## 4 Results and Discussion
From a statistical point of view, the results indicate that the lake's surface area is showing both abrupt and gradual changes, which can be confirmed by the change-detection analysis of Maharlou Lake (Figure 7). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline Jumping point & 2007 & 2007 & 2007 & 2007 & 2007 & 2007 & 2007 & 2007 & 2007 & 2001 & 2006 \\ \hline \end{tabular}
\end{table} 
Table 2: The results of the detected changing points in the lake’s surface area. Figure 6: Prediction results of the lake’s surface area obtained from MLP model I. (**a**) Predictions for the lake’s surface area (km\({}^{2}\)) and (**b**) Residual from the model. In addition to the above analysis, it was also essential to analyze the components of the hydro-climatic time series and, in turn, identify any anomalies in the behaviors of the hydro-climatic variables (e.g., rainfall, temperature, and streamflow) during the study period. An aberration in the natural fluctuations of these variables could signify the presence of external stressors that could change or have changed the _status quo_ of Maharlou Lake. Therefore, a series of statistical tests were used to detect irregularities in the behaviors of the variables. Tables 4-6 summarize the detected changing points among the rainfall, temperature, and streamflow datasets that were deemed statistically significant. While the detected change points occurred from the late 1980s to the mid-2000s, most of the jumps occurred in 1998 and 1999. The temperature data exhibit the highest number of statistically significant jump points, while the rainfall data contain the least number of jump points (all of these occur in March). All of the monitoring stations in the vicinity of the lake (including Dobaneh and Shiraz synoptic stations) reveal that change-points occurred in 1999. The \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline Trend component & \(-3.98\) & \(-3.31\) & \(-4.00\) & \(-3.84\) & \(-5.45\) & \(-7.47\) & \(-8.71\) & \(-8.99\) & \(-8.69\) & \(-8.11\) & \(-5.66\) & \(-5.09\) \\ \hline \hline \end{tabular} Note: These values were deemed statistically significant at \(\alpha=0.05\).  
\end{table} 
Table 3: Detected trend component in the lake’s surface area time series. Figure 7: Change detection of Maharlou Lake’s surface. annual time series of all hydro-climatic variables are homogeneous, and the change-points in these data were not found to be statistically significant. The Mann-Kendall test and Sen's slope estimator were used to identify and measure the gradual changes in patterns in the hydro-climatic data (Tables 7-9). Rainfall shows the most change per unit of time. In contrast, the temperature data displayed the least amount of change, though the gradual temperature change was an upward trend. In the case of streamflow, the data showed only downward trends. The rainfall data displayed a negative trend in March, but an upward trend appeared in November. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline \hline \multicolumn{1}{c}{Cleanar-Sokhteh} & - & - & - & - & - & - & - & - & - & - & 1987 \\ \multicolumn{1}{c}{Cleanar-Sokhteh} & - & - & - & - & 1999 & - & - & - & - & - & - & - \\ \multicolumn{1}{c}{Pol-e-Fasa} & 2006 & 2006 & 1999 & 1999 & 1999 & - & - & - & - & - & - & - \\ \hline \hline \multicolumn{10}{c}{Note: These values were deemed statistically significant at \(\alpha=0.05\).} \\ \end{tabular}
\end{table} 
Table 6: Changing points detected in the monthly streamflow time series of selected stations.