Kelantan Daily Rainfall Datasets: Persistence in Nature

Abstract

There is evidence that a stationary short memory process that encounters occasional structural break can show the properties of long memory processes or persistence behaviour which may lead to extreme weather condition. In this chapter, we applied three techniques for testing the long memory for six daily rainfall datasets in Kelantan area. The results explained that all the datasets exhibit long memory. An empirical fluctuation process was employed to test for structural changes using the ordinary least square (OLS)-based cumulative sum (CUSUM) test. The result also shows that structural change was spotted in all datasets. A long memory testing was then engaged to the datasets that were subdivided into their respective break and the results displayed that the subseries follows the same pattern as the original series. Hence, this indicated that there exists a true long memory in the data generating process (DGP) although structural break occurs within the data series.

Citation

Norrulashikin, S.M., Yusof, F., Yusop, Z., Kane, I.L., Salleh, N. and Jamaludin, A.R. (2018), "Kelantan Daily Rainfall Datasets: Persistence in Nature", Improving Flood Management, Prediction and Monitoring (Community, Environment and Disaster Risk Management, Vol. 20), Emerald Publishing Limited, Leeds, pp. 121-131. https://doi.org/10.1108/S2040-726220180000020021