Contributed by Kerry Emanuel, October 4, 2017 (sent for review September 15, 2017; reviewed by Cindy L. Bruyere, Efi Foufoula-Georgiou, and James A. Smith)
Natural disasters such as the recent Hurricanes Harvey, Irma, and Maria highlight the need for quantitative estimates of the risk of such disasters. Statistically based risk assessment suffers from short records of often poor quality, and in the case of meteorological hazards, from the fact that the underlying climate is changing. This study shows how a recently developed physics-based risk assessment method can be applied to assessing the probabilities of extreme hurricane rainfall, allowing for quantitative assessment of hurricane flooding risks in all locations affected by such storms, regardless of the presence or quality of historical hurricane records.
We estimate, for current and future climates, the annual probability of areally averaged hurricane rain of Hurricane Harvey’s magnitude by downscaling large numbers of tropical cyclones from three climate reanalyses and six climate models. For the state of Texas, we estimate that the annual probability of 500 mm of area-integrated rainfall was about 1% in the period 1981–2000 and will increase to 18% over the period 2081–2100 under Intergovernmental Panel on Climate Change (IPCC) AR5 representative concentration pathway 8.5. If the frequency of such event is increasingly linearly between these two periods, then in 2017 the annual probability would be 6%, a sixfold increase since the late 20th century.
Author contributions: K.E. wrote the paper.
Reviewers: C.L.B., University Corporation for Atmospheric Research; E.F.-G., University of California, Irvine; and J.A.S., Princeton University.
Conflict of interest statement: K.E. is an officer of WindRiskTech, LLC, a firm that provides hurricane risk assessments to clients worldwide. The firm also provides data sets free of charge to scientific researchers.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Contributed by Kerry Emanuel, October 4, 2017 (sent for review September 15, 2017; reviewed by Cindy L. Bruyere, Efi Foufoula-Georgiou, and James A. Smith)
Natural disasters such as the recent Hurricanes Harvey, Irma, and Maria highlight the need for quantitative estimates of the risk of such disasters. Statistically based risk assessment suffers from short records of often poor quality, and in the case of meteorological hazards, from the fact that the underlying climate is changing. This study shows how a recently developed physics-based risk assessment method can be applied to assessing the probabilities of extreme hurricane rainfall, allowing for quantitative assessment of hurricane flooding risks in all locations affected by such storms, regardless of the presence or quality of historical hurricane records.
We estimate, for current and future climates, the annual probability of areally averaged hurricane rain of Hurricane Harvey’s magnitude by downscaling large numbers of tropical cyclones from three climate reanalyses and six climate models. For the state of Texas, we estimate that the annual probability of 500 mm of area-integrated rainfall was about 1% in the period 1981–2000 and will increase to 18% over the period 2081–2100 under Intergovernmental Panel on Climate Change (IPCC) AR5 representative concentration pathway 8.5. If the frequency of such event is increasingly linearly between these two periods, then in 2017 the annual probability would be 6%, a sixfold increase since the late 20th century.
Author contributions: K.E. wrote the paper.
Reviewers: C.L.B., University Corporation for Atmospheric Research; E.F.-G., University of California, Irvine; and J.A.S., Princeton University.
Conflict of interest statement: K.E. is an officer of WindRiskTech, LLC, a firm that provides hurricane risk assessments to clients worldwide. The firm also provides data sets free of charge to scientific researchers.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
