Spatio-temporal nonstationarity analysis and change point detection in multivariate hydrological time-series

Abstract

ABSTRACT The climate change and human activities significantly affect hydrological time series. Due to the mixed impacts of these factors on changing runoff time series, identifying the exact time of starting statistical change in the regime of runoff is usually complicated. The regional or spatial relationship among hydrologic time series as well as temporal correlation within multivariate time series can provide valuable information for analyzing change points. In this paper, a spatio-temporal multivariate method based on copula joint probability namely, copula-based sliding window method is developed for detecting change points in hydrological time series. The developed method can especially be used in watersheds that are subjected to intense human-induced changes. The developed copula-based sliding window method uses copula-based likelihood ratio (CLR) for analyzing nonstationarity and detecting change points in multivariate time series. To evaluate the applicability and effectiveness of the developed method, it is applied to detect change points in multivariate runoff time series in the Zayandehrud basin, Iran. The results indicate that the proposed method could locate three change points in the multivariate runoff time series (years 1985, 1996, and 2003), while the Cramer–von Mises (CvM) criterion method identifies only one of these change points (year 1985).