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steady state impacts in inverse model parameter optimization. - PowerPoint PPT Presentation
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steady state impacts in inverse model parameter optimization
Carvalhais, N., Reichstein, M., Seixas, J., Collatz, G.J., Pereira, J.S., Berbigier, P., Carrara, A., Granier, A., Montagnani, L., Papale, D., Rambal, S., Sanz, M.J., and Valentini, R.(2008), Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval, Global Biogeochem. Cycles, 22, GB2007, doi:10.1029/2007GB003033.
motivation / goals
• CASA model parameter optimization• spin-up routines force soil C pools
estimates• impacts of the steady state in:
– model performance– parameter estimates / constraints
• propagation of C fluxes estimates uncertainties for the Iberian Peninsula
the CASA model
ha RRGPPNEP
APARNPPPARfAPARAPAR
WT *
p
issii MTWkCRh )1(
Potter et al., 1993
OptT wB10QwsA *
= Css∙ ηCns
• inclusion of a parameter that relaxed the steady state approach: η
approach to relax the steady state approach
OptT wB10QwsA *
Fix Steady State
Relaxed Steady State
111
experiment design
• significance of each parameter:– removing one parameter at a time;
• alternatives to η:– replacing by :
• soil C turnover rates;• extra parameters on NPP and Rh
temperature sensitivity.
• Levenberg-Marquardt least squares optimization
site selection and data
• CARBOEUROPE-IP:– 10 Sites
• optimization constraints: NEP• model drivers:
– site meteorological data;– remotely sensed fAPAR and LAI;– different temporal resolutions
effect of η in optimizationadd
ing
η
IT-N
on [
sink:
542
gC
m-2 y
r-1]
determinants of parameter variability: ANOVA
40
165
33
42
*
39
7 1
33
172
Topt
17
9
2
27
39
6
Bw
9
28
056
42
Q10
464
55
19
12
Aws
83
116
80
FST PRM TMR FST*PRM FST*TMR PRM*TMR
site
parameter vector
temporal resolution
site xparameter vector
site xtemporal resolution
parameter vector x temporal resolution
what drives η?r2: 0.76; α < 0.001
model performance improvements
model performance in relaxed > fixed steady state assumptions.
differences in parameter estimates and constraints
ε*
Topt Bwε Q10 Aws
relaxed
fixed
relaxed
fixedε*
Topt Bwε Q10 Aws
P/P SE/SE
↑NPP ↓Rh
total soil C poolsrelaxed fixedmeasurements
steady state approach impacts
• model performance– relaxed > fixed
• parameter estimates– biases
• parameter uncertainties– relaxed < fixed
• soil C pools estimates– relaxed closer to measurements
propagating parameters / uncertainties
spatial simulations
• Iberian Peninsula• optimized parameters per site:
– optimization: naïve bootstrap approach• no assumption on parameters distribution
– GIMMS NDVIg : 8km, biweekly;
• parameter propagation per PFT: – estimating NEP / NPP / Rh
spatial impacts : NPP 1991
relaxed fixed relaxed - fixed
seasonality : NPP : IPrelaxed versus fixed
iav : NEP : IPrelaxed versus fixed
seasonality and iav : IP
var.
inter annual variability
seasonal amplitude
uncertainties
Min max min max min max
NPP -9% 62% -11% 53% -60% -2%
Rh -15% 74% -39% 131% -60% -2%
NEP -10% 63% -10% 91% -60% 6%
(relax – fix) / fix
remarks
• biases in optimized parameters lead to significant differences in flux estimates: seasonality and iav
• uncertainties propagation show significant reductions under relaxed steady state approaches
• impacts in data assimilation schemes
…