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Auflistung nach Autor:in "Siabi, Negar"

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  • Konferenzbeitrag
    Precision analysis for climatological zoning in North East of Iran by using different combinations of environmental parameters in some selected geostatistical methods
    (EnviroInfo Dessau 2012, Part 1: Core Application Areas, 2012) Sanaeinejad, Hossein; Siabi, Negar
    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.
  • Konferenzbeitrag
    Spatial zoning of precipitation by geo-statistical methods and DEM as auxiliary variable in northeast of Iran
    (Innovations in Sharing Environmental Observations and Information, 2011) Sanaeinejad, Hossein; Siabi, Negar; Ghaemi, Marjan
    One of the most important factors in environmental studies is precipitation and its spatial variability. Precipitation data is collected in some individual points in different stations, while these point based data are not often completed in a long term period. In arid and semi-arid area data incompleteness is more serious. There are different methods for reconstruction and zoning of precipitation data, however, it is essential to choose a reliable method that could matches the regional requirements and also provides adequate precision for our applications. Geographical position and topographical factors strongly influence spatial pattern of precipitation. Multivariate geo-statistical method is used to estimate the spatial correlation of two variables that are interdependent in a physical sense. In this paper Ordinary Kriging (OK) as a univariate method and Ordinary CoKriging (OCK) as multivariate method were used for interpolation precipitation. The purpose of this study was to show how the use of a digital elevation model can improve interpolation processes for mapping the mean annual and monthly precipitation amount in the North East of Iran. As most of the study area is mountainous, Digital Elevation Model (DEM) was used as auxiliary variable in the models. Cross validation is used to compare the prediction performances of the geo-statistical interpolation algorithms. In this study Mean Squared Error (MSE) values were used for comparing the models. Cross-validation suggested that OK, which ignores the information on elevation, has lager prediction errors in comparison with prediction errors produced by OCK specially in months with low precipitation. For example MSE values were 0.47, 0.3, 0.25 and 0.5 for March, October, November and December respectively when OCK was applied, while these values were 0.17, 1.8, 1.2 and 1.3 respectively for the same months when OK was applied. Overall, the smallest value of MSE was 0.25 in November when OCK was applied which had the highest correlation coefficient as well. It could be concluded that using of DEM as auxiliary variable improves the results, because of high variability in topography in the study area. The results seem to favor the multivariate geo-statistical method including auxiliary information (related to elevation). We conclude that OCK is a very reliable and robust interpolation method because it can take into account several properties of the landscape; it should therefore be applicable in other mountainous regions, especially where precipitation is an important geomorphological factor.