Increased interest in combining compound flood hazards and social vulnerability has driven recent advances in flood impact mapping. However, current methods to estimate event specific compound flooding at the household level require high performance computing resources frequently not available to local stakeholders. Government and non-government agencies currently lack methods to repeatedly and rapidly create flood impact maps that incorporate local variability of both hazards and social vulnerability. We address this gap by developing a methodology to estimate a flood impact index at the household level in near-real time, utilizing high resolution elevation data to approximate event specific inundation from both pluvial and fluvial sources in conjunction with a social vulnerability index. Our analysis uses the 2015 Memorial Day flood in Austin, Texas as a case study and proof of concept for our methodology. We show that 37% of the Census Block Groups in the study area experience flooding from only pluvial sources and are not identified in local or national flood hazard maps as being at risk. Furthermore, averaging hazard estimates to cartographic boundaries masks household variability, with 60% of the Census Block Groups in the study area having a coefficient of variation around the mean flood depth exceeding 50%. Comparing our pluvial flooding estimates to a 2D physics-based model, we classify household impact accurately for 92% of households. Our methodology can be used as a tool to create household compound flood impact maps to provide computationally efficient information to local stakeholders.

The frequency of major flooding events continues to increase, fueling the already growing concern in numerous fields about quantifying the inequitable distribution of flood hazards. Our previous work of overlaying high resolution flood exposure data with social vulnerability information has already begun to highlight how different communities experience varying levels of risk. However, this fails to capture the complex nature by which flooding affects interconnected infrastructure and service networks which further have an impact on an individual and community’s risk. Our goal is to quantitatively define an individual’s vulnerability to flooding, encompassing how both pluvial and fluvial inundation impacts an individual’s place of residence and disrupts their access to critical resources, including flood, gas, healthcare, and emergency services, while still considering an individual’s socioeconomic standing. With the goal of estimating household level disruption of access to critical resources in near real time, our approach relies on a multilayer network of social vulnerability, transportation infrastructure, essential resources, and emergency services. To estimate inundation in near real time, we utilize the Heigh Above Nearest Drainage (HAND) method and a topographic depression hierarchy algorithm to estimate fluvial and pluvial flooding. Using a minimum cost flow algorithm, we determine an individual’s relative cost to access resources before, during, and after a major flooding event. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This model will be useful in future descriptive and prescriptive analytical frameworks by identifying critical nodes across networks and providing actionable knowledge on at risk communities. Our model will inform agencies involved in flood management, urban planning, and emergency response on where they can best apply resources to increase the resiliency of communities and the infrastructure they rely on.

Depressions are topographic areas that have no outward flow and occur in portions of landscapes with little to no elevation change, or areas with negative relief in relation to surrounding areas. While these depressions are an important part of the hydrological system, they have historically been filled in or ignored during flow routing and other hydrological processing calculations. With the increased prevalence of high-resolution topography data, understanding and evaluating how topographic depressions can impact overland flow is vital for improving hydrological analyses, specifically in the context of flood inundation mapping. Flooding caused from the filling of depressions (pluvial flooding) can have compounding effects when simultaneously occurring with river (fluvial) or coastal flooding. Our goal is to consider both pluvial and fluvial flooding in flat urban environments to identify areas that are significantly more vulnerable to inundation as compared to flood mapping from only one particular source. Our approach relies on a multiplex network that utilizes the Height Above Nearest Drainage (HAND) method as well as a hydraulic head equalization algorithm to estimate inundation patterns. Social vulnerability data are integrated in this framework to identify urban hot spots, defined as areas with a lower relative socioeconomic status in conjunction with a higher probability of inundation. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This approach can help urban flood planners associate social disparities in relationship to flood preparedness and response.