The Chesapeake Bay Program Partnership’s Watershed Model

Gary ShenkPresentation to COG

10/4/2012

2

Chesapeake Bay Partnership Models

Bay WQSTM

NEIEN

CBP Modeling Tools

CASTInteractionTools

DecisionModels/Databases

RelatedTools sparrow

4

Annual, monthly, or daily values of anthropogenic factors:

Land Use AcreageBMPsFertilizerManureTillageCrop typesAtmospheric depositionWaste water treatmentSeptic loads

Hourly or daily values of Meteorologicalfactors:

PrecipitationTemperatureEvapotranspirationWindSolar RadiationDew pointCloud Cover

Daily flow, nitrogen, phosphorus, and sediment comparedto observationsover 21 years

How the Watershed Model Works

HSPF

Calibration Mode

55

Each segment consists of 30 separately-modeled land uses:

• Regulated Pervious Urban• Regulated Impervious Urban• Unregulated Pervious Urban• Unregulated Impervious Urban• Construction• Extractive • Combined Sewer System• Wooded / Open• Disturbed Forest

• Corn/Soy/Wheat rotation (high till)

• Corn/Soy/Wheat rotation (low till)

• Other Row Crops• Alfalfa• Nursery• Pasture• Degraded Riparian Pasture• Afo / Cafo• Fertilized Hay • Unfertilized Hay

– Nutrient management versions of the above

Plus: Point Source andSeptic Loads, and

Atmospheric Deposition Loads Each calibrated to nutrient and

Sediment targets

How the Watershed Model Works

6

Precipitation FertilizerManureAtmospheric deposition

Runoff

How the Watershed Model Works

Hydrologysubmodel

Management filter

RiverSedimentsubmodel Phosphorus

submodel

Nitrogensubmodel

}hourly

Bufferswetlands

7

ELK

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HALIF AX

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CAY UG A

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BRA DF OR D

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ON O ND AG A

GA RR ETT

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HU NTIN G DO N

LY CO M IN G

BED FO RD

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FRA NK LIN

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FLOY DSM YTH

YATE S

HEN R Y

STO KE S

AUG U STA

JE FFER SO N

BATH

HAR D Y

FULTO N

HAM P SH IRE

BLAN D

ALBE MA RLE

SUS Q UEH AN N A

ADA M S

MIF FLIN

HAR FO RD

MO N RO E

WO RC ES TER

LIV ING S TON

CAR O LINE

SCH O HA RIE

CR AIG

LO U DO U NTUC KE R

NO RT HAM P TO N

WAR R EN

AM ELIA

FAIR FAX

PER SO N

HAN O VER

CAR R OLL

GR AN VILLE

CAM P BELL

JU NIA TA

TOM P KIN S

CO LUM B IA

DIN WID DIE

SULLIV AN

SUF FO LK

OR AN G E

MC D OW ELL

BRU N SWIC K

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CAR BO N

BUC H ANA N

ASH E

NELS O N

CAS WE LL

ME CK LEN BU RGPATR IC K

FOR SY TH

BUC KIN G HA M

ESS EX

RO CK ING H AM

RU SSE LL

DAU PH IN

TAZE WELL

GR AN T

SNY DE R

CH EM UN G

ALAM AN C E

CH AR LOTT E

PULA SKI

BO TETO U RT

VAN CE

CAM E RO N

SO UTH AM P TON

RO CK BR IDG EALLEG HA NY

WAS HIN G TO N

LE BA NO N

AM HE RS T

NEW C ASTLE

ANN E A RU ND EL

CALV ER T

WIC OM IC O

FRE DE RIC K

CU LPEP ER

QU EE N AN N ES

LU N EN BUR G

MO N TG OM E RY

PAG E

LA CK AW AN NA

SCH U YLER

JO H NS ON

WAT AU GA

BER KE LE Y

VIRG IN IA BE AC H

CU M BER LAN D

WYO M IN G

PEN DLE TO N

CH EST ERF IELD

DIC KEN SO N

UN ION

HO WA RD

PRIN C E G EO RG ES

FLUV ANN A

NO TTO WA Y

SPO TS YLVA NIA

HEN R ICO

GR AY SO N

STAF FOR D

CH ESA PEA KE

MO R G AN

HIG HLA ND

SHE NA ND O AH

MA TH EWS

NO RT HU M BER LAN D

GILE S

APP OM A TTO X

ISLE O F W IGH T

GO O CH LAN D

PO WH ATA N

CLAR KE

PRIN C E WILLIA M

GR EE NSV ILLE

MIN ER AL

NEW KE NTGLO U CES TER

KING W ILLIAM

PRIN C E ED WA RD

RIC HM O ND

KING A ND QU EE N

MID D LESE X

RO AN O KE

PRIN C E G EO RG E

JA M ES C ITY

WES TM O RE LAND

CH AR LES C ITY

KING G EO R GE

HAM P TO N

MO N TO UR

NO RF OLK

RAP PA HAN N OC K

GR EE NE

NEW PO R T NE WSPO QU O SO N

BO TETO U RT

DAN VILLE

LY NC HB UR G

DIST OF CO LUM B IA

PO RTS M OU THBRIS TO L

HAR R ISO NB UR G

RAD FO RD

WAY NE SBO R O

HO PE WELL

MA NA SSA S

NO RT ON

EM PO RIA

FRE DE RIC KSB UR G

WILLIAM S BU RG

BUE NA VIST A

SO UTH BO ST ON

ARLIN G TO N

SALE M

STAU N TON

PETE RS BU RG

GA LAX

ALEX AN DR IA

MA RT INSV ILLE

WIN CH EST ER

CH AR LOTT ESV ILLE

FAIR FAX CITY

CO LO NIAL H EIG H TS

CO VIN G TONLE XIN G TON

CLIFTO N FO RG E

FALLS C HU R CH

MA NA SSA S P ARK

ELK

TIOG A

ON EID A

YO RK

KEN T

STEU BE N

SUS SE X

HER KIM E R

PO TTER

DELA WA RE

BER KS

OTS EG O

MC KE AN

ACC O M AC K

IND IAN A

WAY NE

HALIF AX

ALLEG AN Y

SO M ERS ET

LE E

CLEA RFIE LD

CAY UG A

BLAIR

LU ZE RN E

BRA DF OR D

CEN TR E

TIOG A

LA NC AS TER

PER RY

BRO O M E

CH EST ER

CH EN ANG O

KEN T

SUR R Y

CAM B RIA

ST M AR YS

CLIN TO N

ON TAR IO MA DIS ON

CEC IL

DO RC H ESTE R

LO U ISA

PITTS YLVA NIA

ON O ND AG A

GA RR ETT

CH AR LES

WISE

SCO TT

PRE STO N

HU NTIN G DO N

LY CO M IN G

BED FO RD

SCH U YLKILL

GU ILFO RD

FRA NK LIN

TALBO T

SUS SE X

WYT HE

BALTIM O R E

FAU QU IER

FLOY DSM YTH

YATE S

HEN R Y

STO KE S

AUG U STA

JE FFER SO N

BATH

HAR D Y

FULTO N

SO M ERS ET

HAM P SH IRE

BLAN D

ALBE MA RLE

SUS Q UEH AN N A

ADA M S

MIF FLIN

HAR FO RD

MO N RO E

WO RC ES TER

LIV ING S TON

CAR O LINE

SCH O HA RIE

CR AIG

LO U DO U N

LY CO M IN G

TUC KE R

NO RT HAM P TO N

CEN TR E

WAR R EN

AM ELIA

FAIR FAX

FRA NK LIN

PER SO N

HAN O VER

CAR R OLL

GR AN VILLE

CAM P BELL

CAR R OLL

JU NIA TA

TOM P KIN S

CO LUM B IA

DIN WID DIE

SULLIV AN

SUF FO LK

OR AN G E

MC D OW ELL

BRU N SWIC K

BED FO RD

CO RT LAND

BATH

CAR BO N

BUC H ANA N

ASH E

SULLIV AN

NELS O N

SUR R Y

CAS WE LL

ME CK LEN BU RGPATR IC K

FOR SY TH

BUC KIN G HA M

ESS EX

RO CK ING H AM

RU SSE LL

DAU PH IN

TAZE WELL

GR AN T

SNY DE R

CH EM UN G

ALAM AN C E

OR AN G E

CH AR LOTT E

PULA SKI

BO TETO U RT

VAN CE

CAM E RO N

SO UTH AM P TON

RO CK BR IDG E

YO RK

ALLEG HA NY

WAS HIN G TO N

LE BA NO N

AM HE RS T

NEW C ASTLE

ANN E A RU ND EL

CALV ER T

WIC OM IC O

FRE DE RIC K

AUG U STA

FRE DE RIC K

RO CK ING H AMCU LPEP ER

NO RT HAM P TO N

QU EE N AN N ES

LU N EN BUR G

MO N TG OM E RY

PAG E

ASH E

WAS HIN G TO N

LA CK AW AN NA

SCH U YLER

CAR O LINE

JO H NS ON

WAT AU GA

BER KE LE Y

VIRG IN IA BE AC H

CU M BER LAN D

FRA NK LIN

BED FO RD

CLIN TO N

BED FO RD

WYO M IN G

PEN DLE TO N

CH EST ERF IELD

HAR D Y

GR AN T

DIC KEN SO N

PEN DLE TO N

UN ION

HO WA RD

PRIN C E G EO RG ES

FLUV ANN A

NO TTO WA Y

SPO TS YLVA NIA

MO N TG OM E RY

HEN R ICO

GR AY SO N

STAF FOR D

CH ESA PEA KE

MO R G AN

HIG HLA ND

SHE NA ND O AH

MA TH EWS

NO RT HU M BER LAN D

GILE S

APP OM A TTO X

ISLE O F W IGH T

ALLEG AN Y

MA DIS ON

PAG E

GO O CH LAN D

RO CK ING H AM

PO WH ATA N

CLAR KE

LE E

PRIN C E WILLIA M

GR EE NSV ILLE

FRE DE RIC K

CU M BER LAN D

GILE S

DAU PH IN

MIN ER AL

NEW KE NT

UN ION

GLO U CES TER

KING W ILLIAM

PRIN C E ED WA RD

ADA M S

RIC HM O ND

LA NC AS TERKING A ND QU EE N

GR AY SO N

MID D LESE X

JE FFER SO N

RO AN O KE

ALLEG AN Y

PRIN C E G EO RG E

BRA DF OR D

GILE S

MIN ER AL

HIG HLA ND

JA M ES C ITY

ALLEG HA NY

WES TM O RE LAND

BED FO RD

NO RT HU M BER LAN D

CH AR LES C ITY

WAR R EN

NELS O N

KING G EO R GE

HAM P TO N

MO N TO UR

SCO TT

PATR IC K

MA DIS ON

SHE NA ND O AH

RU SSE LL

AM HE RS T

WYO M IN G

NO RF OLK

AUG U STA

RAP PA HAN N OC K

GR EE NE

WAR R EN

LU ZE RN E

CU M BER LAN D

FRA NK LIN

TAZE WELL

SM YTH

GR EE NE

BALTIM O R E

SCO TT

ALBE MA RLE

RO AN O KE

NEW PO R T NE WSPO QU O SO N

RIC HM O ND

RAP PA HAN N OC K

BO TETO U RT

ALLEG HA NY

DAN VILLE

FAU QU IER

RO AN O KE

LY NC HB UR G

WYT HE

CAR R OLL

DIST OF CO LUM B IA

PO RTS M OU THWAS HIN G TO N

PETE RS BU RG

BRIS TO L

RAD FO RD

WAY NE SBO R O

HO PE WELL

MA NA SSA S

EM PO RIA

BED FO RD

FRE DE RIC KSB UR G

WILLIAM S BU RG

SO UTH BO ST ON

CH EST ER

RO CK BR IDG E

RO CK ING H AM

ARLIN G TO N

SALE M

STAU N TON

GA LAX

ALEX AN DR IA

HAR R ISO NB UR G

NO RT ON

FRA NK LIN

MA RT INSV ILLE

WIN CH EST ER

CH AR LOTT ESV ILLE

BUE NA VIST A

FAIR FAX CITY

CO LO NIAL H EIG H TS

CO VIN G TONLE XIN G TON

CLIFTO N FO RG E

FALLS C HU R CH

MA NA SSA S P ARK

Two Separate Segmentation Schemes• A land use within a land

segment has the same inputs – atmospheric deposition– fertilizer– manure– precipitation

• Land segmentation driven by availability of land use data

• Land segments determined by– County lines– Rainfall Variances– Federal / Non-Federal

8

Phase 5 river segmentation

• A river segment gathers inputs from the watershed and has one simulated river

• Consistent criteria over entire model domain– Greater than 100 cfs

or– Has a flow gage

9

How do we calibrate?

River Reach

Reasonable values of sediment, nitrogen, and phosphorus

Observations of flow, sediment, nitrogen, and phosphorus

10

Average Targets• Land Use TN TP• Forest 2.0 0.15• Harvested Forest 20.0 0.80• Crop 23.0 2-2.5• Hay 6.0 0.4-0.8• Pasture 4.5 0.7• Urban 9.3 1.5• Extractive 12.5 3.5• Nursery 240 85

• Vary spatially according to input/output

11

Figure 2 Median TP concentration in NPDES Phase 1 storm water data Using data from Pitt, undated. Error bars are one standard deviation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Land Use Category (# of observations)

Med

ian

TN

(m

g/l)

TP

12

Figure 1 Median TN concentration in NPDES Phase 1 storm water data using data from Pitt, undated. Error bars are one standard deviation

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Land Use Category (# of observations)

Med

ian

TN

(m

g/l)

1313

Developed Land Uses

• Regulated vs Unregulated normally corresponds to MS4 and non-MS4. Loading rates are identical so these categories are a convenience for the state partners.

• Combined Sewer land uses have zero loads. The loads from WWTPs and CSOs in combined sewer areas are in the model, so including these would be double counting

• Also broken out as Federal / Non-Federal

• Determined directly from the CBP Land Data Team analysis at roughly 10 year increments

Regulated Unregulated Combined SewerPerviousImperviousConstructionExtractive

14

1515

16

Constant values of anthropogenic factors:

Land Use AcreageBMPsFertilizerManureTillageCrop typesAtmospheric depositionWaste water treatmentSeptic loads

Hourly or daily values of Meteorologicalfactors:

PrecipitationTemperatureEvapotranspirationWindSolar RadiationDew pointCloud Cover

Run for 1984-2000Average 1991-2000For ‘flow-normalized average annual loads’

How the Watershed Model Works

HSPF

Scenario Mode

1717

20.7 18.9 18.8 18.2 17.8 17.1 16.8 16.8 16.5 13.6

120.0 114.7 109.8 109.2 108.4 106.6 105.7 104.4 103.9 102.8

71.4 71.9

8.25.0

3.5 3.6 4.1 3.5 2.9 2.9 2.9 3.5

2.4 2.4

81.1

59.158.1 56.7 57.7 56.9 56.2 53.7 53.2 54.8

37.1 37.3

7.5

7.77.3 7.1 6.8 6.6 6.6 6.7 6.7 6.6

4.8 4.7

90.5

79.078.4 77.8 75.4 74.4 73.1 73.9 73.8 71.9

52.1 51.4

5.9

5.55.1 5.0 4.9

4.9 4.8 4.6 4.6 4.5

3.0 2.9

175

17 130

50

100

150

200

250

300

350

400

1985 2000 2001 2002 2003 2004 2005 2006 2007 2008 Strategy StateCap

Goal

million lbs.

/year

NY PA DC MD WV VA DE

Nitrogen Loads Delivered to the Chesapeake Bay By Jurisdiction Point source loads reflect measured discharges while

nonpoint source loads are based on an average-hydrology year

333.9

289.9 281.1270.2

175

266.3277.7 275.1

262.9 261.9260.7

184.4 183.1

Phase 4.3 Data

18

Scenario Builder

19

Chesapeake Bay Program Partnership

20

Chesapeake Bay Program Partnership

Watershed Technical WorkgroupAgriculture WorkgroupUrban Stormwater WorkgroupForestry WorkgroupSediment WorkgroupAd-Hoc Panels

Modeling Workgroup

21

Agricultural Workgroup• Federal

– USDA, EPA

• State– Chesapeake Bay Commission, Delaware Department of Agriculture, Maryland Department of Agriculture, NY DEC, PA

Department of Environmental Protection, Pennsylvania Department of Environmental Protection, Pennsylvania State Conservation Commission, VA DCR, VA DEQ, West Virginia Department of Agriculture, WV DEP

• University– Chesapeake Research Consortium, Cornell University, Penn State University, University of Delaware, University of

Maryland, West Virginia University

• Industry Groups– Delaware Maryland Agribusiness Association, Delaware Pork Producers Association, Delmarva Poultry Industry, Inc.,

MD Farm Bureau, VA Farm Bureau, VA Grain Producers Producers Association, Virginia Agribusiness Council, Virginia Poultry Association, U.S. Poultry & Egg Association,

• Local organizations– Cortland County Soil and Water Conservation District, Lancaster County Conservation District, Madison Co. SWCD,

Upper Susquehanna Coalition

• NGOs– American Farmland Trust, Environmental Defense Fund, Keith Campbell Foundation for the Environment, MidAtlantic

Farm Credit, PA NoTill Alliance

22

One Ad-Hoc Subgroup of the Agricultural Workgroup

Mid-Atlantic Water Program, U.S. Department of Agriculture-Natural Resources Conservation Service, Virginia Department of Conservation and Recreation, Virginia Department of Forestry, Pennsylvania State Conservation Commission, Pennsylvania

Department of Conservation and Natural Resources, Pennsylvania Department of Environmental Protection, Maryland Department of Agriculture, Maryland Department of

Natural Resources, Maryland Department of the Environment, University of Maryland Cooperative Extension, University of Maryland-College Park, Delaware Department of Agriculture, Delaware Department of Natural Resources and Environmental Control,

Delaware Maryland Agribusiness Association, West Virginia Department of Agriculture, West Virginia Department of Environmental Protection, Cacapon Institute - West Virginia,

New York Department of Environmental Conservation, Upper Susquehanna Coalition, American Farmland Trust, Chesapeake Bay Commission, U.S. Forest Service, U.S. Fish and

Wildlife Service, U.S. Geological Survey, U.S. Environmental Protection Agency, Keith Campbell Foundation for the Environment, Pinchot Institute, Piedmont Environmental

Council

2323

Atmospheric Deposition Estimates

Combining a regression model of wetfall deposition...

…with CMAQ estimates of dry deposition for the base…

…and using the power of the CMAQ model for scenarios.

Land Change Modeling at the CBP

• 1980s – 1990s – simple empirical relationships• CBLCM

– v1 – Sleuth– V2 –empirical relationships– V3 – Patch-based growth

• Existing Lu/Lc• Topographic/Geologic data• Population Projections

Probabilitysurface

25

Forecasted Urban Growth (2000 to 2030)

26

Forecasted Population Growth on Sewer vs. Septic (2000 to 2030)

27

Farmland and Forest Land Loss (2000 to 2030)

28

Estuarine Model

• 57,000 cells• sub-hour hydrodynamics• oysters• menhaden

29

Chesapeake Bay Partnership Models

Use in the TMDL

30

0

5

10

15

20

25

30

35

40

45

1985 Base 2009 Target Tributary Loading Loading Loading E3 All

Scenario Calibration Scenario Load A Strategy Scenario Scenario Scenario Scenario Forest

342TN 309TN 248TN 200TN 191TN 190TN 179TN 170TN 141TN 58TN

24.1TP 19.5TP 16.6TP 15.0TP 14.4TP 12.7TP 12.0TP 11.3TP 8.5TP 4.4TP

Nu

mb

er o

f S

egm

ents

in D

O V

iola

tio

n

Open Water Violations

Deep Water Violations

Deep Channel Violations

Use of modeling suite in the Chesapeake TMDL

Basin-wide load is190 N and 12.7 P (MPY)

31

Nutrient Impacts on Bay WQ

32

Relative effectiveness (Riverine * Estuarine Delivery)

0

1

2

3

4

5

6

7

8

UpE

S, M

DU

pES,

DE

Mid

ES, M

DSu

sq, M

DLo

wES

, MD

Wsh

, MD

UpE

S, P

ALo

wES

, DE

Susq

, PA

PxtB

, MD

EshV

A, V

APo

tB, D

CM

idES

, DE

PotA

, DC

PotB

, MD

PotB

, VA

Susq

, NY

RapB

, VA

PotA

, MD

YrkB

, VA

PotA

, VA

Wsh

, PA

PotA

, WV

PotA

, PA

PxtA

, MD

JmsB

, VA

RapA

, VA

YrkA

, VA

JmsA

, VA

JmsA

, WV

Major River Basin by Jurisdiction Relative Impact on Bay Water Quality

33

TN, p5.3, goal=190, WWTP = 4.5-8 mg/l, other: max=min+20%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10

Relative Effectiveness

Per

cent

red

ucti

on f

rom

201

0 no

BM

Ps

to

E3

All Other

WWTP

4.5 mg/l

8 mg/l

20 percent slope

Allocation Method Agreed to by Majority of Principals’ Staff

Committee Members

Wastewater Loads

All other sources

34

Phosphorus -- phase 5.3 -- Goal=12.67 million lbs

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10

Relative Effectiveness

Pe

rcen

t re

du

ctio

n f

rom

201

0 n

oB

MP

s to

E3

All Other

WWTP

Pollution Diet

by River

Pollution Diet by State

36

Chesapeake Bay WWTP +CSO Loading Trends and WIP Loads

1985 2009 2011 20250.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

89.18

52.18

44.42

38.28

Wastewater TN Delivered Loads (mil lbs/yr)

TN D

eliv

ered

Loa

ds (m

il lb

s/yr

)

1985 2009 2011 20250.00

2.00

4.00

6.00

8.00

10.00

12.00

9.98

3.97

3.15 2.99

Wastewater TP Delivered Loads (mil lbs/yr)

Tp D

eliv

ered

Loa

ds (m

il lb

s/yr

)

Wastewater + CSO TN Load Contributions Among All Sources

1985 2009 2011 20250.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%28.08%

20.05%

17.96%19.71%

Wastewater TN Load Contributions

Perc

enta

ge Agriculture44%

Urban Runoff16%

Wastewater + CSO18%

Septic3%

Other19%

2011 TN Delivered Loads by Sources

Chesapeake Bay Septic System Nutrient Loading Trends and WIP Loads

1985 2009 2011 20250.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

5.16

8.42 8.33

6.19

Septic TN Delivered Loads (mil lbs/yr)

TN D

eliv

ered

Loa

ds (m

il lb

s/yr

)

1985 2009 2011 20250.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.00 0.00 0.00

Septic TP Delivered Loads (mil lbs/yr)

Tp D

eliv

ered

Loa

ds (m

il lb

s/yr

)

Septic System TN Load Contributions Among All Sources

Agriculture44%

Urban Runoff16%

Wastewater + CSO18%

Septic3%

Other19%

2011 TN Delivered Loads by Sources

1985 2009 2011 20250.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

1.62%

3.23%3.37%

3.19%

Septic TN Load Contributions Among All Sources

Perc

enta

ge

Septic N Load (lbs/yr) at the edge of drain field = Pop *8.91586 (lbs/person, yr) *BMP Efficiency (%)

Septic N Load (lbs/yr) at the edge of stream= Pop *8.91586 *BMP Efficiency (%) * Pass-through rate (%)

Phosphorus is assumed to be 100% attenuated by soil.

Septic BMP load reductions Connection 100%Denitrification 50%

Pumping5%

Septic Nitrogen Load Calculation

Septic Nitrogen Pass-Through Rate

State Pass-Through Rate 2011_# Systems

DE 40% 21,735 DC 40% -

MD 30% 241,893

MD 50% 159,783

MD 80% 48,630

NY 40% 96,810

PA 40% 526,721

VA 40% 535,351

WV 40% 62,695

45

Urban % Impervious vs Sediment Load

y = 6.0178x + 98.386

R2 = 0.8965

0

100

200

300

400

500

600

700

800

0 20 40 60 80 100

Urban % Impervious

Sedi

men

t Loa

d (p

ound

s)

Sediment load for several urban land use types were compiled for sites in the mid-Atlantic and Illinois. Langland and Cronin (2003)

When plotted against ‘typical’ impervious percents for those urban land use types, the relationship is striking.

Urban Sediment Targets

By setting pervious urban at the intercept and impervious urban at the maximum, the land use division within each particular segment determines the overall load according to the above relationship.