Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches

Hafsa Aeman; Hong Shu; Hamera Aisha; Imran Nadeem; Rana Waqar Aslam

doi:10.1007/s11356-024-33296-9

Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches

Environ Sci Pollut Res Int. 2024 Apr 25. doi: 10.1007/s11356-024-33296-9. Online ahead of print.

Authors

Hafsa Aeman¹, Hong Shu², Hamera Aisha³, Imran Nadeem⁴, Rana Waqar Aslam²

Affiliations

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China. hafsa.aeman@whu.edu.cn.
² State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
³ World Wildlife Fund for Nature (WWF), Lahore, Pakistan.
⁴ Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria.

PMID: 38662291
DOI: 10.1007/s11356-024-33296-9

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.

Keywords: Accretion; Climate change; EfficientNet; Erosion; Image segmentation; Nature-based solutions; Remote sensing.