineffective impervious surfaces. Therefore, this study tries to understand the effective

impervious surface and their distribution about different land use.

The project involves the following general steps: 1) calculate the percentage of impervious areas

in a drainage sub-basin in Mystic Watershed, 2) break down the impervious surface according to

different categories, i.e. roads, roofs, sidewalks, driveways and others. Assign effective imperviousness

coefficients to these categories to compute the percentage of effective impervious areas; 3) categorize

the breakdowns of step 2 based on land use to estimate the percent of effective imperviousness in each

land use category; 4) compute phosphorus loading for the drainage sub-basin based on the export

coefficients and imperviousness computed from Step 3.

[Literature Review]

Arnold & Gibbons (1996) illustrated why impervious surface becomes a big problem in water

quality and how planners and local officials can mitigate this problem. In the past, stormwater

management has mostly focused on how to get the stormwater out of sight fast without considering its

impact on downstream water quality. The large volume of runoff caused by impervious surface,

together with increased efficiency of water conveyance through pipes, gutters, and artificially straight

channels result in increased severity of flooding and decreased water quality downstream. This paper

reviewed studies conducted in quantifying impervious surface. One of the study found that streets carry

the highest concentration of pollutants among all impervious categories. This is probably caused by the

highest effective imperviousness of streets. Basically stormwater falls on streets are 100% drained away

via water catchments. Roofs are generally low in pollutant loads, which is probably because water flows

from roof is infiltrated through the lawns. Parking lots pollutant loads are moderate.

2

Hatt, et al, (2004) conducted statistical analysis to examine the impacts of urban density and

drainage connection on water quality of streams. It was the first of such study to consider drainage

connection as one important variable. It shows that effective impervious area (EIA) has a better

correlation to water quality indices than total impervious area (TIA). This suggests the importance of

quantifying EIA.

Alley & Veenhuis (1983) is a well cited paper in this area. They studied the TIA, EIA and their

ratio on 19 urban basins. Even though no GIS technique was used in this study, some interesting

patterns were found with regard to land use. For example, for single house families, the ratio of EIA/TIA

does not vary with lot size. Basins with more multifamily houses have greater EIA/TIA than basins with

more single family houses. Commercial and industrial land use have high EIA/TIA ratio and have greater

variability among different developments than residential development. This provides me with some

insights in the relationship between land use and EIA/TIA ratio.

Roy & Shuster (2009) combined the use of GIS data compilation, aerial photo interpretation and

parcel scale field assessment to determine the percentage of EIS in a suburban basin in Cincinnati, Ohio.

The study basically provides a methodology to compute the EIS using combined GIS and field

assessment. The basic GIS layers needed are impervious surface, storm and sewer connection data,

drainage map, and topography layer. An important step mentioned in the paper is the determination of

the surface connectivity, which requires door-to-door interview to property owners. For my project, I

will only do some random survey of buildings within the sub-basin to check the connectivity of drains.

Reference

Alley, W., & Veenhuis, J. (1983). Effective impervious area in urban runoff modeling. Journal of Hydraulic Engineering, 109(17669), 313–319. Retrieved from http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9429(1983)109:2(313)

Arnold, C. L., & Gibbons, C. J. (1996). Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. Journal of the American Planning Association, 62(2), 243–258. doi:10.1080/01944369608975688

Hatt, B. E., Fletcher, T. D., Walsh, C. J., & Taylor, S. L. (2004). The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environmental management, 34(1), 112–24. doi:10.1007/s00267-004-0221-8

Roy, A. H., & Shuster, W. D. (2009). Assessing Impervious Surface Connectivity and Applications for Watershed Management. JAWRA Journal of the American Water Resources Association, 45(1), 198–209. doi:10.1111/j.1752-1688.2008.00271.x

3

[Data Sources]

Table 1 Data layers used in the study

Dataset Name Description Data Source Agency

Key Attributes URL to Metadata

Building Structures (2-D, from 2011-2012 Ortho Imagery)

This vector dataset consists of 2-dimensional roof outlines (roofprints) for all buildings larger than 150 square feet.

MassGIS SHAPE_AREA http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/structures.html

Impervious Surface (2005)

It is a raster layer that illustrates impervious surfaces of Massachusetts. The surfaces were extracted from near infrared orthoimagery.

MassGIS Raster data, 0 & 1 values, no attribute used.

http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/impervioussurface.html

Land Use (2005)

This vector datalayer is a Massachusetts statewide, seamless digital dataset of land cover / land use, created using semi-automated methods, and based on 0.5 meter resolution digital ortho imagery captured in April 2005.

MassGIS FID, LUCODE, LU05_DESC, SHAPE_AREA

http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html

Massachusetts Department of Transportation (MassDOT) Roads (2012)

This vector layer is the official state-maintained street transportation dataset available from MassGIS and represents all the public and a good portion of the private roadways in Massachusetts, including designations for Interstate, U.S. and State highways.

MassGIS SURFACEWIDTH, RIGHTSIDEWALKWIDTH, LEFTSIDEWALKWIDTH

http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/eotroads.html

Drainage Sub-basins (continuous work over past twenty years)

This vector layer was digitized from 1:24,000 USGS paper quad sheets.

MassGIS SubName, SHAPE_AREA http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/subbas.html

4

[Major Steps]

Step 1: Clip data layers to area of interests.

For vector data, use “Clip” tool; for raster data, set the geoprocessing environment to an extent that

covers the area of interests. Set work environment for raster data. Figure 1 shows an example of clipped

data.

5

Figure 1 Impervious Surface Raster Data

Step 2: In roads layer, add additional attribute fields that represent the half surface width of the roads

and the width of sidewalk in meters.

Step 3: create a polygon layer for road surface.

Based on the half width of the road, create a buffer polygon for all road surface (not including sidewalks).

Choose “dissolve all” option so that the result is one whole piece of polygon.

Step 4: create a polygon layer for sidewalks.

Sidewalks are more complicated than road surface. They are not necessary symmetric to road

centerlines. So I created two buffer layers on both sides of the road centerlines (the width includes half

road width and side walk width). Then, I used “union” tool to make one whole layer from these two

separate layers. This single layer represents road surface and sidewalks on both sides. After that, I used

the “erase” tool to deduct the road surface layer from the union layer. The resulting layer from the

erase is for sidewalks only. As I looked into the attribute table, there are three types of polygons

because of the union operation. So I used the “merge” tool in edit to merge different polygon features

of the sidewalk layer into one polygon, just to tidy it up. The result of this step is a sidewalk layer with

just one polygon.

Results of Step 3 and Step 4 are shown below, together with roof prints.

6

Figure 2 Roads, sidewalks and roofs

Step 5: Compute the impervious surface distribution in different categories

From the roof print layer, add an attribute field to calculate the area of roof prints in square meters.

Then, show the field statistics of the sum of all roof areas, which is 755015m2. Similarly, I obtained area

of the road surface and sidewalks based on the respective layers. The total area of impervious surface

can be obtained from the impervious raster layer. Since each cell has an area of 1m2. The number of

cells with the value of 1 is equal to the total area of impervious surface. From these numbers, we can

get a breakdown of impervious surface as shown below.

7

Table 2 The impervious area breakdown based on categories

Area (m2) Percentage to Total Impervious Area

- Road Surface 606,117 26.05%

- Sidewalks 129,210 5.55%

- Roofs 755,015 32.45%

- Others (drive way, parking lots and miscellaneous)

904,360 35.95%

Total Impervious Surface 2326,723 -

Step 6: Landuse categorization.

Phosphorus is a major pollutant of concern in the region, because it’s a limiting component for

eutrophication in Mystic River. A phosphorus export coefficient table was obtained from a study in a

nearby watershed – Charles River Watershed. In order to use that table of coefficients, a crosswalk table

was created to link different landuse categorization, as shown below.

Table 3 Crosswalk table for landuse categories

MassGIS Landuse Export Coefficient Category

P Export Coefficient (kg/ha/yr)

Pervious Impervious

6 Open Land 7 Participation Recreation 17 Transitional 34 Cemetery

1 Open Space/Agriculture

0.24 -

3 Forest 2 Forest 0.17 -

3 Forested Wetland 0.13 -

20 Water 4 Water/Wetland 0.12 -

13 Low Density Residential 5 Low Density Residential

0.31 2.22

12 Medium Density Residential 6 Medium Density Residential

0.5 2.22

11 High Density Residential 7 High Density Residential

0.78 2.22

10 Multi-family residential 8 Multi-Family 1.33 2.22

15 Commercial 16 Industrial 31 Urban Public/Intuitional 39 Junkyard

9 Commercial / Industrial

1.32 2.51

8

The landuse map based on the categorization of export coefficient table is displayed below. As it can be

seen, the residential buildings in the area are mostly high density and multi-family. They surround a

central spine of commercial and industrial area.

Figure 3 Landuse map

Step 7: compute breakdowns of impervious surface within each land use category.

First, create a intersect layer between landuse and road surface to breakdown the roads into different

land use polygons. Then, compute the road areas within each landuse category using “summary

statistics” tool. Do the same thing for sidewalks and roofs.

The method to obtain breakdowns for total impervious surface is slightly different, because it is a raster

layer. “Zonal statistics as table” tool is used in this case to obtain the breakdowns of impervious surface

in each land use category. Use the unique FID code first to get impervious surface for each polygon.

Export the zonal statistics table as dbf and combine it with the land use category table to get the

impervious surface for each land use.

9

Summarize the above four dbase table to obtain the following summary table in terms of breakdowns:

Table 4 Impervious area breakdown based on categories and landuse

LUC

Total Impervious Surface (sqm)

Road Surface (sqm)

Sidewalk (sqm) Roof (sqm)

Others (driveways, parking lots, etc) (sqm)

1 Open Space 84343.00 32273.60 1558.42 2547.60 47963.38

2 Forest 9115.00 5231.47 739.37 118.67 3025.49

5 Low Density Residential 81.00 5254.79 0 46.84 -5220.63 (0)

6

Medium Density Residential 211.00 202.42 32.99 122.02 -146.43 (0)

7 High Density Residential 943210.00 39303.68 60461.15 334489.87 508955.30

8 Multi-Family 600849.00 415220.01 39772.41 218306.39 -72449.82 (0)

9 Commercial / Industrial 688914.00 105309.38 25606.11 199383.84 358614.67

sum 2326723.00 602795.36 128170.45 755015.24 840741.95

percentage - 25.91% 5.51% 32.45% 36.13%

It is worth noting that negative values appear in “Others” category because of the subtraction operation.

The total impervious area in certain land use categories are less than the sum of roads, sidewalks and

roofs. This is because of data quality of the impervious layer and the other vector layers are not perfect.

For ease of assessment purposes, the negative values are corrected to zero in the following analysis.

Step 8: Find coefficients of effective imperviousness for roads, sidewalks, roofs and others from site

survey, with a scale from 0 to 1 (0 being unconnected and 1 being fully connected). The sampling

locations are shown in the figure below. They are scattered around the area and covers the major land

use categories. For each sampling location, I walk for 2 blocks on each direction of the roads to examine

the gutter connection.

10

Figure 4 Sampling locations

Roads:

Roads are considered as 100% connected because all excess water fall on the impervious road surface

will eventually enter the catchment basin. So the coefficients of effective imperviousness for all roads

are assumed to be 1 except those in forest and open space, which are assumed to be zero.

Sidewalks:

Sidewalks are more complicated than roads because some sidewalks are made of pervious materials,

such as bricks, while others are made of impervious materials. Particularly, sidewalks at residential areas

are mostly impervious surface made of concrete. Therefore, the effective imperviousness of sidewalks in

residential area is 1. Sidewalks at commercial areas are mixed, some are made of concrete (about 70%),

while other are made of bricks (about 30%). Assuming an effective impervious coefficient of 0.7 for

bricks and 1 for concrete, we get the overall effective imperviousness of sidewalks in commercial areas

to be 0.8.

11

Sidewalks at residential area made of concrete

Sidewalk at commercial area made of bricks(photo from internet)

Residential buildings:

As shown in the group of photos below, all residential buildings surveyed have unconnected gutters.

Some houses drain rainwater to lawn area, while others drain the water to the driveways. So, I assumed

a coefficient of effective imperviousness of 0.2 for residential buildings in the area (not zero, because

those that drain on driveways will flow onto roads and enter the drainage system).

Unconnected gutter (rainwater guided to lawn)

Unconnected gutter (rainwater guided to lawn)

12

Unconnected gutter (rainwater guided to pavement)

Unconnected gutter (rainwater guided to driveway)

Commercial buildings:

Commercial buildings are divided. About half of the buildings surveyed are connected and half are

unconnected. Most of the unconnected gutters eventually have water flow onto driveways and roads.

Therefore, a coefficient of imperviousness of 1 is assigned for commercial buildings.

13

Connected gutter at Wallgreens

Unconnected gutter at a local gas station

Unconnected gutter of a local restaurant

Connected gutter of another local restaurant

14

Driveways, parking lots and others:

The coefficient for the remaining impervious surface (driveways and parking lots) is set to be 1.

A table that summarizes the coefficient of imperviousness is shown below:

Table 5 Coefficient of imperviouenss based on field survey of connectivity

LUC

Road Surface Sidewalk Roofs Others (driveways, parking lots, etc)

1 Open Space 0 0 0 0

2 Forest 0 0 0 0

5 Low Density Residential

1 1 0.2 0

6 Medium Density Residential

1 1 0.2 1

7 High Density Residential

1 1 0.2 1

8 Multi-Family

1 1 0.2 1

9 Commercial / Industrial

1 0.8 1 1

Step 9: Combine the above two tables to get effective impervious area for each land use category simply

by multiplying the two matrix element by element.

Table of effective impervious area is shown below:

Table 6 Total Effective Impervious Surface (EIS) and its breakdowns

LUC

Total EIS (sqm)

Road Surface EIS (sqm)

Sidewalk EIS (sqm)

Roof EIS (sqm)

Driveway and parking lots EIS (sqm)

1 Open Space 0.00 0.00 0.00 0.00 0.00

2 Forest 0.00 0.00 0.00 0.00 0.00

5 Low Density Residential 5264.15 5254.79 0.00 9.37 0.00

6 Medium Density Residential 259.82 202.42 32.99 24.40 -146.43(0)

7 High Density Residential

675618.10 39303.68 60461.15

66897.97 508955.30

8 Multi-Family 498653.7

0 415220.01 39772.41 43661.2

8 -72449.82(0)

9 Commercial / 683792.7 105309.38 20484.89 199383. 358614.67

15

Industrial 8 84

sum

1863588.56 565290.29 120751.43

309976.87 794973.72

percentage - 30.3% 6.5% 16.6% 42.7%

Combining Table 6 with the land use area, we can find the area of effective impervious surface and

pervious surface of each land use category, shown below:

Table 7 Percent of EIS based on Landuse

LUC

Total Area (sqm)

Total Effective Impervious Surface (sqm)

Total Pervious Area (sqm) Percent of EIS

Phosphorus Load (kg/yr)

1 Open Space 255662

0 255662 0.0% 6.14

2 Forest 161229

0 161229 0.0% 2.74

5 Low Density Residential

791 5264

-4473 665.5% 1.03

6

Medium Density Residential

983 260

724 26.4% 0.09

7 High Density Residential

2362536 675618

1686918 28.6% 281.57

8 Multi-Family 1125450

498654 626796 44.3% 194.07

9 Commercial / Industrial

955599 683793

271806 71.6% 207.51

Sum 4862251 1863589 2998663 693.14

Percentage - 38% 62%

Step 10: Employ the export coefficient table (Table 3) to obtain the total load of the pollutants within

the drainage basin in kg/yr. The result is shown in Table 7. The total phosphorus load from the drainage

subbasin is 0.69 ton/yr.

[Difficulties and Work-arounds]

The MassGIS land use categories are not entirely consistent with land use categories used for

pollutant export coefficient. So I worked around it by building a cross-walk table to connect the two. It is

16

possible that when making the export coefficient table, people used another land use categorization

other than the current one used by MassGIS.

Negative values were encountered when I subtracted the road surface, roof area and sidewalks

from the impervious surface area. The reason is that data quality is not perfect. In some areas, the

impervious surface does not capture all impervious area, which leads to a smaller value than the sum of

the road surface, roof and sidewalks. I worked around it by setting all negative values to zero in

summarizing table.

There is another method to work around the negative values. Instead of subtracting the areas of

the road, sidewalks, and roof polygons from the impervious surface area, extract by overlaying the

polygons on the raster layer. In this way, the remaining values after overlaying will always positive.

However, this also leads to errors. For example, due to data quality constraint, there will be impervious

surface supposed to be roof but not covered by the roof print polygon, which leads to extra area for the

4th category. Either way leads to errors.

[Conclusive Thoughts]

Figure 5 Percentage of effective imperviousness of each land use category

Figure 5 reflects the impact of land use on imperviousness. The commercial/industrial land use

has the highest EIS, followed by multi-family and medium density residential. The percentage of EIS for

low density residential computed is problematic. It’s impossible for the percentage to exceed 100%. The

reason is that there are only a few properties within the subbasin are counted as low density residential.

The area is too small and the error becomes very large. The effective impervious data for low-density

17

residential is therefore meaningless. For a larger scale analysis, for example, the whole watershed, this

kind of error might be reduced because the overall size of the project is bigger. The data would reflect

more of an average characteristic of the watershed.

The total impervious area (based on the raster data) within the subbasin is about 48% of the

total area. When drainage connection is accounted for, the effective imperviousness drops to 38%. In

addition, comparing Table 4 and Table 6, we can find that the weights for roads, sidewalks and roofs are

different when drainage connections are considered. The weight of road effective imperviousness

increases because all roads are considered as 100% connected. The weight of roofs decreases because

many residential roofs are considered as pervious area. The result makes sense. In addition, the

“driveway, parking lots and others” category is the largest component of all four categories, followed by

road surface. This analysis gives us some insights in terms of what areas to focus on first when designing

policy schemes and engineering measures to reduce effective imperviousness.

Table 8 Weights of impervious surface categories

Road Surface Sidewalk Roof

Driveway, parking lots and others

Percentage in Total Impervious Surface 25.91% 5.51% 32.45% 36.13%

Percentage in Effective Impervious Surface 30.3% 6.5% 16.6% 42.7%

There seems to be no standard operation protocol in terms of what kind of land use

categorization to be used in non-point source load estimation in the field of water quality modeling. The

estimation is still at a very rough level, even though high resolution satellite images are available. For

Massachusetts, it is possible to get a better land use categorization based on Level 3 Assessor’s Parcel

Mapping for urban areas, which is parcel level data. For suburban and rural areas, the National Land

cover data can possibly produce better correlations.

The drainage connectivity survey conducted could, to some extent, reflect the connectivity in

the area. However, if there was a drainage map available with all rainwater inlet locations, a flow

analysis could be conducted to see whether the rainwater falls onto the roads and driveways are

actually going into the drains or flowing to nearby lawns. Moreover, the coefficient of effective

imperviousness for sidewalks and driveways are not better than an arbitrary assumption. Remote

sensing analysis could be possibly used to analyze surface porosity and make imperviousness analysis

less arbitrary.