Evaluating Spatial Variability in Sediment and Phosphorus Concentration‐Discharge Relationships Using Bayesian Inference and Self‐Organizing Maps

• Published in: Water resources research Vol. 53; no. 12; pp. 10293 - 10316
• Main Authors: Underwood, Kristen L., Rizzo, Donna M., Schroth, Andrew W., Dewoolkar, Mandar M.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01-12-2017
• Subjects: Basins; Bayesian analysis; Bayesian theory; Biogeochemistry; Clusters; Data processing; Discharge; Exports; Fluctuations; Fluvial sediments; Flux; Frameworks; Hydrologic models; Hydrologic processes; Hydrology; Hydrometeorology; Identification methods; Lakes; Land use; Land use management; Mathematical models; Mineral nutrients; Nutrient cycles; Parameter estimation; Parameter identification; Phosphorus; Probability theory; Regression analysis; Regression models; Sediment; Sediments; Self organizing maps; Slope; Solutes; Spatial variability; Spatial variations; Statistical inference; Suspended particulate matter; Total suspended solids; Watersheds; Lake Champlain
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2017WR021353

Abstract 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 as the default breakpoint in segmented regression models to discern differences between pre and post‐threshold export regimes. We then applied a Self‐Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high‐flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post‐threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high‐flux clusters exhibiting a bimodal concentration‐discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.
Key Points Bayesian regression methods were applied to discern between reactive versus hydrologically driven stages of sediment and nutrient export Identified discharge thresholds differed from the median discharge, used previously to define pre versus post‐threshold export regimes Nonparametric clustering methods identified different biogeophysical drivers of high sediment and nutrient flux