Preprints
https://doi.org/10.5194/egusphere-2022-198
https://doi.org/10.5194/egusphere-2022-198
 
06 May 2022
06 May 2022
Status: this preprint is open for discussion.

DSCIM-Coastal v1.0: An Open-Source Modeling Platform for Global Impacts of Sea Level Rise

Nicholas Depsky1,3,, Ian Bolliger2,3,,a, Daniel Allen3, Jun Ho Choi6, Michael Delgado2, Michael Greenstone4,6, Ali Hamidi2, Trevor Houser2, Robert E. Kopp5, and Solomon Hsiang3,4 Nicholas Depsky et al.
  • 1Energy & Resources Group, University of California, Berkeley
  • 2The Rhodium Group
  • 3Global Policy Lab, Goldman School of Public Policy, University of California, Berkeley
  • 4National Bureau of Economic Research
  • 5Department of Earth & Planetary Sciences and Rutgers Institute of Earth, Ocean and Atmospheric Sciences, Rutgers University, New Brunswick, New Jersey, USA
  • 6Energy Policy Institute, University of Chicago
  • These authors contributed equally to this work.
  • anow at: BlackRock, San Francisco, California, USA

Abstract. 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.

Nicholas Depsky et al.

Status: open (until 30 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-198', Goneri Le Cozannet, 05 Jun 2022 reply
  • RC2: 'Comment on egusphere-2022-198', Anonymous Referee #2, 27 Sep 2022 reply

Nicholas Depsky et al.

Nicholas Depsky et al.

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Short summary
This work presents a novel, open-source modelling platform and associated input dataset of physical and socioeconomic coastal characteristics for over 9,000 discrete segments of global coastlines for evaluating future sea-level rise (SLR) impacts. We performed an initial evaluation of estimated future costs under 110 different future scenarios of SLR and socioeconomic growth trajectories and estimate global annual costs from SLR to range from roughly $200–500 billion by the year 2100.