To effectively cope with the impacts of climate change and increase urban resilience, households and neighborhoods must adapt in ways that reduce vulnerability to climate-related natural hazards. Communities in the United States and elsewhere are exposed to more frequent extreme heat, wildfires, cyclones, extreme precipitation, and flooding events. Whether and how people respond to increased hazard exposure (adaptive behavior) is widely recognized to be driven by their capacity to adapt, perception of the risk, and past experiences. Underlying these important dimensions, however, is social context. In this paper, we examine how social capital and social vulnerability shape risk perception and household flood mitigation actions. The study, based on a metropolitan-wide survey of households in Austin, Texas, USA, suggests that bonding social capital (personal networks, neighborhood cohesion, and trust) is positively related to mitigation behavior and that social vulnerability is negatively related to risk perception. Importantly, our research demonstrates a positive and significant effect of social capital on adaptive behavior even when controlling for social vulnerability of a neighborhood. This suggests that policies and programs that strengthen the social connectedness within neighborhoods can increase adaptive behaviors thus improving community resilience to flood events.

No abstract

No abstract

No abstract

Abstract. 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.