Analytical Reasoning and Fuzzy Cognitive Maps of Barrier Island Response to Natural and Anthropogenic Forcing

障壁岛对自然和人为强迫响应的分析推理和模糊认知图

• 批准号: RGPIN-2017-04920
• 负责人: Houser, Chris
• 金额: $ 2.84万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653898
• 关键词: Analytical; Reasoning; Fuzzy; Cognitive; Maps

Predicting landscape response to a changing climate and anthropogenic forcing is challenged by the mathematical intractability of combining and solving equations for a large range of scale-dependent processes. Numerical modeling requires simplifying assumptions that may not necessarily be representative of process mechanics, or produce results that conform to objective measurements and observations obtained in the field. At the same time, process geomorphologists are able to make relevant assessments of landscape change based on their conceptual knowledge developed through "experience, intuition and tacit knowledge”, but do not have the ability to quantitatively formalize and evaluate their conceptual knowledge and predict complex spatial patterns. It is currently not possible to integrate and numerically model our conceptual understanding of landscape change at scales relevant to geographers, engineers and planners, or to include the results of the model into climate models to predict future climate and landscape change with or without anthropogenic forcing. For example, through our research we have been able to develop robust conceptual models beach-dune interaction, barrier island transgression in response to sea level rise and the impact of anthropogenic forcing?, it is currently not possible to predict coastal response to natural and anthropogenic forcing using this understanding. Analytical Reasoning (AR) and Fuzzy Cognitive Maps (FCMs) represent an emerging geo-computational approach to formalize, implement, and test conceptual models of landscape change4, through integration with remotely sensed imagery. In this Discovery Grant period, we will use AR and FCMs to predict the response of barrier islands to natural and anthropogenic forcing at a range of spatial and temporal scales. Specifically, we will use AR and FCMs to predict: 1 ) alongshore variation in nearshore, beach and dune morphology, in response to offshore bathymetry and framework geology, 2) feedback mechanisms controlling foredune development and recovery, 3) recovery of beach and dune environments over a range of storm forcing and sea level rise, 4) barrier island transgression and evolution with sea level rise, and 5) impact of anthropogenic forcing on island response to sea level and storm activity. In this respect, the proposed research will address the theoretical and methodological shortcomings that have hindered the development of models to quantify and predict spatial patterns of barrier island response to natural and anthropogenic forcing using our conceptual understanding of barrier island geomorphology and evolution in combination with recent advances in geospatial technologies and geo-computation.

预测地貌对气候变化和人为强迫的响应面临着组合和求解大范围尺度相关过程的方程的数学困难的挑战。数值模拟需要简化假设，这些假设可能不一定代表过程力学，或者产生与现场获得的客观测量和观测相一致的结果。同时，过程地貌学家能够基于他们通过经验、直觉和默会知识发展起来的概念性知识对景观变化做出相关评估，但没有能力对他们的概念性知识进行量化形式化和评估，并预测复杂的空间格局。目前还不可能在与地理学家、工程师和规划师相关的尺度上整合我们对景观变化的概念性理解并对其进行数值模拟，也不可能将模型的结果纳入气候模型以预测在有或没有人为强迫的情况下未来的气候和景观变化。例如，通过我们的研究，我们已经能够建立强有力的概念模型，海滩-沙丘相互作用，障碍岛海侵对海平面上升的反应，以及人为强迫的影响，目前不可能用这种理解来预测海岸对自然和人为强迫的反应。分析推理(AR)和模糊认知图(FCMS)代表了一种新兴的地理计算方法，通过与遥感图像的集成来形式化、实施和测试景观变化的概念模型4。在这个探索资助期，我们将使用AR和FCMS来预测屏障岛屿在一系列空间和时间尺度上对自然和人为强迫的响应。具体地说，我们将使用AR和FCMS来预测：1)近岸、海滩和沙丘形态的变化，响应于近海水深和骨架地质；2)控制沙丘发育和恢复的反馈机制；3)在一定范围的风暴强迫和海平面上升期间海滩和沙丘环境的恢复；4)屏障岛屿随海平面上升的海侵和演化；以及5)人为强迫对岛屿对海平面和风暴活动的影响。在这方面，拟议的研究将解决阻碍开发模型的理论和方法缺陷，以便利用我们对堰塞岛地貌和演化的概念性理解，结合地球空间技术和地球计算的最新进展，量化和预测堰塞岛对自然和人为强迫的反应的空间模式。