Comparison between change point detection methods with synthetic rainfall data and application in South Korea

Abstract

Change point analysis is an efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, the result of a reasonable change point detection method can be effectively used in the prediction of flood and drought because it provides a key to resolve the non-stationary or inhomogeneous problem by climate change. However, relatively few studies have addressed the performance of the change point detection skills through comparative study and applications using real data. Therefore, in this study, the comparative study to assess the performance of the three change point detection skills; Cumulative Sum (CUSUM) method, Bayesian Change Point (BCP) method, and segmentation by Dynamic Programming (DP) was performed. After assessment of the performance of the proposed detection skills, the two reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. The three types of the synthetic rainfall data, homogeneous series and inhomogeneous series with a single shift and multiple shifts were generated firstly to assess performance of the detection skills. Especially, the exact number of change points, position error of detected change points and the three indicators were used to assess performance of each detection skill. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in water resources management and design of hydraulic structures.