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https://doi.org/10.5194/hess-2019-308
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-308
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Jun 2019

Submitted as: research article | 28 Jun 2019

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This preprint has been withdrawn by the authors.

Effect of Water Surface Area on the Remotely Sensed Water Quality Parameters of Baysh Dam Lake, Saudi Arabia

Mohamed Elhag1, Ioannis Gitas2, Anas Othman1, and Jarbou Bahrawi1 Mohamed Elhag et al.
  • 1Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • 2Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Greece

Abstract. Water quality parameters help to decide the further use of water based on its quality. Changes in water surface area in the lake shall affect the water quality. Chlorophyll a, Nitrate concentration and water turbidity were extracted from satellite images to record each variation on these parameters caused by the water amount in the lake changes. Each water quality measures have been recorded with its surface area reading to analyses the effects. Water quality parameters were estimated from Sentinel-2 sensor based on the satellite temporal resolution for the years 2017–2018. Data were pre-processed then processed to estimate the Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI). The Normalized Difference Water Index (NDWI), was used to calculate and record the changes in the water surface area in Baysh dam lake. Results showed different correlation coefficients between the lake surface area and the water quality parameters estimated Remote Sensing data. The response of the water quality parameters to surface water changes was expressed in four different surface water categories. MCI is more sensitive to surface water changes rather than GNDVI and NDTI. Neural network Analysis showed a resemblance between GNDVI and NDTI expressed in sigmoidal function while MCI showed a different behavior expressed in exponential behavior. Therefore, monitoring of the surface water area of the lack is essential in water quality monitoring.

This preprint has been withdrawn.
Mohamed Elhag et al.
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Mohamed Elhag et al.
Mohamed Elhag et al.
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Short summary
The current research article tackled successfully the effect of Baysh dam reservoir surface area on the water quality parameters tested in this research. The article addressed the water surface area categorization as a driving force that controls water quality parameters. Partition analysis and Artificial Neural Network Analysis will be used to envisage the water surface area effect on the estimated water quality parameters.
The current research article tackled successfully the effect of Baysh dam reservoir surface area...
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