RESUMO
Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations.
RESUMO
Saprolegniasis is one of the most economical and ecologically harmful diseases in different species of fish. Low water temperature is one of the most important factors which increases stress and creates favourable conditions for the proliferation of Saprolegniasis. Therefore, the monitoring of water surface temperature (WST) is fundamental for a better understanding of Saprolegniasis. The objective of this study was to develop a predictive algorithm to estimate the probability of fish kills caused by Saprolegniasis in Río Tercero reservoir (Argentina). WST was estimated by Landsat 7 and 8 imagery using the Single-Channel method. Logistic regression was used to relate WST estimated from 2007 to 2017 with different episodes of fish kills by Saprolegniasis registered in the reservoir during this period of time. Results showed that the algorithm created with the first quartile (25th percentile) of the WST values estimated by Landsat sensors was the most suitable model to estimate Saprolegniasis in the studied reservoir.