Identification of key factors influencing primary productivity in two river-type reservoirs by using principal component regression analysis.

Abstract

To understand the factors controlling algal production in two lakes located on the Han River in South Korea, Lake Cheongpyeong and Lake Paldang, a principal component regression model study was conducted using environmental monitoring and primary productivity data. Although the two lakes were geographically close and located along the same river system, the main factors controlling primary productivity in each lake were different: hydraulic retention time and light conditions predominantly influenced algal productivity in Lake Cheongpyeong, while hydraulic retention time, chlorophyll a-specific productivity, and zooplankton grazing rate were most important in Lake Paldang. This investigation confirmed the utility of principal component regression analysis using environmental monitoring data for predicting complex biological processes such as primary productivity; in addition, the study also increased the understanding of explicit interactions between environmental variables. The findings obtained in this research will be useful for the adaptive management of water reservoirs. The results will also aid in the development of management strategies for water resources, thereby improving total environmental conservation.