Unraveling the Tropical Peatland Carbon Cycle

Integrated research approach

This research project combines three complementary approaches to improve our understanding of peatland carbon dynamics on the island of Borneo. These approaches include advanced in situ measurement techniques, such as eddy covariance observations, remote sensing–based modelling, and integration with Earth System Models (ECMs), including CLM5 and LPJmL.

• Modern in situ measurement methods, particularly the eddy covariance (EC) technique, enable highly accurate observations of carbon and energy exchanges between ecosystems and the atmosphere. However, these measurements are typically constrained to relatively small spatial footprints, usually covering areas of approximately 1–5 km². Although the foundational concepts of the EC method were first introduced during the late 1950s and early 1960s, broader experimental application and validation emerged in the late 1980s and early 1990s. Since then, the technique has evolved considerably, becoming an increasingly advanced and reliable approach for investigating heat and trace gas fluxes within ecosystem–atmosphere interactions (see Montgomery 1948; Swinbank 1951; Obukhov 1951; Baldocchi 2003; Foken et al., 2011).
• Remote sensing–based models, such as the widely used Vegetation Photosynthesis Model (VPM), provide valuable opportunities to estimate carbon uptake by vegetation through photosynthesis (by utilizing satelite data such as MODIS LAI and FAPAR), commonly referred to as gross primary productivity (GPP). These approaches are essential for improving our understanding of carbon dynamics across larger spatial scales, as they provide crucial information on the spatial and temporal variability of ecosystem productivity.
• Evaluating peatland management strategies requires robust assessments of ecosystem processes. However, single-point measurements, such as CO₂ and CH₄ flux observations, often do not adequately capture the substantial spatial variability across peatland landscapes, leading to considerable uncertainties when extrapolated to larger regions. Therefore, integrating these observations with Earth System Models (ESMs) is essential. As advanced climate modelling tools, ESMs are capable of explicitly representing biogeochemical processes and their interactions with the physical climate system. This enables the simulation not only of physical climate dynamics, but also of the biological and chemical feedback mechanisms associated with ecosystem processes, including the impacts of anthropogenic activities on the Earth system (Flato 2011).