modeling urban watersheds impacted by csos and ssos
Transcript of modeling urban watersheds impacted by csos and ssos
MODELING URBAN WATERSHEDS
IMPACTED BY CSOS AND SSOS
Fifty Years of Watershed Management –
Past, Present and Future
September 24-26, 2012
Ted Burgess
Presentation Agenda
• Current state of modeling software
• Developing dry-weather flow rates
• Rainfall data used in model calibration
• Single-event and continuous model calibration
• Flow data analysis for model calibration
• Modeling green stormwater infrastructure
• Integration of (increasingly digital) field data and
SCADA with collection system models
Urban Watershed / Collection Systems Models
• Aqualyze – h3O
• Bentley – SewerGEMS, SewerCAD
• BOSS – StormNET
• CHI (Canada) – PCSWMM
• DHI (Denmark) – MIKE URBAN
• Delft (Netherlands) – SOBEK
• Innovyze (formerly MWH Soft) – InfoSWMM, InfoSewer, InfoWorks (formerly Wallingford Software, UK)
• U.S.ACE – HEC-HMS / HEC-RAS
• U.S.EPA – SWMM 5
• XP Software (Australia) – xpswmm
DHI MIKE URBANCHI PCSWMM
Innovyze InfoSWMM
U.S.EPA SWMM 5 and Commercial “Spin-offs”
Aqualyze h3Oxpswmm
Bentley SewerGEMS
BOSS StormNET
Gal/day/capita Water/Sewer Billing Analysis
• Data source requirements:
– Water (or sewer) billing records for winter periods
– Geo-reference property addresses to model catchments
Sewer Billing Data – Catchment Level
Sewer Billing Results
Radar-Rainfall Analysis:
2031 grid cells vs. 49 rain gauges
= 29.7%* + 1.5%* + 32.5% * + 36.4%*
Example: Area Weighted Radar-Rainfall
� Basin BW-AL-14 (352.618 ac or 1.427 km2)
Area Weighted Radar Rainfall for the basin is calculated as:
Area covered by radar pixels (1 * 1 km2) Area weights for each pixel
Selecting Calibration Storms
Selecting Calibration Storms (continued)
Unit Hydrograph Methodology in SWMM 5: Continuous
Simulation of Rainfall-Dependent Inflow/Infiltration
Rainfall Data Analysis for Continuous Simulation
Seasonal Wet Weather Response
Growth vs. Dormant Variability in Mean Dmax
U.S.EPA’s Sanitary Sewer Overflow Analysis and
Planning (SSOAP) Toolbox
SSOAP Toolbox - Data Flow
Sewer SystemGIS Database
Flow Monitoring Data
Rainfall
Data
Sewer System
Time Series
FlowVelocity
Depth
Time SeriesRainfall
Hydraulic Analysis
Data
SSO Volume
Capture Flow Volume
Overflow Frequency
Flooding LocationsPipe Capacity
External Data Sources
Database
ManagementTool
RDII AnalysisTool
DWF analysis resultsWet-weather selection results
WWF analysis results
RDII results
Event based RTK parametersRTK predictive analysis results
Internal Data Sources
Sewer System
Flow DataRainfall Data
RDII HydrographGeneration
Tool
RTK parameters
Rainfall DataSewer System
SSOAP-SWMM5Interfacing
Tool
RDII
Hydrograph
SWMM 5 Input File
SWMM 5
SWMM 5 Input File with RDII Hydrograph
SSOAP
SystemDatabase
in MS-ACCESS
0.2
0.1
0.0
0.4
0.6
0.8
1
2
3
4
2
3
4
1 SunJun 2008
8 Sun 15 Sun 22 Sun 1 Tue
OL-UP-10
Ra
inF
all (
in)
De
pth
(ft)
Flo
w (
mg
d)
Ve
locity (
ft/s
)
Date/Time
STA_OL-UP-10 0742S0009 0742S0009 (obs)0742S0010:0742S0009 0742S0010:0742S0009 (obs)
0.15
0.10
0.05
0.00
0.4
0.6
0.8
1
2
3
2
3
4
8 MonDec 2008
15 Mon 22 Mon 1 Thu
OL-UP-10
Ra
inF
all (
in)
De
pth
(ft)
Flo
w (
mg
d)
Ve
locity (
ft/s
)
Date/Time
STA_OL-UP-10 0742S0009 0742S0009 (obs)0742S0010:0742S0009 0742S0010:0742S0009 (obs)
0.3
0.2
0.1
0.0
0.4
0.6
0.8
1
2
3
2
3
4
1 MonSep 2008
8 Mon 15 Mon 22 Mon
OL-UP-10
Ra
inF
all (
in)
De
pth
(ft)
Flo
w (
mg
d)
Ve
locity (
ft/s
)
Date/Time
STA_OL-UP-10 0742S0009 0742S0009 (obs)0742S0010:0742S0009 0742S0010:0742S0009 (obs)
0.075
0.050
0.025
0.000
0.00
0.25
0.50
0.75
0
1
2
3
0
2
4
1 ThuJan 2009
8 Thu 15 Thu 22 Thu 1 Sun 8 Sun 15 Sun 22 Sun 1 Sun
OL-UP-10
Ra
inF
all (
in)
De
pth
(ft)
Flo
w (
mg
d)
Ve
locity (
ft/s
)
Date/Time
STA_OL-UP-10 0742S0009 0742S0009 (obs)0742S0010:0742S0009 0742S0010:0742S0009 (obs)
Example Calibration Results: Continuous RDI/I in SWMM5
Auto-calibration approaches
using Genetic Algorithm-based
techniques and graphical tools
can facilitate this process.
Continuous simulation runtimes for large
networks still require skeletonization
Modeling the relationship between sanitary/combined
sewers and storm sewers for SSO / CSO control
Precipitation
Initial Abstraction
Infiltration
Groundwater Recharge
(Deep Infiltration)
Infiltration to Collection Systems
Infiltration to Stormwater or
Combined Collection System
Infiltration to Sanitary Sewer System
Runoff
Runoff to Stormwater Collection System
Direct Inflow to Sanitary or Combined
Sewer System
19Session II – Modeling Urban Watersheds Impacted
by CSOs and SSOs
Green Stormwater
Infrastructure
Changes in SWMM5 facilitate green stormwater
infrastructure modeling
EffectiveImpervious
(DCIA)Pervious
Inlet
Impervious Pervious
ReceivingCatchment
Transfer
Transfer
Catchment routing –
traditional approach
Catchment routing –
new SWMM 5 options
SWMM5 LID Control Editors
Modeling21
Bio-Retention
Infiltration Trench
Porous Pavement
Rain Barrel
Vegetative Swale
Digital advancements in other sewer system technologies
allows integration of modeling with field information
3D Viewer Tool: Linking system condition and GIS data with sewer network data
SCADA integration example: Key Flow Control Structure
Whittier Street Regulator Gates
To Berliner Park
From/to DSR83
Regulator Gates
N
Whittier Street Storm Tanks
Modeler’s View of Collection System
Operator’s Views of Collection System
SCADA display of selected system conditions
SCADA to SWMM5 Control Rules Editor
Conclusions
• As computers get faster, models get bigger and more detailed (so we still live with long runtimes)
– CSO modeling: Typical year sufficient – no problem
– SSO modeling: Design targets ~ 2-10 year return periods – require much longer simulation periods and impractical runtimes
• Current focus on green stormwater infrastructure imposes new demands on established watershed modeling tools
• Convergence of field data tools (inspection databases, SCADA) and modeling tools is opening up new capabilities
Session II – Modeling Urban Watersheds Impacted
by CSOs and SSOs
EXTRAS
DWF Calibration Approach
DWF = GWI + Diurnal Pattern* Billing Data
– Basin level diurnal pattern and GWI developed from ADWF
flow monitoring data
– Catchment level billing data developed from winter season
water or sewer billing records
– Three parameters used to calibrate DWF:
• GWI Adjustment k1
• Pattern (or Billing) Multiplier k2
• Pattern Time Shift k3
DWF =(GWI + k1)+ (Diurnal Pattern time shift × k2) × k3 × Billing
End
of
Event
Start
of
Event
Rainfall
Dry
Weather
Flow
RDII
Flow
Metered
Flow
RDII Analysis Tool – Determination of RDII Parameters
Catchment Delineation Workflow
Data Process Results
MDC Sewer
Network
MDC Topographic
Data (2 foot contours)
MDC Storm
Network
MDC Property &
Building Polygons
ESRI ArcHydro
ArcGIS Spatial
Analysis
& VBA macro
Manual
Verification
Storm
Catchments
Sanitary/Combined
CatchmentsMDC Orthophotos
Modelers Views of Collection SystemRULE WSST_RG1A IF NODE 0006C0263
DEPTH <= 6.5’ (696’ ele.)THEN ORIFICE
WSST_RegulatorGate1 setting = 1
RULE WSST_RG1B IF NODE 0006C0263
DEPTH > 6.5’ (696’ ele.) AND < 7’ (696.5’ ele.)
THEN ORIFICE WSST_RegulatorGate1
setting = 0.6
RULE WSST_RG1C IF NODE 0006C0263
DEPTH >= 7.2’ (696.7’ ele.) AND <= 8’ (697.5’
ele.) THEN ORIFICE WSST_RegulatorGate1
setting = 0.3
RULE WSST_RG1B IF NODE 0006C0263
DEPTH > 8 (697.5’ ele.) THEN ORIFICE
WSST_RegulatorGate1 setting = 0
RULE WSST_RG2A IF NODE
WSSTControlHouse DEPTH <= 8.65 (700’ ele.)
THEN ORIFICE WSST_RegulatorGate2
setting = 1
RULE WSST_RG2B IF NODE 0006C0263
DEPTH > 6.5 (696’ ele.) THEN ORIFICE
WSST_RegulatorGate2 setting = 0
Columbus Sewer System Model Sanitary sewers: >12” Ø; combined sewers: >18” Ø
Columbus Sewer System Model Summary
• SWMM 5.00.22 engine / PCSWMM 2011 (CHI) interface
• Detailed model: 22,600 nodes / 3820 catchments
• RPM model: 4880 nodes / 1851 catchments
• Continuous simulation of RDII (RTK with IA) and surface
runoff (Green-Ampt)
• Diurnally-varied base flow and seasonally-varied GWI
• Calibration (2008-2009): 212 flow meter sites
• Validation (2010-2011): 60 permanent meter sites
• Radar-rainfall (1 sq km grid): calibration period
• Rain gauge data (49 sites): validation period
Columbus Sewer System ModelKey trunk sewers and flow control structures
Heavy Cleaning vs. Low Velocities
HGL vs Basement Elevations