Uncertainty analysis of rainfall depth duration frequency curves using the bootstrap resampling technique

• Main Authors: Ng, Jing Lin, Abd Aziz, Samsuzana, Huang, Yuk Feng, Mirzaei, Majid, Wayayok, Aimrun, Rowshon, M. K.
• Format: Article
• Language: English
• Published: Springer Verlag 2019
• Online Access: http://psasir.upm.edu.my/id/eprint/82662/1/Uncertainty%20analysis%20of%20rainfall%20depth%20duration%20frequency%20.pdf; http://psasir.upm.edu.my/id/eprint/82662/; https://link.springer.com/article/10.1007/s12040-019-1154-1
• Institution: Universiti Putra Malaysia

Rainfall depth duration frequency (DDF) curves are used extensively in many engineering designs.However, due to the sampling error and the uncertainty associated with the parameter estimation process,the DDF curves are subjected to parameter uncertainty. In this study, an evaluation of the uncertaintyof the DDF curves in the Kelantan river basin was performed using the bootstrap resampling method.Annual maximum rainfall series for durations of 24, 48, 72, 96 and 120 h were derived from the stochasticrainfall model outputs and fitted to the generalised extreme value (GEV) distribution. The bootstrapsamples were generated by resampling with replacement from the annual maximum rainfall series. Therelationships that describe the GEV parameters as a function of duration were used to establish theDDF curves. The 95% confidence intervals were used as an indicator to quantify the uncertainty in theDDF curves. The bootstrap distribution of the rainfall depth quantiles was represented by a normalprobability density function. The results showed that uncertainty increased with the return period andthere was significant uncertainty in the DDF curves. The suggested procedure is expected to contributeto endeavours in obtaining reliable DDF curves, where the uncertainty features are assessed.