Temporal distribution of heavy rainfall for upper Klang catchment - Hong, Jer Lang; Ibrahim, Izihan; Jafri, Azmi; Shafie, Suzana; Naubi, Irena (Science and Engineering Research Support Society, 2018)
Data from 4 pluviograph stations in the upper Klang basin were used to derive time distributions of heavy rainfall using the NOAA method. Rainfall cases for the temporal distribution analysis were selected from the annual maximum series usually used in the rainfall frequency analysis. Each case (i.e., maxima) was the total accumulation over a selected duration (1,6 ,12, 24 hour for this study). For each rainfall case, cumulative rainfall amounts were converted into percentages of the total rainfall amount at specified time increments. All cases for a specific duration were then combined and these rainfall cases were analysed separately, determining the percentage accumulated to 10, 20,30,40,50,60,70,80,90 and 100% of its total duration. For each duration, the percentage was determined by a percentage series of total rainfall, and the probabilities calculated. In order to obtain the values of rainfall based on above definition, linear interpolations were carried out between the probabilities and the immediately previous and subsequent probabilities. The temporal distribution curves for nine deciles (10% to 90%) were plotted in the same graph. Results show that first-quartile and second-quartile storms occurred most frequently with durations less than or equal to 12 hours; and first-quartile and fourth quartile storms most often had durations of 24 hours. Following the principles of Huff (1990), the temporal distribution curves derived in this study are recommended to be used for normal design as follows: • For 1 hour duration, it is recommended that second quartile relations be used to establish typical time distributions. • Time distributions for storms lasting 6 hours ,12 hours and 24 hours are most likely to conform to a first-quartile distribution. For most purposes, the median curves are probably most applicable to design. These curves are more firmly established than the more extreme curves, such as those for the 10% and 90% probability levels, which are determined from a relatively small portion of each quartile’s sample. However, the extreme curves should be useful when runoff estimates are needed for the occurrence of unusual storm conditions, such as typified by the 10% curves.