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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ISSN der Zeitschrift
EnviroInfo Dessau 2012, Part 1: Core Application Areas
ICT and Climate Change
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.