Spatial Analysis of Humidity Propagation over Iran

Document Type : Full length article

Authors

1 Assistance Professor, Department of Geography, Hakim Sabzevari University, Iran

2 PhD Candidate in Agro Climatology, Hakim Sabzevari University, Iran

3 MSc Student in Applied Climatology, Hakim Sabzevari University, Iran

Abstract

Introduction
The consequence of cooperation between environmental factors and circulation patterns in a long time can determine the arrangement of type and manner in humidity in geographical area. The knowledge about space dispersion in geographical areas assists preparing sound programming and proper environmental decision making. Relative Humidity is the most commonly used measurements of moisture content in the air. The key to understand relative humidity is to understand that it is a measure of the ‘actual humidity’, relative to the maximum possible humidity at a given temperature. Let’s explain it a bit further. In this context a number of studies have been conducted to refer this. Some of these studies are Diffenbaugh and et al. (2008), Ohayon (2011), Jia and et al. (2011), Homar and et al. (2010), Chao-bing and et al. (2011), Allard and Soubeyrand (2012), Ageena and et al. (2013), Del Río and et al. (2013), Kim and et al (2014) and Bajat and et al. (2014). This research is fulfilled to detect the temporal and place spatial autocorrelation of humidity in Iran.
 
Materials and Method
In order to reach the expressed goal, the base of network data of relative humidity in Iran has been established. Similarity of data of the stations has been evaluated by the Kolmogorov-Smirnov Test in SPSS software and their similarity has been proved. Then, from the data of the stations a statistical period of 30 years in a daily period from 1982/1/1 until 2012/12/31 is used as the base of the present research and a network in range of 15×15 kilometer have been spread over the study area. In reviewing the changes of Transmittal Humidity of Iran during a year, modern spatial statistics method such as spatial auto correlation global moran, local insulin moral index and hot spots were used by using (GIS) and MATLAB.
 
Results and Discussion
The results of this research showed that the global moran index for each 12 monthes of a year is one more than 0.90. This point indicates that in accordance to global moran, Transmittal Humidity of Iran in the study period has the high cluster pattern in 90, 95 and 99 level percent. Then, the highest index of global moran in scale of 0.97 is related to the February in winter. Z statics for every 12 monthes of a studied statistic period is high and between 247 and 263. Therefore, according to global moran it can be concluded that during a year in the index in Iran shows a very high cluster pattern. Alteration of spatial autocorrelation of Transmittal Humidity of Iran used the local moran index and analysis of hot spots. According to both the indicators, the north, north west, north east, west and south west areas like east Azarbaijan, west Azarbaijan, Ardabil, Zanjan, Guilan, Mazandaran, Ghorgan, Khorasan and Kermanshah stations plays a significant role in forming the Humidity patterns with high cluster. This is in a way that the named areas of Iran have positive spatial autocorrelation. This is while the regions have negative spatial auto correlation or in other words dry humidity in 12 months of a year limited to high regions. Totally, a considerable area of the province in all 12 months is without significant or disciplined pattern or they lack sound virtual spatial autocorrelation statistically. The results of this research showed the humidity pattern is formed through a long time period and under local and distributional elements with a different role.
 
Conclusion
Generally, the geographical arrangement of humidity patterns are formed by regional factors specially heights, latitude and in a clearer explanation formation and structure and the role of latitude. This is while we should not ignore the role of outer factors in formation of humidity patterns. Outer factors or the general circulation atmosphere elements play a significant role in determination of a humidity regime and humidity lapse. If we look at the humidity cluster of Iran we see that the clusters in high and low level are not the same. This contrast is because of influence of circulation element factors.

Keywords

Main Subjects

References

1. علیجانی، ب. (1389). آب‌وهوای ایران. چ10. تهران: انتشارات دانشگاه پیام‌نور.
2. علیجانی، ب. و کاویانی، م. (1385). مبانی آب‌وهواشناسی، تهران: انتشارات سمت.
3. فرج‌زاده اصل، م.، کریمی احمدآباد، م.، قائمی، ه. و مباشری، م. (1388). «چگونگی انتقال رطوبت در بارش زمستانة غرب ایران». مجلة برنامه‌ریزی و آمایش فضا (مدرس علوم انسانی). بهار 13. (پیاپی 60): 217-193.
4. فلاح قالهری، غ. (1393). اصول و مبانی هواشناسی. چ2. سبزوار: انتشارات دانشگاه حکیم سبزواری.
5. قویدل‌ رحیمی، ی. (1391). نگاشت و تفسیر سینوپتیک اقلیم با استفاده از نرم‌افزارGRADS. چ2. تهران: انتشارات سها دانش.
6. مسعودیان، ا. (1390). آب‌وهوای ایران. اصفهان: انتشارات دانشگاه اصفهان.
7. Ageena, I., Macdonald, N. and Morse, A.P.(2013). ''Variability of maximum and mean average temperature across Libya (1945–2009)''. Theoretical and Applied.
8. Alijani, b. (1389). Climate of Iran. tenth edition. Tehran: Payam Noor University Press. (In Persian).
9. Alijani, B, and. Kavyani, M. (1385). The Foundations of climatology. Tehran: Samt. (In Persian).
10. Allard, D. and Soubeyrand, S. (2012). ''Skew-normality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics''. Spatial Statistics. 1: 50-64.
11. Anselin L, Syabri I. and Kho. Y. (2009). GeoDa: an introduction to spatial data analysis. In Fischer MM. Getis A (Eds) Handbook of applied spatial analysis. Berlin, Heidelberg and New York: Springer: 73-89.
12. Bajat, B., Blagojević, D., Kilibarda, M., Luković, J. and Tošić, I. (2014). ''Spatial analysis of the temperature trends in Serbia during the period 1961–2010''. Theoretical and Applied Climatology: 1-13.
13. Chao-bing, H.L. M.D. and Ning, L.I. (2011). ''A review on the hotspot issues of urban heat island effect''. Journal of Meteorology and Environment. 4. 011.
14. De Lucena, A.J., Rotunno Filho, O.C., de Almeida França, J.R., de Faria Peres, L. and Xavier, L.N.R. (2013). ''Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro''. Theoretical and applied climatology. 111 (3-4): 497-511.
15. Del Río, S., Herrero, L., Pinto-Gomes, C. and Penas, A. (2011). ''Spatial analysis of mean temperature trends in Spain over the period 1961–2006''. Global and Planetary Change. 78 (1): 65-75.
16. Diffenbaugh, N.S., Giorgi, F. and Pal,J.S. (2008). "Climate change hotspots in the United States". Geophysical Research Letters. 35 (16).
17. Fallah Ghalheri, Gh. (1393). Essentials and Fundamentals of Meteorology. 2nd edition. Sabzevar: Hakim Sabzevari University Press. (In Persian).
18. Farajzadeh asl, M., Karimi Ahmedabad, M., Ghaemi, H. and Mobasheri, M. (1388). "The transfer of moisture in winter rainfall west of Iran". planning and preparation space (Humanities Madras). spring 13 (successive 60): 193-217. (In Persian).
19. Getis A. and Aldstadt, J. (2004). ''Constructing the spatial weights matrix using a local statistic''. Geogr Anal. 36 (2): 90-104.
20. Ghavidel Rahimi, y. (1391). Mapping and interpretation of synoptic climate using software GRADS. 2nd edition. Tehran:Suha knowledge Press. (In Persian).
21. Homar, V., Ramis, C., Romero, R. and Alonso, S. (2010). ''Recent trends in temperature and precipitation over the Balearic Islands (Spain)''. Clim Change. 98: 199–211.
22. Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Chichester: John Wiley and Sons.
23. Jacquez, G.M. and Greiling, D.A. (2003). ''Local clustering in breast, lung and colorectal cancer in Long Island, NewYork''. Int J Health Geographics. 2: 3.
24. Jia, S., Zhu, W., Lű, A. and Yan, T. (2011). ''A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China''. Remote sensing of Environment. 115 (12): 3069-3079.
25. Killeen, T.J., Douglas, M., Consiglio, T., Jorgensen, P.M. and Mejia, J. (2007). "Dry spots and wet spots in the Andean hotspot''. Journal of Biogeography. 34 (8): 1357-1373.
26. Kim, S. and Singh, V.P. (2014). ''Modeling daily soil temperature using data-driven models and spatial distribution''. Theoretical and Applied Climatology: 1-15.
27. Lindesay J.A. and Dabreton P.C. (1993). ''Water vapor transport over southern Africa during wet and dry early and late summer months''. In. j. climatology. Vol. 13.
28. Masoudian, A. (1390).climate of Iran. Isfahan: Isfahan Publishing Daneshgah. (In Persian).
29. Mitchell, A. (2005). The ESRI guide to GIS analysis. Vol. 2: spatial measurements and statistics. ESRI: Redlands [CA].
30. Nemec, J., Gruber, C., Chimani, B. and Auer, I. (2013). "Trends in extreme temperature indices in Austria based on a new homogenised dataset". International Journal of Climatology. 33 (6): 1538-1550.
31. Ohayon, B. (2011). Statistical Analysis of Temperature Changes in Israel: An Application of CHange Point Detection and Estimation Techniques.
32. Robeson, S.M., Li, A. and Huang, C. (2014). Point-pattern analysis on the sphere. Spatial Statistics.
33. Rogerson, P.A. (2006). Statistics Methods for Geographers: students Guide. California: SAGE Publications. Los Angeles.
34. Waagepetersenand, R. and Schweder, T. (2006). "Likelihood-based inference for clustered line transect data". Journal of Agricultural, Biological, and Environ- mental Statistics. 11: 264–279.
35. Wheeler, D. and Paéz, A. (2009). Geographically Weighted Regression. In Fischer MM. Getis A (Eds) Handbook of applied spatial analysis. Heidelberg and NewYork: Springer. Berlin: 461-486.
36. Xiangde. X.U., Miao Q., Wang J. and Zhang X. (2003). "The water vapor transport model at the regional boundary during the meiyu period". Advances in Atmospheric Science. Vol. 20. No. 3.
37. Zhang, R. (2001). "Relations of water vapor transport from Indian monsoon with that over East Asia and the summer rainfall in china". Advances in Atmospheric Science. Vol. 18. No. 5.
38. Zhang, C., Luo L., Xu, W. and Ledwith, V. (2008). "Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway". Ireland. Sci Total Environ. 398 (1-3): 212-221.
Physical Geography Research
Volume 47, Issue 4 - Serial Number 4
January 2016
Pages 637-650

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History

  • Receive Date: 04 March 2015
  • Revise Date: 08 July 2015
  • Accept Date: 21 July 2015

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