Flooding poses a significant threat to infrastructure, ecosystems, and communities, exacerbated by climate change and urbanization. Effective flood governance requires coordinated action across sectors, yet current approaches remain fragmented and fail to address critical socio-environmental factors shaping collaboration. Despite increased awareness, the complexity of flood governance—driven by diverse stakeholders, varied risk perceptions, and uneven institutional capacities—remains a challenge. This study addresses these gaps by applying network analysis and Exponential Random Graph Models (ERGMs) to examine relationships among flood governance actors in the Beaumont-Port Arthur region. Our unique approach analyzes both the structural features of governance networks and the role of shared perceptions of flood risk drivers and impacts, offering insights into socio-environmental factors shaping collaboration. The paper identifies the structural characteristics of governance networks across flood preparation, mitigation, response, and recovery phases, while evaluating how perceptions of risk influence network ties. Our findings reveal significant governance gaps in addressing socio-economic and ecological impacts, with lower network connectivity in these areas. Conversely, infrastructure considerations play a central role in fostering partnerships, particularly in long-term planning efforts. Organizations with aligned perceptions of key drivers, such as infrastructure and precipitation risks, form more cohesive and responsive networks. These results emphasize the need for inclusive, adaptive governance frameworks that integrate diverse perspectives and strengthen local engagement. This study provides valuable insights for policymakers, offering pathways to enhance flood resilience by addressing the socio-environmental dynamics shaping collaborative networks.

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.