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Urban Watershed Challenge

Urban Watershed Challenge. Storm Sewers & Watershed Models. Delineation Questions. Height-of-land delineation is altered by storm sewer Gravity and force main Do we need to correct for storm sewers? Significance of storm sewers is scale dependent Can we correct for storm sewers?.

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Urban Watershed Challenge

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  1. Urban Watershed Challenge Storm Sewers & Watershed Models

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

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

  4. Manually delineated boundary Stream modified DEM boundary Boundary Disagreement

  5. Storm Sewer Data • Acquire data • Mostly CAD format • Import to GIS • Georeference • no metadata • unknown coordinate systems

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

  7. Georeferenced Data

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

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

  10. Example: Revised Delineation

  11. Limited use of directionality Challenge #3: Directionality

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

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

  14. Summary of Challenges • Unknown coordinate systems • Overlapping jurisdictions • Lack of directionality • Lack of connectivity • Inconsistent and sparse attributes

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

  16. EMC • Simplest approach - event mean concentration (EMC) • Many published values • Often monitoring is land use specific • EMCs area-weighted based on land use

  17. EMC Land Use Specific EMCs (mg/L) Adapted from Harper, H. H. (1998).

  18. EMC • Advantages • Allows evaluation of various land use scenarios • It’s simple (cheap) • Disadvantages • Too simple? • Ignores high variability (spatially and temporally) • No statistically significant difference between urban land uses (NURP) • Examples – Pondnet (Walker)

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

  20. 1000 1000 100 100 TSS (mg/L) Chloride (mg/L) 10 1 10 1 10 100 1000 1 10 100 1000 Flow (cfs) Flow (cfs) Regression Models

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

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

  23. 25 20 15 Mass (kg/m2) 10 5 0 0 10 20 30 40 50 Antecedent Dry Days 1 0.8 0.6 Fraction Mass Remaining 0.4 0.2 0 0 0.5 1 1.5 2 2.5 Daily Rainfall Intensity (in/hr) Build-Up / Wash-Off Build-Up Wash-Off

  24. Build-Up / Wash-Off • Advantages • More mechanistic approach • Hopefully more broadly applicable • Disadvantages • More complicated • Lack the data needed to calibrate this model • Doesn’t address contributions from pervious areas • Examples – P8, SLAMM, SWMM, SWAT

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