Biological Systems Engineering Invent the Future ...€¦ · VirginiaTech Biological Systems...

Invent the FutureBiological Systems EngineeringVirginiaTech

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Watershed Science and Engineering Group

Can Hydrologic Complexity Simplify Field Level Modeling?

[email protected]

Zachary M. EastonBiological Systems Engineering, Virginia Tech,


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Models

If land use is your only variable, management options are extremely limited

% Forest

% Agriculture

Total P

(mg/L)

100%

100%0%

0%

0.00

0.30

How is management evaluated?


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• As a result, we have sometimes dogmatically* developed nonpoint source pollution control practices based on specific land uses and ignored the interaction between land management and physical, landscape scale processes.

*dog.ma …, n. … 2. a specific tenet of doctrine authoritatively put forth: the dogma of the Assumption. ‐The Random House Dictionary

X


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Watershed Science and Engineering Group Easton et al., 2008. J. Hydrol.

The HRU concept• Many models define HRUs

as the coincidence of soil type and landuse• Hydrological/chemical

properties are defined at the HRU

• So runoff/P loss is the same here (lowland pasture)

• As here (upland pasture)

Soils Landuse

HRUs


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Models Need to:•Consider spatially distributed properties other than land use and soils


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Models Need to:•Consider the spatial distribution of hydrological patterns

•Account for locations of features (e.g., potential pollutant sources) relative to hydrologically active zones

“Pollutant Sources”Ditch ‐ Stream


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Models Need to:•Consider the spatial distribution of hydrological and chemical patterns

•Account for locations of features (e.g., potential pollutant sources) relative to hydrologically active zones

•Minimize (or eliminate) model calibration


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Hillside

Flood Plain(valley bottoms)

Stream

Baseflow(groundwater)

Shallow bedrock, fragipan, or other restrictive layer

HillsideDrainage

HydrologyScientific Background


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Rain

Infiltration

“Runoff”

CN‐method based on this theory (Horton 1933, 1940)Common Perception of Runoff

Infiltration Excessa.k.a. Hortonian Flow

Scientific Background


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March

April

August

September

October

November

1 10 100

20

0

30

50

40

70

60

80

100

90

10

Return Period (yr)

% o

f Are

a G

ener

atin

g H

orto

nian

Flo

wIs Infiltration Excess Runoff

Common?

78%


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Rain

Subsurfacewaterrises

Some areas saturate to the

surface

Saturation Excess RunoffScientific Background


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Rain

Rain on saturated areas becomes overland flow

Upland interflow may exfiltrate

Dunne and Black. 1970. Water Resour. Res.

Saturation Excess RunoffScientific Background


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Variable Source Areas

Most Watershed Models were not intended to capture this complexity•Soil and Water Assessment Tool (SWAT)•General Watershed Loading Function (GWLF)•Agricultural Nonpoint Source Pollution Model (AGNPS)•Hydrologic Simulation Program Fortran (HSPF)



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tanln

a

High :

Low :

Topographic Index

33.10

3.52

Topographic Index

10=f(S)

=f(S)

=f(S)=f(S)

=f(S)=f(S)

=f(S) =f(S)f(S)

=f(S)

Easton et al. 2008. J. HydrolEaston et al. 2011. Hydrol. Proc

Wetness IndexClasses

Wetness Index Classes

10921

S

∑

=

i Local StorageS = Watershed Storage



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LanduseTI

HRUs

• VSA concept defines HRUs as the coincidence of topographic index and landuse

• So runoff/P loss is now not the same here (lowland pasture)

• As here (upland pasture)• Better Assumption?

Soils


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Variable Source Area HydrologyUSDA WE38, PA


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Topography to explain soil depth and fractions

Using measured pedon data from ARS long term watershed

TI Class (Dry to Wet) TI Class (Dry to Wet)

San

d or

Cla

y (%

)

Soi

l Dep

th (c

m)

Collick et al. 2015. Hydrol Proc


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ln atan

High :

Low :

Topographic Index

33.10

3.52

Soil genesis as explained byTopography!

Easton et al. 2008 J. HydrolCollick et al. 2015. Hydrol ProcFuka et al. 2015. Hydrol Proc


10

1

Zi = local soil depth

Z2= 80 cm

Z6=100 cm

Z10= 130 cm


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Watershed Science and Engineering Group Collick et al., 2015. Hydrol Proc


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Hortonian Dominated Systems Riesel, TX


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2 4 6 8 10

800

1000

1200

1400

1600

TI Class

Dep

th to

C H

or.(m

m)

R^2=0.28y=50.80 x + 1025.52

(a)

2 4 6 8 105

10

15

20

25

TI ClassM

eas.

Ksa

t(mm

/hr)

R^2=0.19y=−0.88 x + 17.00

(b)

2 4 6 8 1025

30

35

40

45

TI Class

Fiel

d C

apac

ity a

t 33k

pa(%

)

R^2=0.79y=−2.17 x + 46.00

(c)

2 4 6 8 10

1.65

1.70

1.75

1.80

1.85

TI Class

Bulk

Den

sity

(g/c

m^3

)

R^2=0.17y=−0.01 x + 1.75

(d)

2 4 6 8 10

0.48

0.50

0.52

0.54

0.56

TI Class

Poro

sity

(%)

R^2=0.50y=0.01 x + 0.47

(e)

2 4 6 8 100.06

0.07

0.08

0.09

0.10

0.11

0.12

0.13

TI ClassAW

C(v

olum

e)

R^2=0.71y=−0.01 x + 0.13

(f)

Soil Depth

AWC

Field CapacityKsat

PorosityBulk Density

TI Class

Soi

l Dep

th (c

m)

Ksa

t(m

m/h

r)

Fiel

d ca

paci

ty (3

3kpa

%)

Bul

k D

ensi

ty (g

cm

-3)

Por

osity

(%)

AWC

(cm

3cm

-3)

Fuka et al. 2015 Hydrol Proc

TI Class TI Class

TI Class TI Class TI Class


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ln atan

High :

Low :

Topographic Index

33.10

3.52

Soil moisture as explained byTopography!


AWC2= 0.12AWC6=0.09

AWC10= 0.06

Easton et al. 2008 J. HydrolCollick et al. 2015. Hydrol ProcFuka et al. 2015. Hydrol Proc


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TI adjusted FAO Soils ( ) vs base SSURGO Soils ( )

●

●

● ●

●

●

●

●

800 1000 1200 1400 1600 1800 2000

800

1000

1200

1400

1600

1800

2000

Depth to C Hor.(mm)

Est

. ZM

X

R^2=0.28y=0.33 x + 538.88

(a)Soil Depth

● ●●●●

●

●

●

0 10 20 30 40 50 600

10

20

30

40

50

60

Meas. Ksat(mm/hr)

Est

. Ksa

t(mm

/hr) R^2=0.59

y=2.07 x + −15.38

(b)Ksat

Soi

l Bas

ed S

oil D

epth

(mm

)

Soi

l Bas

ed K

sat(

mm

/hr)

Pit Soil Depth (mm) Pit Ksat (mm/hr)

How does SSURGO perform?

Fuka et al. 2015 Hydrol Proc


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Jan 20 Feb 10

Y=125.0ha

−4

−2

0

2

4

Jan 20 Feb 10

W1=71.2ha

−4

−2

0

2

4

Jan 20 Feb 10

Y2=53.4ha

−4

−2

0

2

4

Jan 20 Feb 10

W6=17.1ha

−4

−2

0

2

4

Jan 20 Feb 10

Y10=8.5ha

Jan 20 Feb 10

Y6=8.5ha

−4

−2

0

2

4

Jan 20 Feb 10

Y8=8.4ha

−4

−2

0

2

4

Jan 20 Feb 10

W10=8.0ha

−4

−2

0

2

4

Jan 20 Feb 10

Y13=4.6ha

−4

−2

0

2

4

Jan 20 Feb 10

W13=4.6ha

W12=4.0ha

−4

−2

0

2

4

Y14=2.3ha

−4

−2

0

2

4

SW17=1.2ha

−4

−2

0

2

4

SW12=1.2ha

−4

−2

0

2

4

−−−

Distr.SSURGOMeas.

PS=0.4ha


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0.00

0.00

0.00

0.00

0.01

0.10

1.00

2/26/04 2/26/06 2/26/08 2/26/10 2/26/12

Solu

ble

P lo

ss, m

g L-

1

ObservedStd P routinesNew P routines

0.00

0.00

0.00

0.01

0.10

1.00

2/26/04 3/30/07 3/3/08 10/5/09 3/25/10 12/23/1

Solu

ble

P lo

ss, m

g L-

1


Commercial fertilizer Corn-Hay

Commercial fertilizer and poultry litter Corn-Hay

Collick et al. 2015 JEQ


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No manure application

0.0

1.0

2.0

3.0

4.0

1/1/2010 2/20/2010 4/11/2010

P lo

ss, m

g L-

1


Poultry litter/manure application in January (2 tons per ac)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1/1/2010 2/20/2010 4/11/2010

P lo

ss, m

g L-

1


0

10

20

30

40

50 Prec

ipita

tion,

mm

Collick et al. 2015 JEQ


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Discussion• TI initialization exhibited strong correlations with field level measurements • Resulted in improved predictions of field level contaminant transport, particularly for P

• SSURGO initialization captured soil properties and thus the hydrologic response poorly • Sometimes under predicting and sometimes over predicting, in part due to the overestimated soil depth and AWC

• These results indicate that adjusting model parameters based on topography can result in more accurate P estimation at the field scale


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Closing Thought"Many... modelers seem to follow... the example of Pigmalion, the sculptor... who carved a statue so beautiful that he fell deeply in love with his own creation... It is feared... hydrologists fall in love with the models they create. In hydrology... the proliferation of models has not been matched by the development of criteria for [evaluating] their effectiveness..." (Dooge 1986, Water Resour. Res. 22‐page S49)

Biological Systems Engineering Invent the Future ...€¦ · VirginiaTech Biological Systems...

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