Grouping river flow patterns based on nonlinear features of short time series data

Abstract

River flows provide information on the availability of water and its variability in space and time. Identifying similar grouping of river flow patterns is useful so that the knowledge of homogeneous sites can be used to manage water resources more efficiently. However, the grouping of river flow is not easy because river flow can contain several regimes to account for different behaviours. In this study we use a two-stage approach to identify groupings of river flow patterns on a short time series record for rivers located in the state of Johor, Malaysia. Specifically, we use significance tests to detect any nonlinearity features and existence of structural change in the river flow time series. Then, we fit a Self-Exciting Threshold Autoregressive (SETAR) model to identify the regime switching model for rivers that exhibit nonlinearity features. The results show that there are two main groups amongst the rivers in Johor. All but one river in Johor for the period considered in the study can be explained by significant nonlinear features in their river flow process. Specifically, Johor river, Sayong river and Segamat river that exhibit nonlinear feature patterns, can be further categorized into two different regimes of time series based on the fitted SETAR models.