Olası iklim değişikliği senaryoları altında Gediz havzası sulama suyu ihtiyacının tahminlenmesi
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Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ege Üniversitesi, Fen Bilimleri Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Çalışmada, olası iklim değişikliğinin Gediz Havzası’nda bulunan sulama
birliklerinin net sulama suyu ihtiyaçlarına etkisinin değerlendirilmesi
amaçlanmıştır. Bu amaçla, öncelikle havzada bulunan Manisa, Menemen, Salihli
ve Turgutlu meteoroloji istasyonları yağış ve sıcaklık değerlerinin tahminlenmesi
için yapay sinir ağlarına dayalı ölçek indirgeme modelleri kurulmuştur.
Modellerde, tahminleyici olarak NCEP/NCAR re-analiz değişkenleri kullanılmış,
bu değişkenlerden ölçek indirgeme modellerinde hangilerinin kullanılacağına
doğrusal regresyon analizi ve performans kriterleri kullanılarak karar verilmiştir.
Kurulan ölçek indirgeme modelleri, ECHAM5 iklim modelinin 20C3M 1961-
1990 referans dönemi ve 2021-2100 yılları A2, A1B ve B1 senaryo sonuçlarıyla
çalıştırılarak istasyonlara ait yağış ve sıcaklık tahminleri elde edilmiştir.
Sonuçlardaki yanlılık miktarını azaltmak için bias düzeltme işlemi uygulanmıştır.
Düzeltilmiş yağış ve sıcaklık değerleri kullanılarak Blaney-Criddle yöntemiyle
bitki su tüketimleri ve net sulama suyu ihtiyaçları hesaplanmıştır. Sonuçlar
değerlendirildiğinde, uygulama bölgesi genelinde A2, A1B ve B1 senaryolarına
göre, 2021-2100 dönemi ortalama sıcaklıklarının sırasıyla 2.9, 3.2 ve 2.5 oC
artabileceği, yağışların ise %17.8, %29.2 ve %16.7 azalabileceği tahminlenmiştir.
Bu olası etkiler altında A2, A1B ve B1 senaryolarına göre ortalama net sulama
suyu ihtiyacında %14.9, %17.5 ve %12.5 artış olması beklenmektedir.
In the study, it was aimed to prepare net irrigation water demand projections under different climate change scenarios and projections were examined through an exemplary application on irrigation unions in Gediz Basin. In the study, downscaling models based on artificial neural networks (ANN) were established for rainfall and temperature projections of Manisa, Menemen, Salihli and Turgutlu meteorological stations in the basin. In the models, NCEP/NCAR re-analysis variables were used as predictors. All possible lineer regression relations and performance criteria have been determined from these variables to be used in the downscaling models. The downscaling models calibrated with the optimal predictors convert the coarse resolution results of both reference period (20C3M) and future period (A2, A1B and B1) scenarios of ECHAM5 climate model to the station scale rainfall and temperature forecasts. Correction of biases in the forecasts are achieved by using cumulative distribution functions. Finally, corrected rainfall and temperature forecasts were taken as input in the Blaney- Criddle method and then net irrigation water needs were calculated. According to the A2, A1B and B1 scenarios, the mean temperatures of 2021-2100 period could increase by 2.9, 3.2 and 2.5 oC respectively and the precipitation could decrease by 17.8%, 29.2% and 16.7%, respectively, when the obtained results were evaluated over study region. Under these probable impacts, it is expected that the average net irrigation water need will increase by 14.9%, 17.5% and 12.5% according to the scenarios A2, A1B and B1, respectively.
In the study, it was aimed to prepare net irrigation water demand projections under different climate change scenarios and projections were examined through an exemplary application on irrigation unions in Gediz Basin. In the study, downscaling models based on artificial neural networks (ANN) were established for rainfall and temperature projections of Manisa, Menemen, Salihli and Turgutlu meteorological stations in the basin. In the models, NCEP/NCAR re-analysis variables were used as predictors. All possible lineer regression relations and performance criteria have been determined from these variables to be used in the downscaling models. The downscaling models calibrated with the optimal predictors convert the coarse resolution results of both reference period (20C3M) and future period (A2, A1B and B1) scenarios of ECHAM5 climate model to the station scale rainfall and temperature forecasts. Correction of biases in the forecasts are achieved by using cumulative distribution functions. Finally, corrected rainfall and temperature forecasts were taken as input in the Blaney- Criddle method and then net irrigation water needs were calculated. According to the A2, A1B and B1 scenarios, the mean temperatures of 2021-2100 period could increase by 2.9, 3.2 and 2.5 oC respectively and the precipitation could decrease by 17.8%, 29.2% and 16.7%, respectively, when the obtained results were evaluated over study region. Under these probable impacts, it is expected that the average net irrigation water need will increase by 14.9%, 17.5% and 12.5% according to the scenarios A2, A1B and B1, respectively.
Açıklama
Anahtar Kelimeler
ECHAM5, Yapay Sinir Ağları, Ölçek İndirgeme, Bias Düzeltme, Net Sulama Suyu İhtiyacı, Artificial Neural Networks, Downscaling, Bias Correction, Net İrrigation Demand
