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Landscape Carbon Modelling


Landscape-scale carbon modelling techniques assess the C exchange between atmospheric and terrestrial pools (vegetation and soil), taking into account human activities, natural disturbances as well as climate change effects. Landscape modelling integrates spatial variability of land cover (e.g. agriculture, grassland, and forestry) and land use (e.g. fertilisation, irrigation, etc.), with the inherent spatial features and heterogeneity of land surfaces (e.g. topography), as well as key environmental controls (e.g. rainfall, temperature, solar radiation, and soil moisture).

Mitigation of climate change promotes C storage and sequestration in the terrestrial pool. While the term “storage” does not necessarily imply a net removal of CO2 from the atmosphere, “sequestration” does. Sequestration of C, especially in soils, may feature residence times ranging from years to millennia while enhancing at the same time soil quality and ecosystem functioning, resulting in tangible co-benefits (e.g. reduced erosion and degradation), or enhanced/stabilised ecosystem services (e.g. nutrient cycling, water retention).

Landscape C modelling is a relatively complex endeavour, as both organic C sequestration in the living biomass (above- and belowground) and soils should be considered, for completeness. Different approaches are available for modelling these elements, which are constantly tested and improved by researchers and practitioners.

C sequestration in biomass is closely linked to plant growth and the corresponding growth models are well known (e.g. sigmoid curves for annual crops, bi-linear curves for grasslands, and Weibull or Chapman-Richards-style asymptotic exponential curves for trees). With increasing computational power, non-linear asymptotic models have become a preferred option. For modelling aboveground C, allometric equations, harvest indices, and C partitioning among plant organs are commonly used to estimate the amount of biomass per individual plant and plant compartment (e.g. leaf, stem, root). It permits extrapolation at the desired geographic scale through assumptions on species diversity, density and age. At the landscape level, often even-aged stands are modelled, at least for forestry applications. Based on aboveground C estimations, it is relatively straightforward to derive below-ground C based for instance on allometric equations. The evolution of below-ground C over time can also be modelled using root growth models.

A plethora of soil models, from the simplest soil organic matter (SOM) models (e.g. Hénin-Dupuis, AMG, RothC) to the most complex agro-ecosystem or soil-plant models (e.g. CENTURY, DAISY, STICS), offer the possibility of predicting SOM turnover based on organic C inputs to soil (i.e. organic fertilisers, plant residues, other biomass) and different pedoclimatic parameters that typically include climate, moisture, and other soil properties. Modelling SOM turnover is more complex because not all relevant mechanisms are fully understood and/or suitable operational models to represent them are scarce. The influence and effect of soil biota activity, for instance, is an active research field. Research on the determination of a potential C saturation threshold in soils is another field under continuous development.

To model the evolution of soil carbon stocks, at any scale, an initial estimation is necessary. Data are obtained via field measurements (sampling) or via time- and cost-efficient techniques such as remote sensing, aimed at limiting physical sampling. Remote sensing is widely used to estimate, with great accuracy, the amount of aboveground biomass in a given area, as well as certain characteristics of (bare) soils, including organic carbon in the first centimetres of soil. Finally, assimilation of earth observational data into appropriate process models permits not only spatialisation of model outcomes, but also location-dependent parameterisation of dynamic models.