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Urban Watershed Challenge Storm Sewers & Watershed Models.

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Presentation on theme: "Urban Watershed Challenge Storm Sewers & Watershed Models."— Presentation transcript:

1 Urban Watershed Challenge Storm Sewers & Watershed Models

2 Delineation Questions  Height-of-land delineation is altered by storm sewer Gravity and force main Gravity and force main  Do we need to correct for storm sewers? Significance of storm sewers is scale dependent 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 Boundary Disagreement Stream modified DEM boundary Manually delineated boundary

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

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

7 Georeferenced Data

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

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

10 Example: Revised Delineation

11 Challenge #3: Directionality  Limited use of directionality

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

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

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) Event-mean concentration (EMC) Regression model (rating curve) Regression model (rating curve) Build-up / wash-off 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 TNTPTSSBOD Low-density residential Single family residential Multi-family residential Low-intensity commercial High-intensity commercial Industrial Highway Pasture General agricultural Open space Adapted from Harper, H. H. (1998). Land Use Specific EMCs (mg/L)

18 EMC  Advantages Allows evaluation of various land use scenarios Allows evaluation of various land use scenarios It’s simple (cheap) It’s simple (cheap)  Disadvantages Too simple? Too simple? Ignores high variability (spatially and temporally) Ignores high variability (spatially and temporally) No statistically significant difference between urban land uses (NURP) 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 Flow Land use Land use Soils Soils Climate Climate

20 Regression Models Flow (cfs) TSS (mg/L)

21 Regression Models  Advantages Allows evaluation of various land use & soils Allows evaluation of various land use & soils Still pretty simple Still pretty simple  Disadvantages Can account for spatial and temporal variability Can account for spatial and temporal variability Not mechanistic 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 Accumulation Non-runoff removal

23 Build-Up / Wash-Off Antecedent Dry Days Mass (kg/m2) Daily Rainfall Intensity (in/hr) Fraction Mass Remaining Build-Up Wash-Off

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


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