Urban Watershed Challenge Storm Sewers & Watershed Models

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?

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

Boundary Disagreement Stream modified DEM boundary Manually delineated boundary

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

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

Georeferenced Data

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

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

Example: Revised Delineation

Challenge #3: Directionality  Limited use of directionality

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

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

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

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

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

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)

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)

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

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

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

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

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

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