Rangeland Carbon Fluxes in the Northern Great Plains

Wylie, B.K., T.G. Gilmanov, A.B. Frank, J.A. Morgan, M.R. Haferkamp, T.P. Meyers, E.A. Fosnight, L. Zhang

US Geological SurveyNational Center for Earth Resources Observation Science (EROS)

• USGS: Earth Surface Dynamics, Land Remote Sensing, and Geographic Analysis and Monitoring

• National Science Foundation/EPSCoR Grant No. 0091948 through the South Dakota Center for Biocomplexity Studies and by the State of South Dakota

• USAID GL-CRSP project Grant No. PCE-G-00-98-00036-00 to Univ. of California, Davis• USDA Agricultural Research Service Agriflux network• US Agency for International Development Grant No. PCE-G-98-00036-00, Global Bureau,

Office of Agriculture and Food Security

U.S. Department of the InteriorU.S. Geological Survey

Objectives

Exhaustively model and map Carbon fluxes in rangelands to quantify the fluxes and Investigate temporal and patterns to understand climate and environmental influences.

If NDVI < 0.40C fluxes = f(X1, X2, X4)

If NDVI >= 0.40 C fluxes = f(X1, X4, X6)

Localized Detailed Measurements

Spatial Variables Through Time

C Flux Mapping Model Tree

C Flux Maps

METHODS: Develop Empirical Model Tree Algorithms to Predict Flux Tower Carbon Observations From Spatial Data

Northern Great Plains

Lethbridge (LE), Canada: Larry Flanagan

Ft. Peck (FP), MT; Tilden Meyers

Mandan (MA), ND; Al Frank

Cheyenne (CN), WY: Jack Morgan

Miles City (MC), MT: Marshall Haferkamp

Cottonwood, SD

Brookings, SD

Flux Tower Data is used to Develop Models for the Mapping of 10-day Carbon Dynamics on Rangelands

New Towers

METHODS: Tower C Fluxes Associated with Basic Ecosystem Functions of Gross Primary Production (Pg) and Total Ecosystem

Respiration (Re) are Derived From Light Response Curves on 30 min. Datasets.

Tagir Gilmanov, SDSU, coordinator of the FLUXNET WORLDGRASSFLUX working group

Derives estimates of daytime respiration

independentindependent of nighttime flux data

(others often use relationships derived from nighttime fluxes and

apply them to daytime observations)

PROVIDES:1) Tower Pg, Re, and NEE2) Facilitates flux tower data gap filling3) Quantifies ecophysiological parameters

(quantum yield (α), maximum gross photosynthesis (β), & daytime respiration (γ))

P(Q,Ts ; α,β,γ 0,θ,κ )=

12θ

αQ + β − (αQ + β )2 − 4αβθQ⎛ ⎝

⎞ ⎠ − γ0 eκTs ,

NEE

-2

-1

0

1

2

3

4

5

6-Dec

25-Jan

15-Mar

4-May

23-Jun

12-Aug

1-Oct

20-Nov

9-Jan

g C

m-2

day

-1

NEEPgRe

Ecosystem Process Carbon Partitioning: Added Value

Bkwd_finalGPP_RE_NEEmodel4c.xls

Sou

rce

S

ink

Flux Tower Pg Group: Significant Early Rain,

High Pg0

1

2

3

4

5

6

7

8

1-Jan 20-Feb 10-Apr 30-May 19-Jul 7-Sep 27-Oct 16-Dec

Pg

(g C

m-2

/day

-1)

0

20

40

60

80

100

120

140

10-d

ay p

reci

pita

tion

(mm

)

CH 1998 PgMA 1999 PgCH 1998 pptMA 1999 ppt

Flux Tower Pg Group: Moderate Rain,

Moderate Pg

0

1

2

3

4

5

6

7

8

1-Jan 20-Feb 10-Apr 30-May 19-Jul 7-Sep 27-Oct 16-Dec

Day of Year

Pg (g

C m

-2/d

ay-1

)

0

20

40

60

80

100

120

140

10-d

ay p

reci

pita

tion

(mm

)

LE 2000 PgLE 2001 PgMC 2000 PgMC 2000 pptLE 2000 pptLE 2001 ppt

Flux Tower Pg Group: Low Rain, Low Pg

0

1

2

3

4

5

6

7

8

1-Jan 20-Feb 10-Apr 30-May 19-Jul 7-Sep 27-Oct 16-Dec

Day of Year

Pg

(g C

m-2

/day

-1)

0

20

40

60

80

100

120

140

10-d

ay p

reci

pita

tion

(mm

) FP 2000 PgMA 2000 PgMA 2001 PgMC 2001 PgFP 2000 pptMA 2000 pptMA 2001 pptMC 2001 ppt

Model Development Data Set Consists of Flux Tower Pg, Re, and NEE Combined With Spatial Variables from Remote Sensing and GIS

(10-day Time Step)

Variable name DefinitionSeasonally Dynamic

Solar Radiation function of DOY and latitudend Smoothed NDVIppt precipitationtemp temperaturepar PAR

Seasonally Dynamic but dependent on timesinceSost Days since Start of Season (NDVI metric)DOY day of year = photoperiodismsolstice days prior or post summer solstice

Annually Dynamicsosn NDVI at Start of Seasonsost Start of Season DOYtin Time Integrated NDVIyear Categorical variable for testing year effects

Site Dynamicsitecat Categorical variable for testing site effectsc4pct % C4 from STATSGOco3surf Surface calcium carbonates from STATSGOpctclaysurf % clay in surface layer from STATSGOpctgrass % grass from STATSGOomernick Categorical variable for testing ecoregion effectsElevationAspect

Tower versus Spatial Data- Takes into account the accuracy of spatial data sets when developing model.- Process-based models typically rely heavily on precipitation - Precipitation can have high spatial and temporal variability that is difficult to map accurately.

Precipitation

y = 1.0331x + 2.3167R2 = 0.4247

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90

Mapped Precipitation (mm)

Tow

er P

reci

pita

tion

(mm

) .

Temperature

y = 1.0171x + 0.2487R2 = 0.9852

-10

0

10

20

30

-10 0 10 20 30

Mapped Temperature (oC)

Tow

er T

empe

ratu

re (

o C)

Distinct Model Trees

• Models that predict:– Net Ecosystem Exchange (NEE)– Gross Primary Production (Pg)– Respiration (Re)

Empirical Piecewise Regression Model

• Cubist– Parsimonious and transparent selection of variables– Stratification of data into homogeneous information

spaces– Multiple regression defined for each information

space

NEE Model example (g CO2 m-2 day-1)

Rule 1: [98 cases, mean -1.1538022, range -6.22692 to 5.404624, est err 1.2296274]

if nd <= 140 and sinceSost > 85then

NEE = 6.25352 + 0.138 nd - 0.091 ppt - 0.19 sosn - 0.009 par- 0.01 temp - 0.0015 sinceSost

Rule 2: [21 cases, mean 0.7320258, range -3.83196 to 4.013414, est err 1.8628249]

if nd > 140 and par <= 100then

NEE = 1.82742 + 0.046 par - 0.04 temp + 0.033 nd - 0.03 sosn- 0.008 ppt - 0.0014 sinceSost

Rule 3: [60 cases, mean 0.9998901, range -4.301141 to 7.026846, est err 1.7766731]

if nd <= 140 and sinceSost <= 85then

NEE = 3.42815 + 0.051 par - 0.046 temp - 0.054 tin + 0.043 nd- 0.04 sosn - 0.012 ppt - 0.0019 sinceSost

Flux Tower Training Data Set and Respective NEE Rules

-2

-1

0

1

2

3

4

6-Dec25-Jan15-Mar4-May23-Jun12-Aug1-Oct20-Nov9-Jan28-Feb

Date

Tow

er N

EE

(g C

m-2

day

-1)

rule 1, Rel. Err. 0.11rule 2, Rel. Err. 0.24 rule 3 Rel. Err. 0.16rule 4 Rel. Err. 0.25rule 5 Rel. Err. 0.22April - Oct.

Rules or respective “information spaces”

Theme Count Pct. Weight Pct.---------------------------------------------

par 429 34.3 1.712 13.2nd 390 31.2 2.981 22.9

sinceSost 379 30.3 0.732 5.6temp 40 3.2 3.360 25.8ppt 11 0.9 1.636 12.6tin 0 0.0 1.385 10.7sosn 0 0.0 1.193 9.2

Variables used to predict NEE

Theme Count Pct. Weight Pct.---------------------------------------------

nd 683 37.8 7.031 33.5par 508 28.1 5.322 25.3doy 177 9.8 2.231 10.6temp 136 7.5 2.222 10.6

sinceSost 132 7.3 1.437 6.8c4pct 111 6.1 0.448 2.1sost 60 3.3 0.827 3.9tin 0 0.0 1.482 7.1

Variables used to predict Pg

Variables used to predict Re

Theme Count Pct. Weight Pct.---------------------------------------------

temp 2848 40.4 3.540 4.0 nd 2244 31.8 22.192 25.2ppt 991 14.1 8.395 9.5par 343 4.9 10.789 12.3

sinceSost 284 4.0 12.412 14.1 doy 255 3.6 10.735 12.2

pctgrass 81 1.1 14.612 16.6tin 0 0.0 5.326 6.1

Model Accuracy Assessment

1/n | observed − predicted |∑MAD =

Range = 95th percentile - 5th percentile

Relative Error = MAD / Range

NGP winter NEE

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

-20 -15 -10 -5 0 5 10

Temperature (C)

NEE

(g C

m-2

day

-1)

Winter fluxes difficult to estimate….

30 min. data sets indicate snow depth and wind speed important

Winter fluxes (Nov. - Mar.) are need toconvert model tree maps (Apr. - Oct.) to Annual Fluxes

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0Mandan 01-02 Mandan 96-00 Miles City Lacombe Average

Winter C flux Grassland Studies

Ave

rage

NE

E (g

C m

-2 d

ay-1

)

Mandan 01-02, Gilmanov 2002; Mandan 96-00, Frank et al. 2002, Miles City, Gilmanov 2002; Lacombe, Baron et al. 2004

Cross validation

Five-fold NEE (g C m-2 day-1) cross-validation (random)

• Methodology to quantify robustness of small training data sets• Random cross-validation produces realistic error estimates• Cross-validation by sites evaluates influence of individual sites• Cross-validation by year evaluates influence of individual years

Average MAD = 0.48

Relative Error

0.6000.380.480.430.49Mean Absolute Difference

4444434343Hold out data sample size

Fold 5Fold 4Fold 3Fold 2Fold 1Random sample as test

Relative Error

0.6000.380.480.430.49Mean Absolute Difference

4444434343Hold out data sample size

Fold 5Fold 4Fold 3Fold 2Fold 1Random sample as test

Results

0.150.160.290.17Relative Error (test)0.480.490.910.55Mean Absolute Difference (test)

074972521Sample size (test)0.110.120.140.100.12Relative Error (train)0.350.390.440.330.37Mean Absolute Difference (train)

217143120192196Sample size (train)

None2001200019991998Year withheld as test

0.150.160.290.17Relative Error (test)0.480.490.910.55Mean Absolute Difference (test)

074972521Sample size (test)0.110.120.140.100.12Relative Error (train)0.350.390.440.330.37Mean Absolute Difference (train)

217143120192196Sample size (train)

None2001200019991998Year withheld as test

Cross-validation NEE (g C m-2 day-1) by site

Cross-validation NEE (g C m-2 day-1) by years0.180.170.100.210.14Relative Error0.590.550.310.660.46Mean Absolute Difference

04421217358Sample size (test)0.110.120.120.110.090.14Relative Error (train)

0.350.380.370.360.290.45Mean Absolute Difference (train)

217173196196144159Sample size (train)NoneMCCNFPMALESite withheld as test

0.180.170.100.210.14Relative Error0.590.550.310.660.46Mean Absolute Difference

04421217358Sample size (test)0.110.120.120.110.090.14Relative Error (train)

0.350.380.370.360.290.45Mean Absolute Difference (train)

217173196196144159Sample size (train)NoneMCCNFPMALESite withheld as test

Pg: Jack_site_summary3.xls

Robustness of Pg model tree: Cross Validation using Sites

• Cheyenne and Mandan are influential sites (high Pg in CH 1998 and MA 1999)(MA is only high precipitation, eastern site)

•Relative Errors on the other sites was from 7 to 12 %

Site Withheld CN FP LE MA MC NoneMAD (g C m-2 day-1) 0.94 0.32 0.46 1.65 0.53 0.32Relative Error 0.21 0.07 0.10 0.37 0.12 0.07N 21 21 58 73 43 217

NGP Modeled April - Oct. average Re (g C m-2 day-1) versus Observed Re

y = 0.8382x + 0.3206R2 = 0.639

1

1.5

2

2.5

1 1.5 2 2.5

Modeled Re

Tow

er R

e

MC 2000

MC 2001

MA 2001

MA 2000

FP 2000

MA 1999

1:1 Line

RegressionMiles City 2000 flux data begins 4/25

ResultsHow Well do 10-day Model Tree Predictions Averaged over

the Growing Season Compare with Flux Tower Observation?

NGP Modeled April - Oct. average Pg (g C m-2 day-1) versus Observed Pg, MAE = 0.15

y = 0.9602x + 0.1677R2 = 0.9049

1

1.5

2

2.5

3

1 1.5 2 2.5 3

Modeled Pg

Tow

er P

g

MC 2000MC 2001MA 2001MA 2000FP 2000MA 19991:1 LineRegression

Miles City 2000 flux data begins 4/25

Twrlocalbxy_gpp_nee01_Re.xls

NGP Modeled April - Oct. average NEE (g C m-2 day-1) versus Observed NEE

y = 1.0274x + 0.0279R2 = 0.9545

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-0.4 -0.2 0 0.2 0.4 0.6 0.8

Modeled NEE

Tow

er N

EE

MC 2000MC 2001MA 2001MA 2000FP 2000MA 19991:1 LineRegression

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Tower Pg (g C m-2 day-1)

Estim

ated

Pg

(g C

m-2

day

-1)

LEMAMC1:1 Line

MAD = 0.32Relative Error = 0.08

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Tower Pg (g C m-2 day-1)

Estim

ated

Pg

(g C

m-2

day

-1)

LEMAMC1:1 Line

MAD = 0.56Relative Error = 0.16

Model Tree (trained from all towers) MODIS GPP (withheld 2001 MAD was 0.48)

Model Tree Predictions Match 2001 Flux Tower ObservationsBetter than MODIS Net Photosynthesis

• Captures General Tend in Tower Pg• Predictions seem site specific

MODIS

Regional Comparison of 2001 MODIS GPP and Model Tree Pg on Rangelands

Pg (g C m-2 day-1)

• General Agreement • MODIS had outlier values > 6 g C m-2 day-1

ModelTree

1998 = -15.5

NEE (g C m2 yr-1)

1999 = 16.82000 = -59.42001 = -6.0Average = -16.0

Ppt_temp_monthly_rangelands.xls0

10203040

5060708090

0 2 4 6 8 10 12

Month

Pre

cipi

tatio

n (m

m)

2001200019991998

Ppt_temp_monthy_rangelands.xls

1998 = -15.5

NEE (g C m2 yr-1)

1999 = 16.82000 = -59.42001 = -6.0Average = -16.0

Why was 2000 NEE so low?

• Low June precipitation

• Low previous fall precipitation

0

10

20

30

40

High 1 High 2 Low 1 Low 2

Perc

ent C

lay

High NEE 1 and 2

Low NEE 1 and 2

Investigating Causes of Sinks and Sources: 1999

-2

-1

0

1

2

3

2/9 3/31 5/20 7/9 8/28 10/17 12/6

Date

NEE

(gC

m-2

day

-1)

High 1High 2Low 1Low 2

0

0.1

0.2

0.3

0.4

0.5

0.6

2/9 3/31 5/20 7/9 8/28 10/17 12/6

Date

ND

VI

High 1High 2Low 1Low 2

STATSGO Range Production Normal Year

Kg ha-1

1998-2001 April-June Precipitation

mm

<= 525

>= 50

Percent clay

STATSGO percent clay

Environmental Drivers of 1998-2001 Source Areas

Average Temperature (Apr. - Oct., 1998-2001)

13.7

15.5

17.4

18.9

(C)

Conclusions

• Model Tree predictions of Pg and NEE agreed well with tower observations

• Heavy reliance of Model Tree predictions on NDVI insures tracking of temporal and spatial dynamics not evident in climatic data sets

• Winter flux estimates need improvement

• N. Great Plains Rangelands from 1998-2001 were a WEAK source of C.