Urban Watershed Challenge Storm Sewers Watershed Models Delineation

Urban Watershed Challenge Storm Sewers & Watershed Models

Delineation Questions Ø Height-of-land delineation is altered by storm sewer l Gravity and force main Ø Do we need to correct for storm sewers? l Significance of storm sewers is scale dependent Ø Can we correct for storm sewers?

Semi-Automated Delineation Ø Burn streams into DEM Ø Run initial delineation on modified DEM Ø Check with local sources and experts Ø Review DOQs Ø Modify streams and repeat the process

Boundary Disagreement Manually delineated boundary Stream modified DEM boundary

Storm Sewer Data Ø Acquire data l Mostly CAD format Ø Import to GIS Ø Georeference l l no metadata unknown coordinate systems

Challenge #1: Georeferencing Spatial adjustment tool used to fix georeferencing problem

Georeferenced Data

Example: Effect of Lift Stations Manually delineated boundary Stream modified DEM boundary

Challenge #2: Jurisdictional Issues Manually delineated boundary. City of Edina Storm Sewer Hennepin County Storm Sewer Stream modified DEM boundary

Example: Revised Delineation

Challenge #3: Directionality Ø Limited use of directionality

Challenge #4: Connectivity Ø Interrupted by other feature types l Ø maintenace access holes Interrupted by missing surface water feature l open ditch

Challenge #5: Attributes Inconsistent attributes between sources Ø Typically limited attributes Ø Attributes may be as graphical annotation Ø

Summary of Challenges Ø Unknown coordinate systems Ø Overlapping jurisdictions Ø Lack of directionality Ø Lack of connectivity Ø Inconsistent and sparse attributes

Urban Watershed Models Ø Three basic algorithms for water quality modeling of urban watersheds l Event-mean concentration (EMC) l Regression model (rating curve) l Build-up / wash-off

EMC ØSimplest approach - event mean concentration (EMC) ØMany published values ØOften monitoring is land use specific ØEMCs area-weighted based on land use

EMC Land Use Specific EMCs (mg/L) Land Use TN TP TSS BOD Low-density residential 1. 77 0. 18 19. 1 4. 4 Single family residential 2. 29 0. 3 27 7. 4 Multi-family residential 2. 42 0. 49 71. 7 11 Low-intensity commercial 1. 18 0. 15 81 8. 2 High-intensity commercial 2. 83 0. 43 94. 3 7. 2 Industrial 1. 79 0. 31 93. 9 9. 6 Highway 2. 08 0. 34 50. 3 5. 6 Pasture 2. 48 0. 476 94. 3 5. 1 General agricultural 2. 32 0. 344 55. 3 3. 8 Open space 1. 25 0. 053 11. 1 1. 45 Adapted from Harper, H. H. (1998).

EMC ØAdvantages l Allows evaluation of various land use scenarios l It’s simple (cheap) ØDisadvantages l Too simple? l Ignores high variability (spatially and temporally) l No statistically significant difference between urban land uses (NURP) ØExamples – Pondnet (Walker)

Regression Models Ø Another approach is to develop empirical relationships between runoff concentration and predictor variables l l Flow Land use Soils Climate

Regression Models 1000 Chloride (mg/L) TSS (mg/L) 1000 10 10 1 1 10 100 Flow (cfs) 1000

Regression Models Ø Ø Advantages l Allows evaluation of various land use & soils l Still pretty simple Disadvantages l l Ø Can account for spatial and temporal variability Not mechanistic Examples - Tasker & Driver (1988), SWMM, SWAT

Build-Up / Wash-Off Build-up & wash-off Ø Mass balance of pollutants on impervious surfaces Ø A constant rate of accumulation Ø A first-order rate of non-runoff removal Ø Accumulation Non-runoff removal

Build-Up / Wash-Off Mass (kg/m 2) 25 20 15 10 Build-Up 5 0 Fraction Mass Remaining 0 10 20 30 40 50 Antecedent Dry Days 1 0. 8 0. 6 Wash-Off 0. 4 0. 2 0 0 0. 5 1 1. 5 2 Daily Rainfall Intensity (in/hr) 2. 5

Build-Up / Wash-Off Ø Advantages l l More mechanistic approach Hopefully more broadly applicable Ø Disadvantages l l l More complicated Lack the data needed to calibrate this model Doesn’t address contributions from pervious areas Ø Examples – P 8, SLAMM, SWAT