Sevinc Asilhan Sirdas

Prof. Dr. Sevinc Asilhan Sirdas


Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 November 2007 in Journal of Hydrologic Engineering
Verified
Journal Article
Closed access
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
ZŞZekai Şen
Zekai Şen
This technical note presents a combination of isohyetal map, kinematic wave, and rational methods for estimating flash flood synthetic hydrographs in arid regions. They are combined in a logical sequence for the calculations of time of concentration and synthetic flood hydrograph. This empirical approach relates flood peaks to the average temporal and areal intensities of storm rainfalls. The application of the methodology is presented for two arid region wadis in the central western part of the Arabian Peninsula. Hydrograph peaks yield practically reliable estimations of the observed flood magnitudes from individual storms.

17 February 2017 in Natural Hazards
Verified
Journal Article
Closed access
PDF file
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
EÖEmrah Tuncay Özdemir
Emrah Tuncay Özdemir
ISİsMail Sezen
İsMail Sezen
Bahtiyar EfeBahtiyar Efe
Bahtiyar Efe
VKVinay Kumar
Vinay Kumar

28 August 2007 in Hydrological Processes
Verified
Journal Article
Closed access
PDF file
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
ASAhmet Duran Sahin
Ahmet Duran Sahin

28 September 2013
Verified
Book Chapter
Closed access
PDF file
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
ZŞZekâi Şen
Zekâi Şen
AÖAhmet Öztopal
Ahmet Öztopal

23 August 2018
Verified
Book Chapter
Closed access
PDF file
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
ANAkatas Nilcan
Akatas Nilcan
IEIzgi Ercan
Izgi Ercan

16 October 2016 in Environment and Natural Resources Research
Verified
Journal Article
Open access
PDF file
SSSevinc Asilhan Sirdas
Sevinc Asilhan Sirdas
ADAli Behzat Diriker
Ali Behzat Diriker
VKVinay Kumar
Vinay Kumar
The prediction and understanding of the extreme weather events became one of the top priorities of the forecasting centers. To analyze the heavy rainfall events, the data from reanalysis and station datasets are utilized. The linear regression analysis and principle component analysis (PCA) are applied on precipitation, temperature, Arctic Oscillation Indices (AO) and North Atlantic Oscillation Indices (NAO) datasets. The results indicate that the relationship between these variables can be expressed neither by linear regression nor by PCA, which falls in short in capturing the complexity of the problem. The results indicate the importance of low-level jet in two cases out of three cases considered here.