# Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
## 3 Results
### Meteorological Factors Associated with WUE Change  
The following results refer to the descriptive statistics of the WUE, precipitation, and LST, shown in Table 3 and Figure 3. Significant differences in WUE were observed between the dry (Figure 3a) and rainy seasons (Figure 3d); in this case, the maximum, minimum, and mean values show variations of 1.33 gC/mm\(\cdot\)m\({}^{2}\), 0.45 gC/mm\(\cdot\)m\({}^{2}\), and 0.93 gC/mm\(\cdot\)m\({}^{2}\), respectively. This result indicates a possible influence of precipitation on these values since the mean total precipitation between the rainy and dry seasons differed by approximately 136 mm. The low precipitation in the dry season, associated with high temperatures (a difference of 4.2 \({}^{\circ}\)C between the dry and rainy seasons), stimulates biophysical processes, resulting in the highest WUE values in dry periods, as shown in Table 3.  
Figure 3 displays the spatial variability of WUE during the dry and rainy seasons. During the dry season, the precipitation volume is less than 15 mm in the western portion, and, at the same time, there are temperatures between 30 \({}^{\circ}\)C and 35 \({}^{\circ}\)C resulting from the dry period. When combined, these conditions promote an increase in the amount of energy available in biophysical processes, contributing to higher WUE values (close to 4 gC/mm\(\cdot\)m\({}^{2}\)), as seen in Figure 3a. However, in the rainy season, excess rain (Figure 3f) contributes to a decrease in temperatures (a decrease of approximately 5 \({}^{\circ}\)C in the maximum and minimum values). This behavior further interferes with the carbon absorption cycle and, consequently, WUE values (<2 gC/mm\(\cdot\)m\({}^{2}\), illustrated in Figure 3b). This interaction between WUE and temperature agrees with the results found by [54], who pointed out that increases in temperature and ET result in higher ecosystem productivity; that is, these climatic variations have a strong influence on the vegetation, and consequently, on WUE. The influence of LST and precipitation on WUE can be seen in Figure 4. The averages of WUE, LST, and precipitation for the 19 years of study (2001 to 2019) were used for preparation. After the construction of the average images (WUE, LST, and precipitation),  
Figure 3: Spatial variation of WUE (gC/mm\(\cdot\)m\({}^{2}\)), LST (\({}^{\circ}\)C) and Precipitation (mm) in the dry and rainy seasons. (**a**) WUE dry, (**b**) LST dry, (**c**) Precipitation dry, (**d**) WUE rainy, (**e**) LST rainy and (**f**) Precipitation rainy. Average monthly data between the years 2001 and 2019 were used. \begin{table}
\begin{tabular}{c c c c c}  & **Minimum** & **Median** & **Mean** & **Maximum** \\ WUE (Dry period) & 1.95 & 2.83 & 2.84 & 3.69 \\ WUE (Rainy period) & 1.50 & 1.89 & 1.92 & 2.36 \\ Precipitation (Dry period) & 2.72 & 5.60 & 6.30 & 12.42 \\ Precipitation (Rainy period) & 102.90 & 136.90 & 136.90 & 180.10 \\ LST (Dry period) & 26.96 & 30.09 & 30.14 & 33.30 \\ LST (Rainy period) & 23.02 & 25.75 & 25.91 & 28.62 \\ \end{tabular}
\end{table} 
Table 3: Comparison of WUE, precipitation, and LST indices during the dry and rainy season. 220 random points were extracted referring to the study area (Figure 4a). Subsequently, the scatter plot and the calculation of Pearson's correlation (r) were performed. When we look at Figure 4b, we can see the inverse relationship between the highest WUE values and the lowest precipitation values, confirmed by the moderate negative correlation (\(-0.57\)), emphasizing that as the volume of precipitation increases, there are decreases in the WUE values. The correlation between WUE and LST was high (0.66), implying higher WUE values as LST values increase. It is observed that the influence of these factors can explain the variation of WUE in rainy and dry periods through the coefficient of determination (R\({}^{2}\)), with 0.33 (precipitation) and 0.43 (LST).