Dataset: Climate Change and Flood Susceptibility in Bocas del Toro, Panama: A Multi-Criteria Spatial Analysis Approach

This collection comprises a suite of geospatial datasets developed to support flood‑susceptibility assessment in Bocas del Toro Province, Panama. It encompasses the ALOS PALSAR RTC Mosaic, the Normalized Difference Vegetation Index (NDVI), forest‑cover and land‑use data, three extreme‑rainfall datasets, and the administrative boundary shapefile of Bocas del Toro Province. The boundary shapefile of Bocas del Toro Province was clipped from the 2024 district‑level shapefile of Panama, published by Milton Solano on the STRI GIS Open Data Portal.The ALOS PALSAR RTC Mosaic (GeoTIFF), sourced from the Alaska Satellite Facility, provides high‑resolution terrain information suitable for detailed topographic characterization and surface‑feature delineation. The NDVI dataset, derived from Sentinel‑2 quarterly mosaic imagery, offers quantitative indicators of vegetation condition, ecosystem health, and spatial patterns of vegetative cover. The collection also includes a 2021 forest‑cover and land‑use raster for Bocas del Toro, clipped from the 2021 forest‑cover and land‑use dataset of Panama prepared by MiAmbiente.The rainfall component is subdivided into three datasets that capture both historical and future hydroclimatic extremes. The 90th Percentile Historic Rainfall dataset characterizes long‑term extreme precipitation patterns, while the SSP2‑4.5 and SSP5‑8.5 90th Percentile Projected Rainfall datasets represent anticipated extreme rainfall conditions for the 2040–2060 period under mid‑range and high‑emissions climate scenarios. All three rainfall datasets were clipped from the 90th percentile historic and projected rainfall surfaces developed by Hidalgo et al. for Central America.All datasets are organized to enable seamless integration across diverse spatial analyses, supporting investigations of land‑cover, ecological resilience, terrain–environment interactions, and climate‑impact assessments. Each dataset is accompanied by detailed metadata to ensure transparency, reproducibility, and methodological clarity. Collectively, these datasets enable full replication of the analytical workflow presented in our study and can also support a broad range of related geospatial and environmental research applications. Each ZIP archive contains the primary raster dataset along with associated auxiliary files (.xml,. aux.xml) and metadata required for proper rendering, interpretation, and reuse within standard GIS environments.