Determination of sediment deposition of Hasanlar Dam using bathymetric and remote sensing studies

Yakup Darama (), Zeliha Selek, Bulent Selek, Mehmet Ali Akgul and Murat Dagdeviren
Additional contact information
Yakup Darama: Atilim University
Zeliha Selek: Gazi University
Bulent Selek: General Directorate Water Management (SYGM)
Mehmet Ali Akgul: General Directorate of State Hydraulic Works (DSI)
Murat Dagdeviren: General Directorate of State Hydraulic Works (DSI)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 97, issue 1, No 11, 227 pages

Abstract: Abstract Hasanlar Dam and Hydroelectric Power Plant are located on Küçük Melen Creek in the Western Black Sea Basin of Turkey. The dam was constructed in 1974 to provide domestic water needs of the Düzce Province, to supply irrigation water need, to control and mitigate floods and to produce hydroelectric power. This dam has been subjected to severe sedimentation since its construction in 1974. Therefore, bathymetric field survey studies were conducted to determine storage loss in the Hasanlar Dam reservoir by sedimentation. Bathymetric survey data from the reservoir site of the Hasanlar Dam were obtained in 1979, 1999 and 2014. Analysis of the bathymetric data, GIS and remotes sensing techniques showed that storage loss in reservoir active volume between 1974 and 1999 was 24% and between 1974 and 2014 storage loss was 26%. Analysis of the bathymetric maps also showed that sediment accumulation is severe near and around the dam body and the spillway whose discharge capacity was decreased by sediment accumulation. This is extremely critical because the flood of May 1998 caused the high risk of collapse of dam due to reduced capacity of the spillway. Remote sensing technique was used to determine the future deposition of sediment in the reservoir. For this purpose, 35 points in the reservoir area were determined by comparing the relative water depths and actual water depths using satellite image of the bathymetry in July 2017 and Lake Observation Station. High correlation (R2 = 0.833) was calculated by using logarithmic nonlinear regression analysis between actual and relative water depths for those 35 control points. The average of absolute values of differences between the estimated and actual water depths was found as 1.06 m, and RMSE was calculated as 1.25 m. This analysis shows that in the future, remote sensing data can be used in the studies of determining the depth of water and the total sediment thickness. In addition, the volume of the entire reservoir can be predicted by measuring the actual water depth only at those 35 control points without making a bathymetric map of the whole dam reservoir.

Keywords: Dam sedimentation; Bathymetric measurement; Satellite image; Remote sensing; Hasanlar Dam (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-019-03635-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:97:y:2019:i:1:d:10.1007_s11069-019-03635-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-019-03635-y

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().