Relative Linkages of Chlorophyll-a with the Hydroclimatic and Biogeochemical Variables across the Continental U.S. (CONUS)

Chlorophyll-a (Chl-a) is a key indicator for stream water quality and ecological health. The characterization of interplay between Chl-a and its numerous hydroclimatic and biogeochemical drivers is complex, and often involves multicollinear datasets. A systematic data analytics methodology was employed to determine the relative linkages of stream Chl-a with its dynamic environmental drivers at 50 stream water quality monitoring stations across the continental U.S. Multivariate statistical techniques of principal component analysis (PCA) and factor analysis (FA), in concert with Pearson correlation analysis, were applied to evaluate interrelationships among hydroclimatic, biogeochemical, and biological variables. Power-law based partial least square regression (PLSR) models were developed with a bootstrap Monte Carlo procedure (1000 iterations) to reliably estimate the comparative linkages of Chl-a by resolving multicollinearity in the data matrices (Nash-Sutcliff efficiency = 0.50-87). The data analytics suggested four environmental regimes of stream Chl-a, as dominated by nutrient, climate, redox, and hydro-atmospheric contributions, respectively. Total phosphorous (TP) was the most dominant driver of stream Chl-a in the nutrient controlled regime. Water temperature demonstrated the strongest control of Chl-a in the climate-dominated regime. Furthermore, pH and stream flow were found to be the most important drivers of Chl-a in the redox and hydro-atmospheric component dominated regimes, respectively. The research led to a significant reduction of dimensionality in the large data matrices, providing quantitative and qualitative insights on the dynamics of stream Chl-a. The findings would be useful to manage stream water quality and ecosystem health in the continental U.S. and around the world under a changing climate and environment.