An Integrated Evaluation Framework based on Generalized Likelihood
Uncertainty Estimation for Quantifying Uncertainty in Flood Modeling
Abstract
Evaluation of the performance of hydrologic and hydraulic models is a
crucial step in the modeling process. Considering the limitations of
single statistical metrics, such as the Nash Sutcliffe efficiency (NSE),
the Kling Gupta efficiency (KGE), and the coefficient of determination
(R2), which are widely used in the evaluation of model performance, an
evaluation framework that incorporates multiple criteria and based on
the generalized likelihood uncertainty estimation (GLUE) is proposed to
demonstrate the uncertainty in the evaluation criteria and hence to
quantify the overall uncertainty of flood models in a comprehensive way.
This framework is applied to the one-dimensional HEC-RAS models of six
reaches located in States of Indiana and Texas of the United States to
quantify the uncertainty associated with the channel roughness and
upstream flow input. Specifically, the effects of different prior
distributions of the uncertainty sources, multiple high-flow scenarios,
and various types of measurement errors (white noise, positive bias, and
negative bias) in observations on the evaluation metrics are
investigated by using the bootstrapping method and Monte Carlo
simulations. The results show that the model performances based on the
uniform and normal priors are comparable. The distributions of all the
evaluation metrics in the framework are significantly different for the
flood model under different high-flow scenarios, and it further
indicates that the metrics are essentially random statistical variables.
Additionally, the white-noise error in observations has the least impact
on the metrics, while the positive and the negative biases would have
opposite impacts, which depends on whether the model overestimated or
underestimated the hydrologic variable.