Comparative analysis of the structures and outcomes of geophysical flow models and modeling assumptions using uncertainty quantification

TL;DR

This paper introduces a statistical method to evaluate and compare different geophysical flow models and their assumptions, using uncertainty quantification to determine model suitability across various flow regimes.

Contribution

It develops a novel probabilistic approach for characterizing and comparing geophysical flow models based on their underlying assumptions and observational data.

Findings

Models show distinct performance differences across flow regimes.

The method identifies dominant modeling assumptions in different scenarios.

Application to volcanic and inclined plane data demonstrates effectiveness.

Abstract

We present a new statistically driven method for analyzing the modeling of geophysical flows. Many models have been advocated by different modelers for such flows incorporating different modeling assumptions. Limited and sparse observational data on the modeled phenomena usually does not permit a clean discrimination among models for fitness of purpose, and, heuristic choices are usually made, especially for critical predictions of behavior that has not been experienced. We advocate here a methodology for characterizing models and the modeling assumptions they represent, using a statistical approach over the full range of applicability of the models. Such a characterization may then be used to decide the appropriateness of a model and modeling assumption for use. We present our method by comparing three different models arising from different rheology assumptions, and the data show unambiguously the performance of the models across a wide range of possible flow regimes. This comparison is facilitated by the recent development of the new release of our TITAN2D mass flow code that allows choice of multiple rheologies The quantitative and probabilistic analysis of contributions from different modeling assumptions in the models is particularly illustrative of the impact of the assumptions. Knowledge of which assumptions dominate, and, by how much, is illustrated in two different case studies: a small scale inclined plane with a flat runway, and the large scale topography on the SW slope of Volc\'{a}n de Colima (MX). A simple model performance evaluation completes the presentation.