## 5 Conclusions  
Multivariate statistical techniques help researchers to scrutinize the relationships between parameters in a broader fashion by applying different approaches such as cluster analysis, correlation, factor analysis, discriminant analysis, and multiple regressions to determine the association between dependent and independent variables. They reduce the dimensionality of data so that the whole picture can be visualized more easily than looking at specific cases allows. Furthermore, multivariate techniques provide powerful significance testing compared to univariate techniques. Despite their various merits, the results of multivariate statistical modeling are not easy to interpret and require a large data set to get meaningful results due to the high standard errors. In particular, PCA/FA is likely to lose information if PCs or factors are not chosen judiciously. This study was conducted to evaluate seasonal and spatial variations in water quality and to identify potential sources of pollution using multivariate statistical techniques for the Lake Hawassa Watershed. The results of this study show that the condition of Lake Hawassa Watershed was classified into moderately and highly polluted categories in both dry and wet seasons. In data-limited developing countries such as Ethiopia, it is especially clumsy to identify possible sources of pollution due to certain contaminants, as this requires frequently monitored water quality data, which are often not available. To address this serious problem, this study applied MVST. Multivariate statistics were used to  
Figure 5: PCA biplots (**a,b**) suggest the projection of the monitoring sites (blue dots) and the variable loadings of the primary components (F1 and F2). The biplots additionally display the relationship between highly correlated variables and monitoring stations for dry (**a**) and wet (**b**) seasons. High and low values indicate strong positive and negative correlation, respectively, while values close to 0 imply weak correlation between F1 and F2 and the respective parameter. perform temporal and spatial assessment of surface water quality to reduce the number of monitoring stations and chemical parameters in LHW. In this study, we used Pearson correlation, PCA/FA, CA, and DA to evaluate spatial and temporal variance in surface water quality. CA grouped the monitoring stations into two statistically significant clusters for the dry and wet seasons, labelled MP and HP, using PI. Accordingly, this resulted in a dendrogram with two clusters for the dry and wet seasons. The findings of the study revealed that rivers in the upstream and middle portion of the lake watershed and Lake Hawassa were moderately polluted (MP), while point sources (industries, hospitals, and hotels) in the middle of the LHW were found to be highly polluted (HP). DA was used to identify the most critical parameters to investigate the spatial variations and extracted seven significant parameters: EC, DO, COD, TN, TP, Na+, and K+, with spatial variance to distinguish the pollution statuses of the groups obtained using CA. PCA/FA techniques helped to identify the potential sources of water quality degradation. This study comprehensively analyzed the water quality of LHW and identified three significant sources responsible for pollution of Lake Hawassa Watershed in dry and wet seasons affecting the water quality. Accordingly, the pollution is due to mixed sources including point sources such as municipal and industrial effluents, natural processes, livestock, urban runoff, and non-point sources from agricultural activities. Poor industrial effluent management combined with non-point sources from agriculture and urban runoff contribute significantly to the pollution of Lake Hawassa. Discharge of industrial effluents into the surface water system is the largest point source of anthropogenic pollution. Diffuse sources that contribute enormously to LHW come from agricultural activities, i.e., intensive farming and livestock (F1, F2, and F3).