## 4 Results and Discussion
### Cluster Analysis  
Spatial and Temporal Similarities  
Cluster analysis was applied to find out if the monitoring stations had similar characteristics in terms of water quality parameters. It was implemented with the water quality data set to group comparable monitoring sites (spatial variability) spread over the watershed. Results from CA display high homogeneity within clusters and high heterogeneity between clusters [64]. Hierarchical agglomerative CA was carried out with the normalized data set employing [PERSON]'s method, using Euclidean distances as a measure of similarity. In this approach, the analysis of variance method is used to evaluate the distances between \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Parameters** & **Seasons** & **Rivers** & **Lake Hawassa** & **PS** \\ \hline \multirow{2}{*}{DO (mg/L)} & dry seasons & 4.2 & 4.2 & 1.7 \\  & wet seasons & 6 & 4.3 & 2.1 \\ \multirow{2}{*}{BOD\({}_{5}\) (mg/L)} & dry seasons & 19.7 & 28.1 & 116.2 \\  & wet seasons & 6.9 & 19.1 & 111.6 \\ \multirow{2}{*}{SS (mg/L)} & dry seasons & 30.6 & 19.7 & 29.3 \\  & wet seasons & 51.1 & 20.9 & 28.1 \\ \multirow{2}{*}{NH\({}_{3}-\)N (mg/L)} & dry seasons & 0.2 & 0.8 & 1.2 \\  & wet seasons & 0.002 & 0.71 & 14.4 \\ \multirow{2}{*}{PI} & dry seasons & 4.5 & 5 & 7.3 \\  & wet seasons & 3.3 & 5 & 6.8 \\ \multirow{2}{*}{Rank} & dry seasons & MP & MP & HP \\  & wet seasons & MP & MP & HP \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average concentrations of monitoring stations for rivers, Lake Hawassa, and point sources (PS) observed in both dry and wet seasons. clusters, attempting to reduce the sum of squares of all clusters that can be made at each step. In this method, the clusters are grouped sequentially, beginning with the most comparable pair of objects and establishing better clusters one after the other, demonstrated through a dendrogram [2; 65]. The dendrogram presents a visual summary of the clustering processes and provides the map of the groups with a dramatic reduction in the dimensionality of the original records [2; 5; 32; 43; 44]. The CA grouped all 19 monitoring stations into two statistically significant clusters for the dry and wet seasons in LHW, and the dendrogram displays the grouping of stations for the wet and dry seasons, as demonstrated in Figure 2. Regarding the clustering for the dry and wet seasons, monitoring stations from most of the watershed upstream, from the eastern and western sides of the lake, and from the center of Lake Hawassa have been grouped in Cluster 1. Stations in these clusters typically consist of rivers and Lake Hawassa and are categorized as moderately polluted. The monitoring stations in these clusters are MS1-MS3, MS6-MS14, and MS16-MS18, which can be labeled as \"moderate anthropogenic effect\". This cluster received pollution from point sources and non-point sources, consisting of animal waste and runoff. It is characterized by moderate anthropogenic impact and labelled as moderately polluted. The pollution sources for monitoring stations MS1-MS3 were mainly anthropogenic activities from non-point pollution sources such as agricultural and sewage pollution, whereas pollution sources for monitoring stations MS6 (Tikur-Wuha river) and Lake Hawassa (MS7-MS14, MS16-MS18) were mainly industrial pollution, dispersed point sources, agricultural pollution, urban runoff, and sewage pollution. Owing to their relative sources, all stations in this cluster were rivers and lakes, suggesting that clustering is reasonable for both dry and wet seasons. Figure 2: Dendrogram for LHW based on [PERSON]’s method showing the clustering of 19 monitoring stations into two significant clusters for both dry (**a**) and wet (**b**) seasons. The spatial trend of water quality was generally driven by anthropogenic activities from point and non-point sources of pollution, especially anthropogenic activities with respect to pollutant loading and land use.