Probabilistic Flood Mapping and Impact Analysis from Downscaled Compound Flood Ensembles in Southeast Texas

Flooding is one of the costliest natural disasters; from 1980 to 2024 it has cost the United States over $200 billion cumulatively according to NOAA. Texas has been impacted by extreme flood events, including Hurricane Harvey (2017), Tropical Storm Imelda (2019), and the July 2025 floods which tragically caused over 100 fatalities. We focus on Southeast Texas, where low-relief terrain contributes to compound floods driven by fluvial and pluvial forcings. This work is part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL), where we collaborate with regional stakeholders—representing local government agencies, practitioners, community organizations, and industry partners—through task forces with whom we identify areas of concern, share scientific findings, and co-create actionable flood information. Compound flooding is computationally expensive to model at high resolution because coupled physical models are necessary to accurately capture compound flood processes and feedbacks. We downscale coarser results from the Advanced Terrestrial Simulator (ATS), a fully distributed surface-subsurface hydrologic model that includes compound fluvial-pluvial flood processes, to map flood inundation at residential block scale (1 to 3 m). We force ATS with ensembles of synthetic storm events generated using flood frequency analysis and stochastic storm transposition. We develop and apply a volume-conservative downscaling technique to the ensembles of ATS flood output, increasing resolution from an unstructured mesh with element edge length O(100 m) to a regular grid with element edge length O(1 m). We compute probabilistic flood maps by calculating the annual recurrence interval (ARI) at each pixel in our downscaled product, and validate against an extensive local gage network and FEMA's 100-year ARI floodplain. To translate probabilistic flood maps into actionable information co-developed with stakeholders, we perform impact analysis on urban centers within our study area: we intersect downscaled inundation maps with population data, building footprints, and transportation infrastructure. We also classify flood depths using human-meaningful thresholds to communicate flood impacts intuitively. This approach provides a nuanced understanding of flood risk by illustrating spatial variations in flood probability and quantifying impacts on people and infrastructure.