Selection of minimum indicators of hydrologic alteration of the Gorai river, Bangladesh using principal component analysis

Abstract

The use of a few indicators of hydrologic alteration can minimize statistical redundancy which can lead to take effective environmental flow management decisions. The aim of the research is to select representative dominant indicators of hydrological alteration through reducing the dimensionality of multivariate data for the Gorai river, Bangladesh. The required mean daily discharge data (1976–2006) of the Gorai have been collected from the Bangladesh Water Development Board. The Range of Variability Approach and anomaly of the river flow show hydrologic alteration in recent years comparing with past. Principal component analysis (PCA) in Kendall and Pearson methods has been performed by the XLSTAT software (version 2.9). Depending on eigenvalues (>1.0), seven and six Principal components (PCs) are selected for Kendall and Pearson methods, respectively, for further analysis. Since the eco-flow metrics of eco-deficit of the river has poor correlation with the indicators of hydrologic alteration, the resultant loadings are subjected to the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy (MSA) method which is classified into marvelous, meritorious, middling, mediocre, and miserable based on the PC loading values. Seven indicators have been found whose PC loadings are in marvelous class and are common for both Kendall and Pearson methods. These indicators, named March mean flow, 1-day minimum mean, 3-day minimum mean, 7-day minimum mean, 30-day minimum mean, 90-day minimum mean, and 30-day maximum mean flows, are the desired indicators of hydrologic alteration of the Gorai river.

Appendices

Appendix A1

See Table 7.

Table 7 Loadings for the first seven PCs of the data set determined by Kendall method

Appendix A2

See Table 8.

Table 8 Loadings for the first six PCs of the data set determined by Pearson method