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Appendix P. 

Setting the SAV/Water Clarity Criteria Based Sediment Allocations

Introduction

The scale of the Chesapeake Bay Program partnership’s models extend from the extreme of the 

continental scale of the Community Multiscale Air Quality Bay Airshed Model and watershedwide scale of the Phase 5.3 Bay Watershed Model to the other extreme of the narrow ribbon of 

shallow water adjacent to the Bay’s over 11,000 miles of tidal shoreline. The ribbon of shallow 

water of 2 meters or less in depth is the region where the jurisdictions’ SAV/water clarity criteria 

are applied to assess protection of the shallow-water bay grass designated use. This region of a 

convoluted shoreline is spatially and temporally more heterogeneous than the rest of the 

Chesapeake Bay Water Quality and Sediment Transport Model domain covering the open and 

contiguous waters of the Chesapeake. Episodic loads from shoreline erosion, resuspension and 

watershed inputs all transit this narrow band of land and water interface.

The challenge of assessing SAV and water clarity criteria at these scales has only recently been 

taken up by the Chesapeake Bay Program partnerships in the past five years. Monitoring, 

modeling and research in these shallow-water systems is in its relative infancy compared to the 

more mature environmental science surrounding dissolved oxygen in eutrophic estuarine 

ecosystems. In addition, while moving toward these finer scales, the retention of system-wide 

representation of loading sources, boundary conditions must be preserved.

Key Model Refinements in the Simulation of Water Clarity-SAV 

The Bay Water Quality Model used in setting the 2003 Chesapeake Bay nutrient and sediment 

allocations (Cerco and Noel, 2004; Linker et al., 2000; Cerco et al., 2004) was refined to include 

full sediment transport of four classes of inert particulates approximating the settling and 

transport behavior of sand, silt, clay, and a sediment fraction of slowly settling clay. The 

resulting Chesapeake Bay Water Quality and Sediment Transport Model was capable of 

resolving turbidity maximum zones in the Bay and appropriately setting the boundary conditions 

for the shallow water region of the SAV/water clarity criteria. Resuspension of sediment was 

generated by currents, both tidal and residual, and by waves. Additional refinements included 

high resolution at half meter depths of the shallow water SAV grow areas (Figure P-1), a 

advanced optics model of underwater light attenuation, improvements to the SAV simulation, 

and refinements to shoreline erosion. These model refinements and additions are shown 

schematically in Figure P-2. 
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Source: Carl Cerco, 2009. Presentation at CERF Conference.

Figure P-1. A schematic of the half meter depths of the SAV sub-grid unit cells mapped to the WQSTM grid 

cell which provides light attenuation and other model state variables the SAV growth cell.
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CH3D

Hydrodynamic

Model

WindGenerated 

Wave Model

Bottom Shear 

Stress, 

Currents

ICM 

Eutrophication 

Model

SAV Model

ROMS Bed 

Model

Resuspension

Optical Model

Source: Cerco 2009.

Figure P-2. A schematic of the Water Quality and Sediment Transport Model refinements applied for the 

simulation of the SAV/water clarity water quality standard.

Refinements to Shore Erosion Estimates

Consistent temporal and spatial data for erosion rates, bank heights, shoreline protection, and 

sediment type were needed for the entire Chesapeake Bay to better estimate the role of shoreline 

erosion in the overall sediment budget (Hennessee et al., 2006; Hardaway et al., 1992). The 

refined shoreline sediment load estimates included both bank load (e.g. fastland erosion) and 

nearshore erosion (Figure P-3). Spatially explicit erosion rates by ―reach‖ that allowed for 

variance with bank height, shoreline orientation, and sediment composition were calculated. 

Best estimates of the actual shoreline lengths were used, including reduced erosion rates for 

enclosed minor inlets where reduced wave and current erosion would be expected. The different 

shoreline loading estimates were then incorporated into the appropriate Water Quality and 

Sediment Transport Model cells.
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Figure P-3. Example of fastland and nearshore components of the shoreline sediment loads.

Source: Hopkins and Halka (2007). 

For unprotected shorelines the shoreline erosion computation was as follows:

- Eroded Fastland Volume = Shoreline Length x Elevation x Erosion Rate/Day

- Total Eroded (Fastland + Nearshore) = Fastland Mass/0.65

- Eroded Silt/Clay Mass = Total Eroded Volume x Bulk Density x Silt Clay %

- Different silt/clay proportions for bank and marsh sources (Applied to Maryland portion 

tidal shoreline only)

- Different silt/clay proportions for north and south banks of each major river (Applied to 

Virginia tidal shoreline only)

For protected shorelines everywhere the assumption was that fastland erosion was eliminated,

but that nearshore erosion continued. Nearshore erosion was estimated for protected shorelines 

by using adjacent or nearby unprotected shoreline.

Simulation of SAV 

The unit SAV simulation computes SAV density (mass/unit area) as a function of irradiance and 

nutrients for SAV shoot and roots as shown in Figure P-4. Irradiance and epiphytes are 

calculated separately and the SAV model fully interacts with water column and bed sediments

(see Figure P-2).
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P-5 September 24, 2010

Source: Cerco 2009.

Figure P-4. The Chesapeake Bay Water Quality and Sediment Transport Model’s SAV unit model.

The current simulation of SAV considers light to be the sole determinant of SAV abundance, but 

other factors such as composition of bottom substrate, SAV community structure and seed bank 

availability are significant. These factors not explicitly simulated in the Water Quality and 

Sediment Transport Model but are accounted for via an empirical ―probability of success‖. 

The probability function was empirically set to best represent SAV biomass under current 

nutrient loads and adjusted to improve the probability of SAV growth under conditions that are 

more representative of mid-1900s Chesapeake nutrient loads (Hagy et al., 2004). The use of the 

empirically set probability function for SAV allowed appropriate SAV levels to best simulate 

water clarity, which was solely used to assess the water clarity criteria. Moving forward, in the 

next generation of the Bay Model the probability function will be replaced with salient first 

principal forcing functions.

Process of Assessment of the Water Clarity-SAV Criteria 

There are three methods to assess attainment of the Bay jurisdictions’ SAV/water clarity water 

quality standards. Either one of the following three methods can be used to determine whether 

the SAV and water clarity goal is achieved. The SAV/water clarity criteria assessment applied to 

the Bay Water Quality and Sediment Transport Model scenario output is always on the combined 

SAV and water clarity criteria assessment method. 
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Using only acres of SAV coverage: A segment attains if the SAV acreage of single best year 

in the segment is met in the preceding 3 years (including the current year) (USEPA 2003b).

Using only water clarity acres: A segment attains if the single best year water clarity acreage in 

the preceding 3 years exceeds 2.5 times the SAV restoration acreage. The water clarity acres 

for a year are assessed based on the arithmetic mean of monthly water clarity in the criteria 

months that meets the water clarity criteria (see Table 3-5) (USEPA 2007). 

 

Using combined SAV and water clarity achievement: This method considers both the achieved 

SAV acreage and water clarity acre in a segment. In the assessment, the water clarity acre can 

be converted to an SAV-equivalent acre by dividing 2.5 of the water clarity acre, which will be 

credited along with the SAV coverage estimated by regression model. 

Estimation of SAV/Water Clarity in a WQSTM Loading Scenario

In the combined SAV and water clarity assessment, both the SAV acres and water clarity acres 

needs to be estimated in load reduction scenarios. The light extinction coefficient, Ke, is the 

metric used to measure water clarity. The Ke in a load reduction scenario is estimated using the 

Chesapeake Bay Water Quality and Sediment Transport Model (WQSTM). The SAV area in a 

load reduction scenario is estimated from a regression model. 

Ke Assessment by the WQSTM

The simulated Ke in the WQSTM is based on the amounts of simulated clay, silt, sand, organic 

particulates, and dissolved organic matters in a model cell. Since the simulated Ke is an 

imperfect representation of the observed Ke, a data correction method is used to obtain adjusted 

scenarioed Ke in each shallow cell for the target loading scenario. While several more 

sophisticated data correction methods were tried a simple proportional adjustment of the shallow 

water Ke to the nearest observed water quality monitoring station was found to provide the best 

shallow water data correction as determined by independent shallow water monitoring sites.

 

The shallow water bay grass designated-use habitat is considered located between the 2-meter 

depth contour and the adjacent shoreline. A segment consists of Bay Water Quality and 

Sediment Transport Model cells. Due to inconsistency between the model cell boundary and the 

2-meter contour area, EPA remapped and extended the model cells to cover tidal water up to the 

shoreline, and subdivided the area into 0 - 0.5 m, 0.5-1.0 m, 1.0-1.5m and 1.5-2.0m depths. For 

each half-meter contour area we apply corresponding Ke criteria (see Section 3, Table 3-5). 

Note that the areas of defined no-growth zone are excluded from the cell/segment area in the 

assessment. 

Credit of SAV Area Based on Observed SAV Area

The projected SAV acreage in a target scenario is based on a regression of observed SAV in the 

Bay segments which, together, compose the major tributaries and the nutrient and sediment loads 

from each corresponding land basin (i.e., the major sub-watershed) of the Chesapeake Bay 

watershed which provides loads to those collective set of segments (Table P-1).

 
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Through the Baywide SAV aerial survey, the partners have access to annual SAV distribution 

and abundance data for almost every year in the past 30 years. The attached Excel file, 

Appendix P SAV Coverage 1971-2009 Spreadsheet.xls, shows observed SAV for Bay segments 

in 1971-2009. The observed SAV areas from 102 segments are aggregated into SAV areas for 

the 8 tidal basins for each year. 

 

TN, TP, and TSS loads from the 8 major basins are estimated from the Phase 5.3 Chesapeake 

Bay Watershed Model’s progress scenarios under years 1985, 1987, 1992, 1998, 2002, 2005, 

2007 and 2009 management conditions. 

 

Linear regression of SAV versus load of TN or TP or TSS, respectively, is conducted for each 

basin, yields:

 SAV = m Load + b 

where, coefficient m is slope and b is intercept from the linear regression. The results are 

presented in Table P-1. 

Table P-1. Results of linear regression of SAV versus TN, TP, and TSS loads for 8 major basins.

Basin Component R2 Slope Intercept

Susquehanna TN 0.8983 -4.16E+02 6.43E+04

Susquehanna TS 0.8049 -2.77E+01 9.11E+04

Susquehanna TP 0.6847 -8.54E+03 5.12E+04

Potomac TN 0.9068 -2.85E+02 2.82E+04

Potomac TS 0.8769 -2.13E+01 6.83E+04

Potomac TP 0.8449 -1.28E+04 7.04E+04

York TN 0.0468 1.09E+03 1.79E+03

York TS 0.8948 -8.81E+01 2.65E+04

York TP 0.7539 -9.38E+03 1.79E+04

Eastern Shore TN 0.1615 -1.87E+03 7.69E+04

Eastern Shore TS 0.5769 -2.29E+02 1.20E+05

Eastern Shore TP 0.3518 -2.18E+04 8.09E+04

Rappahannock TN 0.5900 -6.93E+02 6.57E+03

Rappahannock TS 0.5425 -8.42E+00 7.88E+03

Rappahannock TP 0.6609 -5.44E+03 7.68E+03

James TN 0.9624 -8.54E+00 3.81E+02

James TS 0.8763 -3.59E-01 5.89E+02

James TP 0.7467 -3.27E+01 2.21E+02

MD Western Shore TN 0.5437 -2.35E+02 6.11E+03

MD Western Shore TS 0.7106 -4.79E+01 1.49E+04

MD Western Shore TP 0.5361 -3.50E+03 5.17E+03

Patuxent TN 0.5940 -2.02E+02 9.31E+02

Patuxent TS 0.5693 -3.66E+00 7.66E+02

Patuxent TP 0.3253 -1.38E+03 6.89E+02
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For individual basin, we use the regression of SAV with Load component TN or TP or TSS, 

which has the highest R2 of regression. 

 

The Bay TMDL’s critical period of 1993-1995 is our reference for the TMDL. The load in the 

reference year for each basin can be estimated from Bay Watershed Model calibration, and the 

corresponding SAV is known from the observation. They also have the relationship: 

SAV_ref = m Load_ref + b. 

 

A projected SAV of the basin in a load reduction scenario is: 

Proj_SAV = m Proj_load + b

Therefore, Proj_SAV – SAV_ref = m (Proj_load – Load_ref).

We can calculate the ratio: 

Rate = Proj_SAV / SAV_ref = (Proj_SAV – SAV_ref) / SAV_ref + 1 = m (Proj_load –

Load_ref) / SAV_ref + 1 = (m (Proj_load – Load_ref) + SAV_ref ) / SAV_ref

Thus, the projected SAV of this basin for the target loading scenario can also be estimated by: 

 Proj_SAV = Rate * SAV_ref. 

We assume that the rate calculated from a basin is applicable to individual segments in this 

basin. We use this rate to calculated projected SAV in the reference hydrology year for each 

segment in this basin: 

 Proj_SAV (seg) = Rate * SAV_ref (seg). 

The projected SAV in segments is used for SAV credit in the assessment. 

Assessing Attainment of the SAV/Water Clarity Standard

Before the assessment, EPA converted the SAV restoration goal acreage (see Section 3, Table 

3.6) with a factor of 2.5 to establish the water clarity acre for each Bay segment. 

For individual months, EPA compared the monthly average Ke in a cell at 4-depth-interval areas 

(0-0.5, 0.5-1.0, 1.0-1.5, and 1.5-2.0) with the applicable water clarity criterion for the 4 

application depths (i.e., 0.5, 1.0, 1.5, and 2.0), respectively. If it meets the criterion for that 

depth, the area is accounted. Adding the area achieving the water clarity criterion for each depth 

of all cells in the segment, yield the total area achieving the water clarity criterion for the month. 

Averaging (using arithmetic mean) the monthly achieving areas in the criteria months (i.e., SAV 

growing seasons—see Section 3, Table 3.5), we get the water clarity acres for that year for each 

segment. If the water clarity acre is smaller than the SAV area, we use 2.5 of the assessed SAV 

area as the total water clarity acre from this combined SAV/water clarity assessment in the year. 
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P-9 September 24, 2010

If the water clarity acre is greater than the SAV area, we credit 1.5 of the assessed SAV area, into 

the total water clarity acre of this year for this combined SAV/water clarity assessment. 

 

Finally, the water clarity acre in single best year of the 3 consecutive assessment years (i.e., 

1993-1995 hydrology years) is regarded as the achieved water clarity acreage. If the achieved 

water clarity acre was greater than the water clarity acre goal (i.e., 2.5 times SAV acre goal), 

then the combined SAV/water clarity criteria were projected to be achieved in this segment 

under model loading scenario. Otherwise, i.e., the achieved water clarity acre is less than the 

water clarity acre goal, a percent violation is calculated: 

 

100 * (water clarity acre goal – water clarity acre) / water clarity acre goal. 

References

Cerco, C.F., M. Noel, and L. Linker, 2004. Managing for water clarity in Chesapeake Bay. ASCE 

Journal of Environmental Engineering 130:6, pp1-12.

Cerco, C.F. and M.R. Noel, 2004. The 2002 Chesapeake Bay Eutrophication Model U.S. EPA 

Chesapeake Bay Program Office 349 pages, July 2004, Annapolis, MD

http://www.chesapeakebay.net/pubs/subcommittee/mdsc/doc 

2002_Eutrophication_Model.pdf

Hagy, J.D., W.R. Boynton, C.W. Keefe, and K.V. Wood. 2004. Hypoxia in Chesapeake Bay, 

1950–2001: long-term change in relation to nutrient loading and river flow. Estuaries 27: 

634–658.

Hardaway, C. S.; Thomas, G. R.; Glover, J. B.; Smithson, J. B.; Berman, M. R.; Kenne, A. K. 

1992. Bank Erosion Study. Special Report in Applied Marine Science and Ocean 

Engineering Number 319. Virginia Institute of Marine Science 73 pages. April, 1992 

Gloucester Point, VA

Hennessee, L., K. Offerman, and J. Halka, 2006. Suspended Sediment Load Contributed by 

Shore Erosion Chesapeake Bay, Maryland. Maryland Geological Survey. Coastal and

Estuarine Geology File Report No. 06-03. June 2006. 21 pages. Baltimore, MD.

Hopkins, K. and J. Halka. 2007. Developing Shoreline Sediment Erosion Estimates for the 

Chesapeake Bay. Coastal and Estuarine Research Federation 2007 Conference.

Linker, L.C., G.W. Shenk, R.L. Dennis, and J.S. Sweeney. 2000. Cross-Media Models of the 

Chesapeake Bay Watershed and Airshed. Water Quality and Ecosystem Modeling. 1:1-4 pp 

91-122 March-December, 2000.

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 

Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries. 
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P-10 September 24, 2010

EPA 903-R-03-002. U.S. Environmental Protection Agency, Region 3, Chesapeake Bay 

Program Office, Annapolis, MD.

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Identification of 

Chesapeake Bay Designated Uses and Attainability. EPA 903-R-03-004. U.S. Environmental 

Protection Agency, Region 3, Chesapeake Bay Program Office, Annapolis, MD.

U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved 

Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries—

2007 Addendum. EPA 903-R-07-003. CBP/TRS 285-07. U.S. Environmental Protection 

Agency, Region 3, Chesapeake Bay Program Office, Annapolis, MD.

U.S. Environmental Protection Agency. 2008. Ambient Water Quality Criteria for Dissolved 

Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries–

2008 Technical Support for Criteria Assessment Protocols Addendum. EPA 903-R-08-001. 

CBP/TRS 290-08. U.S. Environmental Protection Agency, Region 3, Chesapeake Bay 

Program Office, Annapolis, MD.