It is flooding more than we know: climate injustice from floods due to sea level rise
Summary
Climate induced sea-level rise (SLR) is increasing coastal flood exposure globally, with tens of millions of people at risk to coastal flooding in the United States alone. The proposed NASA interdisciplinary project aims to address this issue by disseminating maps, real-time data streams, and sensor-based estimates of flood frequency at a community level to support local governments in demonstrating potential inequities and quantifying flood exposure as they seek funding for climate adaptation. Specifically, we propose to provide new observations of flood extent and frequency in marginalized communities distant from tide gauges to support community-based understanding of flood risk. We will integrate NASAs higher spatial and temporal resolution satellite observations with in situ data streams to identify flooding in these communities. We are leveraging newly developed water level sensors and smart (with on-device machine learning) cameras, efforts led by PIs Anarde, Hino, and Goldstein. These sensors offer a unique opportunity to test the effectiveness of commercial SmallSat sensors (e.g., PlanetScope and Capella) and satellite observations (e.g., Sentinel-1 and upcoming NASA ISRO Synthetic Aperture Radar) in capturing flood extent at high resolution across various coastal landscapes, while also providing real-time, privacy-preserving data streams of flood hazards for partner communities. The proposed project will build upon years of regional work on remote sensing tools, socioeconomics, and assessments to move into a new phase that centers NASA ESDs commitment to environmental justice (EJ) and climate justice (CJ) at the forefront of NASA-funded interdisciplinary research, and to address NASAs high priority EJ/CJ needs to study exposure and vulnerability to flooding hazard. The UNC-CH team will lead two components. Co-PI Wang will lead the analysis of remotely sensed data to detect flood extent, and Co-PI Hino will be responsible for integrating flood exposure information with socioeconomic data to characterize inequities in the distribution of flood impacts. They will also support community engagement efforts and dissemination of results.The UNC-CH team associated with this component of the project will include co-PIs Hino and Wang and two graduate students. In Year 1, co-PI Wang and one GRA will develop and validate models to detect flood extent from remotely sensed data, validated using in situ sensors and cameras. This work may include automation of routines to derive flood extent from Capella SAR time-series observations, refinement of code, and preparing the data for use by other team members, all within the time allotted in the budget. In Years 2 and 3, once the model is developed, they will use it to estimate flood frequency and extent across a broader study area. They will participate in team meetings and generate maps and other communication products based on the results. In addition, co-PI Wang and the GRA will participate in conferences to share their work and get feedback from the remote sensing community. Results will be disseminated widely, including at conferences and in peer-reviewed journals. Co-PI Hino will lead the analysis of the inequities in flood impacts, supported by a part-time graduate research assistant in Years 2 and 3. This task will require compiling, cleaning, and analyzing two primary datasets, one containing information on household financial wellbeing, and the other reflecting where government investment in flood risk mitigation has taken place. Co-PI Hino and the graduate student will combine these datasets with the data on flood incidence to identify how flood exposure intersects with low-income households and where government investment is needed (and has not occurred previously). Co-PI Hino will also support community engagement and education through site visits, developing tailored co