Risk-Based Trend Detection for Climate Change Adaptation.
Rosner, Ana.
2012
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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 ... read moreerror 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.read less - ID:
- 1544c132q
- Component ID:
- tufts:20988
- To Cite:
- TARC Citation Guide EndNote
