TY  - JOUR
PY  - 2018//
TI  - An area-based approach for estimating extreme precipitation probability
JO  - Geographical analysis
A1  - Gao, Peng
A1  - Carbone, Gregory J.
A1  - Lu, Junyu
A1  - Guo, Diansheng
SP  - 314
EP  - 333
VL  - 50
IS  - 3
N2  - 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>
LA  - en
SN  - 0016-7363
UR  - http://dx.doi.org/10.1111/gean.12148
ID  - ref1
ER  -