Development of a Watershed-to-Very-Near-Shore Model for Pathogen Fate and Transport

Sheridan K. HaackAtiq U. Syed

Joseph W. DurisUSGS, Lansing, MI

Setting Significant local/regional interest

and historical data Previous studies by Center PIs NOAA SF6 transport study, 2005 May affect beaches at which

USEPA/CDC conducted recent epidemiology studies

Definitely affects beaches where Richard Whitman has developed predictive models

Contributes to portion of Lake Michigan being modeled by Phanikumar Mantha

Watershed is modestly sized, with variable land use Tractable for initial model development Different land uses likely yield different

pathogens Of particular interest for local E. coli

TMDL issues (urban vs nonpoint sources)

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Lake Michigan

Salt Creek

Deep River/Burns Ditch

Turkey Creek

Little Calumet River Basin (West)

Little Calumet River Basin (East)

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Lake Michigan

Salt Creek

Deep River/Burns Ditch

Turkey Creek

Little Calumet River Basin (West)

Little Calumet River Basin (East)

LAND USE CATAGORIES

High Intensity Developed

Low Intensity Developed

Water

Grassland

Cultivated Land

Scrub/Shrub

Deciduous Forest

Evergreen Forest

Mixed Forest

Palustrine Scrub/Shrub W etland

Palustrine Emergent W etland

Palustrine Forested Wetland

Unconsolidated Shore (Beach)

Bare Land

Little Calumet River/Burns DitchIndiana

Rationale

One of overall goals for CEGLHH is to develop models to predict coastal microbiological (particularly, pathogen) water quality

Most current models of microbial fate and transport use E. coli E. coli doesn’t represent many (most?) pathogens Temporal and spatial variability in types and source

loadings of pathogens poorly accounted for In-stream and very-near-shore hydrologic processes that

influence pathogen transport poorly understood

Overall Objectives

Acquire information on selected pathogens in a CEGLHH focus watershed Occurrence Relation to conventional and emerging

(chemicals, pathogen genes) measures of water quality

Develop a model of watershed-to-very-near-shore transport of these pathogens that can be linked to other models and research within CEGLHH

Sampling Took Place Under Three Hydrologic Conditions

1 2 30

1

2

3

4

4-days after rain

(8/14/05)

Base flow

(9/07/05)Rising hydrograph

(9/14/05)

En

tero

cocc

i (C

FU

/ 1

00

ml)

1 2 30

1

2

3

4

4-days after rain

(8/14/05)

Base flow

(9/07/05)

Rising hydrograph

(9/14/05)

E.

coli

(CF

U /

10

0 m

l)

1 2 3

0

1

2

3

4

4-days after rain

(8/14/05)

Base flow

(9/07/05)Rising hydrograph

(9/14/05)

Fe

cal C

olif

orm

(C

FU

/ 1

00

ml)

Correlation between precipitation event and indicator bacteria concentrations, based on contingency tables using flow pattern categories, and bacteria counts from individual sub-basins.

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

B

CAB

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

All enterococci > standardexcept sites 14 & 15

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept sites 14 & 15

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

>1000

B

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

>1000

B

CAB

CA

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

E. coli O157Esp - humanStx1- cattleStx2c - cattleStx2f - pigeonStx2d or e – sheep, pigs, WWTP?

Hydrologic Context

>235 (standard)>500

E. coli (no./100 mL)

All enterococci > standardexcept site 15

>1000

Preliminary Correlation Analysis

Two Groups of Samples: Fecal coliforms/E. coli >enterococci

20 samples: 15/20 downstream, 3 esp, 1 stx1 Fecal coliforms/E. coli < enterococci

25 samples: 18/25 upstream, 7 esp, 6 stx1, Currently conducting a variety of multivariate

analyses to relate patterns of bacterial and gene occurrence to PO4, NO3, NH3, SO4, Turbidity, Color, Dissolved Oxygen,

pH, Specific Conductance, and Temperature Spatial patterns and land use

Conducting more detailed analyses for 9 samples

Preliminary Correlation Analysis

Red = primary wastewater chemicals (Glassmeyer et al. 2005)

Preliminary Correlation Analysis

For base flow and rising hydrograph conditions, chemistry of sites 12 and 14 most closely linked Linkage between watershed and beach

through channel subject to backwater from lake Needs more accurate gauging

Did not sample during a CSO event How would such water mix near the mouth and

what does it carry?

AHTN (musk fragrance) predictor of WWTP effluents, and our own studies shown esp more probable AHTN significantly correlated with Cl

12

14

15

13

Watershed Model

PURPOSE: to predict loading rates for chemical constituents and bacteria from point and non-point sources into Lake Michigan

Flow Model for the study area developed and in process of calibration and sensitivity analysis on a continuous basis using daily time steps

USDA/ARS, Soil Water Assessment Tool (SWAT) Model

Database includes: DEM of the study area, Delineated watershed boundaries, NHD stream network, Land use data, Soils data, Point source discharges, and Weather data, which includes precipitation, temperature, solar radiation,

wind speed, and relative humidity

Comparision of Computed and Observed FLow at the Porter Gaging Station (04094000)

0

100

200

300

400

500

600

700

800

900

2/3/2004 2/23/2004 3/14/2004 4/3/2004 4/23/2004 5/13/2004 6/2/2004 6/22/2004 7/12/2004

Flo

w in

cfs

Computed Flow

Observed Flow at the GagingStation

Comparision of Computed and Observed FLow at the Portage Gaging Station (04095090)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

3/14/2004 4/3/2004 4/23/2004 5/13/2004 6/2/2004 6/22/2004 7/12/2004

Flo

w in

cfs

Computed FlowObserved Flow at the Gaging Station

Preliminary Results Showing Computed and Observed Flow in the Study Area

SWAT model daily mean flow results compared to the observed flow at the Porter gaging station (04094000).

SWAT model daily mean flow results compared to the observed flow at the Portage gaging station (04095090), near Lake Michigan.

Highlights Genes indicating pathogenic E. coli and

enterococci are frequently detected in the watershed In the absence of CSOs Patterns of detection are complex and must be

linked to some predictable or measureable factor esp-AHTN-Cl is one possibility

Watershed model is well-developed and can account for point-source flows (WWTP and CSO)

Challenges

Continued analysis of factors associated with gene occurrence

Sampling of a CSO event Improved measurement of flow dynamics at

the Burns Ditch/Lake interface Linkage of watershed flow model to Lake

Michigan Circulation Model