Investigation of sediment accumulation in Berdan Dam Reservoir using bathymetric measurements and Sentinel-2 Data


Güvel Ş. P., Akgül M. A., Yurtal R.

ARABIAN JOURNAL OF GEOSCIENCES, cilt.14, sa.24, ss.1-7, 2021 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 24
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s12517-021-09089-6
  • Dergi Adı: ARABIAN JOURNAL OF GEOSCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aquatic Science & Fisheries Abstracts (ASFA), Geobase, INSPEC
  • Sayfa Sayıları: ss.1-7
  • Çukurova Üniversitesi Adresli: Evet

Özet

Water depth data of a reservoir is used in calculation of water volume storage and sedimentation volume in reservoirs. The goal of this study is to examine the recent developments in sediment accumulation in Berdan Dam Reservoir as of the year 2019, to examine the risks caused by sedimentation in terms of sustainability within the framework of dam safety principles and to assess the possibilities of using remote sensing data to determine bathymetric water depths. In this study, the water depth of Berdan Dam Reservoir is investigated to determine bathymetric elevations of the reservoir by using the Log Ratio Transformation (LRT) method which is developed by the US National Oceanic and Atmospheric Admisinistration (NOAA). 25 control points were used for calibration of water depth. The depths of these points were selected from the July 2019 bathymetric map of Berdan Dam Reservoir with a depth difference of 1 m between the points. As remote sensing data, archive scanning was performed by considering the date of the bathymetric map and cloudlessness, the image of Sentinel-2 dated 28 July 2019 was selected and analyzed. As a result, high correlation was found between the predicted water depths and surveyed water depths by using logaritmic nonlinear regression analysis for chosen 25 control points. The regression value between the average bathymetric elevations was found to be 0.944 and the root mean square error (RMSE) was calculated as 1.70 m.