DSCIM-Coastal v1.0: An Open-Source Modeling Platform for Global Impacts of Sea Level Rise
Depsky, N., Bolliger, I., Allen, D., Choi, J. H., Delgado, M., Greenstone, M., Hamidi, A., Houser, T., Kopp, R. E., and Hsiang, S.: DSCIM-Coastal v1.0: An Open-Source Modeling Platform for Global Impacts of Sea Level Rise, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-198, 2022.
Published May 6, 2022
Global sea level rise (SLR) may impose substantial economic costs to coastal communities worldwide, but characterizing its global impact remains challenging because SLR costs depend heavily on natural characteristics and human investments at each location—including topography, the spatial distribution of assets, and local adaptation decisions. To date, several impact models have been developed to estimate global costs of SLR, yet the limited availability of open-source and modular platforms that easily ingest up-to-date socioeconomic and physical data sources limits the ability of existing systems to transparently incorporate new insights. In this paper, we present a modular open-source platform designed to address this need, providing end-to-end transparency from global input data to a scalable least-cost optimization framework that estimates adaptation and net SLR costs for nearly 10,000 global coastline segments and administrative regions. Our approach accounts both for uncertainty in the magnitude of global SLR and spatial variability in local relative sea level rise. Using this platform, we evaluate costs across 110 possible socioeconomic and SLR trajectories in the 21st century. We find annual global SLR costs of $180 billion to $200 billion in 2100 assuming optimal adaptation, moderate emissions (RCP 4.5) and middle-of-the-road (SSP 2) socioeconomic trajectories. Under the highest SLR scenarios modeled, this value ranges from $400 billion to $520 billion. We make this platform publicly available in an effort to spur research collaboration and support decision-making, with segment level physical and socioeconomic input characteristics provided at https://doi.org/10.5281/zenodo.6449231, source code for this dataset at https://doi.org/10.5281/zenodo.6456115, the modeling framework at https://doi.org/10.5281/zenodo.6453099, and model results at https://doi.org/10.5281/zenodo.6014086.