Huang Xuanhao, Li Yangfan, Wang Xinwei
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
Sci Total Environ. 2024 Dec 1;954:176257. doi: 10.1016/j.scitotenv.2024.176257. Epub 2024 Sep 15.
Beach erosion is an adverse impact of climate change and human development activities. Effective beach management necessitates integrating natural and anthropogenic factors to address future erosion trends, while most current prediction models focus only on natural factors, which may provide an incomplete and potentially inaccurate representation of erosion dynamics. This study enhances prediction methods by integrating both natural and anthropogenic factors, thereby enhancing the accuracy and reliability of erosion projections. By extracting historical shorelines through CoastSat model from 1986 to 2020, we develop multivariable scenarios with Attention-LSTM model to predict the regional impacts of natural and anthropogenic factors on erosion to sandy beaches along the typical shoreline of Shenzhen in China. Results reveal that Shenzhen's beaches experienced erosion up to 12 m over the past 35 years. Here we project a decrease in the mean erosion rate of the beaches, identifying population growth (21.0 %) as the main controlling factor before the mid-century in a range of scenarios. We find that Attention-LSTM multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of beach erosion compared to scenario prediction model of Attention-LSTM and statistical model of Digital Shoreline Analysis System (DSAS), yielding an average uncertainty of 10.99 compared to 13.29. These insights reveal policies to safeguard beaches because of the rising demand for beaches due to human factors, coupled with decreased impervious surfaces through ecological conservation, lead to mitigation for beach erosion. Accurate forecasts empower policymakers to implement effective coastal management strategies, safeguard resources, and mitigate erosion's adverse effects. Our study offers finely-tuned predictions of coastal erosion, providing crucial insights for future coastal conservation efforts and climate change adaptation along the shoreline, and serving as a foundation for further research aimed at understanding the evolving environmental impacts of beach erosion in Shenzhen.
海滩侵蚀是气候变化和人类发展活动的不利影响。有效的海滩管理需要整合自然和人为因素来应对未来的侵蚀趋势，而目前大多数预测模型只关注自然因素，这可能无法完整且准确地反映侵蚀动态。本研究通过整合自然和人为因素来改进预测方法，从而提高侵蚀预测的准确性和可靠性。通过使用CoastSat模型提取1986年至2020年的历史海岸线，我们利用注意力长短期记忆模型（Attention-LSTM）开发多变量情景，以预测自然和人为因素对中国深圳典型海岸线沙滩侵蚀的区域影响。结果显示，在过去35年里，深圳的海滩侵蚀达12米。在此，我们预测海滩的平均侵蚀速率将会下降，并确定在一系列情景中，人口增长（21.0%）是本世纪中叶之前的主要控制因素。我们发现，与注意力长短期记忆模型的情景预测模型和数字海岸线分析系统（DSAS）的统计模型相比，注意力长短期记忆多模型集成方法在广泛的海滩侵蚀预测中能提供整体更高的准确性和可靠性，其平均不确定性为10.99，而其他模型为13.29。这些见解揭示了保护海滩的政策，因为人为因素导致对海滩的需求增加，同时通过生态保护减少不透水表面，从而减轻海滩侵蚀。准确的预测使政策制定者能够实施有效的海岸管理策略、保护资源并减轻侵蚀的不利影响。我们的研究提供了对海岸侵蚀的精确预测，为未来沿海保护工作和海岸线气候变化适应提供了关键见解，并为进一步研究深圳海滩侵蚀不断演变的环境影响奠定了基础。
