%0 PDF %T Risk-Based Trend Detection for Climate Change Adaptation. %A Rosner, Ana. %8 2017-04-18 %R http://localhost/files/1544c132q %X Abstract: The usual procedure for detecting climate change impacts from historic records chooses statistical criteria (usually alpha=0.05) to minimize the probability of Type I error, claiming a trend exists where it does not. However, it ignores Type II error, failing to detect an increasing trend in storm surges. For coastal climate change adaptation, the physical implication of a Type I error is wasted money on unneeded infrastructure. Repercussions of a Type II error, however, are major storm damages and flooding due to inadequate protection. Decision-makers are poorly served by statistical methods that do not carefully consider this type of error. We propose a new method that combines hypothesis testing, Risk-Based Decision Making, and decision analysis, to evaluate adaptations for a possibly costly but highly uncertain increase in storms. We propose a new metric, Expected Regret, that integrates the statistical certainty and the economic impacts of a trend. This method gives needed attention to the risks of under-preparing; conveys the statistical uncertainty in a physically meaningful way; and addresses the question, "Should we adapt now, despite the uncertainty?"; Thesis (M.S.)--Tufts University, 2012.; Submitted to the Dept. of Civil Engineering.; Advisor: Richard Vogel.; Committee: Paul Kirshen, and Elena Naumova.; Keywords: Environmental engineering, Water resources management, and Statistics. %[ 2022-10-12 %9 Text %~ Tufts Digital Library %W Institution