## 3 Results
### Markov Evaluation of TSS Dynamic  
According to the results of the TSS concentration changes in Hedi Reservoir from 2014 to 2018, except for the water body in Shijiao Town, the concentration of the TSS in other areas has been relatively stable at the level of I. Therefore, this study focuses on the Shijiao Town waters in the high-risk area of TSS as the key monitoring object and quantitatively evaluates and analyzes the dynamic change process of the TSS in this area according to the method in Section 2.3.3. The results of the progress degree \(pd(S)\) for two kinds of TSS in the reservoir area from 2014 to 2018 are as follows in Table 5. The whole results that reflect the progress degree of TSS in Shijiao from 2014 to 2018 can be seen in Figure 11.  
Figure 11 illustrates changes in the TSS progress degree by using the Markov evaluation model and applying two kinds of TSS for that period (from 2014 to 2018). Obviously, in 2018, the progress degree of the pixel-based TSS recorded the highest value, at 10.232, followed by 2014, 2017, 2016 and 2015, with 2.493, 2.042, 0.819 and 0.194, respectively. On the whole, from 2014 to 2018, the progress degree produced by the pixel-based TSS is greater than 0, indicating that the water quality has been improved, and it includes all possible states in the process of water quality improvement. This maximum progress value is 10.23 calculated from the pixel-based TSS of each grid in Shijiao Town, which indicates that the concentration level of TSS has spanned three levels. Similarly, the progress degree produced from the region-averaged TSS in Shijiao Town showed a minimum value (-3.333) in 2015 and a maximum value (0.899) in 2017, followed by 2016 (-2.667), 2014 (0) and 2018 (0.667). However, except for 2014 and 2018, most results of the average TSS are below 0, which represents the deterioration of the water quality. The result of the regional average TSS progress degree recorded at -3.333 in 2015 stated that the water quality had deteriorated and the TSS concentration level had dropped by one level. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Years**} & \multicolumn{2}{c}{**Progress Degree \(pd(S)\)**} \\ \cline{2-3}  & **Pixel-Based TSS** & **Region-Averaged TSS** \\ \hline
2014 & \(pd\)\((S_{2014})=2.493\) & \(pd^{{}^{\prime}}\)\((S_{2014})=0\) \\
2015 & \(pd\)\((S_{2015})=0.194\) & \(pd^{{}^{\prime}}\)\((S_{2015})=-3.333\) \\
2016 & \(pd\)\((S_{2016})=0.819\) & \(pd^{{}^{\prime}}\)\((S_{2016})=-2.667\) \\
2017 & \(pd\)\((S_{2017})=2.042\) & \(pd^{{}^{\prime}}\)\((S_{2017})=0.899\) \\
2018 & \(pd\)\((S_{2018})=10.232\) & \(pd^{{}^{\prime}}\)\((S_{2018})=0.667\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Taking Shijiao Town of Hedi Reservoir as the research area, Markov evaluation was performed on the water quality using the pixel-based TSS and regional-averaged TSS data. According to the evaluation results shown in Figure 11, it can be seen that whether or not the progress degree from the pixel-based TSS or the region-averaged TSS in Shijiao Town was simulated, the above two types of data reach the lowest in 2015. At the same time, the results obtained from the two different types of TSS data have roughly the same trend shown by the red and blue lines in Figure 11, which confirms the validity of the Markov assessment method proposed in this study. The progress degree of the pixel-based TSS in 2014 was higher than in 2017 (2.493 vs. 2.042). On the contrary, different from the pixel-based TSS, the progress degree of region-averaged TSS in 2014 was less than in 2017 (0 vs. 0.899). The difference in the progress degree of the pixel-based TSS (2.493 in 2014, 2.042 in 2017) and the regional average TSS (0 in 2014, 0.899 in 2017) is mainly due to the spatial distribution variance.