ALİ ÇAYLI, ADİL AKYÜZ, Emir Hüseyin KAYA, Yasir ÇİÇEKLİ, Mehmet Çağrı YILDIZ

Bazı Meteorolojik Verilerin Mekânsal Değişkenliği Üzerine Bir Karşılaştırma: Kahramanmaraş Örneği

Tarımsal üretimi etkileyen en önemli parametre çevre koşullarıdır.Uygun iklim koşullarının sağlanması ve bu koşulların takipedilmesi, birçok tarımsal yapıda ve üretim sistemlerinde hayatiönem arz etmektedir. Ancak meteoroloji istasyonlarından eldeedilen veriler çok geniş alanlar için kullanılmaktadır. Bu durumyapılan hassas hesaplamalarda ve analizlerde doğru sonuçlar eldeedilememesine neden olmaktadır. Bu sebeple çalışmada, bölgeselolarak büyük farklılıklar gösterebilen sıcaklık ve oransal nemdeğerlerinin il bazındaki değişimi araştırılmıştır. Bu amaçla,Kahramanmaraş merkez ilçe sınırları içerisindeki MerkezMeteoroloji İstasyonu verileri ile istasyona 10 km uzaklıkta bulunanaraştırma arazisinde ölçülen sıcaklık ve oransal nem verileri ayrıcail merkezinde bulunan Havalimanı Meteoroloji İstasyon verileriistatistiksel olarak karşılaştırılmıştır.Elde edilen bulgular, rakımı 468 m olan araştırma arazisi ileMerkez Meteoroloji İstasyonu verileri arasında günlük ortalamasıcaklıklarda farklılık olmadığı ancak maksimum ve minimumsıcaklıklar ile ortalama, maksimum ve minimum oransal nemdeğerleri arasındaki farkın önemli olduğunu göstermiştir (P

A Comparison on The Spatial Variability of Some Meteorological Data: Kahramanmaras Case Study

The most important parameter affecting agricultural production is environmental conditions. Providing suitable climatic conditions and monitoring these conditions is of vital importance in many agricultural structures and production systems. However, data obtained from official meteorological stations are used for very large areas. Such cases may lead to inaccurate results in the calculations. For this reason, the provincial variation of temperature and relative humidity values, which may have regional differences, were investigated. For this purpose, the temperature and the relative humidity data measured in the survey area located 10 km away from the station and the data of the official meteorological station at the airport in the city center of the province were statistically compared with those of the official meteorological station data in the Kahramanmaras central district borders. The findings showed that the differences between the maximum and minimum temperatures and the mean, maximum and minimum relative humidity values were significant, although there were no differences in the mean daily temperatures between the study site (altitude 468 m) and the central meteorological station (altitude 572 m) data. Also statistically significant differences were found between the Central Station and the Airport Station daily minimum temperature, relative humidity and wind speed data (P

PDF

___

Apaydin H, Sonmez FK, Yildirim YE 2004. Spatial interpolation techniques for climate data in the GAP region in Turkey. Climate Research, 28(1), 31-40.
Bellocchi G, Rivington M, Donatelli M, Matthews K, Bellocchi G, Rivington M, Donatelli M, Matthews K 2010. Validation of biophysical models : issues and methodologies . A review. 30, 109-130. doi: 10.1051/agro/2009001
Bonhomme R 2000. Bases and limits to using 'degree day' units. European Journal of Agronomy, 13, 1-10. doi: 10.1016/S1161-0301(00)00058-7
Dikmen S, Cole JB, Null DJ, Hansen PJ 2013. Genomewide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in Holstein cattle. Plos One, 8(7), e69202.
Fisher DK, Sui R 2013. An inexpensive open-source ultrasonic sensing system for monitoring liquid levels. Agricultural Engineering International: CIGR Journal, 15, 328-334.
Fodor Nn, Kovacs GzJ 2005. Sensitivity of crop models to the inaccuracy of meteorological observations. Physics and Chemistry of the Earth, 30, 53-57. doi: 10.1016/j.pce.2004.08.020
Gallagher M. B, Sandhu S, Kimsey R. 2010. Variation in developmental time for geographically distinct populations of the common green bottle fly, Lucilia sericata (Meigen). Journal of Forensic Sciences, 55(2), 438-442.
Gubbi J, Buyya R, Marusic S, Palaniswami M 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645-1660. doi: 10.1016/j.future.2013.01.010
He H, Yang L, Fan L, Zhao L, Wu H, Yang J, Li C 2012. The effect of intercropping of maize and soybean on microclimate. Computer and Computing Technologies in Agriculture V, 257-263.
Jamieson PD, Brooking IR, Porter JR, Wilson DR 1995. Prediction of leaf appearance in wheat: a question of temperature. Field Crops Research, 41(1), 35-44. doi: http://dx.doi.org/10.1016/0378-4290(94)00102-I
Johansson E, Berglund S, Lindborg T, Petrone J, Van As D, Gustafsson LG, Näslund JO, Laudon H 2015. Hydrological and meteorological investigations in a periglacial lake catchment near Kangerlussuaq, west Greenland–presentation of a new multi-parameter data set. Earth System Science Data, 7(1), 93-108.
Kim KS, Yoo B 2015. Comparison of Regional Climate Scenario Data by a Spatial Resolution for the Impact Assessment of the Uncertainty Associated with Meteorological Inputs Data on. 2015, 249-255. doi: 10.1007/s12892-015-0115-8
Los, S, Pollack N, Parris M, Collatz G, Tucker C, Sellers P, Malmström C, DeFries R, Bounoua L, Dazlich D 2000. A global 9-yr biophysical land surface dataset from NOAA AVHRR data. Journal of Hydrometeorology, 1(2), 183-199.
Mearns LO, Easterling W, Hays C, Marx D 2001. Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part 1. The Uncertainty Due to Spatial Scale. Climatic Change, 51, 131-172. doi: 10.1023/A:1012297314857
Mesas-Carrascosa FJ, Verdú Santano D, Meroño JE, Sánchez de la Orden M, García-Ferrer A 2015. Open source hardware to monitor environmental parameters in precision agriculture. Biosystems Engineering, 137, 73-83. doi: 10.1016/j.biosystemseng.2015.07.005
Monestiez P, Courault D, Allard D, Ruget F, Ruget ËO 2001. Spatial interpolation of air temperature using environmental context: Application to a crop model. Environmental and Ecological Statistics, 8, 297-309. doi: 10.1023/A:1012726317935
Monteith JL 1965. Evaporation and environment. Paper presented at the Symp. Soc. Exp. Biol.
Moorhead JE, Gowda, P. H., Marek GW, Porter, DO, Marek TH 2016. Spatial Uniformity in Sensitivity Coefficient of Reference Et in the Texas High Plains. Applied Engineering in Agriculture, 32(2), 263-269.
Mu Q, Heinsch FA, Zhao M, Running SW 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4), 519-536. doi: http://doi.org/10.1016/j.rse.2007.04.015
Romanovsky V, Drozdov D, Oberman N, Malkova G, Kholodov A, Marchenko S, Moskalenko N, Sergeev D, Ukraintseva N, Abramov A 2010. Thermal state of permafrost in Russia. Permafrost and Periglacial Processes, 21(2), 136-155.
Sethi VP, Sumathy K, Lee C, Pal DS 2013. Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies. Solar Energy, 96, 56-82. doi: 10.1016/j.solener.2013.06.034
Swan JB, Schneider EC, Moncrief JF, Paulson WH, Peterson AE 1987. Estimating Corn Growth, Yield, and Grain Moisture from Air Growing Degree Days and Residue Cover1. Agronomy Journal, 79(1), 53-60. doi: 10.2134/agronj1987.00021962007900010012x
Tan CL, Wong NH, Jusuf SK 2013. Outdoor mean radiant temperature estimation in the tropical urban environment. Building and Environment, 64, 118-129.
TUİK 2016. Tarım alanları. Türkiye İstatistik Kurumu.
Zhao G, Siebert S, Enders A, Rezaei EE, Yan C, Ewert F 2015. Demand for multi-scale weather data for regional crop modeling. Agricultural and Forest Meteorology, 200, 156-171. doi: 10.1016/j.agrformet.2014.09.026