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Temporal dynamic drought interpretation of Sawa Lake: case study located at the Southern Iraqi region

Author

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  • Salih Muhammad Awadh

    (University of Baghdad)

  • Ahmed H. Al-Sulttani

    (University of Kufa)

  • Zaher Mundher Yaseen

    (South Ural State University
    Al-Ayen University
    Asia University)

Abstract

Sawa Lake is a historical, cultural, and environmental landmark of great importance in education and tourism. Its unique advantages make it a potential global natural heritage and geopark in 2021. The current research is devoted to investigate and assess the actual reasons behind the rapid decline of the water level of Sawa Lake, which has begun to drop dramatically in an unprecedented situation. Landsat data are employed for interpreting the temporal and spatial changes of both water and vegetation covers. Shuttle Radar Topography Mission data are used for water volume estimation. Standardized Precipitation Evapotranspiration Index was used in comparing the rainfall rate to the changing of land use-land cover. Additionally, the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index are established on the Landsat OLI imageries using ENVI 5.3 software. The data analysis showed that one of the most important reasons for the Sawa Lake water level drop is the massive pumping of the groundwater from a site located to the south of the lake to feed the Mamlahat Al-Samawa from which NaCl is extracted.

Suggested Citation

  • Salih Muhammad Awadh & Ahmed H. Al-Sulttani & Zaher Mundher Yaseen, 2022. "Temporal dynamic drought interpretation of Sawa Lake: case study located at the Southern Iraqi region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 619-638, May.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:1:d:10.1007_s11069-021-05198-3
    DOI: 10.1007/s11069-021-05198-3
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    References listed on IDEAS

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    Cited by:

    1. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).

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