1. Graduate Students, CEE-6190
Urban Storm Runoff Modeling Using GIS for EPA Storm Water Management Model (SWMM)
Noha Hossam
Muhammad Imran
Graduate Students, CEE-6190
04/25/2016

2. Presentation Outline Introduction Objective Data Collection Methods
Results
Conclusion
Questions

3. Introduction Objective
Urban runoff generated from rain and snow which drains to streams, canals and rivers. This is one of the major source of water pollution. Environment Protection Agency - Storm Water Management Model (SWMM) is used for urban runoff modelling and to estimate pollutant loads associated with runoff
Geographic Information System has strong spatial analysis function, which can be used to prepare the data for the SWMM
Objective
To prepare GIS based model with input parameter information that will be imported into SWMM for modelling of specific watershed in Logan
SWMM will be used to compare its ability and strengths using high temporal and spatial data

4. Study Area
The study area is Logan Northwest Field Canal watershed, with total area of km2

5. Data Collection
The data for this project was provided by Tony Melcher; in form shapefiles and digital elevation model
The shapefiles were produced from LiDAR data obtained from Logan City Department using ArcHydro Tools
The shapefiles comprises of watersheds, streets, buildings, future land use plan, monitoring locations and canals in Logan City
Datasets are in projection NAD 1983 State Plane Utah North FIPS 4301 Feet

6. Methods
Clipping
The first step in the model building was clipping of spatial input parameters to the study area
The reason for doing this step was to get data that is only covers the region of interest which is the study area/watershed shapefile. We clipped the files in ArcMap and saved in the geodatabase as a feature class
Intersection Analysis
Then intersect analysis was done among watershed and clipped shapefiles and saved as feature class in geodatabase
The intersect operation combined data from bounding and data layer. Each resulting polygon in the output layer included attributes of both layers

7. Methods Shapefiles before clipping analysis
Shapefiles after clipping analysis

8. Methods Statistical Analysis and Relationship
After intersection analysis contribution of parameters in each watershed was calculated
For this step, shapefiles generated after intersection analysis were used. Using watershed ID, areas were summarized and exported in form dbase table
These tables were then joined to original watershed shapefile using spatial join feature
Calculations/Analysis
Percentage Impervious
Land use composition
Average slope

9. Methods Percentage Impervious
The shapefiles of streets, buildings and demarcation of impervious region were separate. So, the areas of each of these input parameters in each watershed were calculated
The areas of buildings, streets and impervious areas were added. Using field calculator in attribute table, total impervious area was divided by area of watershed and multiplied by hundred to get percentage values

10. Percentage Impervious
Areas obtained after intersection analysis
Percentage impervious area calculated in ArcGIS

11. Zonal histogram output table
Methods
Land Use Composition
First, the shapefile obtained after intersection analysis between watershed and future land use shapefile was converted to raster from polygon on basis of land use description (LU Description) in attribute table
In next step, zonal histogram analysis was done which created a table and a histogram graph showing the frequency distribution of cell values on the value input (LU description). Using this table fraction of each land use type was calculated to show the composition of each watershed and saved in form of table. Using spatial join relationship, fraction of each land use was linked to respective watershed ID
Zonal histogram output table

12. Methods
Average Slope
To calculate the average slope of each watershed, we first calculated the slope in degrees of digital elevation model (DEM; 2 feet resolution) using the Slope tool in Spatial Analyst
Then using the Zonal Statistic tool, mean values of slope were exported in table format, which was then spatially joined to watersheds using watershed ID

13. Results
The final attribute table of watershed have the percentage of impervious area, average slope and land use composition
These values will be used as input in SWMM for urban runoff modeling

14. Conclusion
GIS performs complex calculations on large datasets and acts as an effective organizing system for our project.
The GIS model that we developed is very important as the output data will be used in SWMM to calculate the pollution in any watershed. That will be useful for Logan City Department

15. Questions?