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h1. N2O emission from nitrification
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h1. Bayesian uncertainty analysis
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# Math markup
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{{>toc}}
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Here is some math markup: $`a^2 + b^2 = c^2`$
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h2. Description
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Or more complicated:
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$`\sum_{i=1}^n i^3 = \left( \frac{n(n+1)}{2} \right)^2`$
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$x^{yield}_{t}$
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It's based on $`\KaTeX`$
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Parameter determination for processes in LPJmL is mostly achieved by
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literature values. In case, data are available, the estimation of
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uncertainties and evaluation of current parameterization is possible by
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Bayesian analysis. Here, we give an example how to use data for a better
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grass parametrization.
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# GitLab-specific markup
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I can refer to a specific commit, just by pasting the hash here: b3ede2e52b64026c68d90519d93b3bce58f4c46d
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h2. Details
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Or here: 80fbf7b1627d45d8d857481585d23430fb9b354b
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a more detailed description of it (data sources, data preparation,
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meaning, usage...). Refer to references if you have any using
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footnotes\[1\]
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I can refer to an issue, starting with the $`#`$: #2
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h2. Technical Note
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Here is another wiki page about [Something Else](something-else)
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What you need are \* data \* the R package FME\[1\]
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# Another heading
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- [ ] a task
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- [x] a completed task
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Following the procedure there, you can \* define a cost function that
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gives the deviation of the model output from the observations \*
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determine the local sensitivity to a large set of parameters \* evaluate
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the most prominent parameters that should be included in the Monte Carlo
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simulations by determining the collinearity \* fit the model to the data
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using the chosen subset of parameters \* perform the Markov chain
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Monte-Carlo simulations \* evaluate the uncertainty of the model output
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due to the parameter values accepted in the Monte Carlo chain.
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========================================
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h2. Developer(s)
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# h1
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Susanne Rolinski, Anja Rammig, Werner von Bloh
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# N2O emission from nitrification
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h2. See Also
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"Parton et al. 1996":http://onlinelibrary.wiley.com/doi/10.1029/96GB01455/abstract gives function
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\[\[Upscaling\]\], \[\[Downscaling\]\], \[\[Wiki\]\], \[\[Mathematical
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Description\]\], \[\[Missing wiki page\]\]
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$`N_{N2O} = N_{H2O} \cdot N_{pH} \cdot N_T \cdot (Kmx + Nmx \cdot N_{NH4})`$
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Links to other Wiki pages, that are related. It doesn't matter if the
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wiki page already exists or not. Also link pages that do not exist yet!
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Links to existing pages are written blue, links to non-existing pages
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are writtn in red.
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with
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$N_{N2O} = ((WFPS-b)/(a-b))^{d\cdot (b-a)/(a-c)} \cdot ((WFPS-c)/(a-c))^d$
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* $WFPS$ is water filled pore space of the soil
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* parameters $a$ to $d$ given for sandy and medium soil
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* source for functions given as Doran et al. 1988
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h2. References
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$N_{pH} = 0.56+1/\pi\cdot \arctan(\pi\cdot 0.45 \cdot(pH-5))$
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* we ignore this limitation
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$N_T = -0.06+0.13\cdot \exp(0.07 \cdot T_{soil})$
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* based on data by Sabey et al. (1959)
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$Kmx = 0.00038$ gN m$^{-2}$ d$^{-1}$
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* N turnover coefficient
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* site specific but given for different sites
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* for natural soils given as 3.8 and 3.9 (gN ha$^{-1}$ d$^{-1}$)
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$Nmx = 0.003$ gN m$^{-2}$ d$^{-1}$
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* maximum nitrification flux of N$_2$O with excess NH$_4$.
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$N_{NH4} = 1 - \exp(-0.0105\cdot NH4)$
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* NH4 here given as $\mu$g N per g soil
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* in the same range as gN m$^{-2}$ so that formula taken as it is
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Process is incorporated in source:branches/nitrogen_rev2142/src/soil/littersom.c after mineralization. |
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\ No newline at end of file |
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fn1. K. Soetaert and T. Petzoldt (2010): Inverse Modelling, Sensitivity
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and Monte Carlo Analysis in R Using Package FME. Journal of Statistical
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Software, 33, 3, 1-28. |
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\ No newline at end of file |
