@article{ref1,
title="An area-based approach for estimating extreme precipitation probability",
journal="Geographical analysis",
year="2018",
author="Gao, Peng and Carbone, Gregory J. and Lu, Junyu and Guo, Diansheng",
volume="50",
number="3",
pages="314-333",
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.<p /> <p>Language: en</p>",
language="en",
issn="0016-7363",
doi="10.1111/gean.12148",
url="http://dx.doi.org/10.1111/gean.12148"
}