Evaluating the impact and risk of pluvial flash flood on intra-urban road net- work: A case study in the city center of Shanghai, China

Urban pluvial flood are attracting growing public concern due to rising intense precipitation and increasing consequences. Accurate risk assessment is critical to an efficient urban pluvial flood management, particularly in transportation sector. This paper describes an integrated methodology, which initially makes use of high resolution 2D inundation modeling and flood depth-dependent measure to evaluate the potential impact and risk of pluvial flash flood on road network in the city center of Shanghai, China. Intensity–Duration–Frequency relationships of Shanghai rainstorm and Chicago Design Storm are combined to generate ensemble rainfall scenarios. A hydrodynamic model (FloodMap-HydroInundation2D) is used to simulate overland flow and flood inundation for each scenario. Furthermore, road impact and risk assessment are respectively conducted by a new proposed algorithm and proxy. Results suggest that the flood response is a function of spatio-temporal distribution of precipitation and local characteristics (i.e. drainage and topography), and pluvial flash flood is found to lead to proportionate but nonlinear impact on intra-urban road inundation risk. The approach tested here would provide more detailed flood information for smart management of urban street network and may be applied to other big cities where road flood risk is evolving in the context of climate change and urbanization

Leveraging Crowdsourced Navigation Data In Roadway Pluvial Flash Flood Prediction

This dissertation develops and tests a new data-driven framework for short-term roadway pluvial flash flood (PFF) risk estimation at the scale of road segments using crowdsourced navigation data and a simplified physics-based PFF model. Pluvial flash flooding (PFF) is defined as localized floods caused by an overwhelmed natural or engineered drainage system. This study develops a data curation and computational framework for data collection, preprocessing, and modeling to estimate the risk of PFF at road-segment scales. A hybrid approach is also developed that couples a statistical model and a simplified physics-based simulation model in a machine learning (ML) model to rapidly predict the risk of roadway PFF using Waze alerts in real-time

A vulnerability assessment of urban emergency in schools of Shanghai

Schools and students are particularly vulnerable to natural hazards, especially pluvial flooding in cities. This paper presents a scenario-based study that assesses the school vulnerability of emergency services (i.e., Emergency Medical Service and Fire & Rescue Service) to urban pluvial flooding in the city center of Shanghai, China through the combination of flood hazard analysis and GIS-based accessibility mapping. Emergency coverages and response times in various traffic conditions are quantified to generate school vulnerability under normal no-flood and 100-y pluvial flood scenarios. The findings indicate that severe pluvial flooding could lead to proportionate and linear impacts on emergency response provision to schools in the city. Only 11% of all the schools is predicted to be completely unreachable (very high vulnerability) during flood emergency but the majority of the schools would experience significant delay in the travel times of emergency responses. In this case, appropriate adaptations need to be particularly targeted for specific hot-spot areas (e.g., new urbanized zones) and crunch times (e.g., rush hours)

Flood risk map from hydrological and mobility data: a case study in S\~ao Paulo (Brazil)

Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This paper proposes an urban flood risk map from hydrological and mobility data, considering the megacity of S\~ao Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.Comment: 22 pages, 20 figure

Evaluating the cascading impacts of sea level rise and coastal flooding on emergency response spatial accessibility in Lower Manhattan, New York City

This paper describes a scenario-based approach for evaluating the cascading impacts of sea level rise (SLR) and coastal flooding on emergency responses. The analysis is applied to Lower Manhattan, New York City, considering FEMA’s 100- and 500-year flood scenarios and New York City Panel on Climate Change (NPCC2)’s high-end SLR projections for the 2050s and 2080s, using the current situation as the baseline scenario. Service areas for different response timeframes (3-, 5- and 8-minute) and various traffic conditions are simulated for three major emergency responders (i.e. New York Police Department (NYPD), Fire Department, New York (FDNY) and Emergency Medical Service (EMS)) under normal and flood scenarios. The modelling suggests that coastal flooding together with SLR could result in proportionate but non-linear impacts on emergency services at the city scale, and the performance of operational responses is largely determined by the positioning of emergency facilities and the functioning of traffic networks. Overall, emergency service accessibility to the city is primarily determined by traffic flow speed. However, the situation is expected to be further aggravated during coastal flooding, with is set to increase in frequency and magnitude due to SLR

Linking a storm water management model to a novel two-dimensional model for urban pluvial flood modeling

This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model (SWMM) and the 2D flood inundation model (ECNU Flood-Urban). The SWMM modeling results (the overflow of the manholes) are used as the input boundary condition of the ECNU Flood-Urban model to simulate the rainfall–runoff processes in an urban environment. The analysis is applied to the central business district of East Nanjing Road in downtown Shanghai, considering 5-, 10-, 20-, 50-, and 100-year return period rainfall scenarios. The results show that node overflow, water depth, and inundation area increase proportionately with the growing return periods. Water depths are mostly predicted to be shallow and surface flows generally occur in the urban road network due to its low-lying nature. The simulation result of the coupled model proves to be reliable and suggests that urban surface water flooding could be accurately simulated by using this methodology. Adaptation measures (upgrading of the urban drainage system) can then be targeted at specific locations with significant overflow and flooding

A network-based analysis of critical resource accessibility during floods

Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events

Vulnerability of the Scottish Road Network to Flooding

Between 2014 and 2021 there were over 600 recorded flooding incidents per year on trunk roads managed by the four Operating Companies. Whilst many of these incidents were relatively minor in nature, around 9% of incidents resulted in a reduction in road width or at least one lane closure, and just over 1% led to a road or carriageway closure. Around half of all recorded flooding incidents had a duration of more than one hour, and 3.4% were longer than 6 hours. On average, there was at least one flooding incident on the trunk road network every two days. The maximum number of recorded incidents in a single day was 51, which occurred on 31st December 2015 during Storm Frank, and there were more than 15 flooding incidents in a single day on 40 occasions in the period 2014-2021

An integrated framework to assess compound flood risks for interdependent critical infrastructure in a coastal environment

Compound flooding refers to flood events caused by multiple factors, including marine processes (e.g. storm tides and waves), hydrometeorological signals (e.g. rainfall and river flows) among others. Saint Lucia is a tropical island in eastern Caribbean Sea, which is frequently affected by weather-related extreme events such as tropical storms and the associated risks are exacerbated due to its mountainous topography and high concentrations of infrastructure and human communities close to the coast. At the southern coast of Saint Lucia, significant infrastructures such as Hewanorra International Airport and Vieux Fort Seaport, and human settlements such as towns of Vieux Fort and La Tourney are located at low-lying areas and are at risk of compound flooding. A hydrologic model (i.e. HYdrological MODel) and a two-dimensional hydrodynamic model (i.e. LISFLOOD-FP) are set up and calibrated to investigate the combined effects of storm tides, wave run-up, rainfall, and river flows on flood risks in Saint Lucia. Results indicate the necessity to consider multiple contributing factors as well as to characterize the effects of uncertain boundary conditions. In flood-prone areas, there are infrastructures supporting major services in the study area, and by extension, the economy of the Island. A network-based model, which considers direct and indirect connections between infrastructures, is set up to explore risks of assets in conditions of non-flooding and flooding. Modelling results reveal the fundamental importance of various components including electricity distribution, flood control, information and communication services, transportation, housing and human settlements, tourism, and particularly the normal operations of Hewanorra International Airport. Prioritization of risks is critical for developing effective mitigation methods for infrastructure networks