Bacterial TMDL Model for Copano Bay

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Bacterial TMDL Model for Copano Bay

• Research performed by Carrie Gibson at Center for
  Research in Water Resources
• Schematic processor tool developed by Tim
  Whiteaker at CRWR
• Research supported by Texas Commission for
  Environmental Quality

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Presentation Outline

• Background
• Scope of Work
• Bacterial Loading Water Quality Model
• Non-Point Source Bacterial Loading
  Calculations/Results
• Point Source Bacterial Loading Calculations/Result
  s
• Modeling Bacteria Transport Schematic Processor
• Calibration of Model
• Conclusions
• Future Work

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Project Location

• Copano Bay watershed

Copano Bay
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Background

• Section 303(d) of 1972 Clean Water Act (CWA)
• Texas Surface Water Quality Standards
• Fecal coliform bacteria
• Oyster water use
• Contact Recreation Use

Mission River
Aransas River
Copano Bay
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Existing Monitoring Data
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Scope of Work

• Identify major bacterial sources in Copano Bay
  watershed.
• Calculate total bacterial loadings, Total Maximum
  Daily Loads (TMDLs), from bacterial sources.
• Determine amount of load reductions that is
  needed to meet water quality standards.

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Potential Bacteria Sources

• Non-point bacteria sources
• Point sources
• Concentrated Animal Feedlot Operations (CAFOs)
• Livestock (cattle, goats, horses, sheep, hen,
  hogs, and chickens)
• Wastewater Treatment Plants (WWTPs)
• Septic Systems
• Waterbirds

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Non-Point Bacterial Loadings

• Basic Equation
• L Q C
• L Bacterial loadings (cfu/year)
• Q Runoff (m3/year)
• C Fecal coliform concentration (cfu/m3)

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Runoff (Q) Calculations

• Rainfall-runoff equations derived by Ann Quenzer
• Based on land use and precipitation

Quenzer Equations
Runoff, Q (m3/year)
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EMC (C) Calculations
Land Use Code Category Fecal Colonies per 100 mL
11 Open Water 0
21 Low Intensity Residential 22,000
22 High Intensity Residential 22,000
23 Commercial/Industrial/Transportation 22,000
31 Bare Rock/Sand/Clay 0
32 Quarries/Strip Mines/Gravel Pits 0
41 Deciduous Forest 1,000
42 Evergreen Forest 1,000
43 Mixed Forest 1,000
51 Shrubland 2,500
61 Orchards/Vineyards/Other 2,500
71 Grasslands/Herbaceous 2,500
81 Pasture/Hay 2,500
82 Row Crops 2,500
83 Small Crops 2,500
85 Urban/Recreational Grasses 22,000
91 Woody Wetlands 200
92 Emergent Herbaceous Wetlands 200

• From Reem Jihan Zouns thesis, Estimation of
  Fecal Coliform Loadings to Galveston Bay
• Modified dbf table in order not to account for
  livestock fecal wastes twice

0
0
0
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Creation of EMC Grid
Join based on land use code
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Non-Point Bacterial Loading Grid
Annual Bacterial Loading per Grid Cell

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Non-Point Loading per Watershed
Delineated Watersheds using WRAP Hydro
Annual Bacterial Loading per Watershed (cfu/year)
Zonal Statistics
Annual Bacterial Loading per grid cell (cfu/year)
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Point Source Calculations Livestock

• Cattle, goats, horses, sheep, layers, hogs,
  chickens
• Data (annual animal count per county) from
• 2002 Census of Agriculture, National Agricultural
  Statistics Service (NASS)
• 2004 Texas Livestock Inventory and Production,
  United States Department of Agriculture (USDA),
  NASS, Texas Statistical Office

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Livestock Loading Results

• Results
• Add cfu/year
• to non-point
• bacterial loading
• calculations

Livestock Bacterial Loadings
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Point Source Calculations Avian

• Texas Colonial Waterbird Census (TCWC)

Breeding Pair Locations
Locations of Applied Avian Loads
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Avian Loading Results

• Results
• Add cfu/year
• to non-point
• bacterial loading
• calculations

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Bacterial Loading to Watersheds

• Results

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Water Quality Model

• Created Water Quality Model using Model Builder

Cumulative Runoff per Watershed
Runoff (m3/yr)
Schematic Processor
Load (cfu/year)
Concentration (cfu/m3)
Cumulative Loading per Watershed
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Bacterial Loading Transport using Schematic
Processor

• Creation of Schematic Network

Watershed Drainage Junction Bay
Watershed to Junction Junction to
Junction Junction to Bay
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Schematic Processor Implements DLLs

• Dynamic linked libraries, DLLs
• First-order decay
• Simulates decay of bacteria along stream segments
• loadpassed loadreceived e-kt
• k first-order decay coefficient (day-1) -
  stored as attribute in SchemaLink
• t travel time along streams, t (days) - stored
  as attribute in SchemaLink

Decay
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Copano Bay acts as CFSTR

• CFSTR
• Assumptions
• Bay is completely mixed and acts as Continuous
  Flow, Stirred Tank Reactor (CFSTR)
• Inflow Outflow
• c L/(QkV)
• c concentration in bay (cfu/m3)
• L bacteria load entering bay (cfu/yr)
• Q total flow (m3/yr) stored as attribute in
  SchemaNode
• k first-order decay coefficient (day-1) -
  stored as attribute in SchemaNode
• V volume of bay (m3) stored as attribute in
  SchemaNode

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Schema Links and Nodes
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Computations along the network
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Moving material through links and nodes
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Processing Steps
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DLLs have the processes in them
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Schematic Processor Parameters

• Parameters (Inputs)
• SchemaLink (SrcTypes 1 and 2)
• Residence Time (t in days), Decay Coefficient (k
  in day-1)
• SchemaNode
• SrcType 3 Copano Bay
• Volume (V in m3), Decay Coefficient (k in day-1)
• Cumulative Runoff (Q in m3/year)
• SrcType 1 Watersheds
• Bacterial Loading per Watershed (L in cfu/year)

Determined by User Calculated from Previous Steps
in Model Builder
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Model Calibration Aransas River

• Calibration Locations (Four)

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Model Calibration Aransas River

• Goal Adjust upstream k and t values of each
  calibration location until median concentration
  of existing data is achieved.
• Then set k and t parameter values and work on
  the next downstream calibration location
  (bacteria monitoring station.)

Nodes/Links parameters that can be varied for
each bacteria monitoring station calibration
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Modeled versus Existing Data
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Modeled versus Existing Data
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Conclusions

• Major point and non-point source bacterial
  loadings have been calculated.
• Bacterial Loadings Water Quality Model has been
  created.
• Model has been calibrated (adjusting k and t
  parameters) to existing median bacteria
  monitoring data.
• There is uncertainty in the calculations of
  bacterial loadings and in the determination of
  parameters.

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Future Work

• Determine reasonable decay coefficient (k) values
  for rivers and compare to k values in calibrated
  model.
• Conduct parameter optimization and a Monte Carlo
  simulation on the model.
• Determine the current load, allowable load, and
  the load reductions necessary to meet water
  quality standards for each TCEQ segment.