Performance of region‐of‐influence approach of frequency analysis of extreme rainfall in monsoon climate conditions

Abstract

This study investigates the applicability of the region‐of‐influence (ROI) approach of frequency analysis of extreme rainfall in a monsoon‐dominated country and to find the most appropriate scheme of the method. Seven different ROI pooling schemes are examined which differ in their similarly distance measure containing a range of different site descriptors (also known as pooling variables). The site descriptors also include average annual monsoon rainfall (AAMR) which is introduced to assess its suitability in this climatic condition. The ROI schemes are evaluated against traditional regional as well as at‐site frequency analysis. The performance of the selected scheme in terms of homogeneity and frequency behaviour is also assessed. In order to determine the performance, the study uses annual maximum rainfall data of 1‐day duration at 34 stations in Bangladesh, which has a tropical monsoon climate. It is found that the ROI method of any variants is superior to the traditional geographical approach of regional analysis as well as to the at‐site analysis. The AAMR based pooling scheme has provided the best result among the considered ROI schemes. The pooling groups delineated based on AAMR has also passed the homogeneity test and conveyed a desirable frequency behaviour. All results indicate that the AAMR as a pooling variable, i.e. information about monsoon rainfall, should be accommodated to the estimation of extreme rainfall in a monsoon‐dominated region.

An assessment of temporal effect on extreme rainfall estimates

“…2 identifies the generalized extreme value (GEV) as the most suitable distribution for the Yangtze Basin. The formula for estimating growth factors and the associated quantiles based on the GEV can be obtained from Das and Cunnane (2011).…”

Extreme rainfall estimation at ungauged sites: Comparison between region‐of‐influence approach of regional analysis and spatial interpolation technique

“…The AM rainfall series of 1‐day duration were extracted from the above data sets for the study purpose. It is to be mentioned that the same AM data sets were employed earlier to assess rainfall frequency in Bangladesh (Das, ). A general description of the data sets can be obtained from that study.…”

“…AM series length of each station with the initial and ending record years, after Das () [Colour figure can be viewed at wileyonlinelibrary.com]…”

“…The region‐of‐influence (ROI) technique (Burn, ), which is considered to be superior in several studies (e.g., Institute of Hydrology, ; Gaál et al ., ; Kyselý et al ., ; Das, ) to the other forms of regional analysis (e.g., traditional fixed regional analysis), identifies a region of influence for a target location based on similarity distance measure in the site descriptor space. Climatological descriptors such as average annual rainfall (AAR; Gaál and Kyselý, ), average annual monsoon rainfall (Das, ); and geographical descriptors such as geographical location (Reed et al ., ; Kyselý et al ., ) produced promising results in delineating homogeneous groups. The latter has the advantage of transferring the approach in an ungauged case because geographical coordinates are the only requirement to form groups.…”

“…Bangladesh is a low‐lying tropical country where extreme rainfall plays a principal role in producing huge flooding almost every other year. A recent study (Das, ) shows ROI can be a promising tool in estimating design rainfall in Bangladesh conditions. However, no attempts have been made to find a suitable method in estimating the same for ungauged sites.…”