land use acreage bmps fertilizer manure atmospheric deposition point sources septic loads
DESCRIPTION
Rainfall Snowfall Temperature Evapotranspiration Wind Solar Radiation Dewpoint Cloud Cover. Land Use Acreage BMPs Fertilizer Manure Atmospheric Deposition Point Sources Septic Loads. Quick overview of watershed model operation. 1. Watershed Divided into sub-watershed ‘segments’. - PowerPoint PPT PresentationTRANSCRIPT
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