Nature and Society Relations Commons^™

Evaluating Spatial Variability In Sediment And Phosphorus Concentration-Discharge Relationships Using Bayesian Inference And Self-Organizing Maps, Kristen L. Underwood, Donna M. Rizzo, Andrew W. Schroth, Mandar M. Dewoolkar

College of Engineering and Mathematical Sciences Faculty Publications

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously …