Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference

The common practice of infrequent (e.g., monthly) stream water quality sampling for state of the environment monitoring may, when combined with high resolution stream flow data, provide sufficient information to accurately characterise the dominant nutrient transfer pathways and predict annual catchment yields. In the proposed approach, we use the spatially lumped catchment model StreamGEM to predict daily stream flow and nitrate concentration (mg L-1 NO3-N) in four contrasting mesoscale headwater catchments based on four years of daily rainfall, potential evapotranspiration, and stream flow measurements, and monthly or daily nitrate concentrations. Posterior model parameter distributions were estimated using the Markov Chain Monte Carlo sampling code DREAMzs and a log likelihood function assuming heteroscedastic, t-distributed residuals. Despite high uncertainty in some model parameters, the flow and nitrate calibration data was well reproduced across all catchments (Nash-Sutcliffe efficiency against Log transformed data, NSL, in the range 0.62-0.83 for daily flow and 0.17-0.88 for nitrate concentration). The slight increase in the size of the residuals for a separate validation period was considered acceptable (NSL in the range 0.60-0.89 for daily flow and 0.10-0.74 for nitrate concentration, excluding one data set with limited validation data). Proportions of flow and nitrate discharge attributed to near-surface, fast seasonal groundwater and slow deeper groundwater were consistent with expectations based on catchment geology. The results for the Weida Stream in Thuringia, Germany, usingmonthly as opposed to daily nitrate data were, for all intents and purposes, identical, suggesting that four years of monthly nitrate sampling provides sufficient information for calibration of the StreamGEM model and prediction of catchment dynamics. This study highlights the remarkable effectiveness 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. (C) 2017 Elsevier B.V. All rights reserved.