Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling

TL;DR

This paper introduces a spatial global sensitivity analysis method for high-resolution 2D urban flood models, producing sensitivity maps that reveal the influence of topographic data uncertainties and modeller choices on flood predictions.

Contribution

The paper develops a novel spatial GSA approach using Sobol indices to map the influence of input uncertainties on flood model outputs, emphasizing modeller decisions over data accuracy.

Findings

Modeller choices have a greater impact than measurement errors.

Sensitivity maps reveal spatial variability in parameter influence.

The approach enhances understanding of model limitations and uncertainties.

Abstract

This paper presents a spatial Global Sensitivity Analysis (GSA) approach in a 2D shallow water equations based High Resolution (HR) flood model. The aim of a spatial GSA is to produce sensitivity maps which are based on Sobol index estimations. Such an approach allows to rank the effects of uncertain HR topographic data input parameters on flood model output. The influence of the three following parameters has been studied: the measurement error, the level of details of above-ground elements representation and the spatial discretization resolution. To introduce uncertainty, a Probability Density Function and discrete spatial approach have been applied to generate 2, 000 DEMs. Based on a 2D urban flood river event modelling, the produced sensitivity maps highlight the major influence of modeller choices compared to HR measurement errors when HR topographic data are used, and the spatial variability of the ranking. Highlights $\bullet$ Spatial GSA allowed the production of Sobol index maps, enhancing the relative weight of each uncertain parameter on the variability of calculated output parameter of interest. 1 $\bullet$ The Sobol index maps illustrate the major influence of the modeller choices, when using the HR topographic data in 2D hydraulic models with respect to the influence of HR dataset accuracy. $\bullet$ Added value is for modeller to better understand limits of his model. $\bullet$ Requirements and limits for this approach are related to subjectivity of choices and to computational cost.