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Zoltán BARCZA 2019-01-22 15:58:37 +01:00 committed by GitHub
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@ -138,7 +138,7 @@ plot(gpp4*1000,type="l")
Assume that we would like to dig a bit deeper with the model and understand the effect of changing ecophysiological variables on the model results. This can easily be performed with RBBGCMuso. Execute the following command in R/RStudio: Assume that we would like to dig a bit deeper with the model and understand the effect of changing ecophysiological variables on the model results. This can easily be performed with RBBGCMuso. Execute the following command in R/RStudio:
#+BEGIN_SRC R :eval no #+BEGIN_SRC R :eval no
musoQuickEffect(calibrationPar = 25, startVal = 0,endVal = 9,nSteps = 5,outVar = 3009) musoQuickEffect(calibrationPar = 25, startVal = 0, endVal = 9, nSteps = 5, outVar = 3009)
#+END_SRC #+END_SRC
This command selects the 25th line in the ecophysiological constants (EPC) file (this is base temperature), then it starts to replace the original value from 0 to 9 in 5 consecutive steps. In this example GPP is selected (variable number 3009, which is the 27th variable), so the effect of varying base temperature on GPP is calculated using 9 simulations. The result is a spectacular plot where color coding is used distinguish the parameter values. This command selects the 25th line in the ecophysiological constants (EPC) file (this is base temperature), then it starts to replace the original value from 0 to 9 in 5 consecutive steps. In this example GPP is selected (variable number 3009, which is the 27th variable), so the effect of varying base temperature on GPP is calculated using 9 simulations. The result is a spectacular plot where color coding is used distinguish the parameter values.