Simulation of peat accumulation dynamics: Insights from a Pole Forest and Palm Swamp in Amazonia

Tropical peatlands are among the most carbon-dense terrestrial ecosystems, but we lack an understanding of their sensitivity to environmental change. Dynamic peat models provide a way to understand the tipping points in peatland formation, and can be constrained using a palaeoecological approach, namely the peatlands' successional stages of development identified through peat core (pollen) analysis. 

Using the 1D-HPMtrop dynamic peat model we simulate the peat accumulation process in two ecosystems of the Peruvian Amazon: a Pole Forest (PF) (7809-cal yr BP) and Palm Swamp (PS) (1215-cal yr BP), parameterized using net primary productivity (NPP) and decomposition rate (k_exp) data from field plots. We incorporate a novel concept of palaeoecological period within the existing model code (derived from pollen diagrams at the two sites). Period-specific parameters were derived based on the assumption that current ecosystem parameters could represent similar conditions in the past and the calibration of sensitive variables to reflect these conditions accurately.  

This implementation enhances the HPMtrop structure, making it a more flexible model capable of generating age-depth curves based on period-specific parametrization. The curves can be compared with the reference age-depth curve derived from radiocarbon dating. Therefore, it is important to understand the nature of the ecosystem in each period or stage, including the parameters associated with each ecosystem type and the driving conditions. A reliable simulation of the peat-depth curve enables sensitivity analysis, allowing us to develop hypotheses about changes in peat accumulation under changes in key parameters.  

We vary key drivers and parameters including anoxia rate, NPP, k_exp, and precipitation to understand the vulnerability of these two tropical peatlands to environmental change. We find highly variable sensitivity to different parameters with NPP being the most influential variable. The impact also varies in magnitude between PS and PF ecosystems. These ecosystems face a variety of threats, and our results could potentially be used to inform conservation and management strategies in some of the most carbon-dense ecosystems on Earth.