Peat Carbon Vulnerability to Projected Climate Warming in the Hudson Bay Lowlands, Canada: A Decision Support Tool for Land Use Planning in Peatland Dominated Landscapes

Abstract

Peatlands help regulate climate by sequestering (net removal) carbon from the atmosphere and storing it in plants and soils. However, as mean annual air temperature (MAAT) increases, peat carbon stocks may decrease. We conducted an in-depth synthesis of current knowledge about ecosystem controls on peatland carbon storage and fluxes to constrain the most influential parameters in probabilistic modelling of peat carbon sinks, such as Bayesian belief networks. Evaluated parameters included climate, carbon flux and mass, land cover, landscape position (defined here as elevation), fire records, and current and future climate scenarios for a 74,300 km2landscape in the Hudson Bay Lowlands, Canada. The Bayesian belief network was constructed with four tiers: 1) exposure, expressed as MAAT, and the state variables of elevation and land cover; 2) sensitivity, expressed as ecosystem conditions relevant to peat carbon mass and its quality for decomposition, peat wetness, and fire; 3) carbon dioxide and methane fluxes and peat combustion; and 4) vulnerability of peat carbon sink strength under warmer MAAT. Simulations were conducted using current (−3.0 to 0.0°C), moderately warmer (0.1–4.0°C), and severely warmer (4.1–9.0°C) climate scenarios. Results from the severely warmer climate scenario projected an overall drying of peat, with approximately 20% reduction in the strong sink categories of net ecosystem exchange and peat carbon sink strength for the severely and, to a lesser degree, the moderately warmer climate scenarios relative to current MAAT. In the warmest temperature simulation, probability of methane emission decreased slightly and the probability of the strong peat carbon sink strength was 27% lower due to peat combustion. Our Bayesian belief network can assist land planners in decision-making for peatland-dominated landscapes, such as identifying high carbon storage areas and those projected to be at greatest risk of carbon loss due to climate change. Such areas may be designated, for example, as protected or reduced management intensity. The Bayesian belief network presented here is built on an in-depth knowledge synthesis to construct conditional probability tables, so is expected to apply to other peatland-dense jurisdictions by changing only elevation, peatland types, and MAAT.