hydrology and sediment calibration status

Hydrology and Sediment Calibration Status

Gary ShenkJing Wu

Lewis LinkerRoss MandelDoug MoyerJeff Keaton

Hui LiuJeff Raffensperger

Modeling Subcommittee4/4/05

Hydrology Calibration

• Method has been stable for months

• Received final land use

• Final check of parameters

• New elevation data

• Received upgraded rainfall

• Ran iteration routine

Efficiency of Feb 2005

0

5

10

15

20

25

30

35

40

45

50

-103% -83% -63% -43% -23% -3% 18% 38% 58% 78% 98%

Efficiency of April 2005

0

5

10

15

20

25

30

35

40

45

50

-103% -83% -63% -43% -23% -3% 18% 38% 58% 78% 98%

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

rag

e E

ffic

ien

cy Feb Calibration

New Rain and Land Use

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 Feb Calibration

New Rain and Land Use

New Land Use, Old Rain

Frequency of Surface Runoff in Inches/yr

0%

2%

4%

6%

8%

10%

12%

14%

0 2 4 6 8 10 12 14

Inches

April

Feb

April Feblow 14% 17%good 70% 70%high 16% 13%

Sediment Pathway in Phase 5

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field

Edge of Stream

In Stream Concentrations

Sediment Pathway in Phase 5

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field Targets

Edge of Stream

In Stream Concentrations

Needed EOF targets for 13 land uses

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

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

Agriculture Other

Needed EOF targets for 13 land uses

NRI gave estimates of

Pasture and Cropland for each county

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

Agriculture

Needed EOF targets for 13 land uses

• Pasture => Pasture• Bad 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%

Needed EOF targets for 13 land uses

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

Other65%

0.65%

0.65%

0.52%

0.11%

7.1%

1.2%

FOREST

• NRI estimates exist for phase 4 for the CB watershed. Use these where available

• Southern Virginia– Calculate forest rate using USLE– Ratio the results so that the range is equal to the

rest of the CB watershed

HARVESTED FOREST

• Bare ground erosion rates of forest soils 3 to 4 orders of magnitude greater than base forest erosion rates

• Current practice in Mid-Atlantic Region does not reduce forests to bare ground except for roads and harvest machinery

• Use 3.4 t/ac/yr as target rate—one order of magnitude greater than base forest rate

NATURAL GRASS(fallow land, horse pasture)

RECOMMENDATION:

Use Basin-wide average Pasture erosion rate for 1982-1997, inclusive

= 1.5 t/ac/yr

BARE = CONSTRUCTION

• 38 – 78 t/ac/yr (Guy and Ferguson, 1962)

• 7.2 – 500 t/ac/yr ( U. S. EPA, 2002)

• 35 – 45 t/ac/yr (MWCOG, 1987)

• Choose 40 t/ac/yr

EXTRACTIVE(mines, sand and gravel pits)

• Active Operations: Permit limits of 70 mg/l TSS ≈ 0.16 t/ac/yr

• Abandoned Mines: Waste piles and non-vegetated area more like construction areas

• Assume average of one-fourth construction site erosion rate: 10 t/ac/yr

URBAN

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

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

Needed EOF targets for 13 land uses

• Forest => Use P4 NRI• Harvested Forest => Mean Forest * 10 = 3.4 t/ac/yr• Natural grass => Mean Pasture = 1.5 t/ac/yr• Bare => 40 t/ac/yr• Extractive => 10 t/ac/yr• Pervious Urban => 0.74 t/ac/yr• Impervious Urban => 5.18 t/ac/yr

Other

ATMOSPHERIC DEPOSITION ON WATER

RECOMMENDATION:Use Constant Daily Rate of 10 lb/ac/day

(Langland and Cronin, 2003)

Sediment Pathway in Phase 5

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field Method

Edge of Stream

In Stream Concentrations

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

Rule 1 - 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

wa

ter

(in

/d);

Se

d (

t/d

); S

ed

Sto

r (t

on

s)

surodetswssd

vanSickle and Beschta (1983)Allen Gellis (pers communication)

Decision Rules for Four Key land Parameters:

Rule2 – 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

Rule3 – 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

Strategy: Reduce the Parameter Set

1. Assume ratio of NVSI : EOF target

2. Assume ratio of KSER : KRER

3. 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

Two strategies to optimize ratios:

• Does the model correctly capture the dynamic of land process?

• Do the different land calibrations make a difference in the correlation between simulated and observed?

- Dynamic of land process Land segment A42033 - pasture, KSER/KRER = 5

Detached Storage goes down to zero sometimes

0

0.5

1

1.5

2

2.5

3

0 1000 2000 3000 4000 5000 6000 7000

suro

dets

wssd

0

0.4

0.8

1.2

1.6

2

0 100 200 300 400 500 600

suro

dets

wssd

- Dynamic of land process Land segment A42033 - pasture, KSER/KRER = 5

Detached Storage remains high, never goes down to zero

0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000

suro

dets

wssd

0

0.4

0.8

1.2

1.6

2

0 100 200 300 400 500 600

suro

dets

wssd

Correlation vs NVSI:Target [ Fake ]

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1 1.5 2 2.5 3

NVSI : Target

Ave

rag

e C

orr

elat

ion

Bet

wee

n

Sim

ula

ted

an

d O

bse

rved

Correlation vs KS:KR [ Fake ]

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25

KS : KR ratio

Ave

rag

e C

orr

elat

ion

Bet

wee

n

Sim

ula

ted

an

d O

bse

rved

Status and expected steps

• Computer programs ready

• EOF calibration can be done in two weeks once final hydrology parameters are available.

Land-River Connection

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field

Edge of Stream

In Stream Concentrations

Proposed method:Relate delivery to average distance from land to river by segment

Single factor for each land use in each segment

• Related to watershed size

• Unique for each Land use

• Assume mean distance is

the radius of a circle

A = π*(mean distance)^2

DF = 0.417762 * A -0.134958 - 0.127097

Land-River Connection

BMP Factor

Land Acre FactorDelivery Factor

Edge of Field

Edge of Stream

In Stream Concentrations

critz

w

eMass

1

1

River Cohesive Sediment Simulation

SuspendedSediment

Bed Storage(unlimited)

Outflow

Sco

ur

Dep

osit

ion

Inflow

1*

crit

AreaM

Goals of River Calibration

• Find sensitive parameters

• Find numeric calibration measures

• Make system of rules for their interaction

Data

• Calibrating now with 50 stations

• Have data coming in for approx 200 stations

Default Values

• Adjust M values only to 0.4

Reduce Erodibility

• Scour Tau and deposition tau set to 99th percentile

• M = 0.4

• Reduce frequency of Scour

• Adjust deposition and scour to 95th percentile.

• M = 0.4

• Depositional 95% of time and erosional 5% of time.

• High frequency / Low Mass Scour

• M set to default

• Tau scour set to 99th percentile

• Low Frequency / High Mass Scour

Strategy for completion

• Extra staff

• Investigate various strategies– KS test– Shear Stress vs Error– Optimize 1:1 plot

• Develop decision tree method that can be followed by calibrator or automated