Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty

High-frequency water quality monitoring is increasingly used in examining the nutrient fluxes within catchments. Despite this, no studies have assessed the impact of monitoring frequency on the uncertainty of nitrate estimates obtained from distributed or semi-distributed catchment models. This study aims to evaluate the impacts of two different frequencies of nitrate sampling on the performance of a catchment hydrology model, including the uncertainty in both predictions and calibrated parameters. The investigation uses the HYPE model to simulate streamflow and nitrate concentrations (2010–2015) in the Selke catchment, a heterogeneous mesoscale catchment in central Germany. The Bayesian inference scheme of the DREAM code was employed for calibration and uncertainty analysis, and to explore differences between fortnightly and daily nitrate sampling strategies. The results indicate that: (a) the posterior uncertainty intervals of nitrogen-export process parameters were narrower when the model was calibrated to daily nitrate measurements, while similar maximum likelihood parameter values were obtained regardless of the sampling frequency; (b) the model calibrated using daily nitrate data better represented both daily and fortnightly nitrate measurements relative to that obtained using fortnightly sampling; (c) the daily nitrate dataset produced significantly smaller parametric prediction uncertainty, but only modest reduction in total prediction uncertainty, relative to the fortnightly nitrate dataset; (d) model structural error and measurement errors are the primary sources of total prediction uncertainty, and these combine to inhibit the benefits of high-frequency monitoring. We conclude that the adequacy of sampling frequency is dependent on model structure and measurement errors, such that higher-frequency nitrate monitoring may not markedly reduce the uncertainty of nutrient predictions, depending on other levels and sources of uncertainty.