Is comprehensive event sampling necessary for constraining process models of water quality? A comparison of high and low frequency phosphorus sampling programs for constraining the HYPE water quality model

Abstract

Model parameter calibration is an important step in process-based watershed water quality modelling. Calibration is commonly performed using readily available water quality data from long-term monitoring programs that collect samples periodically at a relatively low frequency (i.e. monthly). Higher frequency synoptic and targeted event data is becoming available from more intensive monitoring programs, yet there remains little consensus on whether relatively lower frequency data are sufficient to constrain process-based watershed water quality models. To investigate the effects of using water quality data from differing sampling regimes for calibration, we use an implementation of HYPE (HYdrological Predictions for the Environment), a process-based watershed water quality model, in Southern Ontario, Canada. HYPE was calibrated with two stream water quality datasets, one associated with the long-term Ontario Provincial (Stream) Water Quality Monitoring Network and characterized by routine approximately monthly sampling frequency, and one associated with the Multi-Watershed Nutrient Study and characterized by semi-regular synoptic sampling during baseflow and at increased frequency (∼4 to 8 h) during event flow. Performances varied widely between sites, with validation ranges of daily predictions having Nash-Sutcliffe Efficiencies (NSE) from > −1 to 0.48 for flow and > −1 to 0.98 for total Phosphorus. Medians for daily data during validation ranged from 0.15 to 0.24 for flow and from 0.19 to 0.36 for total phosphorus. We found that model performance and simulated total phosphorus loads were similar for the two calibration datasets, potentially suggesting that the dataset with approximately monthly water quality sampling was adequate to constrain the HYPE model. We attribute this to a combination of similar levels of statistical variability in the calibration datasets and prior knowledge in the HYPE model structure. Furthermore, while model performance was similar when HYPE was calibrated with datasets of differing sampling strategies, there was a large range in model performance within each modelling domain. Results suggest that this range in performance could be due in part to poor representation of cold-weather processes in the HYPE model, as sites with higher mean annual temperature and fewer freezing days had better model performance.