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Title: Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods

Abstract

Probable maximum precipitation (PMP) is the primary criterion used to design flood protection measures for critical infrastructures such as dams and nuclear power plants. Based on our analysis using the Stage IV (ST4) quantitative precipitation estimates, precipitation associated with Hurricane Harvey near Houston, Texas, represents a PMP-scale storm and partially exceeds the Hydrometeorological Report No. 51 (HMR51) 72-h PMP estimates at 5,000 mi 2 (ST4=805 mm; HMR51=780 mm) and 10,000 mi 2 (ST4=686 mm; HMR51=673 mm). We also find statistically significant increasing trends since 1949 in the annual maximum total precipitable water and dew point temperature observations along the US Gulf Coast region, suggesting that, if the trend continues, the theoretical upper bound of PMP could be even larger. Our analysis of Hurricane Harvey rainfall data demonstrates that an extremely large PMP-scale storm is physically possible and that PMP estimates should not be considered overly conservative. Furthermore, this case study highlights the need for improved PMP estimation methodologies to account for long-term trends and to ensure the safety of our critical infrastructures.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1493141
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Hydrologic Engineering
Additional Journal Information:
Journal Volume: 24; Journal Issue: 4; Journal ID: ISSN 1084-0699
Publisher:
American Society of Civil Engineers
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Probable maximum precipitation; Hurricane Harvey; Stage IVQPE; infrastructure safety

Citation Formats

Kao, Shih -Chieh, DeNeale, Scott T., and Watson, David B. Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods. United States: N. p., 2019. Web. doi:10.1061/(ASCE)HE.1943-5584.0001768.
Kao, Shih -Chieh, DeNeale, Scott T., & Watson, David B. Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods. United States. doi:10.1061/(ASCE)HE.1943-5584.0001768.
Kao, Shih -Chieh, DeNeale, Scott T., and Watson, David B. Mon . "Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods". United States. doi:10.1061/(ASCE)HE.1943-5584.0001768.
@article{osti_1493141,
title = {Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods},
author = {Kao, Shih -Chieh and DeNeale, Scott T. and Watson, David B.},
abstractNote = {Probable maximum precipitation (PMP) is the primary criterion used to design flood protection measures for critical infrastructures such as dams and nuclear power plants. Based on our analysis using the Stage IV (ST4) quantitative precipitation estimates, precipitation associated with Hurricane Harvey near Houston, Texas, represents a PMP-scale storm and partially exceeds the Hydrometeorological Report No. 51 (HMR51) 72-h PMP estimates at 5,000 mi2 (ST4=805 mm; HMR51=780 mm) and 10,000 mi2 (ST4=686 mm; HMR51=673 mm). We also find statistically significant increasing trends since 1949 in the annual maximum total precipitable water and dew point temperature observations along the US Gulf Coast region, suggesting that, if the trend continues, the theoretical upper bound of PMP could be even larger. Our analysis of Hurricane Harvey rainfall data demonstrates that an extremely large PMP-scale storm is physically possible and that PMP estimates should not be considered overly conservative. Furthermore, this case study highlights the need for improved PMP estimation methodologies to account for long-term trends and to ensure the safety of our critical infrastructures.},
doi = {10.1061/(ASCE)HE.1943-5584.0001768},
journal = {Journal of Hydrologic Engineering},
number = 4,
volume = 24,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
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This content will become publicly available on April 1, 2020
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