Abstract
Insufficient freshwater recharge and climate change resulted in seawater intrusion in most of the coastal aquifers in Pakistan. Coastal aquifers represent diverse landcover types with varying spectral properties, making it challenging to extract information about their state hence, such investigation requires a combination of geospatial tools. This study aims to monitor erosion along the major coastal aquifers of Pakistan and propose an approach that combines data fusion into the machine and deep learning image segmentation architectures for the erosion and accretion assessment in seascapes. The analysis demonstrated the image segmentation U-Net with EfficientNet backbone achieved the highest F1 score of 0.93, while ResNet101 achieved the lowest F1 score of 0.77. Resultant erosion maps indicated that Sandspit experiencing erosion at 3.14 km2 area. Indus delta is showing erosion, approximately 143 km2 of land over the past 30 years. Sonmiani has undergone substantial erosion with 52.2 km2 land. Miani Hor has experienced erosion up to 298 km2, Bhuri creek has eroded over 4.11 km2, east Phitii creek over 3.30 km2, and Waddi creek over 3.082 km2 land. Tummi creek demonstrates erosion, at 7.12 km2 of land, and East Khalri creek near Keti Bandar has undergone a measured loss of 5.2 km2 land linked with quantified reduction in the vertical sediment flow from 50 (billion cubic meters) to 10 BCM. Our analysis suggests that intense erosions are primarily a result of reduced sediment flow and climate change. Addressing this issue needs to be prioritized coastal management and climate change mitigation framework in Pakistan to safeguard communities. Leveraging emerging solutions, such as loss and damage financing and the integration of nature-based solutions (NbS), should be prioritized for the revival of the coastal aquifers.
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Data availability
The datasets used during this study will be made available from the corresponding author on reasonable request.
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Hafsa Aeman: conceptualization, data collection, analysis, and writing. Hong Shu: conceptualization, review, and supervising. Hafsa Aeman: visualization, analysis, and writing. Imran Nadeem: review and editing. Hamera Aisha: ground data collection, designing and writing NbS recommendations, and validation. Rana Waqar Aslam: editing. As the corresponding author, Hafsa Aeman bears full responsibility for the submission and confirms that all authors listed on the title page have contributed notably to work. Finally, all authors read and approved the manuscript.
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Aeman, H., Shu, H., Aisha, H. et al. Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33296-9
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DOI: https://doi.org/10.1007/s11356-024-33296-9