Sensitivity and identifiability of rheological parameters in debris flow modeling

Abstract

<jats:p>Abstract. Over the past decades, several numerical models have been developed to understand, simulate and predict debris flow events. Typically, these models simplify the complex interactions between water and solids using a single-phase approach and different rheological models to represent flow resistance. In this study, we perform a sensitivity analysis on the parameters of a debris flow numerical model (FLO-2D) for a suite of relevant variables (i.e., maximum flood area, maximum flow velocity, maximum flow velocity, deposit volume). Our aims are to (i) examine the degree of model overparameterization, and (ii) assess the effectiveness of observational constraints to improve parameter identifiability. We use the Distributed Evaluation of Local Sensitivity Analysis (DELSA) method, which is a hybrid local-global technique. Specifically, we analyze two creeks in northern Chile that were affected by debris flows on March 25, 2015. Our results show that SD and β1 – a parameter related to viscosity – provide the largest sensitivities. Further, our results demonstrate that equifinality is present in FLO-2D, and that final deposited volume and maximum flood area contain considerable information to identify model parameters. </jats:p>

This paper gives an inkling of the kinds of insights all models will someday provide about data and simulated processes

“…Commonly this is called a Value of Improved Information (VOII) analysis, and statistics such as OPR (Observation-PRediction) and PPR (Parameter-PRediction) could be used to explore the distribution of uncertainty measures throughout parameter space using the DELSA approach. OPR and PPR are described by Tiedeman et al (2003Tiedeman et al ( , 2004, and Tonkin et al (2007). Parameter-value dependence of these or other statistics with similar goals has received little attention to my knowledge.…”