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Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia

Author

Listed:
  • Mohamed Elhag

    (King Abdulaziz University)

  • Ioannis Gitas

    (Aristotle University of Thessaloniki)

  • Anas Othman

    (King Abdulaziz University)

  • Jarbou Bahrawi

    (King Abdulaziz University)

  • Aris Psilovikos

    (University of Thessaly)

  • Nassir Al-Amri

    (King Abdulaziz University)

Abstract

The monitoring of inland water resources in arid environments is an essential element due to their fragility. Reliable prediction of the water quality parameters helps to control and manage the water resources in arid regions. Water quality parameters were estimated using remote sensing data acquired from the beginning of 2017 until the end of 2018. The prediction of the water quality parameters was comprehended by using an adjusted autoregressive integrated moving average (ARIMA) and its extension seasonal ARIMA (S-ARIMA). Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) were the tested water quality parameters using Sentinel-2 sensor on temporal resolution basis of the sensor. Results indicated that the implementation of the ARIMA model failed to sustain a reliable prediction longer than one-month time while S-ARIMA succeeded to maintain a robust prediction for the first 3 months with confidence level of 96%. MCI has its ARIMA at (1,2,2) and S-ARIMA at (1,2,2) (2,1,1)6, GNDVI has its ARIMA at (2,1,2) and S-ARIMA at (2,1,2) (2,2,2)6, and finally, NDTI has its ARIMA at (2,2,2) and S-ARIMA at (2,2,2) (1,1,2)6. The accuracy of S-ARIMA predictions reached 82% at 6-month prediction period. Meanwhile, there was no solid prediction model that lasted till 12 months. Each of the forecasted water quality parameters is unique in its prediction settings. S-ARIMA model is a more reliable model because the seasonality feature is inherited within the forecasted water quality parameters.

Suggested Citation

  • Mohamed Elhag & Ioannis Gitas & Anas Othman & Jarbou Bahrawi & Aris Psilovikos & Nassir Al-Amri, 2021. "Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1392-1410, February.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:2:d:10.1007_s10668-020-00626-z
    DOI: 10.1007/s10668-020-00626-z
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    References listed on IDEAS

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    1. Aris Psilovikos & Mohamed Elhag, 2013. "Forecasting of Remotely Sensed Daily Evapotranspiration Data Over Nile Delta Region, Egypt," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4115-4130, September.
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    Cited by:

    1. Zhi Yang & Wenping Li & Liangning Li & Shaogang Lei & Jiawei Tian & Gang Wang & Xuejia Sang, 2022. "Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China," Sustainability, MDPI, vol. 14(15), pages 1-19, August.

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