Flood Outlier Detection Using PCA and Effect of How to Deal with Them in Regional Flood Frequency Analysis via L-Moment Method

摘要

Regional flood frequency analysis (RFFA) is the major instrument for studying of the flood regime at locations where little or no information is available. Outliers have an important effect on the flood frequency analysis and the existence of them in hydrologic data sets affects the regional flood frequency analysis. Outlier analysis is composed of two steps, outlier detection and outlier treatment. After detecting outlier, one should determine how to deal with it. This has the particular role in outlier analysis. In this research, flood frequency analysis was done for the Dez River basin located in south-western Iran. First of all, the studied basin was separated in two hydrological homogeneous regions according their physiographical, climatic and vegetation characteristics. Then, outliers were detected in each region by using Principal component analysis method (PCA). We considered two approaches to survey and compare the effect of the outliers in RFFA. In the first approach outliers were retained, while in the second approach frequency analysis was done after removal the years which contain outliers. Result of RFFA via L-moment method showed that the estimated quantiles in two approaches, particularly in highly return period have a lot of difference. These results illustrate that dealing with the outliers in flood frequency analysis is of the special importance.