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Paper Details


Ismail Bulent Gundogdu

Journal Title:Global Journal of Engineering Science and Research Management (GJESRM)

Modelling of meteorological data is very important both for the evaluation of meteorological events and for the observation of its effects on the environment. Generally, classical interpolation methods are insufficient but multivariate geostatistical methods can be more effective, especially when studying secondary variables, because secondary variables might affect directly the model precision. In this study, the mean annual and mean monthly precipitation data from 264 meteorological stations in Turkey have been used for producing maps predicting meteorological precipitation. In addition to using linear regression (LR), the methods of inverse square distance (ISD) and ordinary co-kriging (OCK) were used and elevation, slope and aspect data for each point were added to the database as secondary variables. Cross-validation indicates that OCK yields the smallest prediction error. Standard errors verified that the best model could be produced with aspect as a secondary variable. Consequently, an aspect standard error map was produced to evaluate which points are more effective in the model. It is concluded that OCK is a very flexible method because it can account for several properties of the landscape. Therefore, it should be applicable in similar regions and a wider context, especially where precipitation is an important factor in water erosion.