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Regionalization of extreme rainfall in India
Author(s) -
Bharath R.,
Srinivas V. V.
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4044
Subject(s) - quantile , environmental science , climatology , latitude , extreme value theory , homogeneity (statistics) , longitude , geographic coordinate system , statistics , geography , mathematics , geology , cartography , geodesy
Regionalization of extreme rainfall is useful for various applications in hydro‐meteorology. There is dearth of regionalization studies on extreme rainfall in India. In this perspective, a set of 25 regions that are homogeneous in 1‐, 2‐, 3‐, 4‐ and 5‐day extreme rainfall is delineated based on seasonality measure of extreme rainfall and location indicators (latitude, longitude and altitude) by using global fuzzy c‐means ( GFCM ) cluster analysis. The regions are validated for homogeneity in L ‐moment framework. One of the applications of the regions is in arriving at quantile estimates of extreme rainfall at sparsely gauged/ungauged locations using options such as regional frequency analysis ( RFA ). The RFA involves use of rainfall‐related information from gauged sites in a region as the basis to estimate quantiles of extreme rainfall for target locations that resemble the region in terms of rainfall characteristics. A procedure for RFA based on GFCM ‐delineated regions is presented and its effectiveness is evaluated by leave‐one‐out cross validation. Error in quantile estimates for ungauged sites is compared with that resulting from the use of region‐of‐influence ( ROI ) approach that forms site‐specific regions exclusively for quantile estimation. Results indicate that error in quantile estimates based on GFCM regions and ROI are fairly close, and neither of them is consistent in yielding the least error over all the sites. The cluster analysis approach was effective in reducing the number of regions to be delineated for RFA .