Mitra Bijoy, Rahman Muhammad Muhitur, Khan Aftab Ahmad, Rahman Syed Masiur
Department of Geography and Environmental Studies, University of Chittagong, Bangladesh.
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.
Heliyon. 2024 Jun 15;10(12):e33120. doi: 10.1016/j.heliyon.2024.e33120. eCollection 2024 Jun 30.
This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995-2014) and IPCC-projected (2020-2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km (5 % confidence on SSP1-1.9) to 7150.8 km (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.
本研究调查了海平面上升（SLR）对印度河三角洲的影响，该三角洲是一个重要的生态系统，越来越容易受到气候变化影响。本研究的目的是根据政府间气候变化专门委员会（IPCC）第六次评估报告，全面评估各种共享社会经济路径（SSP）情景下的淹没区域。该研究采用基于地理信息系统（GIS）的浴缸模型，利用卡拉奇、坎德拉和奥卡站的历史（1995 - 2014年）和IPCC预测（2020 - 2150年）潮汐数据，确定受沿海洪水威胁的潜在淹没区域。此外，它还分析了陆地卫星（LANDSAT）获取的多光谱图像，以识别海岸侵蚀热点和景观变化。使用监督随机森林分类器对主要地貌进行分类，并了解土地覆盖的变化。此外，基于神经网络的细胞自动机模拟被用于预测2050年、2100年和2150年面临淹没风险的未来土地覆盖情况。结果表明，在不同的SSP情景下，估计的淹没土地面积从307.36平方公里（SSP1 - 1.9情景下5%置信度）到7150.8平方公里（SSP5 - 8.5情景下95%置信度）不等。到2150年，该地区将损失超过550平方公里的农业用地和535平方公里的红树林（平均海平面上升预测）。这项工作强调识别对海平面上升引起的沿海洪水敏感的土地覆盖。它可能会推动未来的政策制定和建模工作，以减少海平面上升的不确定性，并建立有效的沿海淹没缓解方法。
