ABSTRACT
This study investigates the change in dependence structure of bivariate flood flow characteristics namely magnitude, frequency and timing in the Godavari river basin using a copula based likelihood ratio (CLR) test. Parametric bootstrap was used to obtain critical values of CLR test for the best-fitted copula. The performance of the CLR method was also evaluated for simulated synthetic bivariate series with a known change-point location in the dependence (copula parameter). Then the methodology was applied to two streamflow monitoring sites Bhimkund and Wagholi-Butti, located in the Godavari river basin in India. Streamflow data for 33 years from 1977 to 2009 was analyzed, by extracting the series of flow characteristics of magnitude, frequency and timing. Initially univariate change-point detection (CPD) test namely standard normal homogeneity test was applied to detect abrupt change-point in mean of the flow series. At Bhimkund site, there was abrupt increase in mean of magnitude, frequency and timing series after the identified change-point year. However, at Wagoli-Butti site, there was abrupt decrease in mean of magnitude and frequency series although timing series got delayed (i.e., abrupt increase). After univariate CPD in mean, the bivariate series i.e., magnitude-frequency and magnitude-timing pairs for these sites were analyzed to detect the change-points in dependence in terms of copula parameters using the CLR method. The results showed that change-points in the copula parameters were detected at year 2003 and 2004 for Wagoli- Butti and Bhimkund sites respectively, and appear to be jointly non-stationary due to human induced change at these two sites. The results of study for detection of the change-point location in the dependency structure of flow characteristics would be useful for flood risk assessment in the basin.
ABSTRACT
The present study focuses on change detection and attribution analysis for a case study of Allegheny river catchment (at two unregulated sites namely Eldred and Salamanca) in USA. The proposed methodology involves, multiple change-point detection (MCPD) techniques i.e., Binary Segmentation based cumulative sum algorithms with Monte Carlo based threshold (BSCSth) and Bayesian Information Criteria (BIC) based penalty (BSCSBIC) and validating the two techniques using standard performance measures for different hypothetical riverflow time-series. The proposed BSCSth technique was applied to the Allegheny river at two sites for the series of different hydrological alteration indicators to identify the location of change-point in mean, and the hydrological regime shifts. To perform attribution analysis, hydrologic simulations were carried out using Sacramento model for the identified segments. The overall hydrologic alterations of selected annual flow metrics extracted from daily simulated flows and observed flow values were also estimated. The results of MCPD analysis showed that overall three hydrological regimes comprising of three segments i.e., near natural period (NNP, 1940-1955), low impact period (LIP, 1956-1966) and high impact period (HIP, 1967-2014) were identified for both the sites. The results of attribution analysis for the three cases (case-1: NNP & LIP; case-2: LIP & HIP; case-3: NNP & HIP) showed that for the case-1, the changes of flow regimes in Eldred and Salamanca were predominantly affected by climate-induced processes. But, for case-2 and case-3, it was seen that there were magnification of human-induced changes probably due to the landuse change (forest and rangeland increased by 18.31% (19.79%) for Eldred (Salamanca) from 1940 to 1992), construction of Kinzua dam (in the year 1966) and other anthropogenic pressures. Thus, the results of the study can be helpful for quantifying the main drivers of hydrological changes in river basin, and planning suitable water resource management strategies in the basin.
