LCFR Water Quality ModelingProject Report
Jim Bowen, UNC Charlotte
LCFRP Advisory Board/Tech. Comm. Meeting, October 30, 2008
Raleigh, NC
Outline of Presentation
• A Quick Review of the LCFR Model
• Summary of Model Report
• Questions/Suggestions
Basis of Presentation
TechnicalReportDraft
(available on web)
LCFR Dissolved Oxygen ModelThe big picture
Estuary PhysicalCharacteristics: e.g. length,width, depth,roughness
EFDC SoftwareAdjustable Parameters:(e.g. BOD decay, SOD, reaeration)
Hydrologic Conditions
RiverFlows,Temp’s,Conc’sTides Time
“Met” DataAir temps,precip,wind,cloudiness
Time
State Variables
nutrientsDO, organic C
Time
Dissolved Oxygen Conceptual ModelBOD Sources
Sediment
Cape Fear, Black & NECF BOD Load
Muni & Ind. BOD Load
decaying phytopl.
Estuary Inflow BOD Load
Dissolved Oxygen Conceptual ModelBOD Sources, DO Sources
Sediment
Cape Fear, Black & NECF BOD Load
Ocean Inflows
SurfaceReaeration
Phytoplank. ProductivityMuni & Ind.
BOD Load
decaying phytopl.
MCFR Inflows
Estuary Inflow BOD Load
BOD Consumption
Dissolved Oxygen Conceptual ModelBOD Sources, DO Sources & Sinks
Sediment Sediment O2 Demand
Cape Fear, Black & NECF BOD Load
Ocean Inflows
SurfaceReaeration
Input of NECF & Black R. Low DO Water
Phytoplank. ProductivityMuni & Ind.
BOD Load
decaying phytopl.
MCFR Inflows
Estuary Inflow BOD Load
Steps in Applying a Mechanistic Model
1. Decide on What to Model
2. Decide on Questions to be Answered
3. Choose Model
4. Collect Data for Inputs, Calibration
5. Create Input Files
6. Create Initial Test Application
7. Perform Qualitative “Reality Check” Calibration & Debugging
Steps in Applying a Mechanistic Model, continued
8. Perform quantitative calibration & model verification
9. Design model scenario testing procedure (endpoints, scenarios, etc.)
10. Perform scenario tests
11. Assess model reliability
12. Document results
Description of Model Application
Open BoundaryElevation Cond.
Lower Cape Fear RiverEstuary Schematic
Black River FlowBoundary Cond.
Cape Fear R. FlowBoundary Cond.
NE Cape FearFlow Boundary Cond.
Description of Model Application
• Flow boundary condition upstream (3 rivers)
• Elevation boundary condition downstream
• 20 lateral point sources (WWTPs)
• Extra lateral sources add water from tidal creeks, marshes (14 additional sources)
• 37 total freshwater sources
Model State Variables• Water Properties
– Temperature, salinities
• Circulation– Velocities, water surface elevations
• Nutrients– Organic and inorganic nitrogen, phosphorus, silica
• Organic Matter– Organic carbon (labile particulate, labile and
refractory dissolved), phytoplankton (3 groups)
• Other– Dissolved oxygen, total active metal, fecal coliform
bacteria
Water Quality Model Schematic
Data Collected to Support Model
• Data Collected from 8 sources– US ACoE, NC DWQ, LCFRP, US NOAA, US
NWS, USGS, Wilmington wastewater authority, International Paper
• Nearly 1 TB of original data collected
• File management system created to save and protect original data
Observed Data Used to Create Model Input Files• Meteorological forcings (from NWS)
• Freshwater inflows (from USGS)
• Elevations at Estuary mouth (from NOAA)
• Quality, temperature of freshwater inflows and at estuary mouth (from LCFRP, USGS, DWQ)
• Other discharges (from DWQ)
EFDC Input Files & Data Sources
Lower Cape Fear
River Program
Sites Used
USGS Continuous Monitoring and DWQ
Special Study
Stations Used
New Cross- Sections Surveyed
by NC DWQ
SOD Monitoring
Stations Performed
by NC DWQ
LCFR Grid• Channel
Cells in Blue• Wetland
Cells in White
• Marsh and Swamp Forest in Green, Purple
LCFR Grid Characteristics• Grid based on NOAA bathymetry and previous
work by TetraTech• Off-channel storage locations (wetland cells)
based on wetland delineations done by NC DCM• 1050 total horizontal cells (809 channel cells,
241 wetland cells) • 8 vertical layers for each horizontal cell• Used a sensitivity analysis to locate and size
wetland cells
Model Grid
Showing Location and Size
of Wetland
Cells
Riverine Swamps
and Saltwater
Marshes in Estuary
(NC DCM)
Input File Specification
• Inflows
• Temperatures and Water Quality Concentrations at Boundaries
• Water quality mass loads for point sources
• Benthic fluxes
• Meteorological data
Riverine Inflow Specification
• Flows based on USGS flow data
• Flows scaled based upon drainage area ratios
• 17 total inflows– 3 rivers, 14 estuary sources
Subwatersheds Draining Directly to the Estuary
Subwatersheds Draining Directly to the Estuary
Temperature and Concentration Specification
• 5 stations used (3 boundaries, 2 in estuary)
• Combined USGS and LCFRP data
• Point source specification tied to closest available data
Procedure for creating water quality mass load file (WQPSL.INP)
• Used an automated procedure based upon available data (LCFRP, DMR’s)
Use data interpolation and estimation to create a monitoring data set with no data gaps, enter data into Excel spreadsheet, one spreadsheet for each source
Use data interpolation and estimation to create a monitoring data set with no data gaps, enter data into Excel spreadsheet, one spreadsheet for each source
For each source, create a data conversion matrix to estimate each model constituent from the available parameters in the source dataFor each source, create a data conversion matrix to estimate each
model constituent from the available parameters in the source data
For source data given as a concentration time history, multiply concentrations by flows to get mass loads
For source data given as a concentration time history, multiply concentrations by flows to get mass loads
Collect mass load time histories and reformat, then write into WQPSL.INP file using Matlab script
Collect mass load time histories and reformat, then write into WQPSL.INP file using Matlab script
An Example Conversion Matrix (Cape Fear River Inflow)
Benthic fluxes and meteorological data• Used a prescriptive benthic flux model
• SODs time varying, but constant across estuary
• SOD values based upon monitoring data
• Met data constant across estuary
• Met data taken from Wilmington airport
Model Calibration and Confirmation• 2004 calendar year used for model
calibration
• Nov 1, 2003 to Jan. 1 2004 used for model startup
• 2005 calendar year used for confirmation run (a.k.a. verification, validation run)
Streamflows during Model Runs
• 2004 dry until October
• Early 2005 had some high flows
• Summer 2005 was dry
Hydrodynamic Model Calibration
• Examined water surface elevations, temperatures, salinities
• Used LCFRP and USGS data for model/data comparisons of salinity temperature
• Used USGS and NOAA data for model/data comparisons of water surface elevation
• USGS data based on pressure measurements not corrected for barometric changes
Monitoring Stations Used
for Hydrodynamic
Calibration
Simulation of Tidal Attenuation in Estuary
• Varied wetland cell widths to determine effect on attenuation of tidal amplitude
• Wider wetland cells gave more attenuation, as expected
• Also tried different distribution of wetland cells within estuary
M2 Tidal Amplitude for Various Cell Width Scenarios
M2 Tidal Amplitude for Various Cell Distribution Scenarios
M2 Tidal Amplitude for Various Cell Distribution Scenarios
Width * 2, v1 chosen as best overall (in green)
Example Time Series Comparison – Black at Currie (upstream), 2004
Example Time Series Comparison – NECF at Wilmington, 2004
Example Time Series Comparison – Cape Fear at Marker 12, 2004
Example Time Series Comparison – Black at Currie (upstream), Jan. 04
Example Time Series Comparison – Wilm. Tide Gage, Jan. 04
Example Time Series Comparison – Cape Fear at Marker 12, Jan. 04
Example Time Series Comparison – Salinities at Navassa, 2004
Example Time Series Comparison – Salinities at NECF Wilm., 2004
Example Time Series Comparison – Salinities at Marker 12, 2004
Calibration Statistics, Salinity
Salinity Scatter Plot
Temperature Scatter Plot
Calibration Statistics, Temperature
Water Quality Calibration
• Added a second category of dissolved organic matter (refractory C, N, P)
• Split between labile and refractory based upon longer-term BOD measurements from LCFRP, IP, Wilmington wastewater authority
• Accounted for effects of NBOD in these tests
Water Quality Model Schematic
Water Quality Model Schematic
State Variables UsuallyUsed to Simulate Organic Matter Load
Water Quality Model Schematic
State Variables UsuallyUsed to Simulate Organic Matter Load
Additional State Variables Used (settling velocity = 0.0)
Partitioning Organic Matter into Labile and Refractory Parts• Fit data to 2 component model for BOD
exertion, using equation
€
CBOD(t)= rBODu(1− e−kdrt )+ rBODukdr
kdr − kdl(e−kdl t − e−kdrt )+ lBODu(1− e−kdl t )
Example: Long-term BOD, IP discharge, 7/20/2003
Partitioning Organic Matter into Labile and Refractory Parts• Fit data to 2 component model for BOD
exertion, using equation
€
CBOD(t)= rBODu(1− e−kdrt )+ rBODukdr
kdr − kdl(e−kdl t − e−kdrt )+ lBODu(1− e−kdl t )
Loading Breakdown for DOC
Loading Breakdown for Refractory DOC
Loading Breakdown for NH4
Also implemented time variable SOD (varies w/ temperature)
Example Time Series Comparison – DO at Navassa, 2004
Example Time Series Comparison – DO at NECF Wilm., 2004
Example Time Series Comparison – DO at Marker 12, 2004
Calibration Statistics, DO
DO Scatter Plot
DO Percentile Plot
Calibration of Other WQ Constituents• Show some key constituents
– Ammonia, nitrate+nitrite, total phosphorus, chlorophyll-a
• Show only at Navassa (more plots in report)
• Overall, water quality model predicts each of the constituents well
Example Time Series Comparison – Ammonia at Navassa, 2004
Example Time Series Comparison – NOx at Navassa, 2004
Example Time Series Comparison – TP at Navassa, 2004
Example Time Series Comparison – Chl-a at Navassa, 2004
Confirmation Run Results
• Ran model for calendar year 2005, with parameters determined from calibration
• USGS continuous monitoring data ended by then, used LCFRP data instead
• Show time histories only at Navassa (more in report)
Example Time Series Comparison – Salinities at Navassa, 2005
Example Time Series Comparison – Temperatures at Navassa, 2005
Example Time Series Comparison – DO at Navassa, 2005
Model Fit Statistics, DO, 2005 Confirmation Run
DO Percentile Plot, Predicted vs. Observed, 2005 Confirmation Run
Sensitivity Testing
• Examined effect of varying SOD on model DO predictions and sensitivity of system to changes in organic matter loading
• SOD had an significant impact on model predictions
• Effect of changing SOD on effect of load changes shown in next section (scenario testing)
Scenario Tests - Methods
• In general, test effect of changing wastewater input on water quality of system
• Changed loads only for oxygen demanding constituents (DOC, RDOC, Ammonia
• Examine DOs during warm weather period (April 1 – November 1) at 18 stations spread across impaired area
• Look at predicted DOs in each layer
• 6 scenario tests done so far
Six Scenario Tests Done so Far1. Changes in Flow (and load) of Brunswick Co.
WWTP
2. Removal of load from all WWTPs, and from 3 (IP, Wilm NS & SS)
3. Removal of Ammonia load from all WWTPs
4. Increase all WWTPs to maximum permitted load
5. Reduction in load from rivers, tidal creeks, wetlands
6. Reduction in loads for various SOD values
1. Changes in Flow (and load) of Brunswick Co. WWTP • Base case flow = 0.38 MGD
• Three increased flows1. 4.3 times
base
2. 12.1 times base
3. 39.1 times base
2. Removal of load from all WWTPs, and from 3 (IP, Wilm NS & SS) • Completely removed CBOD & ammonia load from all WWPTS
• Tried turning off just IP, just Wilm NS & SS
3. Removal of Ammonia load from all WWTPs • Removed ammonia load from all 20 WWTP inputs
• No changes to CBOD load
4. Increase all WWTPs to maximum permitted load • Increased all flows and loads to maximum permitted values
• Assumed constant load at maximum permitted value
5. Reduction in load from rivers, tidal creeks, wetlands • Manipulated concentrations (& loads) of all 17 freshwater inputs (3 rivers, 14 estuary sources)
• Reduced loads by 30% and 50%
6. Reduction in loads for various SOD values • Varied SOD above and below calibrated value
• Observed effect of turning all WWTP loads off for each SOD case
Summary & Conclusions• Successfully created a simulation model of
dissolved oxygen in Lower Cape Fear River Estuary• Model testing included calibration, confirmation,
and sensitivity analyses• Scenario tests used to investigate system sensitivity
to changes in organic matter and ammonia load• System found to be only moderately sensitive to
changes in WWTP load
Additional Work Ongoing• Working to finalize modeling report and
other publications
• Will work with DWQ personnel to incorporate model results into TMDL
• Training DWQ personnel to run LCFR model and analyze additional scenarios
Additional Work Ongoing• Working to finalize modeling report and
other publications
• Will work with DWQ personnel to incorporate model results into TMDL
• Training DWQ personnel to run LCFR model and analyze additional scenarios
Questions?
Additional Work Ongoing• Working to finalize modeling report and
other publications
• Will work with DWQ personnel to incorporate model results into TMDL
• Training DWQ personnel to run LCFR model and analyze additional scenarios
• Additional analyses done that are not in report
Effect on DO of deepening navigation channel • Entrance channel deepened from 40 to 44 feet
• Remainder of channel (up to CF Mem. Br.) deepened from 38 to 42 feet
0
0.2
0.4
0.6
0.8
1
3 3.5 4 4.5 5 5.5 6
Base Case
Dredged Channel
Dissolved Oxygen (mg/L)
April through October Simulated Dissolved Oxygen Concentrations in the Impaired Area, Lower Cape Fear River Estuary
Effect of Changing River Load and SOD • Considers possible cleanup of sediments
• SOD lowered by same percentages (30% and 50%) as riverine loading 0
0.2
0.4
0.6
0.8
1
3 3.5 4 4.5 5 5.5 6 6.5 7
Base Case30% Reduction River Load30% Reduction River Load & SOD50% Reduction River Load50% Reduction River Load & SOD
Dissolved Oxygen (mg/L)
April through October Simulated Dissolved Oxygen Concentrations in the Impaired Area, Lower Cape Fear River Estuary:
Clean Rivers Scenario
Analysis of DO deficit in the impaired region • Examined summer
average DOs (surface) at 3 sites in impaired region
• Used linear sensitivity analysis to attribute deficit to either WWTPs, SOD, or river loads 0
2
4
6
8
10
NECF at Wilm.CF at HBCF at Nav
WWTP deficitRiver Load DeficitSOD deficitAvg. Conc.
Location
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