Extension of Bayesian chemistry-assisted hydrograph separation to reveal water quality trends (BACH2)

Abstract

A Bayesian chemistry-assisted hydrograph separation (BACH) approach was previously demonstrated using 15 years of monthly total phosphorus (TP) and total nitrogen (TN) data from eight mesoscale catchments in New Zealand’s North Island. Calibration was done separately for three 5-year data periods, and in each period, concentrations of the two tracers (TP and TN) discharged from each of the three separated flow paths—fast (event-response near-surface flow), medium (seasonal shallow local groundwater flow), and slow (persistent deeper regional groundwater flow)—were assumed to be constant. This approach has now been extended to reveal non-linear trends in the tracer concentrations in each flow path, each represented using a four-parameter curve (initial and final values of a linear trend plus two harmonics). The extended method (called BACH2) identified clear TP and TN concentration trends in the medium and slow flow paths in most of the eight catchments. TP and TN concentration trends in the fast flow path were generally uncertain, however, due to the infrequency and inherent variability of concentrations sampled during high flow conditions. Concentrations closely matched previously published results from the constant-concentration BACH model calibrated to shorter data series. The BACH2 approach is a powerful tool for revealing concentration trends in the different pathways that sustain stream flow using commonly available water quality and flow data. This type of analysis has not previously been available outside of complex distributed simulation models.