How vulnerable are peatlands to rapid climate change and human and natural disturbance, and how can we protect their existing carbon stocks and reduce their carbon emissions? By taking a 'smart-data' approach, it is possible to integrate long-term data on carbon emissions with causal knowledge of physical and socio-economic influences. This integration will lead to better models for predicting future peatland carbon emissions and for assessing policy and management interventions for climate action.
Explaining the science
Peatlands are vulnerable to rapid climate change and to disturbances such as wildfire and drainage, with the risk that their huge carbon stocks could be released to the atmosphere very rapidly, further accelerating global warming. To counteract this threat, government, environmental organisations and industry are investing in climate actions to protect undisturbed peatlands and to restore those that have been damaged by human activities such as drainage and afforestation. At present, there is large uncertainty on future peatland carbon emissions and on the potential benefits of conservation and restoration activities.
This research will draw together quantitative, semi-quantitative and qualitative information on peatlands and their carbon emissions to scope a high-level Bayesian network. The network will sketch out how government policy, specific management interventions and natural mechanisms of resilience are linked to peatland carbon emissions via causal relationships. This causal knowledge will be based on numerical modelling, scientific understanding of peatland functioning and expert advice on management activities and socio-economic factors. By integrating these different types of knowledge, this 'smart-data' approach should lead to better predictive models that can accurately predict future peatland carbon emissions and the benefits of specific policies and interventions.
This aim of this project is to develop a framework to integrate different types of information on peatlands: quantitative data on greenhouse gas fluxes and environmental drivers, such as temperature and precipitation; semi-quantitative understanding of controls and interactions, such as feedbacks between peat formation and water loss; and qualitative information on socio-economic factors, such as land-use policy and site-level management actions.
One part of the research will assimilate long-term records of carbon emissions from two Scottish peatlands into a process-based model. This data assimilation will allow for the identification of key environmental drivers and for quantifying uncertainties on peatland carbon emissions. The other part of the work will develop a causal framework that links carbon emissions predicted by the process-based model to the broader ecological and hydrological functioning of peatlands and human interventions. This AI-based ‘smart data’ approach will enable better understanding of interactions between environmental, climatic, social and economic processes that drive policy and management of peatlands and their carbon emissions.
The proposed research will provide proof of concept on a 'smart data' approach, with potential for future wide-ranging impacts on climate change science and policy-making. The innovative approaches for quantifying uncertainty and risk have potential to transform predictions of peatland response to climate change and management or policy interventions. The approach is, in principle, readily transferable to other ecosystems whose behaviour can suddenly and dramatically change due to sustained pressures or rare events.