The impact of considering uncertainty in measured calibration/validation data during auto-calibration of hydrologic and water quality models

Abstract

The importance of uncertainty inherent in measured calibration/validation data is frequently stated in literature, but it is not often considered in calibrating and evaluating hydrologic and water quality models. This is due to the limited amount of data available to support relevant research and the limited scientific guidance on the impact of measurement uncertainty. In this study, the impact of considering measurement uncertainty during model auto-calibration was investigated in a case study example using previously published uncertainty estimates for streamflow, sediment, and NH4-N. The results indicated that inclusion of measurement uncertainty during the auto-calibration process does impact model calibration results and predictive uncertainty. The level of impact on model predictions followed the same pattern as measurement uncertainty: streamflow < sediment < NH4-N; however, the direction of that impact (increasing or decreasing) was not consistent. In addition, inclusion rate and spread results did not indicate a clear relationship between predictive uncertainty and the magnitude of measurement uncertainty. The purpose of this study was not to show that inclusion of measurement uncertainty produces better calibration results or parameter estimation. Rather, this study demonstrated that uncertainty in measured calibration/validation data can play a crucial role in parameter estimation during auto-calibration and that this important source of predictive uncertainty should be not be ignored as it is in typical model applications. Future modeling applications related to watershed management or scenario analysis should consider the potential impact of uncertainty in measured calibration/validation data, as model predictions influence decision-making, policy formulation, and regulatory action.