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Evaluation of the effects of land-use change and increasing deforestation in the Sapanca Basin on total suspended solids (TSS) movement with predictive models

Abstract

Sapanca Lake is a tectonically sourced freshwater resource and one of the rare natural water resources used as a source of drinking water. This study examined the change of land use and lake area in the natural water source basin subjected to human pressure for years. Landsat 5 TM (1987) and Landsat 8 TM (2010) satellite images were used. Satellite images were analyzed using ArcGIS 10.1 software. As a result of the analysis, it was observed that the natural vegetation was significantly destroyed between 1987 and 2010. Besides, the bathymetry maps of Lake Sapanca belonging to the years 1990 and 2010 were also examined, and accordingly, it was determined that there was a 2% reduction in the lake surface area. The decrease in the volume of the lake was thought to be due to sedimentation movement caused by land-use change, and the total amount of suspended solids, grain size, discharge, and temperature measurements were made between 2012 and 2014 in 12 streams which are sources of Sapanca Lake. Sediment prediction models have been developed under two different scenarios using measurement data from side streams. Artificial neural networks (ANN), Sediment rating curve, and multiple linear regression models were examined within the scenario models, and comparisons were made between the models. It was determined that ANN achieved the closest results with the measurement data.

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Acknowledgements

This study was the product of the corresponding author's doctoral dissertation and thanks to Osman Sonmez and Mucahit Opan for their contributions to the study on-field and modeling.

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Correspondence to Temel Temiz.

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Edited by Dr. Mehdi Abdolmaleki (ASSOCIATE EDITOR) / Prof. Savka Dineva (CO-EDITOR-IN-CHIEF).

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Temiz, T., Sonmez, O., Dogan, E. et al. Evaluation of the effects of land-use change and increasing deforestation in the Sapanca Basin on total suspended solids (TSS) movement with predictive models. Acta Geophys. 70, 1331–1347 (2022). https://doi.org/10.1007/s11600-022-00783-x

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  • DOI: https://doi.org/10.1007/s11600-022-00783-x

Keywords

  • Suspended sediment
  • Land-use
  • Sediment rating curve
  • Multilinear regression
  • Artificial neural network