Farklı yanlılık düzeltme yöntemlerinin istatistiksel ölçeğe indirgenmiş yağış projeksiyonlarına uygulanması

Yıl 2019, Cilt: 21 Sayı: 2, 868 - 881, 28.06.2019

Umut Kırdemir Umut Okkan

https://doi.org/10.25092/baunfbed.654535

Cited By: 2

Öz

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.

Anahtar Kelimeler

Yanlılık düzeltme, ölçek indirgeme, iklim modelleri, Gediz Havzası

Teşekkür

Bu çalışma, ikinci yazar tarafından yürütülen, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)-Çevre, Atmosfer, Yer ve Deniz Bilimleri Araştırma Destek Grubu (ÇAYDAG) tarafından desteklenen 114Y716 numaralı tamamlanmış proje içeriğinden hazırlanmıştır. Yazarlar çalışmayı özenle değerlendiren iki hakeme ayrıca müteşekkirdir.

Implementation of different bias correction methods to statistically downscaled precipitation projections

Öz

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.

Anahtar Kelimeler

Bias correction, downscaling, climate models, Gediz Basin

Kaynakça

• IPCC, Climate Change 2013 - The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1535, (2013).
• Ghosh, S. ve Mujumdar, P.P., Statistical downscaling of GCM simulations to streamflow using relevance vector machine, Advances in Water Resources, 31, 1, 132–146, (2008).
• Okkan, U. ve Kirdemir, U., Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs, Meteorological Applications, 23, 3, (2016).
• Okkan, U. ve Kirdemir, U., Investigation of the Behavior of an Agricultural-Operated Dam Reservoir Under RCP Scenarios of AR5-IPCC, Water Resources Management, 32, 8, 2847-2866, (2018).
• Gudmundsson, L., Bremnes, J. B., Haugen J. E., ve Engen-Skaugen, T., Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods, Hydrology and Earth System Sciences, 16, 9, 3383–3390, (2012).
• Lenderink, G., Buishand, A. ve Van Deursen, W., Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach, Hydrology and Earth System Sciences, 11, 3, 1145–1159, (2007).
• Leander, R. ve Buishand, T.A., Resampling of regional climate model output for the simulation of extreme river flows, Journal of Hydrology, 332, 3–4, 487–496, (2007).
• Leander, R., Buishand, T.A., van den Hurk, B.J.J.M. ve de Wit, M.J.M, Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output, Journal of Hydrology, 351, 3–4, 331–343, (2008).
• Schmidli, J., Frei, C., ve Vidale, P.L., Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods, International Journal of Climatology, 26, 5, 679–689, (2006).
• Ines, A.V.M. ve Hansen, J.W., Bias correction of daily GCM rainfall for crop simulation studies, Agricultural and Forest Meteorology, 138, 1–4, 44–53, (2006).
• Jakob Themeßl, M., Gobiet, A. ve Leuprecht, A., Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, International Journal of Climatology, 31, 10, 1530–1544, (2011).
• Teutschbein, C. ve Seibert, J., Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, Journal of Hydrology, 456–457, 12–29, (2012).
• Cannon, A.J., Sobie, S.R. ve Murdock, T.Q., Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?, Journal of Climate, 28, 17, 6938–6959, (2015).
• Chen, J., Brissette, F.P., Chaumont, D. ve Braun, M., Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resources Research, 49, 7, 4187–4205, (2013).
• Fang, G.H., Yang, J., Chen, Y.N. ve Zammit, C., Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China, Hydrology and Earth System Sciences, 19, 6, 2547–2559, (2015).
• Hempel, S., Frieler, K., Warszawski, L., Schewe, J. ve Piontek, F., A trend-preserving bias correction – The ISI-MIP approach, Earth System Dynamics, 4, 2, 219–236, (2013).
• Block, P.J., Souza Filho, F.A., Sun, L. ve Kwon, H.H., A streamflow forecasting framework using multiple climate and hydrological models, Journal of the American Water Resources Association, 45, 4, 828–843, (2009).
• Boe, J., Terray, L., Habets, F. ve Martin, E., Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies, International Journal of Climatology, 27, 1643–1655, (2007).
• Sennikovs, J. ve Bethers, U., Statistical downscaling method of regional climate model results for hydrological modelling, 18th World IMACS/MODSIM Congress, 3962–3968, Cairns, Australia, (2009).
• Maraun, D., Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue, Journal of Climate, 26, 2137–2143, (2013).
• Okkan, U. ve Inan, G., Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: Use of machine learning methods, multiple GCMs and emission scenarios, International Journal of Climatology, 35, 11, 3274–3295, (2015).
• Okkan, U. ve Inan, G., Bayesian Learning and Relevance Vector Machines Approach for Downscaling of Monthly Precipitation, Journal of Hydrologic Engineering, 20, 4, (2015).
• Kirdemir, U., İklim değişikliğinin baraj havzası hidrolojisi üzerindeki olası etkilerinin modellenmesi: AR5-RCP senaryoları ve Demirköprü Barajı örneği, Yüksek Lisans Tezi, Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, Balıkesir, (2017).
• Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D. ve Veith, T.L., Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE, 50, 3, 885–900, (2007).
• Knutti, R. et al., Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections, (2010).
• Wang, L. and Chen W., Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation, Atmospheric Science Letters, 15, 1–6, (2014).
• Li, H., Sheffield J. ve Wood, E.F., Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching, Journal of Geophysical Research, 115, (2010).
• Olsson, J., Berggren, K., Olofsson, M. ve Viklander, M., Applying climate model precipitation scenarios for urban hydrological assessment: A case study in Kalmar City, Sweden, Atmospheric Research, 92, 3, 364–375, (2009).
• Yilmaz, B., ve Harmancioglu, N., Multi-criteria decision making for water resource management: a case study of the Gediz River Basin, Turkey, Water SA, 36, 5, 563-576, (2010).
• Yilmaz, B., ve Harmancioglu, N., An indicator based assessment for water resources management in Gediz River Basin, Turkey, Water Resources Management, 24, 15, 4359–4379, (2010).