Fine-resolution precipitation mapping over Syria using local regression and spatial interpolation

Annual precipitation at 1 km2 spatial resolution have been produced over Syria for a referenced period of 1975?2010. The observations from 410 rain-gauges were interpolated over a regular grid by applying multivariate regression models (PSMRM) and local equations for sub-regions of the study area. This statistical method aims to model the influences of the essential geographical and topographical climatic factors, such as longitude, latitude, elevation, slopes, and aspects on the precipitation field in multiple local regions. The PSMRM is composed of two steps, (i) a potential surface of precipitation is calculated through multi-linear local regressions based on geographical and topographical information, then (ii) a kriging and IDW interpolation is applied to adjust the potential surface so as to better fit the station residuals (i.e. the difference between the observed values and the predicted values which are obtained from PSMRM). Ultimately, the models? accuracy was evaluated by 43 stations. The PSRMR-IDW-3 is found to be superior to all other models; the value of RMSE was 92.5 mm and the Nash-Sutcliffe efficiency NSE was 0.9187, while the Willmott index of agreement was 0.9808. In contrast, the PSMRM-OK-EXP was only superior to other models with the least mean absolute error (MEA) and the mean absolute percentage error (MAPE); the difference was 64.07 mm, i.e. 11.44%. However, all the proposed models were shown to be highly efficient compared to global models and can be considered an appropriate alternative to studying precipitation variability spatially over Syria.

Automated summary

A study on annual precipitation in Syria from 1975 to 2010 used multivariate regression models to calculate precipitation at 1 km^2 spatial resolution, with the PSRMR-IDW-3 model found to be superior based on accuracy evaluations from 43 stations. The PSMRM-OK-EXP model had the least mean absolute error and mean absolute percentage error compared to other models.