Regional frequency analysis with development of region-of-influence approach for maximum 24-h rainfall (case study: Urmia Lake Basin, Iran)

Abstract

In this study, region-of-influence (ROI) approach was used for modeling extreme values of 24-h maximum rainfall using seven groups of defined attributes including climatic, geographical, and statistical attributes and their hybrids. According to the degree of importance and the role of each defined attribute, different weighting scenarios were defined for each of the attributes relating to the corresponding group. The goal of this paper is to select the best weighting scenario for attributes in the ROI model for estimating more accurate and reliable quantiles of 24-h maximum rainfall over Urmia Lake Basin. The investigation of 140 pooling groups built with the ROI approach showed that each of the seven groups of defined attributes has a different performance in the estimation of quantiles. Moreover, results showed that, in most cases, performance of the models with weighted attributes was better than that of the non-weighted ones. Among the seven categories, the statistical attributes and their combination with climatic and geographical attributes had the best estimation of quantiles in terms of low relative error. In addition, results showed that skewness plays an effective role in the estimation of quantiles. In general, it can be concluded that by applying appropriate weights for available attributes in regions without data, the acceptable and reliable results of extreme values can be earned at ungauged stations. Furthermore, results showed that the use of median of metric distance as one of the threshold options can obtain better results in the estimation of quantiles.