@article { author = {Siabi, N. and Sanaeinejad, H.}, title = {An investigation into using of combined geostatistical methods to increase precision in climatological classification and climatic parameters zoning in great Khorasan}, journal = {Journal of Climate Research}, volume = {1392}, number = {15}, pages = {81-32}, year = {2013}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Introduction: 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. Some geo-statistical models are used to interpolate the data. The ability of these models for spatial interpolation increases significantly, if co-variables are used (Daly et al. 1994). In Kriging methods the sparsely sampled variables can be completed by secondary attributes that are more densely sampled. Topography and weather-radar observations could be used as secondary information in these models. Material and methods: The study area is Khorasan province (Northeast of Iran, longitude 55◦W to 61◦E and latitude 38◦S to 30◦N). The area is approximately 248,000 km2 in a semiarid climate.  The monthly and annual precipitation has been averaged for the climate normal period of 1993 – 2009. We were very strict in data selection, only keeping weather stations with complete years. After assuring the raw data quality, monthly and annual climate data averages were calculated. This information was loaded to the spatial database and used as the source of input data for the gridding process. We used geostatistic 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 was 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. Results and discussion: According to MSE values, COK and IDW had the highest and lowest accuracy among the methods for temperature respectively. The pattern of MSE changes were also similar for all of the four methods when MSE values increased from January to June showing that the accuracy of the models decreased from cold to warm season. OK and IDW showed more errors in the warm months than in cold months for precipitation, while KED and COK with elevation as ancillary variable showed better results. Considering all of the variables, KED provided the most accurate spatial interpolation among all of the applied models. However, COK was more accurate for evapotranspiration interpolation with the minimum MSE with increasing toward warm months as other variables. There is only one exception in applying COK method for evapotranspiration and temperature where MSE is almost the same in cold and warm seasons. For interpolating of relative humidity, there was not a substantial difference between K and KED, while COK and IDW showed smaller values of MSE. In this case MSE values decreased from clod to warm months. All of the four interpolation methods were used for climatological zoning based on Thornthwaite Climatological Index values. MSE values decreased in order of IDW, K, KED and OCK respectively. Using meteorological parameters such as temperature and evapotranspiration as ancillary variables in multivariable methods such as COK and KED showed a substantial improvement in the accuracy of climatological zoning. 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, 0.004 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. Conclusion: 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.  }, keywords = {evaluation of accuracy,spatial analysis,climatic parameters,Classification of Climate}, title_fa = {بررسی روش های ترکیبی زمین آمار در افزایش دقت طبقه بندی اقلیمی و نیز پهنه بندی عناصر اقلیمی شمال شرق ایران}, abstract_fa = {      متغیر های اقلیمی به یکدیگر و نیز به وضعیت سطح زمین مانند ارتفاع و پوشش گیاهی وابسته اند. این در حالی است که این متغیر ها به صورت نقطه ای در ایستگاه های هواشناسی اندازه گیری می شوند. برای انجام مطالعات محیطی و تحقیقات کشاورزی، داشتن درک صحیحی از تغییرات پیوسته مکانی و زمانی این متغیر ها از اهمیت بسزایی برخوردار است. از طرفی طبقه بندی های اقلیمی به دلیل استفاده از روابط ساده و متغیر های کم از دقت بالایی برخوردار نیستند، از این رو در این تحقیق دو هدف دنبال شده است: اول اینکه از الگوریتم های زمین آمار برای درون یابی، ارزیابی و تهیه نقشه های تغییرات مکانی و زمانی متغیر های اقلیمی در شمال شرق ایران استفاده شد و آنگاه روش تورنت وایت[1] برای طبقه بندی اقلیمی انتخاب و درجه تاثیر هر متغیر اقلیمی در افزایش دقت طبقه بندی اقلیمی با استفاده از روش های چند متغیره بررسی شد. روش ها ی درون یابی در این تحقیق کریجینگ معمولی ([2]OK) ، کو کریجنگ ([3]COK)، روش وزن دهی عکس فاصله ([4]IDW) و روش ([5]KED) بود. با استفاده از روش های چند متغیره (COK,KED)، وابستگی متغیر هایی مانند (تبخیر، دمای هوا، بارندگی و رطوبت نسبی) به ارتفاع به عنوان متغیر ثانویه با گام های زمانی ماهانه و سالانه مورد بررسی قرار گرفت. مقدار MSE برای مقایسه نتایج مدل ها استفاده شد و نتایج متفاوتی برای هر متغیر به دست آمد. روش COK برای دمای هوا نتایج بهتری را نشان داد، در حالی که روش KED برای بارندگی نتایج دقیق تری را حاصل کرد. به عنوان مثال MSE برای برای دما از روش های K، COK و KED در ماه ژانویه به ترتیب مقادیر 19/2، 004/0 و 1 ، در ماه فوریه 63/2، 005/0 و 27/1 و در ماه مارس 51/2، 004/0 و 33/1 به دست آمد. همچنین نتایج نشان داد که مقادیر MSE از ماه مارس تا جولای افزایش می یابد، بدین معنی که استفاده از ارتفاع در این مدل برای تخمین دما در این ماه ها دقت کمتری دارد. همچنین مشاهده شد که توزیع زمانی و مکانی بارندگی نسبت به سایر متغیر های مورد مطالعه، بیشترین تاثیر پذیری را از تغییرات ارتفاع دارد. قابل ذکر است که بر اساس این تحقیق تبخیر در طول ماه های سرد از ارتفاع تاثیر می پذیرد( اکتبر تا مارس). و از میان متغیر های محیطی به ترتیب تبخیر، ارتفاع، رطوبت نسبی و بارندگی در تغییر پذیری زمانی و مکانی اقلیم در منطقه مورد مطالعه بیشترین تاثیر را دارند. دما نتایج متفاوتی بسته به شاخص اقلیمی مورد استفاده برای پهنه بندی اقلیمی حاصل کرد. Email: negarsiabi63@gmail.com [1]. Thornthwaite [2]. Ordinary kriging [3] .Co-Kriging [4] .Inverse Distance Weighted [5] .Kriging with an External Drift}, keywords_fa = {ارزیابی دقت,تحلیل مکانی,عناصر اقلیمی,زمین آمار,طبقه بندی اقلیمی}, url = {https://clima.irimo.ir/article_14957.html}, eprint = {https://clima.irimo.ir/article_14957_a2f66ae403f7010630fb057d73ccd10b.pdf} }