Modeling the high-resolution dynamic exposure to flooding in a city region

Abstract

Abstract. Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment.

A comprehensive system for semantic spatiotemporal assessment of risk in urban areas

“…Recent works address the problem of spatiotemporal assessment of risks either from a quantitative perspective (Zhu et al., 2019) or by simulating cascades of faults (Grafenauer, König, Rass, & Schauer, 2018; König & Schauer, 2019) due to critical infrastructure interdependencies. In Zhu et al.…”

“…In Zhu et al. (2019), the spatiotemporal distribution of flooding is simulated by using the LISFLOOD‐FP model and the spatial movements of residents are simulated during the urban flooding time period. Instead, in Grafenauer et al.…”

Flood Inundation Mapping at Ungauged Basins Using Coupled Hydrometeorological–Hydraulic Modelling: The Catastrophic Case of the 2006 Flash Flood in Volos City, Greece

“…Over the last decades, there has been an increasing number of studies on flood events for which a sufficient amount of data for verification is available (e.g., stage and discharge relationships, discharge data, accurate rainfall data, satellite flood extent, flood peak watermarks) [9,[21][22][23][24]. However, in ungauged or poorly gauged catchments, flood-related data (i.e., hydrometeorological data and/or flood inundation data such as flooded area and measured water depth-watermarks) are inadequate, both quantitatively and qualitatively.…”

Flood-pedestrian simulator for modelling human response dynamics during flood-induced evacuation: Hillsborough stadium case study

“…focus on assessing and minimising the direct risks of floodwater on people, especially when they are under immediate evacuation condition. For this purpose, computer models have been used to evaluate evacuation strategies, including for the analysis of the potential variability in individual people's flood risk state, the lowest-risk pathways, and the time window for issuing an early warning (Aboelata and Bowloes, 2008;Lumbroso et al, 2011;Dawson et al, 2011;Mas et al, 2015;Liu and Lim, 2016;Bernardini et al, 2017;Zhu et al, 2019).…”

“…For regional scale applications, ABM-based models were developed to simulate immediate crowd evacuation from a city, focusing on moving groups of individuals or household units using cars within a city road network to analyse response time of aware and unaware people to the immediate evacuation warning (Dawson et al, 2011;Mas et al, 2015;Liu and Lim, 2016;Zhu et al, 2019). These simulation models only consider vehicular emergency evacuation, which makes them not suited to simulate the interactive and the collective responses of moving individuals, or pedestrians, in and around small hubs (< 0.5 km × 0.5 km in size), such as shopping centres or sports venues.…”