The Effect of Involving Exceptional Outlier Data on Design Flood Magnitude

Abstract

The term "outlier" is generally used to refer to single data points that appear to depart significantly from the trend of the other data. Outliers are classified into three types: incorrect observations, rare events resulting from essentially the same phenomena as the other maxima, and rare events resulting from a different phenomenon. Flood frequency analysis was first performed on complete data series (including the outlier) and then on the series with the outlier removed. Results revealed that omission of the outlier data didn’t affect the probability distribution function (Log-Pearson type III), but the design discharge reduced by 60 percent in 10000 year return period from 3320 (m3/s) to 1340 (m3/s). Furthermore, the method proposed by the U.S. Water Resources Council (WRC), and the HEC-SSP software were applied in order to compose outlier data with other systematic data and to modify the parameters of the statistical distribution. Using WRC method, the estimated 10000-year flood was equaled to 1907 (m3/s) by designating the outlier as the 200-year return period and revising the parameters of Log-Pearson type III distribution; that is about 43 percent decrease over the scenario involving the outlier.

Impact of Climate Variability on Rainfall Characteristics in the Semi-Arid Shashe Catchment (Botswana) from 1981–2050

“…Hydrometeorological disasters in Sub-Saharan Africa are fast changing. Despite much research on various aspects of floods, the world still witnesses severe floods [16][17][18][19][20][21]. Vulnerability and exposure to these disasters are driven by climate variability and land use changes due to rapid population growth and economic expansion.…”