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An Area‐Based Approach for Estimating Extreme Precipitation Probability
Author(s) -
Gao Peng,
Carbone Gregory J.,
Lu Junyu,
Guo Diansheng
Publication year - 2018
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12148
Subject(s) - environmental science , precipitation , storm , flood myth , flooding (psychology) , return period , meteorology , bootstrapping (finance) , magnitude (astronomy) , point estimation , statistics , climatology , mathematics , geography , geology , econometrics , psychology , physics , archaeology , astronomy , psychotherapist
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.