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

abstract

  • 2015, Springer-Verlag Berlin Heidelberg. 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

published proceedings

  • STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

altmetric score

  • 0.5

author list (cited authors)

  • Yen, H., Hoque, Y., Harmel, R. D., & Jeong, J.

citation count

  • 13

complete list of authors

  • Yen, Haw||Hoque, Yamen||Harmel, Robert Daren||Jeong, Jaehak

publication date

  • October 2015