SWAT Carbon Presentation

24
Integrating Nitrogen and Integrating Nitrogen and Phosphorus with Carbon Cycling in SWAT Cycling in SWAT Armen R. Kemanian, Texas AgriLife Research, Temple TX Stefan Julich, Justus-Liebig University, Giessen, Germany Jeffrey G. Arnold, USDA-ARS, Temple TX 2009 SWAT Conference July 8, 2009 Boulder, CO Improving Life through Science and Technology

Transcript of SWAT Carbon Presentation

Integrating Nitrogen andIntegrating Nitrogen and Phosphorus with Carbon Cycling in SWATCycling in SWAT

Armen R. Kemanian, Texas AgriLife Research, Temple TXgStefan Julich, Justus-Liebig University, Giessen, GermanyJeffrey G. Arnold, USDA-ARS, Temple TX

2009 SWAT ConferenceJuly 8, 2009 Boulder, CO

Improving Life through Science and Technology

Outline

• Carbon cycling modeling relevanceB i f hi t i l b k d• Brief historical background

• Challenges• Sub-module description• Sub-module description• Sample simulations• Concluding remarksg

Photo courtesy of Hyrum Johnson

Carbon cycling modeling relevance

• Key component of soil productivity and environmental integrityenvironmental integrity

• C, N, and P cycling closely linked• Dissolved C role in pollutants transport and in-

tstream processes• Soil C storage role in regulating atmospheric CO2

concentration• Biomass harvest and the soil C balance

More than a century of research

• Hénin and Dupuis (1945): carbon balanceJ (1958) t i t• Jansson (1958): tracer experiments

• Swift (1979): the decomposition cascade• Jenkinson and Rayner (1977): multiple carbon• Jenkinson and Rayner (1977): multiple carbon

pools, Roth-C model• Paul & coworkers (1979 - present)• Phoenix model (McGill et al. 1981)• Century, NCSoil, Verberne et al. (1980-90)• Hassink & Withmore (1997): Saturation• SOC composition revisited (2000 ….)

What is soil organic matter?

A continuous of products from decaying organisms, and their exudates and excreta sometimes charredand their exudates and excreta, sometimes charred

Concept of SOM as polymeric macromolecular structures transitioned to non-covalent association ofstructures transitioned to non covalent association of low-molecular-mass, recognizable biomolecules in various stages of decomposition (Hedges et al., 2000; Burdon, 2001; Wrobel et al., 2003; Sutton and Piccolo, 2001; Smejkalova and Piccolo, 2008)

Most processes directly affecting SOM formation p y goccur at nanometer- and millimeter-scales, at specific locations in the soil matrix

Modeling – the balance equation

dCs/dt = hCi – kCs

Rate of Change = Inputs – Outputs

Inputs: vegetation residues, …

Outputs: erosion, microbes respiration, …

Modeling – the balance equation

dCs/dt = hCi – kCs

0.12

0.16

nc

SOIL A

SOIL B

Soil organic carbon is composed of

0.00

0.04

0.08

Freq

uencomposed of

fractions with varying turnover

t 0.001 10 100 1000 10000

Turnover Time (year)

rates

SWAT sub-model approach

• Soil organic matter composed of one pool with variable properties:pool, with variable properties:–variable C:N ratio

variable C:P ratio–variable C:P ratio–variable turnover rate

• Residues humification controlled by soil organic carbon content (saturation g (content)

SWAT carbon sub-model approach

Litter Litter

Ci

ManureRootsPOM*

NiNO3

ManureRootsPOM*

i

CO2

Cs

iNH4

Ns

humificationdecomposition

Microbial biomassHumus

Microbial biomassHumus

mineralization -immobilization

•Cs and texture control the humification

•Cs, tillage, texture,

•Residues C:N and mineral N control the C:N ratio of the newly formed SOMCs, tillage, texture,

temperature, moisture, control the decomposition

y

Core soil carbon balance equation

Change Carbon Storage = Inputs – Outputs

dCs/dt = hCi – kCs

h = hc[1 – (Cs/Cx)n]k = f ftk (C /C )mk feftkx(Cs/Cx)

hc depends on soil textureCx depends on soil texture (Hassink and Withmore, 1997)fe soil temperature and water content factorf f ti f till t l d b f ti (NRCS)ft function of tillage tool and number of operations (NRCS)

Controls: Tillage

1 0tThe effect of tillage and SOM

decomposition rate

0.4

0.6

0.8

1.0

enha

ncem

ent

On each layer, tillage accelerates decomposition; soil settling returns this variable to zero

0.0

0.2

0 1 2 3

Dec

omp

Cumulative mixing efficiencyCumulative mixing efficiency

The tillage enhancement effect12

15

men

t

Texture effect on tillage

The tillage enhancement effect depends on the layer texture

3

6

9

12

Max

enh

ance

m

0

3

0.0 0.2 0.4 0.6

M

Fractional clay content

Controls: residue C:N ratio

Residue C:N ratio and N The lower the C:N ratio of the residue, and the higher the mineral nitrogen concentration, th l th C N ti f th

13

15

:N ra

tio

mineral on C:N ratio of SOM 1 ppm N4 ppm N32 ppm N the lower the C:N ratio of the

newly formed SOM

7

9

11

SOM

C

32 ppm N

0 50 100 150 200Residue C:N ratio

Testing: Pendleton OR (1931)

Climate: semi-arid, winter precipitation

S il W ll W ll ilt lSoils: Walla Walla silt loam

Original vegetation: shrub / sagebrush –grassland

R t ti i t h t / f llRotation: winter wheat / summer fallow

Tillage: moldboard plow in fall + operations to control weeds during p gsummer

Source: Rasmussen and Smiley, 1996 and others

Testing: Pendleton OR

Treatment: 0 kg N ha-1

50

60

a

Sim top 30 cm Sim 30 to 60 cmObs top 30 cm Obs 30 to 60 cm

Obs Sim Diff

Yield, Mg ha-1 2.6 3.2 +0.6

Stover, Mg ha-1 4.5 4.5 +0.030

40

50

SOC

Mg/

ha

S o e , g a 5 5 0 020

1920 1940 1960 1980 2000Year

60

40

50

60

C M

g/ha

Treatment: 45 / 90 kg N ha-1

Obs Sim Diff

Yield, Mg ha-1 3.7 4.2 +0.5

20

30

1920 1940 1960 1980 2000SO

Yield, Mg ha 3.7 4.2 0.5

Stover, Mg ha-1 4.9 4.8 -0.1

Year

Testing: Pendleton OR

Manure 110 kg N ha-1 + 1.5 Mg C ha-1

50

60

a

Obs Sim Diff

Yield, Mg ha-1 4.2 4.4 +0.2 30

40

50

SOC

Mg/

ha

Stover, Mg ha-1 7.0 6.1 -0.9 201920 1940 1960 1980 2000

Year

40

50

60

C M

g/ha

Treatment: 45 / 90 kg N ha-1, starting on 1894

comment on top layercomment on sub-soilsame difficulties expected in N-based

20

30

1880 1900 1920 1940 1960 1980 2000SO

same difficulties expected in N based models

Year

Testing: Pendleton OR

Winter SummerSpring Winter SummerSpring WinterFallFall

6ac

tor

Wheat WheatMechanical Fallow

2

4

Tilla

ge F

a

0 - 10 cm10 - 20 cm

T = tillage event

0

2

1 101 201 301 401 501 601 701

T 10 20 cm20 - 30 cm

TT T T T

1 101 201 301 401 501 601 701

Day Count Progression

Testing: Pendleton OR

Testing: College Station, TX

Climate: subtropical, hot and humid summer: 940 mm / year, 20 C MATsummer: 940 mm / year, 20 C MAT

Soils: Weswood

Original vegetation: grassland, woodland

R t ti h t ltRotation: wheat monoculture

Tillage: three to four operations per yearSource: Dou et al 2007 Soil Science 172 124-131Source: Dou et al., 2007. Soil Science 172, 124 131

Testing: College Station, TX

12

14

16kg

-1so

il0 - 5 cm 5 15 cm

NT in 2002: 15.0 g kg-1

CT i 2002 11 1 k 1

8

10

bon,

g C

k 5 - 15 cm15 - 30 cm30 - 55 cm55 - 80 cm

CT in 2002: 11.1 g kg-1

NT = CT in 2002: 0.83 g kg-1

2

4

6

gani

c C

arb

80 - 105 cm

00 50 100 150 200

Org

Yearea

Testing: Central Texas Vertisols

Climate: subtropical, dry summer

P i it ti 750 900 /Precipitation 750 - 900 mm / year

Temperature 18 - 20 C MAT

Original vegetation: tallgrass prairieOriginal vegetation: tallgrass prairie

R t ti i l t d tiRotation simulated: continuous corn

Tillage: three to four operations per yearSource of SOC data: Potter et al 1999Source of SOC data: Potter et al., 1999

Testing: Central Texas Vertisols

0 0 0 5 1 0 1 5 2 0Organic Carbon, g C kg-1 soil

0.0

0.2

0.0 0.5 1.0 1.5 2.0

simulation year 1

0.4

0.6pth,

m

simulation year 25simulation year 50TempleBurleson

0.8

1.0

Dep Riesel

1.2

Concluding Remarks

AdvancesAdvances

• Integration of N, P, and C in SWAT

• The effect of tillage on several processes can be now simulated

Notes of caution

C• Carbon cycling below the plow layer is not clearly understood

• Uncertainties in all components of the C (or N) balance p ( )make accurate predictions of C accumulation rate difficult

• Forest soils and organic horizons not tested yetForest soils and organic horizons not tested yet

Acknowledgements

IndividualsNancy SammonsNancy SammonsShawn Quisenberry

InstitutionsTexas AgriLife ResearchUSDA-ARSJustus-Liebig University

Questions