A human-environmental network model for assessing coastal mitigation decisions informed by imperfect climate studies

A Bayesian network model is developed to explore the interaction between physical and social processes that influence mitigation decisions and outcomes for extreme events. The network includes statistical relationships for event occurrence and magnitude; uncertainty in the parameters of these models; a high degree of variability in the sequence of events that occurs in any given time interval, and the possibility of long-term trends in the frequency, magnitude and impact of events. The model is applied to coastal storm surge events in the New York City (NYC) area. A 50 cm increase in sea level is predicted to approximately double the expected cumulative damage over a 40-year period. A 20% increment in storm frequency yields a further predicted increase of about 18% in the cumulative damage. The uncertainties in long-term trends associated with climate change may be reduced by scientific studies. However the value of this information is affected both by study accuracy and the extent of its trust, acceptance and utilization by decision makers. Implications of this are assessed in the model, showing that the probability of regret is notably reduced when climate study results are used to support mitigation decisions. This is demonstrated even when the studies have relatively low accuracy, moreso when they exhibit good or perfect accuracy. Based on model insights and limitations, further research needs are identified to better understand extreme event risk perception and management in coupled human-environmental systems.