Improved Understanding of River Ice Processes Using Global Sensitivity Analysis Approaches

Abstract

The high impact of river ice phenomena on the hydrology of cold regions has led to the extensive use of numerical models in simulating and predicting river ice processes. Consequently, there is a need to utilize efficient and robust sensitivity analysis (SA) methods to characterize the role of different parameters on the functioning of these models. To gain greater insight into how the internal parameters affect a river ice model’s behavior, this paper presents a comparative performance investigation of the two global SA methods: (1) the recently proposed variogram analysis of response surfaces (VARS); and (2) the widely used regional sensitivity analysis (RSA). The methods were benchmarked on a one-dimensional hydrodynamic river ice model of the Lower Dauphin River, Manitoba, Canada. Furthermore, using a bootstrapping strategy, a procedure was developed to estimate confidence intervals on the resulting sensitivity indices and evaluate reliability of the inferred parameter rankings. Results show that (1) the water levels simulated by the river ice model are most sensitive to the ice cover characteristics (i.e., porosity and thickness at the ice cover front) and upstream discharge; (2) the hydraulic roughness parameters and slush ice properties (i.e., porosity and thickness of the slush pans) are medium- and low-sensitivity parameters, respectively; (3) the VARS and RSA methods provide contradictory assessments regarding the sensitivity of the model output to variations in the slush ice porosity and ice roughness parameters; and (4) the VARS method appears to be superior to RSA in terms of generating robust estimates of the parameter sensitivity rankings.