04/18/23

A GAP-FILLING MODEL (GFM) FOR TOWER-BASED NET

ECOSYSTEM PRODUCTIVITY MEASUREMENTS

Zisheng Xinga, Charles P.-A. Bourquea, Fanrui Menga, Roger M. Coxb, and D. Edwin Swiftb

a Faculty of Forestry & Environmental Management, University of New Brunswick, Fredericton, New Brunswick, CANADA, E3B 6C2

b Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Centre, P.O. Box 4000, Fredericton, New Brunswick, CANADA, E3B 5P7

Jena Workshop,

Sep. 18-20, 2006

What is GFM• A simple, process-based model

to predict net ecosystem productivity (NEP).

• Uses climatic data, simple site and soil descriptors.

• Runs at half-hourly time steps.• Automatically sets equation

parameters with available data.• Generates:

• NEP• Ecosystem respiration• Soil respiration

Overview of GFM

• Multiple layer canopy; PAR partitioning into direct & diffused components

• Variable LAI• NEP - Environmental control feedback• Ecosystem respiration; canopy + soil

respiration

Model Structure

Light Response Module

Temperature Modifier Module

Canopy Conductance Module

Soil Respiration Module

NEP

Soil Moisture

Multiple-layerDiff & Dir PAR

Air Temperature

RelativeHumidity

Vapor Density Deficit

Soil Temperature

Canopy Respiration Module

LeafTemperature

Daily LAI

CO2 Module

Light Response Module

• Partition total LAI into multiple layers (6)

• Calculate half-hour zenith angles

• Separate diffuse and direct PAR– If diffuse & direct PAR are not available, use

empirical formulation for PAR partitioning

• Calculate absorbed PAR for each layer

Air Temperature Modifier f(T)

)/)exp((/1)( 20 rtopta KTTTf

)/()(1

minmax

max

max

min

min ))(()(TTTT

TTTT

TTTTa optopt

opt

a

optTf

Topt=20

where Ta is air temperature, Topt, Tmax, Tmin are the optimum, maximum and minimum air temperatures for growth, and Krt is an equation parameter.

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Air Temperature (°C)

0.00

0.20

0.40

0.60

0.80

1.00

f(T)f(T):krt=100f(T):krt=200f(T):krt=300f(T):krt=400f(T):krt=500

opt

opt

TTTfTfTTTfTfTf

)()()()*()(

0

01

fsm Moisture Modifier

If sm = Ψ, then fsm = 1; if sm = 0, fsm = 0.0

))1(,0.0max( /1 smf

minmax

min

smsmsmsm

minmax

min

smsmsm

/1)1(1

1

0.00 0.2 0.4 0.6 0.8 1.0

Soil Moisture (v/v)

0.00

0.20

0.40

0.60

0.80

1.00

fsm ψ=0.2 ψ=0.35 ψ=0.5

ψ=0.65 ψ=0.8

Canopy Conductance Modifier (fcond)

)**)100/1(exp( ksatcond VeRHf

273.16))Ln( 5.0208-273.16)/(4985.697057633.52exp( aasat TTe

where RH is the relative humidity

esat is the saturation vapor pressure

Vk is a weight factor

Ta is the air temperature0.00 20.00 40.00 60.00 80.00 100.00

RH(%)

0.00

0.20

0.40

0.60

0.80

1.00

fcond:Ta=5

fcond:Ta=13

fcond:Ta=22

fcond:Ta=30

Soil Respiration))13.22716.273/(1(exp(max sssss TRR

where

•Rsmax is a parameter defining maximum soil respiration at the optimum temperature

• αs and βs are equation parameters.

•Ts is the soil temperature at 10-cm depth below ground 0.00 5.00 10.00 15.00 20.00 25.00 30.00

Soil Temperature (°C)

0.00

0.50

1.00

1.50Rs:a=250

Rs:a=265

Rs:a=280

Rs:a=295

Rs:a=310

Canopy Respiration

)))(exp(1/(max coptaccp RTRRR

Where

•Rcmax is the maximum canopy respiration, Rcβ is an equation parameter,

•Rcopt is the temperature where respiration is greatest, and

0.00 10.00 20.00 30.00 40.00

Air Temperature (°C)

0.00

0.50

1.00

1.50Rp

Rp: Rcβ=0.0575

Rp:Rcβ=0.105

Rp:Rcβ=0.1525

Rp:Rcβ=0.2

Governing Equation

smsdconp

n

i smcondshadeshade

sunsun fRnLAIfRfTffiPARiL

iPARiLNEPNEP */*)*)

*)(**)))][(*exp(*][

)])[(*exp(*][1(*((

1

max

where

• NEPmax is the maximum NEP of each layer

• Lsun and Lshade is the proportions of sunlit and shade leaves of each layer

• PARsun and PARshade are the PAR absorbed by sunlit and shade leaves

• Г is the light compensation point.

Optimizing

• P is the projected or modelled NEP

• O is the observation or targeted NEP

• Er is the error

,

2

2

2

Er

OP jj

Model Fit Flowchart

Variable data & target data

Filter

Model run with auto parameterize

(multiple dimensionSimplex)

Project with new parameters

Fill gaps Output

Sample of Model Run

0 300 600 900 1200 1500

Half Hour Time Series

-20.0

-10.0

0.0

10.0

20.0

30.0

Measured NEP

Modelled NEP

NWL site

Environmental Modifiers

0.00 500.00 1000.00 1500.00 2000.00

Half Hour Time Series

0.00

0.20

0.40

0.60

0.80

1.00

SW_factorAll_factorsConductance_factorTemperature_factor

Modelled and Measured NEP

y = 0.9677x + 0.2378

R 2 = 0.78

-20

-10

0

10

20

30

-10 0 10 20 30

Modelled NEP

Me

as

ure

d N

EP

Fitting to De3_2002 Site

0.00 500.00 1000.00 1500.00 2000.00

Half Hour Series (Day 187-229)

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00Measured NEPModelled NEP

Fitting to Be_2001 file

0.00 500.00 1000.00 1500.00 2000.00

Half Hour Time Series (Day 187- 229)

-20.00

-15.00

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Measured NEPModelled NEP

Advantages of the method• Catches most of the variation

• Obtains reasonable fits (r2>0.60) with minimum number of inputs (e.g., PAR, Ta, LAI)

• Can be quickly adapted to various forest ecosystems

•Has great flexibility for many kinds of gap sizes for any NEP datasets

• Simplify model parameter setting (automatically done through model running)

• Addresses flexible time steps

Current model weaknesses

• Some uncertainty exists in the data fitting during nighttime and winter periods;

• Nighttime model results may be improved with access to soil chamber measurements and refinement of soil respiration prediction

• Winter period is reasonably modelled if the target dataset extends over a full year

Further Work

• Refine the gap filling process for different tree species

• Add CO2 NEP-modifier to address the CO2 fertilizing effect

• Incorporate LAI (biomass) feedback in the model

• Incorporate species aging effect on NEP

THANKS!

QUESTIONS?