home.md

-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.
 
-# GitLab-specific 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](something-else)
+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
+Monte-Carlo 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
+\[\[Upscaling\]\], \[\[Downscaling\]\], \[\[Wiki\]\], \[\[Mathematical
+Description\]\], \[\[Missing wiki page\]\]
 
-$`N_{N2O} = N_{H2O} \cdot N_{pH} \cdot N_T \cdot (Kmx + Nmx \cdot N_{NH4})`$
+Links to other Wiki pages, that are related. It doesn't matter if the
+wiki page already exists or not. Also link pages that do not exist yet!
+Links to existing pages are written blue, links to non-existing pages
+are writtn in red.
 
-with
-$N_{N2O} = ((WFPS-b)/(a-b))^{d\cdot (b-a)/(a-c)} \cdot ((WFPS-c)/(a-c))^d$
-* $WFPS$ 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
 
-$N_{pH} = 0.56+1/\pi\cdot \arctan(\pi\cdot 0.45 \cdot(pH-5))$
-* 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, 1-28.
\ No newline at end of file