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 sletsing@indiana.edu Ph: 812.855.1356 Office: Geology S-301A February 23, 2010

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

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!

Impervious Surfaces

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

Photograph courtesy of Dale T. Johnson, Baltimore County DEPRM

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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

Sprawl, impervious area, and impairment Sensitive Impacted Non-supporting Stream Quality Good Fair Poor Watershed Impervious Cover 10% 25% 40% 60% 100% Urban Drainage Center for Watershed Protection 2003

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

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

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

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

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

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

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

Tillage Practices Affect Runoff

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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

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

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

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

Example Watershed: Youngs Creek Watershed, Johnson County, Indiana

Young’s Creek Watershed Johnson County, Indiana

Youngs Creek Watershed: ISA fraction for Subwatersheds Conventional Tillage Assumed

Youngs Creek Watershed: ISA fraction for Subwatersheds Conservation Tillage Assumed

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

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

Approach: Impervious Surface Cover (ISA)-based Calculations

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

Distributed input data Calculations Distributed output

Volume of water produced in each cell

Area-weighted runoff calculations

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)

Impervious Surface Area: Runoff Simple Method: Runoff coefficient Rv = 0.05 + 0.9 ISA Rv = runoff coefficient ISA = Impervious surface area (fractional)

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)

National Event Mean Concentrations Center for Watershed Protection (2003)

Mass of pollutant produced in each cell

Impervious Surface Area: Pollutant Loads Simple Method: Pollutant Load (Chemical) L = 0.001 * 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

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

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

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

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

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.

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.

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

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."

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.

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”

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

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

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

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

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

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.

Raster Data Model Thematic data (integer)

Raster Data Model Continuous data (floating point or integer)

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

ArcGIS: Spatial Analyst Extension

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

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

Sally Letsinger, Ph.D., LPG, GISP Research Hydrogeologist -------------------------------- Center for Geospatial Data Analysis Indiana University Indiana Geological Survey sletsing@indiana.edu Ph: 812.855.1356 Office: Geology S-301A Workshop data, workbook/tutorial, and presentation slides available for download. http://mypage.iu.edu/~cgda/impervious_surface_workshop.shtml