Preprints
https://doi.org/10.5194/egusphere-2025-1413
https://doi.org/10.5194/egusphere-2025-1413
12 May 2025
 | 12 May 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

INSYDE-content: a synthetic, multi-variable flood damage model to household contents

Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini

Abstract. This paper introduces INSYDE-content, a novel, probabilistic multi-variable synthetic flood damage model designed to analyze physical damage to household contents on a component-by-component basis. The model addresses a critical gap in current modeling tools, which often overlook the significance of household contents in overall damage assessments. Developed through an expert-based approach and grounded in the scientific and technical literature, INSYDE-content leverages desk-based data to characterize model features, including uncertainty treatment arising from incomplete input data. A sensitivity analysis and a benchmark test against observed losses demonstrate the robust performance of the model and highlight the contribution of different features to damage mechanisms affecting house contents. While in this study INSYDE-content is tailored for illustrative purposes to the hazard, vulnerability and exposure characteristics of Northern Italy, the model is highly adaptable, allowing for its application to different regional contexts through appropriate customization.

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Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini

Status: open (until 23 Jun 2025)

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Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini

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Short summary
INSYDE-content is a novel probabilistic model designed to estimate flood damage to household items with a component-based approach. By incorporating multiple variables and addressing uncertainties, the model enables more comprehensive and insightful damage assessments by accounting for an often-overlooked asset
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