Improving hydrological model predictions by incorporating rating curve uncertainty

Abstract

This study identified and analysed the sources of error in runoff estimation via rating curves. This was undertaken by applying the BATEA framework to the rating curve model used to transform river stage to runoff for the Mahurangi catchment - a heavily instrumented experimental catchment in New Zealand. The systematic errors due to rating curve uncertainty were the dominant source of uncertainty. This rating curve uncertainty is due to a limited number of uncertain runoff gaugings. The contribution of the random error component was small. This contradicts the common assumption of random runoff measurement error used in hydrological model calibration. The impact of rating curve uncertainty was incorporated into hydrological model calibration for the hourly Mahurangi data using the BATEA framework. Comparing these results to the typical standard least squares approach (with fixed rating curve) illustrated that incorporating rating curve uncertainty improved the reliability of the hydrological model predictions and caused a shift in the parameter distributions. These results have implications for hydrological model calibration. Runoff data are typically assumed to be “observed” but rating curve uncertainty induces significant systematic runoff errors which can have a significant impact on hydrological model predictions.