Sea-level rise and high tide flooding inundation probability and depth statistics at Big Cypress National Preserve, Florida
Dates
Publication Date
2024-01-05
Start Date
2014-07-30
End Date
2020-01-25
Citation
Thurman, H.R., Enwright, N.M., Osland, M.J., Passeri, D.L., Day, R.H., Simons, B.M., Danielson, J.J., and Cushing, W.M., 2024, Sea-level rise and high tide flooding inundation probability and depth statistics at Big Cypress National Preserve, Florida: U.S. Geological Survey data release, https://doi.org/10.5066/P9JH8KQN.
Summary
This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Big Cypress National Preserve. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined [...]
Summary
This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Big Cypress National Preserve. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2050 and 2100 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major high tide flooding thresholds defined by the National Oceanic and Atmospheric Administration (NOAA). We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-level rise and high tide flooding water level estimates were also propagated based on uncertainty in the sea-level rise estimate (Sweet and others, 2022) and tidal datum transformation, respectively. Moreover, the probability of a pixel being inundated was calculated by summing the binary simulation outputs and dividing by 1,000. Following, probability was binned into the following classes: 1) Unlikely, probability ≤0.33; 2) Likely as not, probability >0.33 and ≤0.66; and 3) Likely, probability >0.66. Finally, depth statistics were only recorded when depth was equal to or greater than 0. We calculated the median depth, 25th percentile, 75th percentile, and interquartile range using all the pixels that met this criterion. When utilizing the depth statistics, it is important to also consider the probability of this pixel being flooded. In other words, the depth layers may show some depth returns, but the pixel may have rarely been inundated for the 1,000 iterations.
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Purpose
This project was funded by the U.S. Geological Survey’s Natural Resources Preservation Program. These layers were developed as a first-cut approximation of park areas that may be inundated from sea-level rise and high tide flooding and can be used to understand potential exposure to cultural and natural resources within the Preserve.
