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

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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.

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Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. KEP-MSc-01-155-38. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

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Authors and Affiliations

  1. Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Mohamed Elhag, Anas Othman, Jarbou Bahrawi & Nassir Al-Amri

  2. Laboratory of Forest Management and Remote Sensing, School of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Ioannis Gitas

  3. Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, 38445, Volos, Magnesia, Greece

    Aris Psilovikos

Authors
  1. Mohamed Elhag
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  2. Ioannis Gitas
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  3. Anas Othman
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  4. Jarbou Bahrawi
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  5. Aris Psilovikos
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  6. Nassir Al-Amri
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Contributions

M.E. and I.Y. involved in conceptualization; M.E. N. A. and A.O. took part in methodology; J.B., A.P. and M.E. and N.A. participated in validation; M.E. and A.O. took part in formal analysis; M.E. involved in writing—original draft preparation; M.E. and A.O. contributed to writing—review and editing.

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Correspondence to Mohamed Elhag.

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Elhag, M., Gitas, I., Othman, A. et al. Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia. Environ Dev Sustain 23, 1392–1410 (2021). https://doi.org/10.1007/s10668-020-00626-z

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  • DOI: https://doi.org/10.1007/s10668-020-00626-z

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