Predicting chlorophyll-a concentrations in two temperate reservoirs with different trophic states using Principal Component Regression (PCR)

Relationships between chlorophyll-a (chl-a) concentrations and 16 physicochemical variables in temperate eutrophic Çygören and mesotrophic Ikizcetepeler reservoirs (Turkey) were determined using Principal Component Analysis (PCA). PCA was used to simplify the complexity of relationships between water quality variables. Principal component scores (PCs) were used as independent variables in the multiple linear regression analysis (MLR) to predict chl-a in both reservoirs. This procedure is called Principal Component Regression (PCR). In the eutrophic Çygören Reservoir, chl-a was significantly (p < 0.05) correlated with nitrite-nitrogen (NO2), ammonium-nitrogen (NH4), phosphate (PO4), total suspended solids (TSS), pH, Secchi disk transparency, total dissolved solids (TDS) and total phosphorus (TP). In the mesotrophic Ikizcetepeler Reservoir, chl-a was significantly (p < 0.05) correlated with TSS, NO2, chemical oxygen demand (COD), sulfate (SO4), TDS, pH and the Secchi disk. In the eutrophic Çaygören Reservoir, six PCs explained 71% of the total variation in the water quality, while in the mesotrophic Ikizcetepeler Reservoir, six PCs explained 75% of the variation. This study has shown that PCR is a more robust tool than direct MLR to simplify the relationships between water quality variables and to predict chl-a concentrations in temperate reservoirs with different trophic states.

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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).