Gördes Havzası Akışlarının Modellenmesinde Era5-Land Verilerinin Performans Değerlendirmesi

Destina Dilan Ekinci; Okan Fıstıkoğlu

doi:10.24012/dumf.1327255

Gördes Havzası Akışlarının Modellenmesinde Era5-Land Verilerinin Performans Değerlendirmesi

Öz

İklim değişikliğinin akarsu akımları üzerindeki etkilerinin hidrolojik modeller yardımıyla belirlenmesinde küresel atmosferik veri setlerinden sıklıkla faydalanılmaktadır. Sunulan çalışmada, Ege bölgesinde yer alan Gördes Havzası akımlarının hem küresel veri setleri hem de lokal istasyon verileri ile akışlarının modellenmesi ve bu model sonuçları dikkate alınarak performansın değerlendirmesi amaçlanmıştır. Küresel veri seti olarak ECMWF (European Centre for Medium-Range Weather Forecasts) tarafından sunulan 1959-2022 yılları arasında veri sağlayan ERA5 Land veri seti ve hidrolojik model olarak da abcd aylık yağış akış modeli kullanılmıştır. Model performansını değerlendirmek için Nash-Shutcliffe performans fonksiyonu seçilmiş olup optimum model parametrelerin tahmini için Parçacık Sürü Optimizasyonu Algoritması (PSO) kullanılmıştır. Çalışmada elde edilen bulgular ERA5 Land veri setinin Gördes Havzası’nın aylık akımlarının modellenmesinde havzanın hidrolojik özelliklerini başarılı bir şekilde yansıttığı ve bu veri setini kullanmanın modelleme çalışmalarında kolaylaştırıcı olacağı gösterilmiştir.

Anahtar Kelimeler

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Details

Primary Language

Turkish

Subjects

Civil Engineering (Other)

Journal Section

Research Article

Authors

Destina Dilan Ekinci ^*
0009-0001-6794-6373
Türkiye

Okan Fıstıkoğlu
0000-0002-9483-1563
Türkiye

Early Pub Date

September 30, 2023

Publication Date

September 30, 2023

Submission Date

July 15, 2023

Acceptance Date

September 1, 2023

Published in Issue

Year 2023 Volume: 14 Number: 3

DOI

https://doi.org/10.24012/dumf.1327255

IZ

https://izlik.org/JA32RE26TY

Cite

RIS / Bibtex

IEEE

[1]D. D. Ekinci and O. Fıstıkoğlu, “Gördes Havzası Akışlarının Modellenmesinde Era5-Land Verilerinin Performans Değerlendirmesi”, DUJE, vol. 14, no. 3, pp. 507–517, Sept. 2023, doi: 10.24012/dumf.1327255.

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