## 3 Results
### Spatial-Temporal Patterns of Response sAET, sGPP, and sWUE to the SPEI  
For evaluating the water loss from the ecosystem under drought disturbances, the correlation between sAET and the SPEI-6 was calculated, as can be seen in Figure 6a. The highest significant positive correlation (\(r\) > 0.29, \(p\) < 0.05) was observed between April and September (a decrease in the SPEI value corresponds to a decrease in the amount of actual evaporation), where \(r\) ranged between 0.5 and 1 (\(p\) < 0.05) and covered the majority of the study area. Figure 6b depicts the temporal relationship between sGPP and the SPEI-6 timescale. The highest positive correlation (red color) was recorded from January to June and dominated in the central part of the study area, especially in January. In contrast, the highest significant negative values were recorded between August and December, whereas October, November, and December exhibited the highest negative correlations, which dominated in the northern part of the study area, including the majority of Turkey and the northwest part of Iran. However, the positive correlation (\(r\)), ranging between 0.3 and 0.5, was more intense, especially in the central part of the study area. For instance, in January, the majority of Iraq, the eastern part of Syria, the central part of Turkey, and the western part of Iran exhibited the highest correlation. Figure 5: Anomalies (%) of accumulated AET, GPP, and WUE over 6-month periods compared to the long-term averages for each day of the year (DoY) spanning the 9-year period (2001–2009) in the Yatir Forest, a semi-arid shrubland in the Eastern Mediterranean. The SPEI-6 values correspond to the same timeframe, with emphasis on the 2008–2009 extreme drought event (shaded area). The red dashed line in the lower panel represents the threshold level for DS calculation. Furthermore, to evaluate the tradeoff between carbon uptake and water loss during photosynthesis under drought disturbances, the correlation between sWUE and the SPEI-6 was calculated, as can be seen in Figure 6c. The most significant negative correlation (\(r<-0.3\), \(p<0.05\)) was observed between April and August, especially in the border area between Jordan, Iraq and Syria, eastern Iran and southern Jordan (April-August), where \(r\) ranged between \(-0.5\) and \(-1\) (\(p<0.05\)). Furthermore, to highlight the maximum correlations (Max. \(r\)) between the values of sAET, sGPP, and sWUE against the SPEI-6 series as presented in Figure 6d. The results of the maximum correlations for sAET vs. SPEI-6 showed that the majority of the study area, greater than (\(>\)90%), falls into the \"High positive significant response\" pattern, while the highest (Max. \(r\)) positive significant response of the sWUE to SPEI-6 was only recorded in small areas within the study area. In contrast, the majority of the study area showed a negative significant response (sWUE vs. SPEI-6) (Figure 6d). The central part of Iran, the  
Figure 6: Correlation between the sGPP, sAET, and sWUE and the monthly SPEI-6 timescale series (**a–c**) from 1982 to 2017 (lagging effects). The bold colors of brown, red, and blue denote significant correlations with a critical value of 0.3 at a 0.05 significance value. Also, the spatial distribution of maximum correlations (Max. corr., \(r\)) (**d**), and the sWUE/sGPP response pattern to the SPEI-6 timescale series for the studied period (**e**). sP: significant positive, sN: significant negative, and ins. : insignificant. southern part of Jordan, the northwest of Turkey, and the northeastern part of Syria exhibited the lowest significant values (bold blue). On the other hand, the spatial distribution of the maximum correlations between sGPP and SPEI-6 reveals that most of the study area has a positive significant response in the southern part of Iran, the eastern part of Syria, the central part of Turkey, and the northern part of Iraq.