Farklı yanlılık düzeltme yöntemlerinin istatistiksel ölçeğe indirgenmiş yağış projeksiyonlarına uygulanması

İstatistiksel ölçek indirgeme modelleri kaba çözünürlüklü iklim modellerinin yerel ölçeğe indirgenmesinde oldukça etkili araçlar olup, iklim değişikliği çalışmalarında sıklıkla yararlanılmaktadır. Çeşitli hidro-meteorolojik değişkenlerin projeksiyonlarında kullanılan farklı iklim modelleri kendi bünyesinde barındırdıkları yanlılık sebebiyle ölçek indirgeme modellerinin performanslarını etkilemekte ve tahminlere ait hassasiyeti azaltabilmektedir. Bu nedenle, ölçek indirgeme modellerinin yanında yanlılık düzeltme işlemlerine de ihtiyaç duyulmaktadır. Bu çalışmada, Hükümetlerarası İklim Değişikliği Paneli’ne (IPCC) ait 5. Değerlendirme Raporu’na göre farklı emisyon senaryoları çerçevesinde hazırlanmış iklim modelleri ve farklı yanlılık düzeltme yöntemleri ile Gediz Havzası’na ait yağış projeksiyonları elde edilmiş ve bunu takiben farklı yanlılık düzeltme yöntemlerinin yağış tahminlerine olan etkileri araştırılmıştır. Bunun için öncelikle, Gediz Havzası yağışlarını temsil eden tahminleyici seçimi yapılmış, daha sonra ilgili yağış tahminleyicileri ile kaba çözünürlüklü iklim modelleri istasyon ölçeğine indirgenmiştir. Çalışmada 2015-2050 gelecek dönemine ait kaba çıktıları bulunan 12 adet farklı küresel iklim modelinden faydalanılmış ve bu iklim modellerinden türetilen projeksiyonlar birleştirilerek daha kuvvetli tahminler elde edilmesi amaçlanmıştır. Çoklu iklim modellerinin birleşiminden sonra tahminlerde var olan yanlılıklar Kantil Haritalama (QM), Eş Oran Kantil Haritalama (ERQM), Trendsizleştirilmiş Kantil Haritalama (DQM) ve Kantil Delta Haritalama (QDM) yöntemleri ile ayrı ayrı düzeltilmiştir. Tüm performans indislerini kapsayan bulgulara göre, QM yönteminin en büyük hata değerlerini veren yaklaşım olduğu görülmüştür. Diğer yandan, QDM yöntemininise rölatif değişimleri diğer yöntemlere göre daha iyi yansıtabildiği sonucuna varılmıştır. Ekstrem süreçleri temsil eden performans indisleri incelendiğinde de, QDM’nin ortalama tabanlı yağış projeksiyonlarının değerlendirilmesinde daha üstün olduğu gözlenmiştir.

Implementation of different bias correction methods to statistically downscaled precipitation projections

Statistical downscaling models are very effective tools for downscaling coarse-resolution climate models to local scale and are widely used in climate change studies. The different climate models used in the projections of various hydro-meteorological variables affect the performance of the downscaling models due to their inherent bias and can reduce the precision of predictions. Due to this reason, bias correction methods are needed in addition to the downscaling models. In the study prepared, the precipitation projections were obtained by the climate models derived within the framework of different emission scenarios in terms of the 5th Assessment Report of Intergovernmental Panel on Climate Change (IPCC) and the effects of different bias correction methods on precipitation estimations were investigated as well. For this purpose, firstly, the predictor selection which represents the precipitation of Gediz Basin was carried out and then the coarse-resolution climate models were downscaled to station scale by means of the related precipitation predictors. In the study, 12 different global climate models having raw outputs of 2015-2050 future period were utilized and it was aimed to obtain stronger predictions by combining the projections which were derived by these climate models. Subsequent to combination of multi-model projections, the bias existing in predictions were corrected by Quantile Mapping (QM), Equiratio Quantile Mapping (ERQM), Detrended Quantile Mapping (DQM) and Quantile Delta Mapping (QDM), respectively. According to the obtained results including all performance measures, it has been deduced that QM offers the largest error values. On the other side, it has been concluded that QDM method can better reflect relative changes compared to other methods. When performance indices pointing out extreme processes were also investigated, it was observed that QDM was superior in the evaluation of mean-based precipitation projections.

Keywords:

Bias correction, downscaling climate models, Gediz Basin,

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