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    "name": "Modelled storm surge and total water level return periods along the coastline of China, Vietnam, Cambodia and Thailand (1980-2050).",
    "description": "This dataset provides modelled storm surge and total water levels along the South China Sea region (coastline of China, Vietnam, Cambodia and Thailand) for the period 1980-2050. Three return period scenarios are considered: 10% AEP (Annual Exceedance Probability) = 1:10 year return period; 1% AEP = 1:100 year return period; 0.1% AEP = 1:1000 year return period. Projections utilise Representative Concentration Pathway (RCP) 8.5 \u2013 the greenhouse gas concentration trajectory adopted by the Intergovernmental Panel on Climate Change (IPCC). The dataset was produced by forcing a hydrodynamic model underpinned by a new synthetic database representing 10,000 years of past, present and future tropical cyclone activity. The aim of this exercise being to estimate the risks posed by extreme sea levels, especially in tropical regions where cyclones can generate large storm surges and observations are too limited in time and space to deliver reliable analyses. The dataset was produced by Principal Investigators Dr Ivan D Haigh and Dr Melissa Wood (School of Ocean and Earth Science, University of Southampton, UK) in collaboration with partners from the School of Geography and Environmental Science (University of Southampton, UK), Tyndall Centre for Climate Change Research (University of East Anglia, UK), Southern Institute of Water Resource Research (Vietnam) and the Institute for Environmental Studies (Netherlands). Funding was secured through the UK\u2019s Natural Environment Research Council (NERC) Grant \u2018CompFlood\u2019 (grant number NE/S003150/1).",
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            "description": "Wood M.L., Haigh I.D., Quan L., Hung N., Darby S.E., Marsh R., Skliris N., Hirschi J., Nicholls R.J., Bloemendaal N. (2022). Modelled storm surge and total water level return periods along the coastline of China, Vietnam, Cambodia and Thailand (1980-2050). NERC EDS British Oceanographic Data Centre NOC doi: 10.5285/e17e7db6-4a78-1a89-e053-6c86abc0253d",
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