Robust analysis and best practices in flood economic loss assessment using HEC-FIA and GO-Consequence software for climate-ready risk management

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Evaluating economic losses is essential for effective flood mitigation and rescue missions. While tools such as HAZUS and HEC-FIA have been extensively used for estimating economic losses, there remains a notable gap in exploring the nuanced intrinsic data of these software systems. This study evaluates HEC-FIA and GO-Consequence, delving into methodologies governing key parameters, including the depth-damage function, National Structure Inventory (NSI) dataset utilization, structure replacement costs based on occupancy type, foundation height considerations, and nuanced flooded depth calculations. Additionally, the research appraises and validates the recently disclosed NSI dataset by the US Army Corps of Engineers, underscoring its pivotal role in economic loss assessment.The analysis suggests that HEC-FIA overestimates by about 15 % compared to GO-Consequence. This overestimation in loss estimation varies across categories, especially in commercial and public buildings. Identified anomalies in estimated loss values emphasize the importance of validating input data sources and cross-referencing with alternative models. The percentage difference between model estimations increases for higher loss values, suggesting factors such as interpretation of damageable value from depth-damage function, flooded depth, occupancy type, and number of stories influence estimation discrepancies. GO-Consequence aligns closely with actual loss values, showcasing state-of-the-art capabilities, advanced features, and enhanced functionalities. Its integration with open data sources makes GO-Consequence a superior choice for future flood loss assessments. The outlined methodology is a best practice for researchers, engineers, and stakeholders, offering insights into estimated value uncertainties.