The water quality was assessed over 18 months, from January 2024 to June 2025 of the Gosekhurd Reservoir in Maharashtra, India, by examining its hydrological and multivariate characteristics. It employed methods such as Principal Component Analysis (PCA), correlation evaluations, and descriptive statistics to examine various situations. Measurements were taken at five separate sites and focused on six leading indicators: alkalinity, pH, dissolved oxygen (DO), temperature, electrical conductivity (EC), and water clarity. Seasonal and spatial analyses revealed marked variation; for example, mean DO ranged from 4.2 mg L⁻¹ in summer to 8.7 mg L⁻¹ in winter, and transparency varied from 65 cm during monsoon to 158 cm in winter. Temperature increases were strongly and negatively correlated with DO (r = –0.82), alkalinity (r = –0.71), and transparency (r = –0.76), reflecting heightened thermal stress during warmer periods. The results showed that there were big changes depending on the season and the region. For example, there was a negative correlation between rising temperatures and levels of DO, alkalinity, and clarity. This suggests that heat causes stress in hotter times. The connections also showed how carbonate activities help keep the system stable. The PCA helped make the dataset easier to work with by identifying patterns in thermal-chemical interactions and optical-ionic changes, which explained 84% of the observed differences. At sites S4 and S5, there was clear evidence of nutrient overload causing eutrophication, especially during the summer heat and monsoon rains. This study shows that combining traditional assessment methods with modern multivariate methodologies can help find ecological changes more accurately. It also calls for flexible monitoring strategies to protect the health of reservoirs like Gosikhurd.
Suryawanshi et al. (Wed,) studied this question.