

h1. N2O emission from nitrification



h1. Bayesian uncertainty analysis






# Math markup



{{>toc}}






Here is some math markup: $`a^2 + b^2 = c^2`$



h2. Description






Or more complicated:



$`\sum_{i=1}^n i^3 = \left( \frac{n(n+1)}{2} \right)^2`$



$x^{yield}_{t}$






It's based on $`\KaTeX`$



Parameter determination for processes in LPJmL is mostly achieved by



literature values. In case, data are available, the estimation of



uncertainties and evaluation of current parameterization is possible by



Bayesian analysis. Here, we give an example how to use data for a better



grass parametrization.






# GitLabspecific markup



I can refer to a specific commit, just by pasting the hash here: b3ede2e52b64026c68d90519d93b3bce58f4c46d



h2. Details






Or here: 80fbf7b1627d45d8d857481585d23430fb9b354b



a more detailed description of it (data sources, data preparation,



meaning, usage...). Refer to references if you have any using



footnotes\[1\]






I can refer to an issue, starting with the $`#`$: #2



h2. Technical Note






Here is another wiki page about [Something Else](somethingelse)



What you need are \* data \* the R package FME\[1\]






# Another heading



 [ ] a task



 [x] a completed task



Following the procedure there, you can \* define a cost function that



gives the deviation of the model output from the observations \*



determine the local sensitivity to a large set of parameters \* evaluate



the most prominent parameters that should be included in the Monte Carlo



simulations by determining the collinearity \* fit the model to the data



using the chosen subset of parameters \* perform the Markov chain



MonteCarlo simulations \* evaluate the uncertainty of the model output



due to the parameter values accepted in the Monte Carlo chain.






========================================



h2. Developer(s)






# h1



Susanne Rolinski, Anja Rammig, Werner von Bloh






# N2O emission from nitrification



h2. See Also






"Parton et al. 1996":http://onlinelibrary.wiley.com/doi/10.1029/96GB01455/abstract gives function



\[\[Upscaling\]\], \[\[Downscaling\]\], \[\[Wiki\]\], \[\[Mathematical



Description\]\], \[\[Missing wiki page\]\]






$`N_{N2O} = N_{H2O} \cdot N_{pH} \cdot N_T \cdot (Kmx + Nmx \cdot N_{NH4})`$



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wiki page already exists or not. Also link pages that do not exist yet!



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are writtn in red.






with



$N_{N2O} = ((WFPSb)/(ab))^{d\cdot (ba)/(ac)} \cdot ((WFPSc)/(ac))^d$



* $WFPS$ is water filled pore space of the soil



* parameters $a$ to $d$ given for sandy and medium soil



* source for functions given as Doran et al. 1988



h2. References






$N_{pH} = 0.56+1/\pi\cdot \arctan(\pi\cdot 0.45 \cdot(pH5))$



* we ignore this limitation






$N_T = 0.06+0.13\cdot \exp(0.07 \cdot T_{soil})$



* based on data by Sabey et al. (1959)






$Kmx = 0.00038$ gN m$^{2}$ d$^{1}$



* N turnover coefficient



* site specific but given for different sites



* for natural soils given as 3.8 and 3.9 (gN ha$^{1}$ d$^{1}$)






$Nmx = 0.003$ gN m$^{2}$ d$^{1}$



* maximum nitrification flux of N$_2$O with excess NH$_4$.






$N_{NH4} = 1  \exp(0.0105\cdot NH4)$



* NH4 here given as $\mu$g N per g soil



* in the same range as gN m$^{2}$ so that formula taken as it is






Process is incorporated in source:branches/nitrogen_rev2142/src/soil/littersom.c after mineralization. 


\ No newline at end of file 


fn1. K. Soetaert and T. Petzoldt (2010): Inverse Modelling, Sensitivity



and Monte Carlo Analysis in R Using Package FME. Journal of Statistical



Software, 33, 3, 128. 


\ No newline at end of file 