Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings

Abstract

Flood damage assessment (FDA) is an essential tool for evaluating flood damage, vulnerability, and risk to civil systems such as residential buildings. The outcome of an FDA depends on the spatial limits of the study and the complexity of the data. For microscale FDA, a high level of detail is required to assess flood damage. This study reviewed the existing methodologies in microscale FDA based on empirical and synthetic data selection methods for model development. The merits and challenges of these approaches are discussed. This review also proposes an integrated step for assessing the stages of FDA. This study contributes to the literature by providing insights into the methodologies adopted, particularly on a microscale basis, which has not been comprehensively discussed in the previous reviews. The findings of this study reveal that univariate modeling of flood damage is nevertheless popular among researchers. New advanced approaches, such as advanced machine learning and 3D models, are yet to gain prominence when compared with the univariate modeling that has recorded a high success. This review concludes that there is a need to adopt a combined empirical–synthetic approach in the selection of data for developing damage models. Further research is required in the areas of multivariate modeling (advanced machine learning), 3D BIM-GIS modeling, 3D visualization of damages, and projection of probabilities in flood damage predictions to buildings. These are essential for performance flood-based building designs and for promoting building resilience to flood damage.

Ex-Ante Flooding Damages’ Monetary Valuation Model for Productive and Environmental Resources

“…Actual damage is the amount of damage that happened during a specific flood, while potential damage is the amount that would occur if there were no damage reduction measures in place. These damages are evaluated based on the type of data input, which may be real or hypothetical data [18]. Flood damages can also be classified into three levels: microscale, meso-scale, and macroscale [15].…”

Methods for Constructing a Refined Early-Warning Model for Rainstorm-Induced Waterlogging in Historic and Cultural Districts

“…Historic and cultural districts are areas with rich cultural relics, concentrated historic buildings, and the ability to reflect traditional patterns and historic features in a relatively intact and authentic manner at a certain scale. In the context of global climate change, extreme rainfall events are becoming increasingly frequent, and rainstorm disasters and urban waterlogging have become the primary risk threatening the safety of historic and cultural districts [1][2][3]. According to incomplete statistics, in just one year, 2021, there were 223 such events that occurred worldwide, far exceeding the number of rainstorm disasters in the past decade.…”

“…Based on the above research requirements, the objectives of this paper are as follows: (1) To propose a method for constructing a fine-grained model for early warning of rainstorm-induced waterlogging in historical and cultural districts, aiming to establish three sets of fine-grained models, including the building waterlogging early-warning model, road waterlogging early-warning model, and public evacuation early-warning model; (2) To introduce the concept of 1 h rainfall intensity water logging index, aiming to explore a reliable method for determining the response relationship between rainfall intensity and rainwater accumulation based on model simulation and clustering algorithms without relying on monitoring data; (3) To put forward the concepts of building waterlogging risk index, road waterlogging risk index, and public evacuation index, aiming to determine the values of the three indices by assessing the flood resilience of buildings, roads, and communities in the blocks; and (4) To develop early-warning response measures for building waterlogging, road waterlogging, and public evacuation in Beijing's historical and cultural districts based on the four-level warning grades for buildings, roads, and the public. The research results of this paper not only contribute to improving the accuracy and timeliness of early warnings, but also effectively address the issue of how to strengthen the linkage between early warning, emergency management, and meteorological forecasting work.…”