CARBWARE Growth Models

Transcription

1 CARBWARE Growth Models Kevin Black, Michael Hawkins, John Connolly and Jacques Hamel Overview CARBWARE model Why a single tree growth model? Which model and why? Single tree model description Data and variables Sub models (with example) Outputs Delivery 16/05/2008 1

2 CARBWARE-Model Concept Atmosphere Burning Decomposition Other GHGs Cohort 3 Biomass Cohort 2 Above ground Biomass Branches Cohort 1 Above Timber, Biomass ground Leaves Branches Below Above ground ground Timber, Leaves Stumps, Branches Roots Below Timber, ground Leaves Stumps, Below Roots ground Stumps, Roots Mortality, wind throw Timber, thinnings Products use Re-cycle Biomass energy Products In use waste Decomposition Litter, dead wood Landfill Stable humus (soil C) Biomass growth models 16/05/2008 2

3 Single tree models? Why? Can model interactions between species and size classes in complex stands = patterns of succession Applicable to mixed stands (40 % affor, NFI, 2007) No stand management assumptions (dynamic) and age independent Suitable for unconventional management Accounts for site index, climate, competition etc. NFI compatible i.e. model application data set compatible (some problems?) Suitable paramerisation data set (Coillte PSP) Thinning spacing experiments (1960) Diff spp Good for competition sub-model Include GIS attributes Which type? Distance independent Model DBH and H increment- include young stands 16/05/2008 3

4 y = asize + bcomp + Model description csite y is DBH or H increment usually on a 5 year basis y = asize + bcomp + csite 1) SIZE is individual tree size and vigour (DBH or H) 2) COMP is competitive effects (status in relation to neighbouring trees) 3) SITE (plot specific variables) 16/05/ Hasenhauer and Monserand, 1996

5 1) Size (DBH ot H): tree size and vigour 2) 2 ln( DBH DBH + 1 ) ij = ui + a2.ln( DBH ) + a3.ln( CR) + a4. Where CR is 2 logit( CRij ) = ui + a1hd + a2h + adbh + b1bal + b2dens + eij HD height to D, H is Height ccomp = ui + a1. BAL + a2.ln( CCF) CCF is % of max crown area of open grown trees relative to sample plot BAL is the basal area of larger trees (NFI?) measures of social position of tree within canopy 3) csite = ui + a1. SL + a2. ELEV + a3humus + a4az + a5ei Slope, ELEVation, Humus, Wind Index, AZumuth etc Can include climatic zone (ESC) or SI 16/05/ e ij

6 Coillte permanent plot data Data extraction and sub model development DBH, H, CR, Site, mortality, competition Single tree growth models (Parameterisation) NFI 2006 Data extraction DBH, H, CR, Site, mortality, competition, BAL Research Biomass functions Climate change CARBWARE Validation Interpolation of growth NFI 20?? 16/05/2008 6

7 Data extraction- NFI compatibility How do we derive DBH, BAL etc if all trees are not measured? If tree not measured is outside R1 then ratio if missing trees to measured trees (500/28.3)-1:1 BALi = SUMj(I(DBHj>DBHi)*BAj*ratio) where SUMj means sum for all trees excluding the target tree I(.) = 1 if its argument is TRUE, 0 otherwise BAj is the basal area of the jth tree 16/05/2008 7

8 Aim Sub models Derive missing values H and CR for Coillte PSP and NFI Improve generalised DBH-H model in NFI Derive an estimate of Top H with measure of error Test BAL determination on NFI plot data 1) Significant improvement to generalised DBH-H model Yellow: Chapman-Richards (BIC = 79610) Single tree model (BIC = 54138) Black = measured, red = model Pr edi ct ed Val ue 40 HEIGHT H DBH DBHT /05/2008 c ( c c2bal) H = a(1 exp( b. DBH )) H = ( a + a BAL + a DENS + a BA)(1 exp( b. DBH ))

9 Testing the homogeneity assumption Hei ght _m Hei ght _m PSP calibrated model r 2 = 0.81, RMSE = 2.29, slope = NFI calibrated model r 2 = 0.96, RMSE = 1.09, slope = 0.99 Model height Hei ght _m NFI measured height Hei ght _m Is apparent error in the PSP vs NFI calibrated model related to: a) BAL spatial assumption? b) Differences in the forest structure e.g. A larger H range in NFI? 16/05/2008 9

10 Summary of results to date Distance independent single tree approach best suited for our requirements (NFI) Introduction of competition factors significantly improves generalised model predictions for both H and CR Works across all thinning and spacing conditions Validated with non research PSP and NFI plots including mixed stands Imputation of BAL (NFI) may introduce uncertainty but: This needs investigation using sensitivity analysis? The introduced error is much smaller when compared to error of prediction when simpler models are used (i.e.no competition variables)- trade off 16/05/

11 Outputs Generalised single tree growth models for: Spruce (Sept 08), Pine, Douglas Fir, Larch, SGB, FGB Software to be used with NFI (run alongside Field-map) developed by PTR Ltd Generalised DBH-H, CR sub models TOP H estimates for NFI plot productivity est. 16/05/

12 Pre-processing Single-tree model Biomass allocation models NFI Model inputs Growth projection Model outputs Existing Field-Map Software Soils stratification LULUCF UNFCCC 3 Input files GIS iforis LPUS Soils, CORINE Output files Scenarios LULUCF Soil factors LULUCF 2 Stratification Scale-up Scenarios Biomass, deadwood Litter, soil Input file Harvest, fire N fert. KP tables HWP 16/05/