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Precision analysis for climatological zoning in North East of Iran by using different combinations of environmental parameters in some selected geostatistical methods
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Datum
2012
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Shaker Verlag
Zusammenfassung
Climatic parameters’ modeling is very important in environmental data processing. This is a consequence that
climatic parameters vary dramatically in time and space. Moreover, the climate variables are dependent to each other
and also to earth surface conditions such as height. The other problem is that climatic parameters are measured as a
point based variables in weather stations. However, for environmental studies it is crucial to have continues spatial
and temporal perception for these parameters. There are different methods to provide such perceptions from climatic
variables. We used geo-statistic algorithms for assessment, interpolation and preparing spatial and temporal maps for
climatic parameters in North East of Iran. Different interpolation methods including ordinary Kriging (OK), Inverse
Distance Weighted (IDW), Co-Kriging (COK) and Kriging with External Drift (KED) were examined. The
dependence of the variables (including solar radiation, evaporation, air temperature and precipitation) to height as
ancillary variable were also investigated in different monthly and annual time scales. Thornthwaite climate
classification method was used for climate zoning. Then the effect order of each climatic variable in the climate
zoning precision was assessed by using multivariate methods such as COK and KED. Mean Squared Error (MSE)
was used to compare the models results. Different results were obtained for different variables. COK model provided
better results for air temperature, while KED method showed more precision for precipitation. For example the
resulted MSE from K, COK and KED methods for temperature in January was 2.19, 0.004 and 1, in February was,
2.63, 0.005 and 1.27 in March was 2.51, 00.4 and 1.33 respectively. The results also showed that MSE values
substantially increased from March to July which means that using elevation in this model for estimating
temperature during these months provides less precision. It was concluded that temporal and spatial distribution of
precipitation is affected more by elevation among all of the climatic parameters, followed by air temperature,
evaporation and relative humidity respectively . It should be noticed that evaporation is affected by elevation during
cold season (from October to March). Among the environmental parameters, evaporation, elevation, relative
humidity and precipitation had the most effect on spatial and temporal climate variability in the area of study
respectively. Temperature provided different results depending on the climate index that was used for classification
and zoning.