## 3 Results and Discussion
### Morphometric Parameters Analysis  
Watersheds and sub-watersheds can be prioritized on the basis of morphometric parameters. For prioritization of sub-watersheds in water resources management, the morphometric analysis uses certain very crucial linear and shape morphometric parameters. Linear parameters such as the drainage density, stream frequency, bifurcation ratio, and texture ratio have direct relationships with erodibility, whereas shape parameters such as the elongation ratio, circulatory ratio, form factor, and compactness ratio have inverse relationships with erodibility ([PERSON], 2019; [PERSON], 2019). Greater values for linear parameters enhance the runoff potential and the erodibility; hence, each sub-watershed was ranked depending on the values of different geomorphological parameters. The highest value for the linear parameters was given a rating of 1, the next highest value was given a rating of 2, and so on, as these geomorphological parameters generally show positive correlations with soil erosion. The lowest value was rated last in the series of numbers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**MLR**} & \multicolumn{6}{c}{**Landscape Metrics**} & \multirow{2}{*}{**Correlation**} \\ \cline{3-4} \cline{6-7}  & & _PD_ & _ED_ & _LPI_ & **ENN\_AM** & _PLAND_ & **SPLIT** \\ \hline \multirow{2}{*}{Scenario 1} & R\({}^{2}\) & 0.45 & 0.34 & 0.18 & 0.92 & 0.3 & 0.97 & ENN\_AM \\ \cline{2-7}  & \(p\) value & 0.2 & 0.3 & 0.47 & **0.009** & 0.33 & **0.001** & SPLIT \\ \hline \multirow{2}{*}{Scenario 2} & R\({}^{2}\) & 0.4 & 0.5 & 0.38 & 0.028 & 0.56 & 0 \\ \cline{2-7}  & \(p\) value & 0.24 & 0.17 & 0.26 & 0.78 & 0.14 & 0.99 & No correlation \\ \hline \multirow{2}{*}{Scenario 5} & R\({}^{2}\) & 0.42 & 0.31 & 0.15 & 0.9 & 0.28 & 0.95 & ENN\_AM \\ \cline{2-7}  & \(p\) value & 0.23 & 0.32 & 0.51 & **0.014** & 0.35 & **0.004** & SPLIT \\ \hline \multirow{2}{*}{Scenario 6} & R\({}^{2}\) & 0.8 & 0.95 & 0.93 & 0.5 & 0.93 & 0.43 & _PD_, _ED_, _LPI_, _PLAND_ \\ \cline{2-7}  & \(p\) value & **0.039** & **0.004** & **0.006** & 0.14 & **0.008** & 0.22 & _PLAND_ \\ \hline \hline \end{tabular} _NP_: Number of Patches; _PD_: Patch Density; _ED_: Edge Density; _LP1_: Largest Patch Index; ENN\_AM: Euclidean Nearest Neighbor Distance Area-Weighted Mean; _PLAND_: Percentage of Landscape; SPLIT: Split index; Values in bold are significant at \(p\) ¡ 0.05.  
\end{table} 
Table 13: Correlation between landscape metrics values and mean soil erosion. In contrast, for shape parameters, the lowest value was given a rating of 1, the next lowest value was given a rating of 2, and so on, as these parameters show negative correlations with soil erosion. The prioritization of both the catchments was carried out by calculating the compound factor values. The compound factor (Cf) was calculated by adding up all ranks of linear and shape parameters and then dividing this number by the number of all parameters (which here was 10) (Table 14). The highest prioritized rank was assigned to the catchment with the lowest compound factor, and vice versa. According to the ranking, the Pelekaniotis catchment seemed to be more prone to soil erosion than the Kakodikianos catchment. Table 15 summarizes the mean soil erosion rates for each catchment under the different RUSLE scenarios. The prioritization ranking based on morphometric parameters was positively correlated with both scenarios 5 and 6; however, for scenarios 1 and 2, it was negatively correlated (Tables 15 and 16). The same prioritization procedure was likewise conducted at the sub-catchment scale to detect the areas that were highly vulnerable to soil erosion according to the morphometric parameters. Kakodikianos catchment was divided into 52 sub-catchments, while Pelekaniotis catchment was split into 73 sub-catchments.