Regional Frequency Analysis of Rainfall, using L-Moment Method, as A Design Rainfall Prediction

Frequency analysis is a method for predicting the probability of future hydrological events, based on historical data. Generally, frequency analysis of rainfall data and discharge data is performed using the moment method, but this method has a large bias, variant, and slope, thus there is a possibility of producing inaccurate hydrological design magnitudes. Meanwhile, the L-moment method is a linear combination of Probability Weighted Moment, with the ability to process data concisely and linearly. This study was therefore conducted to discover the L-moment method’s capacity to obtain a regional probability distribution and design rainfall, used as a basis for calculating hydrological planning, in anticipation of disasters. The study location, Mount Merapi, was selected to enable a more accurate prediction of maximum rainfall with the capacity to cause cold lava in the area, and consequently, reduce the risk of loss for people living within close proximity. According to the results, the L-moment regional ratio results were τ2R = 0.203, τ3R = 0.166, and τ4R = 0.169. The homogeneity and heterogeneity tests show all rainfall stations are uniform or homogeneous, and no data were released from the discordance test results. Also, the growth factor value increases in each return period design rainfall prediction. In this study, the suitable regional probability distribution for the research area is the Generalized Logistic distribution with formulated design rainfall equation. Regional design rainfall is able to predict possible rainfall within the area. The Test model showed the minimum RBias = 0.45%, maximum RBias = 41.583%, minimum RRSME = 0.45%, and maximum RRSME = 71.01%. Meanwhile, the L-moment method’s stability was shown by the model test minimum error = 1.64% and maximum error = 16.60%. The higher error value in the higher return period shows L-moment is able to reduce bias data, however, this has limitations in the higher return period.

Abstract licence: CC BY-SA

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