Model ve uydu tabanlı yağış ürünlerini değişken iklim ve topoğrafya üzerinde değerlendirmek ve birleştirmek

This study first evaluates and inter-compares a set of nine precipitation products (2 satellite estimation-based, 2 model reanalysis-based, and 5 model forecast-based products) over varying climate and topography by using the in-situ observed precipitation data as truth. The products are then merged, in the form of two groups, using two different merging techniques: 1. Taking ensemble mean (i.e., simple merging); 2. Taking ensemble mean after rescaling them by a linear regression method. The merged products are statistically evaluated and inter-compared with the individual products using the same in-situ precipitation data. The results show that the errors in the products increase, while their correlations with the observed data decrease with the increasing terrain complexity. Comparatively, wetness and terrain slope have a more prominent role than elevation in the error variability of the products. The performance of model-based products is more adversely affected by increasing terrain complexity than that of satellite-based products. Both the merging methods improve the errors and correlations of the products not only over the entire study area, but over all its sub-regions classified based on the wetness, elevation, and terrain slope. Simple merging improves the precipitation detection ability of the individual products the most. Merging the products after rescaling them in the space of ECMWF HRES and IMERG adds the highest improvement in ErrSD (on average 28.5% and 34.8%, resp.) and CC (on average 17.6% and 23%, resp.) of the individual forecasts and individual research products, respectively.