Transcript SimCoP - Capsis

Evaluation of two European growth models
for Douglas fir
Minna Pulkkinen, Holger Wernsdörfer & François Ningre
LERFoB, AgroParisTech, INRA, F-54000, Nancy, France
1
Journées CAQSIS, Montpellier, 5–7/4/2016
Background
 Project "Assessment of new forest production systems
for Douglas fir – towards a simulation tool linking
research, development and teaching"
 General driving problematics: need to renew forest
management
- Increasing demand for wood as renewable material and
source of energy
- Requirement of sustainability, favourable carbon and
nutrient balances, maintenance of biodiversity, ...
2
 Growth models as tools in development of new
management scenarios
- Key question: how well are models able to simulate new
management scenarios?
- Evaluation of model simulations against observed data:
if a model is able to reproduce actualised contrasting
management scenarios, it is likely to be able to simulate
new scenarios as well
- Evaluation data: repeatedly measured field experiments
with (i) highly contrasting initial densities and (ii) varied
timings and intensities of thinnings
- Sensitivity analysis: Identifying relations and phases of
development critical for model performance
 model improvement
3
 Different types of growth models may have different
ability to produce robust results outside their
construction / parameterisation conditions
- modelling paradigm: empirical, semi-functional,
functional
- growth unit and spatial explicitness
- initial state and input data requirements
- time step
- incorporation of randomness in modelled processes
 Aim of this study: evaluate two tree-based European
growth models – fully empirical vs. semi-functional –
against independent data in view of realistic
simulation of new management scenarios for Douglas
fir in France
4
Models: Gymnos
Données initiales:
Age, Nha, I0
- Perin, Ligot et al. (2012)
Initialisation
- For even-aged pure stands
of Douglas fir, Norway
spruce and two larch
species
- Empirical, spatially
inexplicit
- Parameterised with
extensive data sets
from S Belgium
Sorties :
Suppression des
arbres éclaircis
oui
Peuplement :
Gha, Nha, RDI
Hdom, Age, Surface
Liste d’arbres
Cdom, Cmoy, Cg
Age, Hdom,…
Eclaircie ?
Croissance :
non
Autoéclaircie
oui
Nha > Nhamax ?
non
Aget+1 = Aget + 1
Hdomt+1 = Hdomt + dHdomt
Cit+1 = Cit + dCit
(Perin 2013)
- Fundamental relationships:
(i) Hdom growth
(ii) tree circumference growth (as function of Hdom, stand density and tree size)
- Mortality: maximum stand basal area w.r.t. mean tree circumference
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- Initial state at age t: plot size, stand age, tree diameters, site index parameter
of Hdom vs. age curve (H50)
Models: SimCoP
- Ottorini (1991, 1995); based on work
by Mitchell (1975)
- For even-aged pure Douglas fir stands
- Semi-functional, spatially explicit
- Parameterised with very detailed tree
analysis data from N-E France
- Basic idea:
height growth (Hdom growth adjusted
with vigour and foliar volume)
 crown growth in 3D (restricted by
neighbouring crowns)
 “effective” foliar volume
 stem volume growth
- Mortality with (i) suppression by
neighbouring trees and (ii) Reineke’s
self-thinning rule
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- Initial state at age 0: tree coordinates,
tree vigours, site index parameter of
Hdomvs. age curve (H50)
Evaluation data
 A subset of 62 plots from permanent field
experiments in Baden-Württemberg, S-W Germany
(FVA Freiburg)
 Measurements at irregular time intervals
- diameter on all trees at all observation time points
- height on (a subset of) trees at some observation time
points
- cause and time of removal on all trees
 exact information on mortality and thinnings
- co-ordinates of trees not available in all plots
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 Plot selection criteria
- pure one-storey Douglas fir stands
- tree coordinates available
- no interventions before first observation time point
- Hdom known at least at three observation time points
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Ninit
(ha-1)
H50
(m)
No. of.
obs.
time
points
Length
of obs.
period
(years)
No. of
cuttings
Cutting
removal
(%)
Cutting
factor
Min.
150
27.1
3
5
1
0.2
0.013
1st quart.
1000
32.5
6
20
3
16.2
0.802
Median
1056
34.0
7
22
4.5
21.4
0.937
3rd quart.
2017
36.8
7
22
6
28.4
1.026
Max.
4017
39.6
7
24
7
91.7
1.382
Simulations: Initial state & parameterisation
 Initial state in Gymnos: tree diameters at first
observation time point
- measured values from data
 social statuses of trees
- drawn from theoretical distribution
 Initial state in SimCoP: tree vigours (and coordinates)
at age 0
- estimated from data (h/Hdom) at first observation time
point and adjusted for mean and variance of default
theoretical distribution
 social statuses of trees
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- drawn from theoretical distribution
 Site index parameter H50
- estimated by fitting Hdom vs. age curve into measured
data, conditional on default values of other parameters
of curve
- with SimCoP, fitting used also to select Hdom vs. age
curve among three alternatives
 Otherwise original parameterisation used without
modifications
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Simulations: Thinnings
 Trees killed by natural hazards (wind, ice / snow, game
animals, ...) included in thinnings
 introducing additional, sometimes drastic thinnings
in data
 Timing of thinnings determined by age (cf. Hdom, RDI, ...)
 easy comparison with observations
 Intensity of thinnings determined by stocking density to
be removed (cf. target density after thinning)
 no artificial correction of possibly erroneous beforethinning mortality
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 Employing two thinning algorithms (Capsis)
1) List thinning
-
removing exactly same tree individuals as were
removed in data
 close imitation of observed thinnings, but difficult to
use in further work (scenario simulations)
Size-class thinning
2)
-
removing trees in diameter classes in same proportion
as in data
-
selecting removed trees within class randomly
-
spatial control
 fairly good imitation of observed thinnings, possible to
use in further work
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Simulations: Randomness
 Sources of randomness
- Gymnos: generation of tree diameters at initial state
selection of trees in mortality process
- SimCoP: generation of tree vigours at initial state
- Size-class thinning algorithm: selection of trees within
each class
 With each simulation scheme (model + initial state
generation + thinning algorithm) involving randomness
100 repeated simulations per plot
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Source of randomness
Model
Gymnos
Simulation scheme
Initial
state
Mortality
mechanism
Measured diameters &
list thinning
x
Measured diameters &
size-class thinning
x
Random diameters &
list thinning
x
x
Random diameters &
size-class thinning
x
x
Thinning
algorithm
x
x
Adjusted vigours &
list thinning
SimCoP
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Adjusted vigours &
size-class thinning
x
Random vigours &
list thinnings
x
Random vigours &
size-class thinning
x
x
Evaluation criteria
 Evaluation based on stand characteristics computed in a
uniform manner from tree diameters and heights:
stocking density, basal area, Hdom, Ddom
 No extra variation between models due to different
volume, biomass etc. equations
 Output of each simulation scheme: 100 time series
(repetitions) of stocking density, basal area, Hdom, Ddom
in each of 62 plots
 Need to summarise variation (i) over repetitions at
each observation time point in each plot, (ii) over
observation time points in each plot, and (iii) over plots
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 Average performance of each simulation scheme in
each plot
- compare mean simulated time series to observed time
series
- summarise resulting time series of relative errors into
plot-wise diagnostics
(a) mean of relative errors over observation time
points  average systematic error over observation
period ("relative bias")
(b) mean of absolute values of relative errors
 aggregated measure of performance at individual
observation time points
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(c) standard deviation of relative errors
 variation in performance between individual
observation time points ("relative precision")
 Variation between simulated time series of each
simulation scheme in each plot
- compare every simulated time series to observed time
series
- summarise resulting set of relative errors into plot-wise
diagnostics
(a) mean of absolute values of relative errors over
observation time points
(b) standard deviation of relative errors
 With Gymnos, first observation time point (having
perfect match!) not included in diagnostics
computation
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Results: Average model performance in plot
over simulation period
Ddom
Stocking density
List thinning
Size-class thinning
Gymnos SimCoP Gymnos SimCoP
Basal area
List thinning
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Gymnos SimCoP
Size-class thinning
Gymnos SimCoP
List thinning
Gymnos SimCoP
Size-class thinning
Gymnos SimCoP
Both models had slight difficulty to
reproduce observed mortality
( overestimation of stocking
density)
Both models predicted Ddom well
Both models overestimated basal
area
Hdom: Gymnos predicted very well, SimCoP underestimated on average in
majority of plots
Hdom
List thinning
Gymnos SimCoP
Hdom
Size-class thinning
List thinning
Gymnos SimCoP
Gymnos SimCoP
Mean of relative
errors  0.4 %
St. dev. of relative
errors  11 %
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Size-class thinning
Gymnos SimCoP
Small mean of relative
errors over time does
not necessarily mean
good performance at
individual time points!
Results: Effect of initial density
Basal area
Ninit = 150-1200
n = 36
Ninit  2000
n = 22
Ninit  4000
n=4
Gymnos, list thinning
SimCoP, list thinning
Gymnos, size-class thinning
SimCoP, size-class thinning
Initial density affected average model performance: stronger overestimation
of basal area with low densities, underestimation with high densities, best
performance with medium densities (mostly present in parameterisation?)
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Results: Importance of early performance
Basal area
Performance of SimCoP iat early phase of
simulation affected its average performance over
whole simulation period (in basal area and Ddom)
If SimCoP was doing well at 1st observation time
point, it was doing well over the whole observation
period
Basal area: plots with same (small)
error range at 1st obs. time point
Basal area: all plots
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SimCoP
SimCoP
SimCoP
SimCoP
Concluding remarks
 Both models were able to predict relatively well – with
their original parameterisation – stand growth and
thinning reaction of independent contrasting data
 similar performance achieved with very different
parameterisation effort
 Starting from age 0, SimCoP was sensitive to early
phase performance
 Both models can be used for developing new
management scenarios
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Acknowledgements
 Access to growth models and help with implementation:
T. Bronner & J.-M. Ottorini (SimCoP)
J. Perin & G. Ligot (Gymnos)
 Evaluation data: A. Albrecht
 Capsis tools: F. de Coligny
 Funding: Chaire “Forêts pour demain”, DGER, Région
Lorraine, AgroParisTech, INRA
The UMR 1092 LERFoB is supported by a grant overseen by the French
National Research Agency (ANR) as part of the “Investissements d'Avenir”
program (ANR-11-LABX-0002-01, Laboratory of Excellence ARBRE)
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Results: Effect of initial density
Ddom
Stocking density
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