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Moving towards a measure-able #SoilCarbon model, the MEMS #model centers mineral saturation as a primary driver SOC dynamics. Robertson etal 2019 https://bg.copernicus.org/articles/16/1225/2019/ showing that model-data integration doesn't have to be complicated.
#ScientificLiterature #Biogoechemistry
[[Personal note: Ok I will acknowledge that I don't entirely agree with all their approaches/conclusions here, but still a good paper to read.]]
bg.copernicus.orgUnifying soil organic matter formation and persistence frameworks: the MEMS model<p><strong class="journal-contentHeaderColor">Abstract.</strong> Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models
have traditionally been simulated as immeasurable fluxes between conceptually
defined pools. This greatly limits how empirical data can be used to improve
model performance and reduce the uncertainty associated with their
predictions of carbon (C) cycling. Recent advances in our understanding of
the biogeochemical processes that govern SOM formation and persistence demand
a new mathematical model with a structure built around key mechanisms and
biogeochemically relevant pools. Here, we present one approach that aims to
address this need. Our new model (MEMS v1.0) is developed from the Microbial
Efficiency-Matrix Stabilization framework, which emphasizes the importance of
linking the chemistry of organic matter inputs with efficiency of microbial
processing and ultimately with the soil mineral matrix, when studying SOM
formation and stabilization. Building on this framework, MEMS v1.0 is also
capable of simulating the concept of C saturation and represents
decomposition processes and mechanisms of physico-chemical stabilization to
define SOM formation into four primary fractions. After describing the model
in detail, we optimize four key parameters identified through a
variance-based sensitivity analysis. Optimization employed soil fractionation
data from 154 sites with diverse environmental conditions, directly equating
mineral-associated organic matter and particulate organic matter fractions
with corresponding model pools. Finally, model performance was evaluated
using total topsoil (0–20 cm) C data from 8192 forest and grassland sites
across Europe. Despite the relative simplicity of the model, it was able to
accurately capture general trends in soil C stocks across extensive gradients
of temperature, precipitation, annual C inputs and soil texture. The novel
approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to
improve our forecasts of how soils respond to management and environmental
perturbation. Ensuring these forecasts are accurate is key to effectively
informing policy that can address the sustainability of ecosystem services
and help mitigate climate change.</p>