İklim ve hidroloji açısından yağışın çok önemli bir parametre olduğu düşünüldüğünde, bu parametrenin mekãnsal ve zamansal dağılımının ve değişiminin incelenmesi gelecekteki iklim koşulları ve su kaynakları hakkında faydalı fikirler verebilir. Bu nedenle yağışın zamansal, mekãnsal ve mekãnsal-zamansal dağılımlarının doğru bir şekilde haritalanması hidrolojide, iklim biliminde, tarım biliminde, ekolojide ve diğer çevre bilimlerinde birçok uygulamada önemlidir. Bu çalışmada Türkiye’nin toplam yıllık ve uzun yıllar toplam yıllık yağış değerlerinin mekãnsal-zamansal dağılımları ve değişimleri analiz edilmiştir. Çalışmanın ana veri kaynağı meteorolojik istasyonlarda ölçülmüş aylık yağış değerleri ve bununla mekãnsal olarak ilişkili geniş kapsamlı veri setleridir. Bunlar yükseklik, yüzey pürüzlülüğü, deniz kıyısına mesafe, akarsu yoğunluğu, bakı, arazi kullanımı ve ekolojik bölge olarak belirlenmiştir. Fırat havzasının yıllık yağış değerlerine “mekãn-zaman Sıradan Kriging” ve “mekãn-zaman Evrensel Kriging” yöntemleri kullanılmıştır. Enterpolasyon yöntemlerinin karşılaştırılması 10 gruplu çapraz sağlama yöntemi ile yapılmıştır. Doğruluk değerlendirmesi işlemi Kare Kök Ortalama Hata (RMSE) ve R-kare (r2) istatistiksel ölçütler kullanılarak yapılmıştır.

Methods to define and estimate the spatial variability of hydrologic, climatic and other environmental variables and perform spatial interpolation using the quantified spatial variability are abundant in the environmental sciences. Recently, the extension of these methods to variables that vary both in space and time has received increasing attention. Environmental variables like precipitation, temperature, air quality, soil characteristics, etc. are temporally variable parameters which mean that they may have different values at the same place through consecutive time periods. So rather than defining the distribution of environmental variables at a unique date, if sufficient amount of data are available at different time periods and different locations, space-time interpolation may be more beneficial than spatial techniques that use only location information. Snepvangers et al. (2003) compared two Space-time kriging techniques: Space-time Ordinary kriging (ST-OK) and ST kriging with external drift (STKED) on soil water content interpolation. They found out that predictions are more realistically obtained from ST-KED, and prediction uncertainty of this method is lower compared to ST-OK. Jost et al. (2005) performed a study about spatio-temporal distribution of soil water storage by using spacetime kriging methods in a forest ecosystem. Hengl et al. (2012) used ST Regression kriging to predict daily temperatures for 2008 obtained from 159 meteorological stations in Croatia. Accurate mapping of the temporal, spatial and space-time distributions of precipitation is important for many applications in hydrology, climatology, agronomy, ecology and other environmental sciences. In addition, it is an environmental parameter which can be analyzed in space-time context since it has variability in time and space. In space-time (ST) kriging all observations in the past, present and future are used to predict the present situation because of temporal correlation as quantified by the space-time variogram. So spacetime kriging makes use of all observations from all years and locations and if there is indeed temporal correlation then the observations from other times (other years) will be included in making the prediction. In this study, the Euphrates Basin which is the biggest and one of the most important basins of Turkey is selected as a study area to implement space-time interpolation techniques. For this purpose, ST-OK and ST-UK methods are applied to total annual observations for the period of 1970- 2008. The former uses only observed values, the latter uses observed values and secondary information as well. Main data source of the study is point observations of monthly precipitation at meteorological stations and spatially exhaustive covariate data sets. These are elevation, surface roughness, distance to coast, river density, aspect and land use. Comparison of interpolation methods are made with ten-fold cross-validation methodology. Accuracy assessment is done by calculating the Root Mean Squared Error (RMSE), R-square (r2 ) values. ST-UK method was applied twice to precipitation data. At first application elevation, surface roughness, distance to coast, river density, land cover, Year and elevation-distance to coast interaction were used. According to performance assessment results of cross-validation, R-square is calculated as 0.73 and RMSE is 107 mm. In the second application of ST-UK; elevation, surface roughness, distance to coast, river density and elevation-distance to coast interaction were used. The obtaining results are more reliable and accurate. This time R-square is calculated as 0.85 and RMSE is 78 mm. For ST-OK the results of R-square is 0.86 and RMSE is 75 mm. Contrary to expectations, ST-OK method resulted in more accurate prediction values than ST-UK according to R-square and RMSE. Since most of the meteorological stations are located at lower elevations compared to basin’s mean elevation, the secondary variables may not be representative parameters to precipitation prediction in the basin. However prediction maps of ST-UK can be regarded as more realistic than STOK since maps are not so smooth. The prediction maps of ST-OK have smooth appearance as details have disappeared during interpolation.