Monthly water quality sampling at the catchment outlet is carried out at many sites across New Zealand for state of the environment monitoring. This data is used for trend analysis, but little else. We have been exploring approaches for using this data in conjunction with concurrent stream flow data to identify and quantify the principal nutrient transfer pathways within catchments. In particular, monthly data may provide sufficient information for an inverse modelling approach.
Three contrasting mesoscale catchments were chosen for this study: (1) the Tahunaatara Stream (208 km²) in the Upper Waikato subregion, (2) the Puniu River (519 km²) in the Waipa subregion, and (3) the Mangatangi River (195 km²) in the Lower Waikato subregion. By considering four years of monthly water quality data from these catchments, alongside daily rainfall, potential evapotranspiration, and stream flow measurements, we were able to use the daily time step, spatially lumped catchment model “StreamGEM” with the Markov Chain Monte Carlo algorithm “DREAMZS” to predict daily stream flow and nitrate fluxes arriving at the catchment outlet via near-surface (NS), shallow fast seasonal groundwater (F), and deep slow older groundwater (S) flow paths, as well as to estimate the reliability/uncertainty of these predictions.
Despite high uncertainty in some model parameters, the flow and nitrate calibration data was well reproduced across all catchments (Nash-Sutcliffe model efficiency in the range 0.70–0.83 for daily flow, and 0.17–0.88 for nitrate concentration, both on log scale). Proportions of flow attributed to near-surface, fast seasonal groundwater and slow older groundwater were well defined, and consistent with expectations based on catchment geology. Fast groundwater contributed the bulk of the annual average nitrate yield in all of these catchments (range 31–97%), although contributions from slow groundwater were also high at Tahunaatara (range 18–63%), while contributions from near-surface flow were high at Mangatangi (range 24–63%).
This research highlights the potential of process based, spatially lumped modelling with commonly available monthly stream sample data, to elucidate high resolution catchment function, when appropriate calibration methods are used that correctly handle the inherent uncertainties
