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GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

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  • Wenwu Tang

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    Center for Applied GIScience, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Heidi S. Hearne

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    Center for Applied GIScience, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Zachery Slocum

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    Center for Applied GIScience, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Tianyang Chen

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    Center for Applied GIScience, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

Abstract

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics.

Suggested Citation

  • Wenwu Tang & Heidi S. Hearne & Zachery Slocum & Tianyang Chen, 2023. "GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling," Sustainability, MDPI, vol. 15(17), pages 1-25, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12704-:d:1222621
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    References listed on IDEAS

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    1. Wenwu Tang & Minrui Zheng & Xiang Zhao & Jiyang Shi & Jianxin Yang & Carl C. Trettin, 2018. "Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    2. Laura Geselbracht & Kathleen Freeman & Eugene Kelly & Doria Gordon & Francis Putz, 2011. "Retrospective and prospective model simulations of sea level rise impacts on Gulf of Mexico coastal marshes and forests in Waccasassa Bay, Florida," Climatic Change, Springer, vol. 107(1), pages 35-57, July.
    3. Reed Noss, 2011. "Between the devil and the deep blue sea: Florida’s unenviable position with respect to sea level rise," Climatic Change, Springer, vol. 107(1), pages 1-16, July.
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