Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) were applied to water quality parameters in order to interpret complex matrices for better assessment of water quality and environmental status of a watershed. A study was conducted to assess water quality and to establish relationship among water quality parameters in Kelantan River basin. Water quality data was obtained from Department of Environment, (DOE) Malaysia from 2005-2014. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) were applied to 15 water quality parameters in order to interpret complex matrices for better assessment of water quality and environmental status of the watershed. From the results, five PCs were extracted which are collectively accountable for controlling approximately 70% of the watershed’s water quality. Results of cluster analysis indicated that three water quality parameters that included total suspended solids, total solids and turbidity control the water quality of the study area. These parameters were allocated into three clusters based on their similarity. The finding of this study will contribute to existing knowledge of the problems associated with water quality in the basin. This information can be put to use by land use managers and policy makers for future planning and development of the watershed.
Determining the response of basin water resources to rainfall and temperature fluctuations is a crucial source of information for basins water resources planning and management. The study used a descriptive, Mann-Kendall trend test (M-K) and Multiple Linear Regression (MLR). The mean, standard deviations and variations were spatially interpolated using the geostatistical technique. The trend results showed an increase in both rainfall and temperature series. However, the only statistically significant trends were in June and September for rainfall series and in February, May, and April for the temperature series. Rainfall exhibited high temporal variability whereas temperature showed high spatial variability. The intra-annual variability was higher than the inter-annual variability, suggesting that the local climate is largely controlled by natural force. The result of the multiple linear regression (R2=0.431), indicates that the hydrology and water resources of the basin are impacted largely by factors not considered in this study such as land use changes, infiltration, and rate of evaporation among others. However, among the factor considered, rainfall (Beta = 0.505; P = 001) has the highest impacts on the river discharge behavior and should be given preference while addressing water resources predicaments in the catchment.