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    Knowledge-Based Predictive Tools to Assess Effectiveness of Natural and Nature-Based Solutions for Coastal Restoration and Protection Planning

    Source: Journal of Hydraulic Engineering:;2020:;Volume ( 146 ):;issue: 002
    Author:
    Ehab Meselhe
    ,
    Yushi Wang
    ,
    Eric White
    ,
    Hoonshin Jung
    ,
    Melissa M. Baustian
    ,
    Scott Hemmerling
    ,
    Monica Barra
    ,
    Harris Bienn
    DOI: 10.1061/(ASCE)HY.1943-7900.0001659
    Publisher: ASCE
    Abstract: Predictive tools are widely used to study coastal and deltaic systems in support of basic research, planning efforts, engineering design, and the implementation of restoration or protection strategies. They have been extensively used to evaluate the effectiveness of natural and nature-based solutions (NNBS) to support ecosystem functions and services of coastal ecosystems and human communities experiencing increased risk from sea-level rise and severe storms. The potential benefits of NNBS are being increasingly recognized, particularly in remote areas or areas that are either technically or financially infeasible to be protected with levees or other difficult engineering alternatives. Local communities, however, are often excluded from proposing, screening, or evaluating NNBS as restoration and protection strategies. Communities are also not sufficiently involved in the development or application of the predictive tools. This research effort outlines an approach to developing knowledge-based predictive tools and a community engagement process to evaluate NNBS strategies proposed predominantly by local communities. Incorporating knowledge from local communities benefits and potentially improves the performance of predictive tools and their ability to capture visible trends and observations. To illustrate this concept, the authors present landscape models for coastal Louisiana that successfully reproduced the frequency of flooding of local roads, rate of shoreline erosion, salinity pattern changes, and presence/absence of key species (e.g., brown shrimp, oysters, and so forth). While these qualitative measures are not a substitute for well-established rigorous and quantitative model performance assessment approaches, they offer an effective approach to engage local communities and incorporate their knowledge in the development of the predictive models and the proposed protection and restoration strategies to be examined.
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      Knowledge-Based Predictive Tools to Assess Effectiveness of Natural and Nature-Based Solutions for Coastal Restoration and Protection Planning

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4266844
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    contributor authorEhab Meselhe
    contributor authorYushi Wang
    contributor authorEric White
    contributor authorHoonshin Jung
    contributor authorMelissa M. Baustian
    contributor authorScott Hemmerling
    contributor authorMonica Barra
    contributor authorHarris Bienn
    date accessioned2022-01-30T20:37:57Z
    date available2022-01-30T20:37:57Z
    date issued2/1/2020 12:00:00 AM
    identifier other%28ASCE%29HY.1943-7900.0001659.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4266844
    description abstractPredictive tools are widely used to study coastal and deltaic systems in support of basic research, planning efforts, engineering design, and the implementation of restoration or protection strategies. They have been extensively used to evaluate the effectiveness of natural and nature-based solutions (NNBS) to support ecosystem functions and services of coastal ecosystems and human communities experiencing increased risk from sea-level rise and severe storms. The potential benefits of NNBS are being increasingly recognized, particularly in remote areas or areas that are either technically or financially infeasible to be protected with levees or other difficult engineering alternatives. Local communities, however, are often excluded from proposing, screening, or evaluating NNBS as restoration and protection strategies. Communities are also not sufficiently involved in the development or application of the predictive tools. This research effort outlines an approach to developing knowledge-based predictive tools and a community engagement process to evaluate NNBS strategies proposed predominantly by local communities. Incorporating knowledge from local communities benefits and potentially improves the performance of predictive tools and their ability to capture visible trends and observations. To illustrate this concept, the authors present landscape models for coastal Louisiana that successfully reproduced the frequency of flooding of local roads, rate of shoreline erosion, salinity pattern changes, and presence/absence of key species (e.g., brown shrimp, oysters, and so forth). While these qualitative measures are not a substitute for well-established rigorous and quantitative model performance assessment approaches, they offer an effective approach to engage local communities and incorporate their knowledge in the development of the predictive models and the proposed protection and restoration strategies to be examined.
    publisherASCE
    titleKnowledge-Based Predictive Tools to Assess Effectiveness of Natural and Nature-Based Solutions for Coastal Restoration and Protection Planning
    typeJournal Paper
    journal volume146
    journal issue2
    journal titleJournal of Hydraulic Engineering
    identifier doi10.1061/(ASCE)HY.1943-7900.0001659
    page12
    treeJournal of Hydraulic Engineering:;2020:;Volume ( 146 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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