IMPERVIOUS SURFACES

Imperviousness values were assigned to each land use based on the literature. An average was calculated from a range of values for each land use classification. Imperviousness ranged from 0.1 to 0.85 (low to high imperviousness, respectively).

UC BERKELEY NON-POINT SOURCE POLLUTION STUDY

INTRODUCTIONStrawberry Creek is composed of two major branches, the North and South Forks, which flow through the UC Berkeley campus and on to San Francisco Bay. Stormwater runoff (the major contributor to non-point source [NPS] pollution) from the entire watershed is routed directly into Strawberry Creek, causing significant degradation of water quality. Common constituents in urban runoff include suspended solids, bacteria, heavy metals, oxygen-demanding substances, nutrients, oil and grease. These constituents increase chemical oxygen demand, suspended solids, turbidity, organic nitrogen, phosphorus, total and fecal coliform bacteria, as well as trace metals, in Strawberry Creek.

Land use has an important influence on the amount and quality of runoff. The upper Strawberry Creek watershed is approximately 25% impervious. Impervious surfaces associated with urban environments prevent water from infiltrating into the soil; rainfall events quickly generate runoff frequently containing pollutants that wash directly into creeks.

UC Berkeley seeks to more accurately model the flow of water and pollutants in the upper Strawberry Creek watershed above the Oxford Street culvert. This model satisfies this need and serves as a tool for campus planning and stormwater mitigation efforts.

MODEL DEVELOPMENT We chose to model NPS pollution entirely within a GIS for added flexibility, efficiency, and useful visualization capabilities. Our basic approach involved the characterization of: HYDROLOGY

USGS digital line graph hypsography data

Digitally-generated stream network

Hydrology was modeled solely as a function of hypsography, since available storm drain data were incomplete. USGS digital line graph hypsography data were converted into a line coverage and then into a point coverage. This point coverage was used to create a TIN, which was then converted into a grid with 30-foot cells.

Arc/Info-Grid flow direction and flow accumulation commands were used on the resulting grid. The Arc/Info-Grid flow direction function creates a grid of values that represent the direction water would flow from each cell to its steepest downslope neighbor, with eight possible directions. Output cells from the flow accumulation function represent accumulation of all upslope cells and may be used to identify stream channels.

USING GIS TO RELATE LAND USE TO STORMWATER RUNOFF IN THE CAMPUS PLANNING PROCESS

GROUP: GEOFF BURCH ABBY HOATS JEFFREY KING MAUREEN MASON CLIENT: KATHERINE MORTIMER, GEOGRAPHIC INFORMATION SCIENCE CENTER, UC BERKELEY

LA221: QUANTITATIVE METHODS IN ENVIRONMENTAL PLANNING | SPRING 2002

DEPARTMENT OF LANDSCAPE ARCHITECTURE AND ENVIRONMENTAL PLANNING | COLLEGE OF ENVIRONMENTAL DESIGN | UNIVERSITY OF CALIFORNIA BERKELEY

LAND USE

Land-use shapefiles were obtained from our client. The major shapefiles displayed roads, vegetated areas, buildings, and parking lots. All shapefiles were projected into Stateplane NAD 1983 California Zone III (feet), then clipped in ArcMap’s Geoprocessing Wizard based on the watershed boundary.

Strawberry Creek Watershed – Strawberry Creek:blue City streets: cyan. The central campus is located at the east end of the watershed. N

Pollutant Flow Loading [classified manually to display percentiles of the maximum: 0 - 0.0003% (blue), 0.0003% - 0.03% (light blue), 0.03% - 0.1% (green), 0.1% - 0.2% (yellow), 0.2% - 3% (orange), 3% - 100% (red)].  This view displays areas where both flow and pollutant loading are high.  The thresholding is an ordinal ranking, in which the variability is illustrated most clearly.

Phosphorus loading concentrations from each grid cell range from 0.15 mg/L for roofs to 2.67 mg/L for lawns.  Zinc loading concentrations range from 59 mg/L for lawns to 279.5 mg/L for streets.  Cadmium loading concentrations range from 0 mg/L for roofs and lawns to 1.1 mg/L for streets.  Copper concentrations range from 13 mg/L for lawns to 51 mg/L for streets.  The maximum annual pollutant loading values obtained in the watershed follow: phosphorus, 68 g/yr; zinc, 15,000 g/yr; cadmium, 57 g/yr; copper, 2,700 g/yr. 

Each pollutant loading grid was multiplied by the flow accumulation grid in order to determine areas where there is both high pollutant loading and high stream flow. These pollutant flow loading maps can be used to determine the relative contributions of existing land uses to NPS pollution and to evaluate where NPS pollution “hotspots” exist.  

The results from this model may be used to determine the location of water sampling stations for effective monitoring of NPS pollution. By repeatedly executing the model with modified input, the relationship between developed land and polluted runoff becomes clear. Though many areas of the campus have a permanent land use (i.e., buildings), other areas can be built in ways that will influence stormwater runoff quality. This model allows a campus planner to investigate different options and mitigation efforts such as aquatic buffers, filters, open channels and swales so that educated decisions about future land use can be made.  

If further refinement of our model is desired, several issues should be addressed. Storm drains and other non-natural routing of runoff should be included in the digital flow network. A more sophisticated algorithm for predicting the direction and accumulation of flow may be necessary. Future studies should seek greater resolution in several model components, including variability of rainfall over the watershed, characterization of land use, and hypsography. Weighted averaging of imperviousness and pollutant loading values within the grid cells should be performed. Finally, the model should be made more interactive to facilitate its use in campus planning applications.

RESULTS

The Simple Method calculates L, pollutant loading (g/yr) where 1 and 2 are conversion factors, P is average annual precipitation (in/yr), C is imperviousness, K is pollutant loading (mg/L), and A is grid cell area (ft2).

All land use shapefiles were unioned so that C and K values were retained for each polygon. The Arc/Info-Grid shapegrid function was implemented to generate five 30-foot cell grids (one for C value and one for each of the K values). The GRID module was again used to perform the algebraic calculations of the Simple Method for determining annual pollutant loading for each grid cell and for each pollutant.

CALCULATIONS L = 1

. 2 . P . C . K . A

POLLUTANT LOADING VALUES

Four pollutants were modeled in this study: cadmium, copper, phosphorus, and zinc. These pollutants were chosen based on their prevalence in urban runoff and the information available in the literature about their loading levels from different land uses.

Phosphorus Flow Loading

Copper Flow Loading Zinc Flow Loading

Cadmium Flow Loading