Yuk Feng Huang
Articles written in Journal of Earth System Science
Volume 124 Issue 8 December 2015 pp 1623-1638
The Langat River Basin provides fresh water for about 1.2 million people in the Langat and Klang valleys. Any change in the pattern of rainfall could affect the quantity of water in the basin. Studying the pattern of change in rainfall is crucial for managing the available water resources in the basin. Thus, in this study, for the first time, both parametric and non-parametric methods were employed to detect rainfall trend in the basin for the period 1982–2011. The trends were determined at 30 rainfall stations using the Mann–Kendall (MK) test, the Sen's slope estimator and the linear regression analysis. Lag-1 approach was utilized to test the serial correlation of the series. On the annual scale, it was found that most of the stations in the basin were characterized with insignificant trends. The significant trends were found only at the four stations, namely 44301, 44305, 44320 and 2719001. The results of the seasonal trend analysis showed that most of the stations during the northeast monsoon (NEM) and the inter monsoon 1 (INT1) seasons and half of the stations during the southwest monsoon (SWM) season experienced insignificant positive trends. To the contrary, for the inter monsoon 2 (INT2) season, majority of the stations showed negative trends. It was found that during the NEM season the station 44301, for the INT1 season stations 44301, 2719001 and 3118069 were established as having significant changes, while in the SWM season station 2917001 and during the INT2 season, the stations 2615131 and 44301 showed significant trends. It is worth mentioning that the maximum rainfall occurs in inter-monsoon seasons.
Volume 125 Issue 2 March 14 pp 269-283
Climate change is receiving more attention from researchers as the frequency of occurrence of severe natural disasters is getting higher. Tropical countries like Malaysia have no distinct four seasons; rainfall has become the popular parameter to assess climate change. Conventional ways that determine rainfalltrends can only provide a general result in single direction for the whole study period. In this study, rainfall series were modelled using additive Holt–Winters method to examine the rainfall pattern in Langat River Basin, Malaysia. Nine homogeneous series of more than 25 years data and less than 10%missing data were selected. Goodness of fit of the forecasted models was measured. It was found that seasonal rainfall model forecasts are generally better than the monthly rainfall model forecasts. Three stations in the western region exhibited increasing trend. Rainfall in southern region showed fluctuation. Increasing trends were discovered at stations in the south-eastern region except the seasonal analysis at station 45253. Decreasing trend was found at station 2818110 in the east, while increasing trend was shown at station 44320 that represents the north-eastern region. The accuracies of both rainfall model forecasts were tested using the recorded data of years 2010–2012. Most of the forecasts are acceptable.
Volume 128 Issue 5 July 2019 Article ID 0113 Research Article
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 uncertainty of 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 stochastic rainfall model outputs and fitted to the generalised extreme value (GEV) distribution. The bootstrap samples were generated by resampling with replacement from the annual maximum rainfall series. The relationships that describe the GEV parameters as a function of duration were used to establish the DDF curves. The 95% confidence intervals were used as an indicator to quantify the uncertainty in the DDF curves. The bootstrap distribution of the rainfall depth quantiles was represented by a normal probability density function. The results showed that uncertainty increased with the return period and there was significant uncertainty in the DDF curves. The suggested procedure is expected to contribute to endeavours in obtaining reliable DDF curves, where the uncertainty features are assessed.
Volume 129, 2020
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