Land Use AcreageBMPsFertilizerManureAtmospheric DepositionPoint SourcesSeptic Loads

RainfallSnowfallTemperatureEvapotranspirationWindSolar RadiationDewpointCloud Cover

“Average AnnualFlow-Adjusted Loads”

Quick overview of watershed model operation1. Watershed Divided into sub-watershed ‘segments’

2. Model is fed hourly values for meteorological forcing functions 3. Model is fed a

particular snapshot of management options

4. Hourly output is summed over 10 years of hydrology to compare against other management scenarios

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

LA NC AS TER

PER RY

BRO O M E

CH EST ER

CH EN ANG O

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

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

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

CO RT LAND

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

Phase 5 land segmentation isprimarily county-based

• Some counties were divided to accommodate different rainfall patterns.

Reasons why counties are a practical choice for segmentation:– Most counties are completely

within a hydrogeomorphic region

– BMP and Crop data are not known on a finer scale in most cases

– Near the limit of computing capacity

Phase 5 river segmentation

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

or

– Has a flow gage

• Near the limit of meaningful data

A software solution was devised that directs the appropriate water, nutrients, and sediment from each land use type within each land segment to each river segment

ExternalTransferModule

Each land use type simulation is completely independent.Each river simulation is dependent on the local land use type simulations and the upstream river simulations.

0%

10%

20%

30%

40%

50%

forest urban crop other

19822002

ExternalTransferModule

Land use change in the Patuxent Basin 1982-2002

Since this software is outside of HSPF, we can incorporate other features, for example, land use change or BMP change over the course of the calibration 1984-1999

Land cover data provided by the Regional Earth Sciences Application Center (RESAC) at the University of Maryland

Land Use Data Set

100%100%

80%80%

60%60%

40%40%

20%20%

0%0%

RESAC also provided pixel by pixel impervious percentages

Land Use Data Set

Map of New York impervious percentages

+

U.S.Census of Agriculture

19821987199219972002

+ Urban Forecast and Hindcast

The land use data set is a product of land cover, impervious percents, the US census of agriculture and an urban forecast and hindcast based on census data and density analysis

Land Use Data Set

Construction Land Use

• Originally tried to use satellite “BARE” classification

Average Bare Ground vs Urban Growth Percent by Lrseg

y = 0.1713x + 0.0052

R2 = 0.0115

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

-6.000% -4.000% -2.000% 0.000% 2.000% 4.000% 6.000%

Urban Growth per Year

Ave

rag

e B

are

Construction Land Use

• Tied to Change in Impervious

• Have impervious 1990 and 2000, gives change per year

• Assume that the average construction time is 1 year

• Assume that the ratio of disturbed acreage to new impervious is 10:1

Comparisons with MD and VA

• MD has 128,000 permitted disturbed acres in 1999 and 74,000 in 2004– Model has 80,000

• VA estimated 30,000 to 50,000 statewide– Model has 118,000

PERMITS INSPECTOR

MAJOR MINOR TOTALNUMBER ACTIVE

DISTURBED ACREAGE

ANNE ARUNDEL 587 95 682 1,223 7,788BALTIMORE 216 1,475 1,691 328 2,963CALVERT (partial) 0 1,287 1,287 1,253 1,387CARROLL 185 3,744 3,929 178 1,500CECIL (partial) 0 733 733 400 183DORCHESTER 18 108 126 15 94FREDERICK 212 1,607 1,819 153 1,498HARFORD 105 583 688 189 2,300HOWARD 335 137 472 482 1,213KENT 5 143 148 4 30MONTGOMERY 358 531 889 440 2,244PRINCE GEORGE'S 153 3,679 3,832 1,057 11,354WORCESTER 415 60 475 110 1800TOTAL 2,589 14,182 16,771 5,832 34,354

Comparison with MD data

Estimate correlation with MD data

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

1999 MD data

2004

or m

odel

inpu

t dat

a

1:1

2004

model

2004 Correlation with 1999: 0.66model Correlation with 1999: 0.79model correlation with 2004: 0.78

Legend

P5 Calibration Stations

# of Observations

0 - 100

101 - 300

301 - 500

501 - 1000

1001 - 5000

Progress of Iterative Routine

0

0.1

0.2

0.3

0.4

0.5

0.6

0 2 4 6 8 10 12

Iteration

Ave

rage

Eff

icie

ncy

A program was written to automatically calibrate the hydrology for all stations and land segments.

This plot shows average NS efficiency for all 284 calibration stations versus iteration

The calibration becomes stable after approximately ten iterations

Sediment Pathway in Phase 5

BMP Factor

Land Acre Factor

Delivery Factor

Edge of Field

Edge of Stream

1. Sediment processes are simulated on the land surface resulting in an Edge-Of-Field sediment load.

2. A time series of Best Management Practice (BMP) factors is applied based on available data.

3. A time series of land use acreage factors is applied.

4. A delivery factor based on local geometry is applied (see below), resulting in the Edge-Of-Stream load.

5. Processes of deposition and scour are simulated in the stream, resulting in concentrations that can be compared to observations.

In Stream Concentrations

In Stream Concentrations

Sediment Pathway in Phase 5

BMP Factor

Land Acre Factor

Delivery Factor

Edge of Field

Edge of Stream

There are two calibration points in this simulation.

1. Edge-of-Field loads are calibrated to expected values according to land use type or other data.

2. In-stream concentrations are calibrated where data are available (approximately 150 stations)

Needed EOF targets for 13 land uses

• Forest • Harvested Forest• Natural grass• Extractive• Barren• Pervious Urban• Impervious Urban

• Pasture• Poor Pasture• Hay• High till with manure• High till no manure• Low till with manure

Agriculture Other

The National Resources Inventory program of the NRCS provided the CBP with estimates of Pasture and Crop by county, based on an aggregation of point measurements applied to RUSLE.

No such data available. Other data sources or analyses necessary.

13 land uses are being used for the sediment calibration. The other land uses are identical to one of the 13 for sediment purposes.

Needed EOF targets for 13 land uses

• Pasture => Pasture• Poor Pasture => 9.5 * Pasture• Hay => 1/3 Crop (P4 NRI)• High till with manure => 1.25 * Crop• High till no manure => 1.25 * Crop• Low till with manure => 0.75 * Crop

Agriculture

9%

0.05%

7%

4%

1%

4%

Land use % of model Target relative to NRI estimate

NRI provided direct estimates for Pasture. Poor Pasture is pasture that is heavily trampled near streams. It is a small land use that exports at a high rate. NRI provided estimates for Hay for the phase 2 model. The estimates were generally 1/3 of crop for that data set, so the proportion was kept. Low till is generally 40% lower than High Till, so that ratio was applied with an average value of the NRI estimate.

Needed EOF targets for land uses not included in the NRI estimates

• Forest• Harvested Forest• Natural grass• Bare• Extractive• Pervious Urban• Impervious Urban

Other65%

0.65%

0.65%

0.52%

0.11%

7.1%

1.2%

The above land uses are discussed on the following pages

Forest:

NRI estimates exist for phase 4 for the Chesapeake Bay watershed. Use these where available.

For simulated areas outside of the Chesapeake Bay watershed, use USLE populated by GIS estimates of factors. Ratio the results to that the range is equal to the range for the Chesapeake Bay Watershed

Harvested Forest:Bare ground erosion rates of forest soils are three to four orders of magnitude greater than base forest erosion rates, but current practice in Mid-Atlantic Region does not reduce forests to bare ground generally.

Use 3.4 t/ac/yr as target rate, which is one order of magnitude greater than the average base forest rate.

Needed EOF targets for land uses not included in the NRI estimates

• Forest• Harvested Forest• Natural grass• Bare• Extractive• Pervious Urban• Impervious Urban

Other65%

0.65%

0.65%

0.58%

0.11%

7.1%

1.2%

Natural Grass:

Similar to Pasture and probably confused with it in a GIS analysis. Use the NRI pasture numbers by county

Bare:This is simulated as a construction land use with acreage based on the local annual change in urban land. Estimates of sediment export from construction sites in the literature range from 7-500 tons/ac/year. An ‘average’ value of 40 tons/ac/year is assumed.

Extractive: Active mining operations are permitted at low rates of sediment export ≈ 0.16 tons/ac/year. Abandoned mines have waste piles and non-vegetated area that act more like construction sites. With high uncertainty and but low overall load assume that extractive areas have the relatively high load of 10 tons/ac/year

• Forest• Harvested Forest• Natural grass• Bare• Extractive• Pervious Urban• Impervious Urban

Needed EOF targets for land uses not included in the NRI estimates

Other65%

0.65%

0.65%

0.52%

0.11%

7.1%

1.2%

Urban:

Post-construction urban sediment loads is primary due to channel erosion from increased concentrated flow from impervious surfaces.

National Urban Runoff Program data are several decades old limited in applicability.

Large amounts of data were collected under the phase I stormwater regulations, but studies by Penn State and University of Alabama have not been able to make predictive models from the collected data.

Use Langland and Cronin (2003) estimates of urban EOS erosion rates by land use category.

(following page)

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

Se

dim

en

t L

oa

d (

po

un

ds

)

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.

Land Sediment Simulation

KSER

Detached Sediment

Soil Matrix(unlimited)

Wash off

Rai

nfal

lD

etac

hmen

t

Att

achm

ent

KRER AFFIXNVSI

Gen

erat

ion

4 parameters, 1 target

Calibration Goal - Zero Detached Sed after Large Storms

Runoff, Washoff, and Sed Storage

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

3650 3670 3690 3710 3730 3750Days

wate

r (i

n/d

); S

ed

(t/

d);

Sed

Sto

r (t

on

s)

surodetswssd

vanSickle and Beschta (1983)

Allen Gellis (personal communication)

Reduce the parameter set by enforcing calibration rules

Surface Runoff

Detached Sediment Storage

Washoff

Sediment transport has a natural hysteresis whereby storms that happen soon after other storms tend to have lower in-stream concentrations than the antecedent storms. This effect is negligible after 30 days. It is unclear if this is a river or land surface process, but there is no mechanism in the river simulation in HSPF to enforce this.

To make this happen, AFFIX must be appropriately set, NVSI must be significant, and KRER must be small enough relative to KSER to avoid detached sediment buildup

Rule 1: Set AFFIX so that Detached Sediment storage reaches 90% of its max in 30 days

)1( *tAFFIXeAFFIX

NVSIS

SAFFIXNVSIdt

dS*

AFFIX = 0.07673

Detached Storage Vs Time

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 15 30 45 60 75 90

Day

Det

ach

ed S

tora

ge

Basic HSPF equation where S = storage

For storage = 0 at t = 0

AFFIX

NVSIS At dS/dt = 0

Solve equations so that detached storage versus time has the desired properties of reaching 90% of asymptote in 30 days.

Rule 2 – Generation makes up significant

portion of Detached Sediment

• NVSI = [significant fraction] * target load

Detached Sediment

Soil Matrix(unlimited)

Rai

nfal

lD

etac

hmen

tKRERNVSI

Gen

erat

ion

Generation and Rainfall Detachment can both generate detached sediment.

The proportion of the detached sediment made up of generation is positively related to the amount of hysteresis in the simulation.

KSER

Detached Sediment(reservoir available for washoff)

Soil Matrix(unlimited)

Wash off

Rai

nfal

lD

etac

hmen

t

Att

achm

ent

KRERAFFIXNVSI

Gen

erat

ion

Rule 3 – KRER is a percentage of KSER

Excessive KRER relative to KSER will lead to a buildup of detached sediment, so that KRER is a percentage of KSER

Strategy: Reduce the Parameter Set

1. Fix AFFIX

2. Assume ratio of NVSI : EOF target

3. Assume ratio of KSER : KRER

4. Adjust KSER to meet target

KSER

Detached Sediment

Soil Matrix(unlimited)

Wash off

Rai

nfal

lD

etac

hmen

t

Att

achm

ent

KRER AFFIXNVSI

Gen

erat

ion

1 parameter, 1 targetMake several runs with different ratios (#2, 3)

Correlation of sediment concentrations for each of 8 scenarios

Different scenarios make little difference on the correlation of simulated and observed concentrations

Greater effect of plowingMore hysteresis

Average detached storage for each of 8 scenarios

Average detached storage decrease with Decrease of NVSI ratio and Increase of KSER/KRER ratio

Greater effect of plowingMore hysteresis

Land-to-Water Delivery Factors

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field

Edge of Stream

In Stream Concentrations

Land uses are not evenly distributed around a segment and some segments are larger than others.

A method to assign a differential delivery factor for each land cover class and segment is shown on the following slide.

Sediment delivery factors by land use and segmentSeveral publications were found that relate sediment delivery to watershed size. The most appropriate one was judged to be the SCS method:

DF = 0.417762 * Area -0.134958 - 0.127097

The delivery factor needed for the phase 5 model is from land cover pixels to the stream, not to the watershed outlet, so it is not appropriate to use the size of the watershed.

To generate a deliver factor for each land use, the average distance of that land use to the stream was used as radius of a circle. The area of that circle was used to calculate the delivery factor.

To illustrate this approach, suppose a land use type that is scattered throughout a segment were gathered into a single area, but the distances to the stream were preserved. The average delivery of that cluster would be roughly equal to the average delivery from a circle with a center that was co-located with the center of mass of the land use.

River Calibration

Legend

P5 Calibration Stations

# of Observations

0 - 100

101 - 300

301 - 500

501 - 1000

1001 - 5000

critz

w

eMass

1

1

River Cohesive Sediment Simulation

SuspendedSediment

Bed Storage(unlimited)

Outflow

Sco

ur

Dep

osit

ion

Inflow

1*

crit

AreaM

critz

w

eMass

1

1

River Sediment Simulation

Bed Storage(unlimited)

Dep

osit

ion

River Sediment Simulation

Bed Storage(unlimited) S

cour

1*

crit

AreaM

River Sand Simulation

Jvelocityksand }{*][

Adjustments are then made to the bed depth

Calibration Rules

• All rivers segments upstream of a calibration point have identical parameters

• For nested rivers, this applies only to rivers downstream of any upstream gages.

Remington

Robinson

Rappahannock

1

Uncalibrated

Stau 97 percentileCtau 93SW .001 inches/secCW .0001 CM = 1 lb/ft^2/daySM = 1KS = .003 empiricalES = 4

High flow in the ballpark, but low flow is a problem. Lowest concentrations are mostly VSS, which is not yet simulated and are at LOD in the observed

Stau 97Ctau 93SW .001CW .0001CM = 1SM = 1KS = 3ES = 3

To deal with the lack of VSS in the simulation, use SAND to make the low concentrations work out. This can be easily reversed when VSS are simulated

Stau 99Ctau 96SW .001CW .0001CM = 1SM = 1KS = 3ES = 3

Reduce the frequency of scour to deal with over-simulation

Stau 99Ctau 96SW .001CW .0001CM = .5SM = .5KS = 3ES = 3

Reduce the effect of scour to deal with over-simulation

Stau 99.5Ctau 98SW .001CW .0001CM = .5SM = .5KS = 2.8ES = 3

Tighten up the simulation

Issues with simulation

• Low values dominate the CFD, but they are not meaningful from:– A load standpoint– An accuracy of observation standpoint– Simulation standpoint (no VSS)

• Flow not perfectly calibrated– If peak is missed by a day, then the

concentration simulation should not match the observed.

‘Windowed’ comparison

• If simulated or observed value is below 10 mg/l set it to 10 mg/l.

• Check simulation for 24 hours before and after observation and set simulated value to point closest to observation.

Of the highest observed and simulated peaks at all calibration stations, almost as many peaks occurred one day apart, but few occurred on two days apart

One day apart – 83% of the same day figure

Two days apart – 19% of the same day figure

I

Reported Error

Stau 97Ctau 93SW .001CW .0001CM = 1SM = 1KS = .003ES = 4

Starting point

Stau 97Ctau 93SW .001CW .0001CM = 1SM = 1KS = 3ES = 3

Sand Calibration

Stau 99Ctau 96SW .001CW .0001CM = 1SM = 1KS = 3ES = 3

Reduce Frequency of Scour

Stau 99Ctau 96SW .001CW .0001CM = .5SM = .5KS = 3ES = 3

Reduce effect of scour

Stau 99.5Ctau 98SW .001CW .0001CM = .5SM = .5KS = 2.8ES = 3

Tighten up calibration

Other Considerations: Load 1:1 plot, error vs Shear stress, Load Frequency

Choptank RiverLog Sediment Load (log kg/mo)

y = 0.7828x + 1.0642

R2 = 0.7312

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

3 3.5 4 4.5 5 5.5 6 6.5 7

Phase 5 Simulation log kg/mo

ES

TIM

AT

OR

log

kg

/mo

Comparison with Estimator Model