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      Comparing the cost effectiveness of nature-based and coastal adaptation: A case study from the Gulf Coast of the United States

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          Abstract

          Coastal risks are increasing from both development and climate change. Interest is growing in the protective role that coastal nature-based measures (or green infrastructure), such as reefs and wetlands, can play in adapting to these risks. However, a lack of quantitative information on their relative costs and benefits is one principal factor limiting their use more broadly. Here, we apply a quantitative risk assessment framework to assess coastal flood risk (from climate change and economic exposure growth) across the United States Gulf of Mexico coast to compare the cost effectiveness of different adaptation measures. These include nature-based (e.g. oyster reef restoration), structural or grey (e.g., seawalls) and policy measures (e.g. home elevation). We first find that coastal development will be a critical driver of risk, particularly for major disasters, but climate change will cause more recurrent losses through changes in storms and relative sea level rise. By 2030, flooding will cost $134–176.6 billion (for different economic growth scenarios), but as the effects of climate change, land subsidence and concentration of assets in the coastal zone increase, annualized risk will more than double by 2050 with respect to 2030. However, from the portfolio we studied, the set of cost-effective adaptation measures (with benefit to cost ratios above 1) could prevent up to $57–101 billion in losses, which represents 42.8–57.2% of the total risk. Nature-based adaptation options could avert more than $50 billion of these costs, and do so cost effectively with average benefit to cost ratios above 3.5. Wetland and oyster reef restoration are found to be particularly cost-effective. This study demonstrates that the cost effectiveness of nature-based, grey and policy measures can be compared quantitatively with one another, and that the cost effectiveness of adaptation becomes more attractive as climate change and coastal development intensifies in the future. It also shows that investments in nature-based adaptation could meet multiple objectives for environmental restoration, adaptation and flood risk reduction.

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          Future flood losses in major coastal cities

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            Coastal flood damage and adaptation costs under 21st century sea-level rise.

            Coastal flood damage and adaptation costs under 21st century sea-level rise are assessed on a global scale taking into account a wide range of uncertainties in continental topography data, population data, protection strategies, socioeconomic development and sea-level rise. Uncertainty in global mean and regional sea level was derived from four different climate models from the Coupled Model Intercomparison Project Phase 5, each combined with three land-ice scenarios based on the published range of contributions from ice sheets and glaciers. Without adaptation, 0.2-4.6% of global population is expected to be flooded annually in 2100 under 25-123 cm of global mean sea-level rise, with expected annual losses of 0.3-9.3% of global gross domestic product. Damages of this magnitude are very unlikely to be tolerated by society and adaptation will be widespread. The global costs of protecting the coast with dikes are significant with annual investment and maintenance costs of US$ 12-71 billion in 2100, but much smaller than the global cost of avoided damages even without accounting for indirect costs of damage to regional production supply. Flood damages by the end of this century are much more sensitive to the applied protection strategy than to variations in climate and socioeconomic scenarios as well as in physical data sources (topography and climate model). Our results emphasize the central role of long-term coastal adaptation strategies. These should also take into account that protecting large parts of the developed coast increases the risk of catastrophic consequences in the case of defense failure.
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              Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes.

              Several recent models suggest that the frequency of Atlantic tropical cyclones could decrease as the climate warms. However, these models are unable to reproduce storms of category 3 or higher intensity. We explored the influence of future global warming on Atlantic hurricanes with a downscaling strategy by using an operational hurricane-prediction model that produces a realistic distribution of intense hurricane activity for present-day conditions. The model projects nearly a doubling of the frequency of category 4 and 5 storms by the end of the 21st century, despite a decrease in the overall frequency of tropical cyclones, when the downscaling is based on the ensemble mean of 18 global climate-change projections. The largest increase is projected to occur in the Western Atlantic, north of 20 degrees N.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 April 2018
                2018
                : 13
                : 4
                : e0192132
                Affiliations
                [1 ] Institute of Marine Sciences, University of California, Santa Cruz, California, United States of America
                [2 ] The Nature Conservancy, Santa Cruz, California, United States of America
                [3 ] Swiss Federal Institute of Technology, ETH Zurich, Zurich, Switzerland
                [4 ] Department of Ocean Sciences, University of California, Santa Cruz, California, United States of America
                Universidade de Vigo, SPAIN
                Author notes

                Competing Interests: One of the co-authors, David N. Bresch executed this work in his function as a lecturer at ETH Zurich, where he now became full professor. The organization (Swiss Re) David N. Bresch has been employed with in the past did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and did not provide financial support in the form of authors' salaries and/or research materials. And we can thus state that this past commercial affiliation does not alter David N. Bresch's adherence to PLOS ONE policies on sharing data and materials.

                Author information
                http://orcid.org/0000-0001-5526-7157
                Article
                PONE-D-17-09287
                10.1371/journal.pone.0192132
                5894966
                29641611
                4bfcfbe1-f814-4409-8598-0381eda57734
                © 2018 Reguero et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 March 2017
                : 17 January 2018
                Page count
                Figures: 6, Tables: 4, Pages: 24
                Funding
                This work was supported in part by the Anne Ray Charitable Trust, Kingfisher Foundation and the Lyda Hill Foundation as well as the Science for Nature And People Partnership; MWB was also supported by a Pew Marine Conservation Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Earth Sciences
                Hydrology
                Flooding
                Social Sciences
                Economics
                Development Economics
                Economic Growth
                Earth Sciences
                Atmospheric Science
                Meteorology
                Storms
                Ecology and Environmental Sciences
                Aquatic Environments
                Freshwater Environments
                Wetlands
                Earth Sciences
                Marine and Aquatic Sciences
                Aquatic Environments
                Freshwater Environments
                Wetlands
                Earth Sciences
                Geomorphology
                Topography
                Landforms
                Wetlands
                Social Sciences
                Economics
                Economic Analysis
                Cost-Effectiveness Analysis
                Social Sciences
                Economics
                Earth Sciences
                Marine and Aquatic Sciences
                Reefs
                Custom metadata
                All relevant data are indicated within the paper and its Supporting Information files. The input and output data for the model are available at: https://osf.io/d6r5u/ with DOI 10.17605/OSF.IO/D6R5U. The risk Model used in the study can be found at: https://github.com/davidnbresch/climada The coastal hazard module, as used for this work, can be found at: https://github.com/borjagreguero/climada_coastal_hazards_module

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