Novel shoreline retreat assessment considering wind dynamics and nature-based solutions
風動力学と自然に基づく解決策を考慮した新しい海岸線後退評価 (AI 翻訳)
Abderraouf Hzami, Radhouan Ben‐Hamadou, Azzam Abu-Rayash
🤖 gxceed AI 要約
日本語
カタールの563kmの海岸線を対象に、機械学習とリモートセンシングを用いて15年間の海岸線変化を定量化。32箇所の侵食ホットスポットを特定し、特にアルシャマル海岸では年間3m以上の後退を確認。卓越風向や波浪データとの関連を分析し、自然に基づく解決策（NbS）の緊急の必要性を強調。
English
This study quantifies shoreline evolution along 563 km of Qatar's coasts over 15 years using machine learning, remote sensing, and wind-wave data. 32 erosion hotspots are identified, with Al Shamal coast retreating over 3 m/year. The analysis links erosion to dominant N-NW winds and significant wave heights exceeding 3 m, emphasizing the need for Nature-based Solutions (NbS) to enhance coastal resilience.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本も沿岸国として海面上昇の影響を受ける。本論文のRSとMLを用いた海岸線モニタリング手法は、日本の海岸管理や気候変動適応策に応用可能。ただし、日本のGX実務（開示・移行計画）との直接的な関連は薄い。
In the global GX context
Contributes to coastal resilience and adaptation literature, particularly using machine learning for high-resolution shoreline change detection. Adds evidence for nature-based solutions in arid peninsula environments. Relevant for global climate adaptation planning.
👥 読者別の含意
🔬研究者:Provides a methodology combining remote sensing, ML, and wind-wave analysis for shoreline change detection applicable to other coastal regions.
🏢実務担当者:Coastal managers can use the identified erosion hotspots and wind-wave patterns to prioritize nature-based solutions.
🏛政策担当者:Highlights the urgency of investing in nature-based solutions for coastal resilience under climate change, especially for low-lying peninsulas.
📄 Abstract（原文）
Shoreline changes are among the major effects of sea level rise, affecting more than 10% of the global population living in low-lying coastal areas. Coastal dynamics pose a significant risk to human health, biodiversity, and infrastructure resilience. While peninsulas such as Qatar would be ideal for studying the impact of shoreline retreat, this knowledge remains largely unquantified. This peninsula has witnessed rapid urbanization and large-scale economic and industrial development along its coastlines, making it an effective case study for experimenting with vulnerable eroded zones. To address these challenges, high-resolution remote sensing (RS) and a wind-wave database were used to quantify shoreline evolution along 563 km of Qatar’s coasts over the last 15 years. A machine-learning vector classification algorithm, the Digital Shoreline Analysis System (DSAS) tool, remote-sensing indices, and statistical analysis methods were used to assess land-water interfaces. This geospatial assessment was performed using Pleaides spectral bands (0.5 m) and the DSAS shoreline statistical analysis shows that 32 sites are classified as coastal erosion hotspots, with an EPR exceeding -0.5 ± 0.15 m/yr. The most vulnerable area is the low-lying Al Shamal coast, where two hotspot segments have been identified with alarming retreat rates exceeding − 3 m. The dominant wind directions in these hotspot areas are from the N-NW quadrant, reaching 74% in the winter months. Analysis of ERA5 data shows that during strong wind events, significant wave height (SWH) in these areas can exceed 3 m. This study emphasizes the urgent need to implement Nature-based Solutions (NbS) to enhance coastal resilience in eroded hotspot areas amid global climate change.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s10668-026-07766-8first seen 2026-06-17 05:24:47 · last seen 2026-06-17 07:13:39
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