EGU23-10510
https://doi.org/10.5194/egusphere-egu23-10510
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion

Zoe Li1, Pengxiao Zhou2, and Maysara Ghaith3
Zoe Li et al.
  • 1Department of Civil Engineering McMaster University, Hamilton, L8S 4L7, Ontario, Canada (zoeli@mcmaster.ca)
  • 2Department of Civil Engineering McMaster University, Hamilton, L8S 4L7, Ontario, Canada (zhoup8@mcmaster.ca)
  • 3Department of Civil Engineering McMaster University, Hamilton, L8S 4L7, Ontario, Canada (ghaithm@mcmaster.ca)

There are significant uncertainties associated with the estimates of model parameters in hydrological and environmental modeling. Such uncertainties could propagate within a modeling framework, leading to considerable deviation of the predicted value from its real value. Quantifying the uncertainties associated with model parameters could be computationally exhaustive and is still a daunting challenge to hydrological and environmental engineers. In this study, a series of Polynomial Chaos Expansion (PCE) methods, which have a significant advantage in computational efficiency, is developed to assess the propagation of parameter uncertainty. The proposed approaches were applied to two hydrological/environmental modeling case studies. The uncertainty quantification results will be compared with those from the traditional Monte Carlo simulation technique, to demonstrate the effectiveness and efficiency of the proposed approaches. This work will provide an efficient and reliable alternative to assess the impacts of the parameter uncertainties in hydrological and environmental modeling.

How to cite: Li, Z., Zhou, P., and Ghaith, M.: Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10510, https://doi.org/10.5194/egusphere-egu23-10510, 2023.