An Area-Based Approach for Estimating Extreme Precipitation Probability

Peng Gao, Gregory J. Carbone, Junyu Lu, Diansheng Guo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Accurate estimates of heavy rainfall probabilities reduce loss of life, property, and infrastructure failure resulting from flooding. NOAA's Atlas-14 provides point-based precipitation exceedance probability estimates for a range of durations and recurrence intervals. While it has been used as an engineering reference, Atlas-14 does not provide direct estimates of areal rainfall totals which provide a better predictor of flooding that leads to infrastructure failure, and more relevant input for storm water or hydrologic modeling. This study produces heavy precipitation exceedance probability estimates based on basin-level precipitation totals. We adapted a Generalized Extreme Value distribution to estimate Intensity-Duration-Frequency curves from annual maximum totals. The method exploits a high-resolution precipitation data set and uses a bootstrapping approach to borrow spatially across homogeneous regions, substituting space in lieu of long-time series. We compared area-based estimates of 1-, 2-, and 4-day annual maximum total probabilities against point-based estimates at rain gauges within watersheds impacted by five recent extraordinary precipitation and flooding events. We found considerable differences between point-based and area-based estimates. It suggests that caveats are needed when using pointed-based estimates to represent areal estimates as model inputs for the purpose of storm water management and flood risk assessment.

Original languageEnglish (US)
Pages (from-to)314-333
Number of pages20
JournalGeographical Analysis
Volume50
Issue number3
DOIs
StatePublished - Jul 2018
Externally publishedYes

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

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