Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification

Parameter estimation, using historical observed data, is an important part of the environmental modeling. The uncertainty in the parameter estimation limits the applications of environmental models. In this paper, the influence of limited and uncertain calibrated data on the performance of the parameter estimation are systematically investigated. For this purpose, synthetic observations with a given uncertainty and frequency are used to estimate the model parameters of a conceptual water quality (WQ) model of the River Zenne, Belgium. Bayesian inference using Markov Chain Monte Carlo sampling is adopted to simultaneously perform the automatic calibration and the uncertainty analysis. The results highlight the critical roles of measurement frequency and uncertainty in the model calibration. We found that the effect of the measurement uncertainty on the parameter estimation is significant when the calibrated data points are limited (e.g. monthly data). The research findings can be used to support measurement prioritization and resource allocation. •Historical observed data is required for calibration.•Measurement error and limited data frequency result in parameter uncertainty.•The results highlight the critical roles of measurement error and frequency in the calibration.•The effect of the measurement uncertainty is significant when the calibrated data are limited.•The research findings can be used to support measurement prioritization and resource allocation.