Bob McKane, USEPABonnie Kwiatkowski & Ed Rastetter, Marine Biological Laboratory

Marc Stieglitz & Feifei Pan, Georgia Institute of Technology

~ January 30, 2008, Presentation to NSF Riparian Zone Workshop, Indianapolis, IN ~

H 2O

NO 3, N

H 4, D

ON

Nassauer

Application of an Eco-hydrology Model to Riparian Forest Buffers in Agricultural Landscapes

Outline Describe GT-MEL, a spatially-distributed

eco-hydrology model

Demonstrate GT-MEL for a generalized agricultural-riparian system BMPs

Uncertainties & Challenges:

• Controls on C-N-H2O interactions

• Scaling up from reach to watershed

Georgia Tech (GT) Hydrology Model Spatially Distributed Hydrologic Processes

snobear.colorado.edu/IntroHydro/hydro.gif

GT is relatively simple3 “free” parameters vs. dozens for some hydrology models (e.g., HSPF)

434

3323

22212

s1111

QDdt

ds

QDDdt

ds

QETDDdt

ds

QQETDPdt

ds

PET1Qs

D1ET2

D2

Q1

Q2

Q3

s1

s2

s3

Bedrock

Q4

D3

s4

S = storage S = storage P = precipitation P = precipitation D = drainage (infiltration)D = drainage (infiltration)Q = runoffQ = runoffET = evapotranspiration ET = evapotranspiration

Pan, Stieglitz & McKane in prep

Logistic Curves For Drainage & RunoffLogistic Curves For Drainage & Runoff

Water Filled Pore Space (WFPS)0

f(x)

1

fc = soil field capacity, As = fraction of saturation area

Drainage

DDmax fD (s /sm )

0

Dmax

WFPSfc

D

0

Q

Subsurface runoff

QslopeQmax fQ (s /sm )

Qmax

0

1

Surface runoff

Qs P fAs (s /sm )

As

1.0WFPS

fc 1.0WFPSfc 1.0

(WFPS) (WFPS) (WFPS)

0 0 0

Climate Station

GT Applied to GT Applied to HJ Andrews Experimental ForestHJ Andrews Experimental Forest

Western Oregon Cascades

Photo: Al Levno

Multiple soil

types & layers

HJ Andrews Watershed #10, OregonHJ Andrews Watershed #10, Oregon 10-hectare forested catchment, clearcut in 197510-hectare forested catchment, clearcut in 1975

Flexible sub-catchment delineation

Flexible soil layers Flexible soil layers

BedrockBedrock

Daily Stream Hydrograph, 1996 - 2001 HJ Andrews Watershed 10

Str

eam

Dis

char

ge

(mm

/da

y)140

120

100

80

60

40

20

01996 1997 1998 1999 2000 2001

Simulated Annual Surface Runoff & Baseflow

1998

1996 1997 1998 1999 2000 2001

S

trea

m D

isch

arg

e (m

m/d

)

Str

eam

Dis

char

ge

(mm

/d)

So

il M

ois

ture

0.6

0.5

0.4

0.3

0.2

0.1

0Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

100 cm

70 cm

30 cm

N Leaching

Denitrification

Simulates acclimation of plants & microbes to changing resources Resources: H2O, PO4, NH4, NO3, DON, N fixation, CO2, light Effects of climate, land use, & chemicals Daily to century-scale responses Simulates grasslands, forests, tundra, agricultural systems, wetlands...

MEL: Multiple Element Limitation MEL: Multiple Element Limitation ModelModel

Rastetter et al., 2005, Ecological Applications 15(1)

Topographic control of Topographic control of HH22O, C, N, P cyclingO, C, N, P cycling

Linking Hydrology & Biogeochemistry Linking Hydrology & Biogeochemistry within Landscapeswithin Landscapes

StreamStream

NHNH44 , NO, NO

33 , PO, PO

44

DON, DOC

DON, DOC

Plants

Soils

HH22 OO

NHNH44 , NO, NO

33 , PO, PO44

DON, DOC,

DON, DOC,

HH22 OO

Plants

Soils

Stream

Coupling of GT-MEL• Daily time step• Flexible spatial scale

Export of Dissolved Organic Nitrogen (DON)HJ Andrews Watershed 10

DO

N D

isch

arg

e (k

g N

/ha)

Agricultural GT-MEL Demo

1. Identify upland & riparian best management practices

(BMPs tradeoffs in crop yield and water quality)

2. How do C-N-H2O interactions control nitrogen removal

effectiveness of upland & riparian zones?

3. How can buffers be managed to increase their N-removal

effectivenss?

Generalized Agricultural Hillslope for Simulating Upland & Riparian BMPs

Riparian forest, 0.5% slope 3 Buffer widths: 0, 50, 100 m2 Stand Ages: 10 or 100 yr

Corn field, 1% slope3 fertilizer rates: 50, 100, 200

kg N ha-1 y-1 for 20 years

2x3x3 factorial: identify acceptable 2x3x3 factorial: identify acceptable tradeoffs in crop yield and water qualitytradeoffs in crop yield and water quality

NH4 fertilizer

Denitrification

H2O, DIN, DON H2O, DIN, DON

Simulating ten 100 X 100 m hillslope segments

Simulation Matrix:Simulation Matrix: 2 Forest Ages X 3 Buffer Widths X 3 Fertilizer Rates2 Forest Ages X 3 Buffer Widths X 3 Fertilizer Rates

Simulation #

158243

Simulation Matrix:Simulation Matrix: 2 Forest Ages X 3 Buffer Widths X 3 Fertilizer Rates2 Forest Ages X 3 Buffer Widths X 3 Fertilizer Rates

Simulation #

BMPWQ BMPYIELD

Tradeoff: Corn Yield vs. Water QualitySimulations with 100-m mature forest buffer

DIN

Exp

ort

to

Str

eam

(k

g N

ha-1

y-1)

NH4 Fertilizer (kg N ha-1 y-1)

0 50 100 150 200

Corn yield

DIN export

Co

rn Y

ield

(t

DM

ha-1

y-1)

BMP

EPA drinking water standard

Nit

rate

-N (

mg

/L)

Julian Day

Concentration of NO3 Exported to Stream

EPA Drinking Water Standard

No forest buffer, N fert = 200 kg N ha–1 y-1

100-m forest buffer, N fert = 100 kg N ha–1 y-1

NH4 Fertilizer

Does not consider in-stream attenuation of NO3 – Need to link GT-MEL to stream network model

• Where did all the fertilizer N go?Where did all the fertilizer N go?

• What processes were most important What processes were most important for protecting water quality?for protecting water quality?

Corn Field, Segment 98,000

6,000

4,000

2,000

0

10,000

0 5 10 15 20

Years

Total N Input

N Leaching

Denitrification

N Storage

Corn Field, Segment 9Corn Field, Segment 98,000

6,000

4,000

2,000

0

10,000

0 5 10 15 20

Years10,000

8,000

6,000

4,000

2,000

00 5 10 15 20

Years

Total N Input

N Leaching

Denitrification

N Storage

Corn Field, Segment 9

Total N input

N Leaching

Denitrification

N Storage

Mature Forest Buffer

kg N

/ h

akg

N /

ha

(35% less N leaching)

20-yr Cumulative N Inputs & Losses

Denitrification (gN m-2 d-1)

0.0

0.1

0.2

0.3

0.4

0 50 100 150 200 250 300 350 400

WFPS %

40

60

80

100

0 50 100 150 200 250 300 350 400

Soil Respiration (gC m-2 d-1)

0

2

4

6

0 50 100 150 200 250 300 350 400

NO3 (umol N L-1)

0

500

1000

1500

0 50 100 150 200 250 300 350 400

Day

4

2

0

60

40

20

0

Soil Respiration (kg C ha-1 d-1)

Dissolved NO3 (mg N L-1)

302010

0

Mature Forest BufferCorn, Segment 9Denitrification

(kg N ha-1 d-1)

WFPS %

Riparian ManagementRiparian Management

• BMPs maximized denitrification, the main factor limiting N BMPs maximized denitrification, the main factor limiting N export to streams: export to streams:

• Forest buffer width most importantForest buffer width most important

• Mature forest denitrification highest: Mature forest denitrification highest: 20% more than young forest, 500% more than corn 20% more than young forest, 500% more than corn (more detritus = more denitrification) (more detritus = more denitrification)

• Sequestration of N in forest vegetation & soil unimportantSequestration of N in forest vegetation & soil unimportant

Crop ManagementCrop Management

• Fertilization rates, crop yields had to be reduced to meet Fertilization rates, crop yields had to be reduced to meet water quality standard, even with mature forest bufferwater quality standard, even with mature forest buffer

BMP SUMMARYBMP SUMMARY

Chesapeake

Peterjohn & Correll 1984

Willamette Valley

Next: real-world tests of GT-MEL

Chesapeake Willamette Valley

Regional applications of fine-scale process models are computationally expensive

GT-MEL

Simplified model of first-order watersheds

Statistically summarize model output describing fine-scale processes

Regional predictions

Spatial extrapolation

Alexander et al. 2000

snobear.colorado.edu/IntroHydro/hydro.gif

Scaling Up Process Info from Hillslope to Region

Thanks!Thanks!