Water quality assessment using GIS and multivariate statistical techniques for Kenyir Lake

• Main Author: Poornasuthra A/P Subramaniam, Ms.
• Format: text::Thesis
• Language: English
• Published: 2023
• Subjects: Water quality assessment using GIS and multivariate statistical techniques

Water quality monitoring is important for protecting the ecological health of water body and sustainable development of Kenyir Lake. Water quality can be varying in terms of spatially and temporally due to the influences of many factors. Generally, water quality monitoring program requires a lot of monetary budget and man power depends on the sampling frequency and number of water quality parameters. Therefore, it is important to optimize the water quality sampling frequency and location. In this study, water samples were collected at 22 locations within Kenyir Lake during normal, dry and wet seasons. The main objective of this study is to evaluate the spatial and temporal variability of water quality in Kenyir Lake using multivariate statistical techniques. The selected water quality parameters are including temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS) and ammoniacal nitrogen (NH3-N) as well as water quality index. The water quality data are analyzed using GIS software and multivariate statistical techniques which include cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA). In addition, the sediment transport and dam storage capacity are further explored in order to determine the life span of Kenyir reservoir. The results showed that there is only little spatial variation in Kenyir Lake but significant temporal variations in the water quality parameters during the study period. The comparison of water quality between normal, dry and wet seasons by using ANOVA test indicated that water temperature, DO, pH, COD, TSS and WQI are differ significantly. Cluster analysis had grouped the 22 sampling sites into three clusters regions. The stepwise mode of discriminant analysis uses 3 parameters which including pH, temperature and TSS to yield the corresponding classification matrix that assigning 69.7% of the cases correctly. The three significant parameters show the biggest influences in water quality of different locations in Kenyir Lake. PCA/FA did not result in considerable data reduction, as it points to 5 parameters (71 % of original 7) required to explain the 57.1 % of the total variance in the water quality data set. NH3- N and TSS are found as the two significant parameters that will influence the water quality in either normal, dry or wet seasons. The number of year required for Kenyir reservoir to be fully deposited with sediment will be shorten to 3784 years for dead storage if 50% reduction in forest coverage.