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  "name": "Nature-based climate resilience through shoreline transformation: Captain Sinclair\u2019s Recreational Area, VA, USA (2023-2024)",
  "description": "Global climate change has led to many adverse impacts including changing weather patterns and an increase in the severity and frequency of extreme weather events. Traditional hard engineering solutions for coastal protection, such as seawalls and bulkheads, are becoming inadequate as they do not have the capacity to keep pace with the accelerating impacts of climate change. In response, there is growing interest in Natural and Nature-Based Features (NNBF), which offer innovative and adaptive approaches to coastal protection. NNBFs, such as salt marshes or living shorelines, have demonstrated potential for mitigating wave energy, reducing flood risks, and enhancing coastal resilience. However, the effectiveness of these features can vary based on their physical attributes and the specific environmental conditions in which they are implemented. The inconsistent performance of NNBFs has posed challenges for their widespread adoption, partly due to uncertainties in their efficacy and barriers in public policy.\r\n\r\nThis research focuses on evaluating the performance of NNBFs in attenuating waves through the natural environment, highlighting the benefits of hybrid NNBFs. Our findings demonstrate that, when combined, different NNBFs play complementary roles in wave attenuation. Thus, enhancing shoreline resilience and reducing marsh loss under dynamic water level conditions. These results highlight the importance of implementing diverse NNBF systems to address the dynamic nature of coastal conditions. By quantifying the performance of these features, this study offers valuable insights into designing and optimizing shoreline protection strategies that promote climate resiliency.\r\n\r\nHere, a Jupyter Notebook is provided to a) Access the data; b) Generate plots; and c) Perform data analysis.",
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        "name": "George Mason University"
      },
      "name": "de Lima, Andre"
    },
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        "name": "George Mason University"
      },
      "name": "Cardona, Daniel"
    },
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        "name": "George Mason University;Civil, Environmental and Infrastructure Engineering (CEIE)"
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      "name": "Miesse, Tyler Will"
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        "name": "Old Dominion University"
      },
      "name": "Navid Tahvildari"
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      "affiliation": {
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        "name": "Old Dominion University"
      },
      "name": "Ali Shahabi"
    },
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      "email": "cferrei3@gmu.edu",
      "identifier": null,
      "affiliation": {
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        "name": "George Mason University"
      },
      "name": "Ferreira, Celso"
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  "dateCreated": "2025-01-22 19:25:56.438643+00:00",
  "keywords": [
    "hydrodynamics",
    "salt marsh",
    "wave attenuation",
    "oyster reefs",
    "waves"
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    "url": "http://creativecommons.org/licenses/by/4.0/"
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  "contributor": [],
  "publisher": {
    "name": "Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI)",
    "url": "https://www.cuahsi.org/"
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  "datePublished": "2025-01-30 20:05:10.977175+00:00",
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  "inLanguage": "eng",
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  "dateModified": "2025-01-30 20:05:07.580169+00:00",
  "funding": [
    {
      "type": "Grant",
      "description": null,
      "identifier": "NA180AR4170083, Project R/72155Y and 77877B",
      "funder": {
        "type": "Organization",
        "url": "https://vaseagrant.org/",
        "address": null,
        "name": "Virginia Sea Grant, Virginia Institute of Marine Science"
      },
      "name": "Shoreline Transformation Adaptation: Forward Looking Nature-Based Climate Resilience"
    }
  ],
  "temporalCoverage": {
    "endDate": "2024-05-23 00:00:00",
    "startDate": "2023-07-21 00:00:00"
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  "spatialCoverage": {
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    "name": "Severn River in Gloucester County, Virginia",
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  "citation": [
    "de Lima, A., D. Cardona, T. W. Miesse, N. Tahvildari, A. Shahabi, C. Ferreira (2025). Nature-based climate resilience through shoreline transformation: Captain Sinclair\u2019s Recreational Area, VA, USA (2023-2024), HydroShare, https://doi.org/10.4211/hs.060dd0ad11834a2793494c6914eaecf1"
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