Bayesian chemistry-assisted hydrograph separation (BACH) and nutrient load partitioning from monthly stream phosphorus and nitrogen concentrations

A Bayesian chemistry-assisted hydrograph separation (BACH) approach was developed, based on calibration of a three-component recursive digital filter, that requires monthly water quality data only. This enables BACH to be applied to the large number of rural catchments for which continuous flow records and monthly water chemistry time series exist from ‘state of environment’ monitoring programmes, but little supplementary data required for more sophisticated analysis techniques. As well as estimating fast, medium, and slow flow components, BACH also estimates for each flow component a time-invariant concentration of the chemical tracers chosen, allowing flow path-specific loads to be calculated. The method was demonstrated using 15 years of total phosphorus (TP) and total nitrogen (TN) data from eight mesoscale catchments in the Waikato region of New Zealand’s North Island. Calibration was done separately for three 5-year data periods, and validated against data from the following 5-year period. Flow path separation and concentration predictions were consistent between data periods, indicating that the TP–TN combination contained sufficient information to reliably identify three flow paths in each catchment; an event-response near-surface flow path with high concentrations of both phosphorus and nitrogen, a seasonal shallow groundwater flow path with lower concentrations of TP but high concentrations of TN, and a deeper slower groundwater flow path characterised by generally low concentrations of both TP and TN. Based on this analysis, the catchments were able to be grouped in three hydro-types. This shows that commonly available water quality data can support robust, objective flow separation and nutrient load apportionment, even in the absence of other supporting data, provided appropriate modelling methods are used.