The Risk of Peat Erosion from Climate Change: Land Management Combinations—An Assessment with Bayesian Belief Networks

Modeling risk factors to soils is constrained by the lack of key data and understanding that explicitly and quantitatively link specific threats to risk. Peat erosion results from the complex interaction of climatic, topographic, and anthropogenic influences acting over a long period of time. With numerous contemporary factors operating to perpetuate the erosion processes, it is often difficult to identify with certainty what actually are the initial and subsequent drivers of erosion. In this situation, expert opinion forms a vital source of information. Here we demonstrate how Bayesian Belief Networks (BBN) can be used to combine quantitative data from the National Soils Inventory of Scotland (NSIS) with qualitative expert knowledge to estimate risk of peat erosion in Scotland. This model was used to identify the main factors associated with peat erosion. It was shown that climatic variables (increased temperature, decreased precipitation) are the most important risk factors for perpetuating peatland erosion. However, the BBN approach also indicated that maintaining good vegetation cover is a significant mitigating factor. It would follow that land management practices that impact negatively on vegetation cover would also exacerbate peatland erosion given a hot dry climate.