Multivariate Statistical Assessment of Monsoon Driven Water Quality Variability in Aquaculture Ponds of Khammam, Telangana

Abstract

Aquaculture ponds in Telangana experience pronounced seasonal fluctuations in water quality during the monsoon. To quantify these dynamics, ten ponds in Khammam were monitored during southwest and northeast monsoon phases and analyzed using multivariate statistical approaches. In-situ parameters, nutrient species, ionic composition, and organic load indicators were measured, and data were evaluated through correlation matrices, principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). Descriptive statistics indicated stable temperatures but large variability in salinity (0.5–18 ppt), alkalinity (90–160 mg/L as CaCO₃), and dissolved oxygen (3–7 mg/L). Correlation analysis revealed three interacting groups: ionic strength variables, organic load indicators, and nutrient inflows. PCA extracted three dominant components that explained 72.1% of the variance, with PC1 reflecting ionic strength, PC2 organic enrichment, and PC3 nutrient inputs. Cluster analysis resolved the dataset into three pond groups: stress-prone systems with low oxygen and high suspended solids, nutrient-enriched systems with elevated nitrate and phosphorus, and stable systems with higher buffering capacity. Discriminant analysis achieved 87% classification accuracy and identified conductivity, alkalinity, ammonia, TSS, and dissolved oxygen as the most effective variables for classification and rapid monitoring. Seasonal contrasts showed that southwest monsoon was dominated by dilution and ionic fluctuations, while northeast monsoon was characterized by nutrient and organic enrichment linked to canal inflows. The integrated analysis demonstrates that multivariate statistical tools provide a robust framework for understanding monsoon-driven variability and for defining a reduced monitoring set to guide management in intensive pond aquaculture.