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

Authors: YEN, HAW - Texas Agrilife Extension; HOQUE, YAMEN - Texas Agrilife Extension; Harmel, Daren; JEONG, JAEHAK - Texas Agrilife Extension

Submitted to: Stochastic Environmental Research and Risk Assessment (SERRA)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2015
Publication Date: 2/20/2015
Publication URL: https://handle.nal.usda.gov/10113/62429
Citation: Yen, H., Hoque, Y., Harmel, R.D., Jeong, J. 2015. The impact of considering uncertainty in measured calibration/validation data during auto-calibration of hydrologic and water quality models. Stochastic Environmental Research and Risk Assessment (SERRA). 29(7):1891-1901.

Interpretive Summary: The importance of uncertainty in measured data is frequently stated in literature, but it is not often used to adjust and evaluate 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 automated model adjustment (autocalibration) was investigated in a case study example using previously published uncertainty estimates for streamflow, sediment, and ammonium-nitrogen. The results indicated that inclusion of measurement uncertainty during the autocalibration process does impact model calibration results and prediction uncertainty. The level of impact on model predictions followed the same pattern as measurement uncertainty, increasing from streamflow to sediment and ammonium-nitrogen. In addition, there was no clear relationship between prediction 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. 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 prediction uncertainty should 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 measurement uncertainty, as model predictions influence decision-making, policy formulation, and regulatory action.

Technical 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 autocalibration process does impact model calibration results and prediction 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 prediction 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 prediction uncertainty should 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 measurement uncertainty, as model predictions influence decision-making, policy formulation, and regulatory action.