Analysis and modeling of spatially and temporally varying meteorological parameter: Precipitation over Turkey

Thesis Type: Doctorate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Civil Engineering, Turkey

Approval Date: 2013




As precipitation is the very important parameter of climate and hydrology, exploring spatial and temporal distribution and variation of this variable can give an idea about climate conditions and water resources in the future. Therefore 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 this thesis, temporal, spatial and space-time distributions and variations of total annual and long term annual precipitation of Turkey are analyzed. Main data source of thesis 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, land use and eco-region. T-Test and Mann-Kendal tests are used to infer temporal trend of seasonal and annual precipitation observations of Turkey. Multiple linear regression (MLR), Geographically Weighted Regression (GWR), Ordinary Kriging (OK), Regression Kriging (RK) and Universal Kriging (UK) are applied to define spatial distribution and variation of long term annual precipitation observations of Turkey. For the spatio-temporal part of the study Space-time Ordinary Kriging and Space-time Universal Kriging methods are applied to total annual precipitation observations of Euphrates Basin, which is the largest basin in Turkey. 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) and Standardized MSE (SMSE) for spatial interpolation. According to these criteria, Universal Kriging is the most accurate with an RMSE of 178 mm, an R-square of 0.61 and an SMSE of 1.06, while Multiple Linear Regression performed worst (RMSE of 222 mm, R-square of 0.39, and SMSE of 1.44). Ordinary Kriging, UK using only elevation and Geographically Weighted Regression are intermediate with RMSE values of 201 mm, 212 mm and 211 mm, and an R-square of 0.50, 0.44 and 0.45, respectively. The RK results are close to those of UK with an RMSE of 186 mm and R-square of 0.57. For space-time interpolation R-square, RMSE and ME methods are used for accuracy assessment. Space-time Ordinary kriging has yielded more accurate prediction results than Space-time Universal kriging with R-square of 0.86, RMSE of 75 mm and ME of 57 mm.