the chesapeake bay program partnership’s watershed model
DESCRIPTION
The Chesapeake Bay Program Partnership’s Watershed Model. Gary Shenk Presentation to COG 10/4/2012. Chesapeake Bay Partnership Models. CBP Modeling Tools. CAST. Interaction Tools. Decision Models/ Databases. NEIEN. Bay WQSTM. Related Tools. sparrow. How the Watershed Model Works. - PowerPoint PPT PresentationTRANSCRIPT
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
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
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.