1. Watershed Hydrology: Impervious Surface Analysis in ArcGIS 9.3x
Sally Letsinger, Ph.D., LPG, GISP
Research Hydrogeologist
Center for Geospatial Data Analysis
Indiana University
Indiana Geological Survey
Ph:
Office: Geology S-301A
February 23, 2010

2. Welcome Part 3: Spatial Analysis Instructor: Sally Letsinger Restrooms
Schedule
Part 1: Introduction to the topic of impervious surfaces
Part 2: Preparing data for analysis
Exercise 1. Set up the ArcMap session, pre-process vector data for analysis
Exercise 2: Pre-process raster data for analysis
Part 3: Spatial Analysis
Exercise 3: Assign impervious surface coefficients (ISC)
Exercise 4: Calculate impervious surface area (ISA)
Challenge Exercises: Calculate runoff depth and contaminant loads

3. Welcome Part 3: Spatial Analysis Instructor: Sally Letsinger Restrooms
Schedule
Part 1: Introduction to the topic of impervious surfaces
Part 2: Preparing data for analysis
Exercise 1. Set up the ArcMap session, pre-process vector data for analysis
Exercise 2: Pre-process raster data for analysis
Part 3: Spatial Analysis
Exercise 3: Assign impervious surface coefficients (ISC)
Exercise 4: Calculate impervious surface area (ISA)
Challenge Exercises: Calculate runoff depth and contaminant loads
Break!

4. Impervious Surfaces

5. Impervious Surfaces
Contribute to the hydrologic changes that degrade waterways
Represent a major component of the intensive land uses that generate pollutants
Serve as an efficient conveyance system for transporting pollutants into waterways
Prevent “natural” pollutant attenuation by preventing infiltration
Research demonstrates a correlation between the imperviousness of a watershed and the quality of its receiving stream
Imperviousness is a measurable environmental indicator

6. Photograph courtesy of Dale T. Johnson, Baltimore County DEPRM

13. site 38

15. Impacts from Increases in Impervious Surface Coverage (USEPA, 1997).
Increased Impervious
Resulting Effects
Leads to:
Flooding
Habitat Loss
Erosion
Channel Widening
Stream Alteration
Increased Amount of Flow X
Increased Peak Flow
Increased Peak Duration
Decreased Base Flow
Sediment Loading

16. Sprawl, impervious area, and impairment
Sensitive
Impacted
Non-supporting
Stream Quality
Good
Fair
Poor
Watershed Impervious Cover
10% % % % %
Urban Drainage
Center for Watershed Protection 2003

17. Measuring Impervious Surface Area
Total versus Effective or Net ISA
Direct measurements
Inferred measurements
from land use
from road density
from population

22. Uses of ISA estimates
Index or indicator of watershed health or impairment (current or predicted future condition)
Basis for prioritization of locating/establishing best management practices or implementing other watershed management plans
Used in stormwater runoff and contaminant loading calculations

23. Impervious Surface Coefficients
Description
ISC% (Coefficient)
Agriculture 6
Low density residential 10
Medium density residential 30
High density residential 40
Multifamily 60
Industrial 75
Commercial 85
Roadway 50
Urban 38
Open, parks, golf courses 5
Forest - thick 20
Forest - thin 15
Shrub, wetland, grassland
Open water

24. Impervious Surface Coefficients
Description
ISC% (Coefficient)
Agriculture 6
Low density residential 10
Medium density residential 30
High density residential 40
Multifamily 60
Industrial 75
Commercial 85
Roadway 50
Urban 38
Open, parks, golf courses 5
Forest - thick 20
Forest - thin 15
Shrub, wetland, grassland
Open water

25. Impervious Surface Coefficients
Description
ISC% (Coefficient)
Agriculture 6
Low density residential 10
Medium density residential 30
High density residential 40
Multifamily 60
Industrial 75
Commercial 85
Roadway 50
Urban 38
Open, parks, golf courses 5
Forest - thick 20
Forest - thin 15
Shrub, wetland, grassland
Open water

26. Impervious Surface Coefficients
Description
ISC% (Coefficient)
Agriculture 6
Low density residential 10
Medium density residential 30
High density residential 40
Multifamily 60
Industrial 75
Commercial 85
Roadway 50
Urban 38
Open, parks, golf courses 5
Forest - thick 20
Forest - thin 15
Shrub, wetland, grassland
Open water

27. Impervious Surface Coefficients
Description
ISC% (Coefficient)
Agriculture 6
Low density residential 10
Medium density residential 30
High density residential 40
Multifamily 60
Industrial 75
Commercial 85
Roadway 50
Urban 38
Open, parks, golf courses 5
Forest - thick 20
Forest - thin 15
Shrub, wetland, grassland
Open water

28. Tillage Practices Affect Runoff

32. Site 18

33. Conservation Tillage Residue Cover (%) Runoff (% of rain)
Runoff Velocity
(m/sec)
Sediment in runoff
(% of runoff)
Soil Loss
(tons/km2) 45
0.13
3.7
3064.8 41 40
0.07
1.1
790.9 71 26
0.06
0.8
346.0 93
0.5
0.04
0.6
74.1

34. Conservation Tillage Residue Cover (%) Runoff (% of rain)
Runoff Velocity
(m/sec)
Sediment in runoff
(% of runoff)
Soil Loss
(tons/km2) 45
0.13
3.7
3064.8 41 40
0.07
1.1
790.9 71 26
0.06
0.8
346.0 93
0.5
0.04
0.6
74.1

35. Impervious Surface Coefficients
Conventional
Conservation Tillage
Description
ISC% (Coefficient)
Agriculture
Cultivated: corn 6 4
Cultivated: soybeans
Cultivated: other

36. Impervious Surface Area
Assign Impervious Surface Coefficients (ISA): estimated proportion of impervious surface area typically characteristic of land-use or land-cover categories
Calculate land-cover area within watershed for each land-cover category
Calculate impervious surface fraction: proportion of total area covered by impervious surfaces

37. Example Watershed: Youngs Creek Watershed, Johnson County, Indiana

38. Young’s Creek Watershed Johnson County, Indiana

40. Youngs Creek Watershed: ISA fraction for Subwatersheds
Conventional Tillage
Assumed

41. Youngs Creek Watershed: ISA fraction for Subwatersheds
Conservation Tillage
Assumed

42. Youngs Creek Watershed: ISA fraction for Subwatersheds
USGS Estimate of Impervious Surface (Automated mapping)

43. Youngs Creek Watershed: Watershed Health Assessment (ISA)
Healthy or Sensitive
Impacted or Impaired
Non-sustaining

44. Approach: Impervious Surface Cover (ISA)-based Calculations

45. Runoff as a function of Imperviousness
Center for Watershed Protection (2003) after Schueler (1987)

47. Distributed input data
Calculations
Distributed output

48. Volume of water produced in each cell

49. Area-weighted runoff calculations

50. Impervious Surface Area: Runoff
Simple Method:
Runoff depth
RO = P * Pj * Rv
RO = annual runoff depth (meters)
P = annual rainfall (meters)
Pj = Fraction of annual rainfall events that produce runoff (in US, usually around 90%, so we’ll use 0.9)
Rv = runoff coefficient (based on impervious area)

51. Impervious Surface Area: Runoff
Simple Method:
Runoff coefficient
Rv = ISA
Rv = runoff coefficient
ISA = Impervious surface area (fractional)

52. Impervious Surface Area: Runoff
Simple Method:
Runoff volume
R = RO * A
R = runoff volume (cubic meters)
RO = runoff depth (meters)
A = area of watershed (square meters)

53. National Event Mean Concentrations
Center for Watershed Protection (2003)

54. Mass of pollutant produced in each cell

55. Impervious Surface Area: Pollutant Loads
Simple Method:
Pollutant Load (Chemical)
L = * RD * C * A
L = annual load (kg)
RD = runoff depth (meters)
C = pollutant concentration (mg/l)
A = watershed area (m2)
0.001 = unit conversion factor

56. Impervious Surface Area: Pollutant Loads
Simple Method:
Pollutant Load (Bacteria)
L = 1.0 * 10-5 * RD * C * A
L = annual load (billion colonies)
RD = runoff depth (meters)
C = bacteria concentration (#/100ml)
A = watershed area (m2)
1.0 * 10-5 = unit conversion factor

57. Youngs Creek Watershed: Runoff Depth (meters) for Subwatersheds
Conventional Tillage
Assumed

58. Youngs Creek Watershed: Runoff Depth (meters) for Subwatersheds
Conservation Tillage
Assumed

59. Geospatial Data
** Some slides in this presentation were borrowed or modified
from Minnesota Department of Natural Resources

60. Data vs. Information
Data, by itself, generally differs from information.
Data is of little use unless it is transformed into information.
Information is an answer to a question based on raw data.
We transform data into information through the use of an Information System.

61. Geospatial Data
Geographic Information Science is the study of the abstraction and representation of spatial phenomena.
The problem is that things in nature are complex. The accuracy with which they are represented in GIS is never perfect.
Simple geometry only approximates nature.

62. Data Models
A geographic data model is a structure for organizing geospatial data so that it can be easily stored and retrieved.
Geographic coordinates
Tabular attributes

63. Representing Spatial Elements: Vector Data Model
Real World
Spatial data is essentially data with a location. It contains information about the location and shape of, and relationship among geographic features usually stored as coordinates. Spatial data comes in two types, VECTOR and RASTER. Geographic information systems work with two fundamentally different types of geographic models--the "vector model" and the "raster model."

64. Representing Spatial Elements: Vector Data Model
We typically represent vector objects in space as three distinct spatial elements:
Points - simplest element
Lines (arcs) - set of connected points
Polygons - set of connected lines
We need symbolize spatial features in order to be able to associate attribute information.
We can classify different features into different dimensions. Each classification of dimension is a conceptual classification.
Points - “0” dimensionality. No length or Width.
Each point is Discrete in that it can only occupy a given point in space at any given time.
Lines - “1” dimensional. Length, but No Width.
Must have a beginning and an ending point.
Polygons - “2” dimensional. Length and Width.
By adding Width, we can describe a feature as having an area.
Surfaces - “3” dimensional. Length, Width, and Height.
Surfaces have infinite number of values (e.g. Elevation). We say that this type of data is Continuous.
When thinking of spatial elements, we must consider Spatial Scale. Depending on scale, we may want to represent a river as a line or a polygon.
We use these three spatial elements to represent real world features and attach locational information to them.

65. Representing Spatial Elements: Raster Data Model
Raster Data Structure
Much of the data we will use in this class will be “Raster” data.
Raster formatted data is much more suitable for many types of landscape modeling, including hydrologic analysis.
Inputs such as elevation can only be processed as a raster data set
“Raster is Faster,
Vector is Corrector”

66. Representing Spatial Elements: Raster Data Model
Buildings
Stream
Road
Representation of the world as a surface divided into a regular grid of cells.

67. Representing Spatial Elements: Raster Data Model
Cellular-based data structure composed of square cells of equal size arranged in rows and columns.
Cell size

68. Representing Spatial Elements: Raster Data Model
Cellular-based data structure composed of square cells of equal size arranged in rows and columns.
The grid size (number of rows and columns), as well as the value at each cell have to be stored as part of the grid definition.
Number of columns
Number of rows
Cell size

69. Representing Spatial Elements: Raster Data Model
Cellular-based data structure composed of square cells of equal size arranged in rows and columns.
The grid size (number of rows and columns), as well as the value at each cell have to be stored as part of the grid definition.
Number of columns
Number of rows
Cell size

70. Raster Data Model Grids have at least one attribute called value
Value represents the numeric value associated with the grid cell
Values can be real or integer

71. Raster Data Model
Every cell in a data set has a numeric value assigned to it.
Depending on the type of data the cell values can be represented as:
Integer, or
Floating point
Thematic/categorical/discrete data (usually integer)
Data that have well defined boundaries
Cell values are related to categories.
Example: Land use and cover, soils
Continuous, measured data (usually floating point).
Example: Topography, temperature, precipitation, population density.

72. Raster Data Model
Thematic data (integer)

73. Raster Data Model
Continuous data (floating point or integer)

74. Raster Data Model
Grid attribute tables are called value attribute tables (VAT)
Minimum of two fields
VALUE = cells value
COUNT = count of cells with this value
To calculate area multiply cell area * count value

75. ArcGIS: Spatial Analyst Extension

76. ArcGIS Spatial Analyst Extension
Adds a comprehensive, wide range of cell-based GIS operators to ArcGIS.
Utilizes the raster data structure
Spatial Analysis Environment
Raster Analysis
Spatial Analysis Tools

77. ArcGIS Spatial Analyst Extension
Spatial Analyst must be loaded as an extension before you can do raster analysis.
Two ways to use functionality
Spatial Analyst Toolbar
ArcToolbox tools and functions

78. Sally Letsinger, Ph.D., LPG, GISP
Research Hydrogeologist
Center for Geospatial Data Analysis
Indiana University
Indiana Geological Survey
Ph:
Office: Geology S-301A
Workshop data, workbook/tutorial, and presentation slides available for download.