Risk of erosion in peat soils - an investigation using Bayesian belief networks

Peat is an important carbon sink in the context of climate change. However, well-documented examples suggest that risk of peat erosion is widespread and significant. Our understanding of peat vulnerability to erosion is commonly constrained by the complexity of drivers, and their interactions, in this process. However, the key constraints are: limited, consistent and comprehensive quantitative data relating to this process and, more significantly, the explicit relationships between the occurrence of peat erosion and its causes and drivers. Bayesian belief networks (BBNs) provide a methodology for integrating qualitative and quantitative knowledge. BBNs can capture and structure available knowledge and rationalize complex interactions, where empirical data are limited or poorly compatible and processes are complex or uncertain. In this article we explore the BBN potential to advance our understanding and to identify gaps in current knowledge. BBN has been demonstrated to be a useful tool in structuring and utilizing currently available knowledge, often with limited evidence, of peat's actual exposure to erosive forces. Despite considerable research into peat erosion processes and understanding the inherent vulnerability of peat, results presented indicate clear gaps in knowledge regarding the role of land management, spatially explicit data related to land management as well as limited evidence of the relevant relationships between many of the variables. The attention of further research will focus on these gaps. The BBN approach provides a framework in which different scenarios of biophysical, climatic and land management (social and economic) conditions can examine and assess the probability of erosion.