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

Year 2018, Volume: 21 Issue: 2, 175 - 184, 30.04.2018

Ali Çaylı , Adil Akyüz , Emir Hüseyin Kaya Yasir Çiçekli Mehmet Çağrı Yıldız

https://doi.org/10.18016/ksudobil.320511
Cited By: 1

Abstract

The most important parameter affecting agricultural production is
environmental conditions. Roviding 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. This case leads to inaccurate results
in the calculations. For this reason, the provincial variation of temperature
and relative humidity values, which may have regional differences, has been
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.

Keywords

Environmental conditions Spatial climate data Data logger

References

  • 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. Genome-wide 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

Bazı Meteorolojik Verilerin Mekânsal Değişkenliği Üzerine Bir Karşılaştırma: Kahramanmaraş Örneği

Year 2018, Volume: 21 Issue: 2, 175 - 184, 30.04.2018

Ali Çaylı , Adil Akyüz , Emir Hüseyin Kaya Yasir Çiçekli Mehmet Çağrı Yıldız

https://doi.org/10.18016/ksudobil.320511
Cited By: 1

Abstract



Tarımsal üretimi
etkileyen en önemli parametre çevre koşullarıdır. Uygun iklim koşullarının
sağlanması ve bu koşulların takip edilmesi, birçok tarımsal yapıda ve üretim
sistemlerinde hayati önem arz etmektedir. Ancak meteoroloji istasyonlarından
elde edilen veriler çok geniş alanlar için kullanılmaktadır. Bu durum yapılan
hassas hesaplamalarda ve analizlerde doğru sonuçlar elde edilememesine neden
olmaktadır. Bu sebeple çalışmada, bölgesel olarak büyük farklılıklar
gösterebilen sıcaklık ve oransal nem değerlerinin il bazındaki değişimi
araştırılmıştır. Bu amaçla, Kahramanmaraş merkez ilçe sınırları içerisindeki
Merkez Meteoroloji İstasyonu verileri ile istasyona 10 km uzaklıkta bulunan
araştırma arazisinde ölçülen sıcaklık ve oransal nem verileri ayrıca il
merkezinde bulunan Havalimanı Meteoroloji İstasyon verileri istatistiksel
olarak karşılaştırılmıştır.



Elde
edilen bulgular, rakımı 468 m olan araştırma arazisi ile Merkez Meteoroloji
İstasyonu verileri arasında günlük ortalama sıcaklıklarda farklılık olmadığı
ancak maksimum ve minimum sıcaklıklar ile ortalama, maksimum ve minimum oransal
nem değerleri arasındaki farkın önemli olduğunu göstermiştir (P<0.05).
Ayrıca rakımı 572 m olan Merkez Meteoroloji İstasyonu ile rakımı 525 m olan
Havalimanı Meteoroloji İstasyonu verileri arasında yapılan karşılaştırma ise
günlük minimum sıcaklıklar, oransal nem ve rüzgâr hızı verileri arasında
istatistiksel olarak farklılıklar olduğunu görülmektedir (P<0.001). Günlük
ortalama sıcaklıklar arasında ise yıllık bazda farklılık bulunmamasına rağmen
Kasım, Aralık ve Nisan ayları için farklılık olduğu bulunmuştur (P<0.05).

Keywords

Çevre koşulları Mekânsal iklim verileri Veri kaydedici

References

  • 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. Genome-wide 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
There are 24 citations in total.

Details

Primary Language Turkish
Journal Section RESEARCH ARTICLE
Authors

Ali Çaylı

Adil Akyüz 0000-0002-2120-0680

Emir Hüseyin Kaya

Yasir Çiçekli

Mehmet Çağrı Yıldız

Publication Date April 30, 2018
Submission Date June 12, 2017
Acceptance Date August 2, 2017
Published in Issue Year 2018 Volume: 21 Issue: 2

Cite

APA Çaylı, A., Akyüz, A., Kaya, E. H., … Çiçekli, Y. (2018). Bazı Meteorolojik Verilerin Mekânsal Değişkenliği Üzerine Bir Karşılaştırma: Kahramanmaraş Örneği. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 21(2), 175-184. https://doi.org/10.18016/ksudobil.320511

Cited By

Doğu Akdeniz Bölgesi Modern Seralarında Farklı Örtü Malzemesi ile Farklı Isı Perdesi Kullanımının Sera Isı Gereksinimine Etkisinin Belirlenmesi
Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi
https://doi.org/10.18016/ksutarimdoga.vi.1462607
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