6/2/2015 A GAP-FILLING MODEL (GFM) FOR TOWER-BASED NET ECOSYSTEM PRODUCTIVITY MEASUREMENTS Zisheng Xing a, Charles P.-A. Bourque a, Fanrui Meng a, Roger M. Cox b, and D. Edwin Swift b 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 , 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-layer Diff & Dir PAR Air Temperature Relative Humidity Vapor Density Deficit Soil Temperature Canopy Respiration Module Leaf Temperature Daily LAI CO 2 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) T opt =20 where T a is air temperature, T opt, T max, T min are the optimum, maximum and minimum air temperatures for growth, and Krt is an equation parameter Air Temperature (°C) f(T) f(T):krt=100 f(T):krt=200 f(T):krt=300 f(T):krt=400 f(T):krt=500

f sm Moisture Modifier If sm = Ψ, then f sm = 1; if sm = 0, f sm = Soil Moisture (v/v) f sm ψ=0.2ψ=0.35ψ=0.5 ψ=0.65 ψ=0.8

Canopy Conductance Modifier (f cond ) where RH is the relative humidity e sat is the saturation vapor pressure V k is a weight factor T a is the air temperature RH(%) f cond :Ta=5 f cond: Ta=13 f cond :Ta=22 f cond :Ta=30

Soil Respiration where R smax is a parameter defining maximum soil respiration at the optimum temperature α s and β s are equation parameters. T s is the soil temperature at 10-cm depth below ground Soil Temperature (°C) R s :a=250 R s :a=265 R s :a=280 R s :a=295 R s :a=310

Canopy Respiration Where R cmax is the maximum canopy respiration, R cβ is an equation parameter, R copt is the temperature where respiration is greatest, and Air Temperature (°C) RpRp R p : R cβ = R p :R cβ =0.105 R p :R cβ = R p :R cβ =0.2

Governing Equation where NEP max is the maximum NEP of each layer L sun and L shade is the proportions of sunlit and shade leaves of each layer PAR sun and PAR shade 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

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 Half Hour Time Series Measured NEP Modelled NEP NWL site

Environmental Modifiers Half Hour Time Series SW_factor All_factors Conductance_factor Temperature_factor

Modelled and Measured NEP y = x R 2 = Modelled NEP Measured NEP

Fitting to De3_2002 Site Half Hour Series (Day ) Measured NEPModelled NEP

Fitting to Be_2001 file Half Hour Time Series (Day ) Measured NEP Modelled NEP

Advantages of the method Catches most of the variation Obtains reasonable fits (r 2 >0.60) with minimum number of inputs (e.g., PAR, T a, 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 CO 2 NEP-modifier to address the CO 2 fertilizing effect Incorporate LAI (biomass) feedback in the model Incorporate species aging effect on NEP

THANKS! QUESTIONS?