2. Nonpoint Source Pollution Some basic principles Example study of total pollution loads in the Corpus Christi Bay System (Ann Quenzer’s research) –rainfall-runoff relationship –point and nonpoint source loads –connection to bay water quality Example study of total pollution loads in the Copano Bay System (Carrie Gibson and Ernest To’s research) –Combination of spatial and statistical analysis

3. References CRWR Online Report 06-6: Bacterial Loadings Watershed Model in Copano Bay, Carrie Jo Gibson, David R. Maidment, and Mary Jo Kirisits, May 2006CRWR Online Report 06-6 CRWR Online Report 98-1: A GIS Assessment of the Total Loads and Water Quality in the Corpus Christi Bay System, Ann Marie Quenzer, and David R. Maidment, May 1998CRWR Online Report 98-1 Handbook of Hydrology: Sec 14.1 and 14.2 on nonpoint source pollution sources Handbook of Hydrology: Sec 28.6 on design for water quality enhancement http://www.crwr.utexas.edu/online.shtml (Handbook of Hydrology on reserve in Engr Library)

4. Adapt Water to the Land System Land Characterization (Land use, Soils, Climate, Terrain) Water Characterization (water yield, flooding, groundwater, pollution, sediment) Non Point Source Pollution (mean annual flows and pollutant loads)

5. Possible Land-Water Transform Coefficients Water Land

6. Expected Mean Concentration EMC = Load Mass/Flow Volume either on a single event basis or as an annual average QC TT L 0 0 0 T EMC =M/V Flow ConcentrationLoad L(t)=Q(t)*C(t)

7. Map-Based Surface Water Runoff Runoff, Q (mm/yr) Precipitation, P (mm/yr) Accumulated Runoff (cfs) P Q Runoff Coefficient C = Q/P

8. Water Quality: Pollution Loading Module DEM Precip. Runoff LandUse EMC Table Concentration Load Accumulated Load Load [Mass/Time] = Runoff [Vol/Time] x Concentration [Mass/Vol]

9. Expected Mean Concentration Land Use EMC Table derived from USGS water quality monitoring sites

10. Total Constituent Loads Input for Water Quality Model Water Quality: Land Surface -Water Body Connection Bay Water Quality

11. Total Loads and Water Quality in the Corpus Christi Bay System Presented by: Ann Quenzer and Dr. David Maidment Special Thanks: Corpus Christi Bay National Estuary Program Ferdinand Hellweger Dr. Nabil Eid Dr. George Ward Dr. Neal Armstrong

12. Purpose To determine the rainfall/runoff relationship To estimate the point and non-point source loads to the bay system To quantify the relationship between the total loads and the bay system water quality

13. Basic Concept Point and Non-point Estimation Total Loads Routing Water Quality Calculate Flow and Total Loads Linkage of the Two Models Steady-State Model

14. Watershed Delineation Sub-Watersheds

15. Precipitation + = Precipitation Trend over Bay System Merged Precipitation Files Oregon State University Precipitation Data

16. Regression Inputs and Outputs

17. Surface Water Runoff

18. Mean Annual Runoff (mm/yr) Land Use + Precipitation

19. Precipitation and Runoff Gradient Precipitation and Runoff Gradient from South (A) to North (B) along the Bay System Precipitation and Runoff Gradient Locations in the South (A) and North (B)

20. Runoff Into Each Bay System Middle Bay System 24.5 m 3 /s 34% of total flow Entire Bay System = 72 m 3 /s North Bay System 40.5 m 3 /s 56% of total flow South Bay System 7 m 3 /s 10% of total flow

21. Bay System Water Balance Entire Bay System

22. Bay System Water Balance North Bay System Middle Bay System South Bay System

23. Purpose To estimate the point and non-point source loads to the bay system

24. Total Constituent Loading Land Surface Load Point Source Load Atmospheric Load ? Sediment Load ?

25. Land Surface Constituent Loading Load [Mass/Time] = Runoff [Vol/Time] x Concentration [Mass/Vol]

26. Land Use USGS Land Use (1970’s) Addition of Missing Land Use

27. Percent Land Use Total Study Area Legend

28. EMC Table

29. Point Sources Texas Natural Resources Conservation Commission (TNRCC) Water Quality Segmentation

30. Loads Routing

31. Load Routing Methodology

32. Total Constituent Loads Input for Water Quality Model Connection of Both Models Bay Water Quality

33. Total Load to Bay System

34. Atmospheric Contribution Total Nitrogen Atmospheric Load to Land Surface = 2,700 Kg/d which is 35% of Land Surface Load from agricultural land use. This calculation is made assuming the EMC of 4.4 mg/l for agriculture and a Nitrogen concentration of 1.1 mg/l in precipitation

35. Bay System Segmentation Segmentation Used in the CCBNEP Project Clipped Segmentation from Drs. Armstrong and Ward

36. Bay System Model Methodology.

38. Water Quality Analysis Salinity Concentration and Mass Fluxes in Corpus Christi Bay. Fluxes Flow of water Advection Dispersion Loads Transport of Constituents Finite Segment Analysis

39. Observed vs. Expected Total Phosphorus (mg/l) Total Nitrogen (mg/l)

40. Observed vs. Expected Oil and Grease (mg/l)Copper (µg/l)

41. Observed vs. Expected Zinc (µg/l)Chromium (µg/l)

42. Conclusions u Strong South-North gradient in runoff from the land surface u Nearly all water evaporates from bays, little exchange with the Gulf u Nonpoint sources are main loading source for most constituents u Nitrogen, phosphorus, oil & grease loads are consistent with observed concentrations in the bays u Metals loads from land account for only a small part of observed concentrations in bays – it was concluded later that metals concentrations were too high because the samples had not been obtained using “clean” sampling methods.

43. Bacterial TMDL Model for Copano Bay Research performed by Carrie Gibson and Ernest To at Center for Research in Water Resources Schematic processor tool developed by Tim Whiteaker at CRWR Research supported by Texas Commission for Environmental Quality

44. Presentation Outline Background Scope of Work Bacterial Loading Water Quality Model –Non-Point Source Bacterial Loading Calculations/Results –Point Source Bacterial Loading Calculations/Results –Modeling Bacteria Transport: Schematic Processor –Calibration of Model Conclusions Future Work

45. Project Location Copano Bay watershed Copano Bay

46. Background Section 303(d) of 1972 Clean Water Act (CWA) Texas Surface Water Quality Standards –Fecal coliform bacteria Oyster water use Contact Recreation Use Aransas River Mission River Copano Bay

47. Existing Monitoring Data

48. Scope of Work 1.Identify major bacterial sources in Copano Bay watershed. 2.Calculate total bacterial loadings, Total Maximum Daily Loads (TMDLs), from bacterial sources. 3.Determine amount of load reductions that is needed to meet water quality standards.

49. 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

50. Non-Point Bacterial Loadings Basic Equation: L = Q * C L = Bacterial loadings (cfu/year) Q = Runoff (m 3 /year) C = Fecal coliform concentration (cfu/m 3 )

51. Runoff (Q) Calculations Rainfall-runoff equations derived by Ann Quenzer –Based on: land use and precipitation Precipitation Data (inches/year) Land Use Land Cover Data Runoff, Q (m 3 /year) Quenzer Equations

52. EMC (C) Calculations From Reem Jihan Zoun’s thesis, Estimation of Fecal Coliform Loadings to Galveston Bay Modified dbf table in order not to account for livestock fecal wastes twice Land Use Code CategoryFecal Colonies per 100 mL 11Open Water0 21Low Intensity Residential22,000 22High Intensity Residential22,000 23Commercial/Industrial/Transportati on 22,000 31Bare Rock/Sand/Clay0 32Quarries/Strip Mines/Gravel Pits0 41Deciduous Forest1,000 42Evergreen Forest1,000 43Mixed Forest1,000 51Shrubland2,500 61Orchards/Vineyards/Other2,500 71Grasslands/Herbaceous2,500 81Pasture/Hay2,500 82Row Crops2,500 83Small Crops2,500 85Urban/Recreational Grasses22,000 91Woody Wetlands200 92Emergent Herbaceous Wetlands200 0 0 0

53. Creation of EMC Grid Land Use Land Cover Data Join based on land use code EMC dbf table Fecal Coliform Concentration, C (cfu/m 3 )

54. Non-Point Bacterial Loading Grid C (cfu/m 3 )Q (m 3 /year)L (cfu/year) * = Annual Bacterial Loading per Grid Cell

55. Non-Point Loading per Watershed Annual Bacterial Loading per grid cell (cfu/year) Zonal Statistics Annual Bacterial Loading per Watershed (cfu/year) Bacteria Monitoring Stations USGS Gauge Stations Water Segment Endpoints Watersheds Delineated Watersheds using WRAP Hydro

56. 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

57. Livestock Loading Results Results Add cfu/year to non-point bacterial loading calculations Livestock Bacterial Loadings Legend Animal_cfu_year 5.37e+013 - 1.58e+015 1.59e+015 - 7.50e+015 7.51e+015 - 1.35e+016 1.36e+016 - 4.14e+016 4.15e+016 - 1.17e+017

58. Point Source Calculations: Avian Texas Colonial Waterbird Census (TCWC) Breeding Pair LocationsLocations of Applied Avian Loads

59. Avian Loading Results Results Add cfu/year to non-point bacterial loading calculations Legend Birds_cfu_year 1.48e+009 1.49e+009 - 2.22e+009 2.23e+009 - 1.22e+011 1.23e+011 - 2.75e+011 2.76e+011 - 3.96e+011

60. Bacterial Loading to Watersheds Results Legend Livestock Non-Point Avian

61. Water Quality Model Runoff (m 3 /yr) Concentration (cfu/m 3 ) Load (cfu/year) Cumulative Loading per Watershed Cumulative Runoff per Watershed Schematic Processor Created Water Quality Model using Model Builder

62. Bacterial Loading Transport using Schematic Processor Creation of Schematic Network Watershed Drainage Junction Bay Watershed to Junction Junction to Junction Junction to Bay Reference: Whiteaker, T., D.R. Maidment, J. L. Goodall, and M. Takamatsu, “Integrating Arc Hydro features with a schematic network”, Transactions in GIS, Vol. 10, No. 2, pp. 219-238, 2006

63. Schematic Processor Implements DLLs Dynamic linked libraries, DLLs –First-order decay Simulates decay of bacteria along stream segments load passed = load received * 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

64. Copano Bay acts as CFSTR –CFSTR Assumptions –Bay is completely mixed and acts as Continuous Flow, Stirred Tank Reactor (CFSTR) –Inflow = Outflow c = L/(Q+kV) c = concentration in bay (cfu/m 3 ) L = bacteria load entering bay (cfu/yr) Q = total flow (m 3 /yr) – stored as attribute in SchemaNode k = first-order decay coefficient (day -1 ) - stored as attribute in SchemaNode V = volume of bay (m 3 ) – stored as attribute in SchemaNode

65. Schema Links and Nodes

66. Computations along the network

67. Moving material through links and nodes

68. Processing Steps

69. DLL’s have the processes in them

70. Schematic Processor Parameters Parameters (Inputs) –SchemaLink (SrcTypes 1 and 2) Residence Time (  in days), Decay Coefficient (k in day -1 ) –SchemaNode SrcType 3 – Copano Bay –Volume (V in m 3 ), Decay Coefficient (k in day -1 ) –Cumulative Runoff (Q in m 3 /year) SrcType 1 – Watersheds –Bacterial Loading per Watershed (L in cfu/year) Determined by User Calculated from Previous Steps in Model Builder

71. Model Calibration: Aransas River Sta. 12952 USGS08189700 Sta. 17592 Sta. 12945 Sta. 14783 Sta. 12948 Calibration Locations (Four)

72. Model Calibration: Aransas River Goal: Adjust upstream k and  values of each calibration location until median concentration of existing data is achieved. Then “set” k and  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

73. Modeled versus Existing Data Sta. 17592 Existing F.C. Median: 260 cfu/100mL Modeled F.C. Median: 260 cfu/100 mL Sta. 12952 Existing F.C. Median: 72 cfu/100 mL Modeled F.C. Median: 72 cfu/100 mL Sta. 12948 Existing F.C. Median: 96 cfu/100 mL Modeled F.C. Median: 96 cfu/100 mL Copano Bay (Aransas R. Outlet) Existing F.C. Median: 2 cfu/100 mL Modeled F.C. Median: 2 cfu/100 mL

74. Modeled versus Existing Data

75. 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  parameters) to existing median bacteria monitoring data. There is uncertainty in the calculations of bacterial loadings and in the determination of parameters.