## 3 Results and Discussion
### Correlation between Land Use and Land Cover and Water Quality  
Through the general correlation analysis across the MRW (Figure 6), it is observed that the highest positive correlation values occurred between the class \"Native Vegetation\" and \"Total Nitrogen\" (corr = +0.8) and \"Native Vegetation\" with \"Total Solids\" (corr = +0.75), considered a high correlation. Meanwhile, the highest negative correlation occurred between \"Wetlands\" and \"Total Nitrogen\" (corr = \(-\)0.80) and \"Native Vegetation\" with \"WQI\" (corr = \(-\)0.75). The strong negative correlation between WQI and Native Vegetation can be explained by two factors: The first factor is a phenomenon of reduced oxygen levels in more preserved natural regions, called \"decoada\". Considering that oxygen has a very large weight within the WQI, there is therefore a tendency for this index to decrease in specific periods of the year, impacting the final results [32]. Furthermore, the effects of spatial autocorrelation in this type of analysis should be considered whenever possible [33], since areas with a specific type of coverage often tend to be concentrated in more specific regions of a basin. The correlation between \"Total Nitrogen\" and \"Urban Area\" (corr = \(-\)0.49) is considered moderately negative; however, other authors [33; 34] observed positive correlations of the order of +0.65 to +0.73, respectively, for Total Nitrogen and urbanized areas. The negative correlation obtained for this study may be associated with the low population density of the basin, considering that the correlations are general for the entire contributing area. It is important to highlight that urban land cannot be used as the sole estimator of water quality conditions [33]. In light of these observations, it is important to highlight that the arrangement of native vegetation, location, shape, and the presence of riparian forests influence the water  
Figure 5: Variation of the Average of the Water Quality Index (WQI) in the Miranda River Watershed subdivided into 17 Influence Areas. Linear trendline in red. quality in adjacent watercourses. Therefore, the presence of native vegetation in strategic points can positively affect water quality, especially when located in the riparian zone of watercourses [6; 35]. In the study by [PERSON] et al. [11] in the Ribeirao Vermelho Basin in Lavras, Minas Gerais, where the springs and main tributaries are located in the urban region of Lavras, it was found that natural vegetation correlated negatively with nitrogen (corr = \(-0.43\)). Additionally, they did not identify a correlation between \"Native Vegetation\" and \"Total Solids\" (corr = 0.06). The correlation values found in this study suggest that each study region has its own peculiarities, as shown in Figure 7, since the expected correlations may not repeat from one study area to another. In the study conducted by [PERSON] et al. [36], the authors analyzed the correlation between land use and land cover and water quality parameters. The authors found that there is some spatial heterogeneity among the sub-basins studied, so the relationship between land use and water quality is not the same among them, as observed in the delineated Areas of Influence for the MRW in this study. The literature suggests that other relationships should be investigated for a better understanding of the relationship between land use and water quality, such as the type of scale adopted, region topography, and hydrological regime [7; 9]. According to the multi-temporal analysis of land use and land cover, IA17 has the highest proportion of wetland areas due to its spatial location close to the mouth of the Miranda River with the Paraguay River. It is observed that over time in IA17, there is a variation in the quantity of \"Wetland Areas\" from year to year. This variation can be explained by the effect of multi-year flood and drought cycles, where periods of consecutive years of floods alternate with years of drought.