Modeling of the heating degree hours (HDH) at dawn in the dry desert regions of Iran

Document Type : Research Paper

Authors

1 Prof of Climatology, Faculty of Humanities and Social Sciences, Yazd University, Iran

2 Graduate Ph. D. Climate risks, Yazd University, Iran

3 MS graduated in Watershed Management, Yazd University, Iran

10.29252/grd.2018.1235

Abstract

The aim of this modeling initiative is to gain insight into the energy needs at dawn (6.30 o'clock in Tehran) in seven provinces including Qazvin, Qom, Central, Isfahan, Yazd, Fars and Kerman (i.e. Iran's arid and desert regions). For this purpose, the dynamical model data of EH5OM were downloaded from the Max Planck Physics Center under the A1B propagation scenario for the period from 01/01/2015 to 03/13/2010 at 03Z for 03Z. In the next step, for the microscale, the output data of the model were used using the REGCM4 model, and the data of the model were obtained with a spatial resolution of 0.27 × 0.27 arcs and an array of outputs of 13410 × 705. After the temperature of the heating clock (HDH) was calculated, the fractal theory was used to assess the behavioral outlook of this climate parameter. The results show that the maximum demand for the temperature of the hexagonal tail in the arid and desert regions of the country is 595 ° C in December and the minimum is 21 ° C in July. The results of fractal geometry also showed that the temperature of dawn warming in Iran's dry and desert regions serves as a sensitive nonlinear system that is more subject to long-term changes in the early and late months of the year and to short-term changes in summer. The trend evaluation and trend slope proved that, in most months of the year, the degree of warming of the dawn is reduced.

Keywords

References

احمدی، محمود و عباسعلی داداشی رودباری(1395)، دستورالعمل اجرایی ریز پیمانه نمایی آماری سری‌های روزانه آب‌وهوا، انتشارات نوید مهر، تهران، 184 ص.
امیدوار، کمال و رضا ابراهیمی و احمد مزیدی، (1395)، واکاوی اثر گرمایش جهانی بر درجه ساعت‌های گرمایش و سرمایش ماهانه ایران، برنامه ریزی و آمایش فضا، دوره 20، شماره 2، صص 41-64.
امیدوار، کمال، و رضا ابراهیمی و عباسعلی داداشی رودباری و مریم ملک میرزایی، (1394)، واکاوی زمانی-مکانی فرین‌های سرد ایران تحت تأثیر گرمایش جهانی به‌منظور کاهش مخاطرات، دانش مخاطرات، دوره 2، شماره 4، صص 423-437.
داداشی رودباری، عباسعلی و غلامعباس فلاح قالهری و مختار کرمی و محمد باعقیده، (1394)، تحلیل تغییرات بارش حوضه آبریز هراز با استفاده از روش­های آماری و تکنیک تحلیل طیفی، هیدروژومورفولوژی، شماره 7، صص 59-86.
سالینگروس، نیکاس انجلوس، (1390)، تئوری شبکه ای و شهر فراکتال (دیدگاه­های نوین در زمینه برنامه ریزی شهری) ترجمه سیدکورش سرورزاده و علیرضا اشتیاقی، انتشارات نوید شیراز، شیراز، 144ص.
شمسی‌پور، ععلی اکبر، (1393). مدل‌سازی آب و هوایی نظریه و روش، چاپ دوم، انتشارات دانشگاه تهران، تهران، 278ص.
قرخلو، مهدی و سعید زنگنه شهرکی، (1388). شناخت الگوی رشد کالبدی-فضای شهر با استفاده از مدلهای کمی، مجله جغرافیا و برنامه ریزی محیطی، دوره 20، شماره 2، صص 19-40.
مرکز آمار ایران (1390). نتایج آمارگیری از مصرف حامل های انرژی در بخش خانوار در نقاط شهری-1390، انتشارات مرکز آمار ایران، تهران.
میرکتولی، جعفر، رضا بارگاهی و سیده زهرا عقیلی، (1393)، تبیین ابعاد استفاده از هندسه فرکتال در تحلیل های جغرافیا و برنامه ریزی شهری، مجله آمایش جغرافیایی فضا، سال چهارم، شماره 14، صص 55-82.
Al-Sanea, S. A., & Zedan, M. F. (2002). Optimum insulation thickness for building walls in a hot-dry climate. International Journal of Ambient Energy, 23(3), 115-126.
Bakos, G. C. (2000). Insulation protection studies for energy saving in residential and tertiary sector. Energy and buildings, 31(3), 251-259.
Chaturvedi, V., Eom, J., Clarke, L. E., & Shukla, P. R. (2014). Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy, 64, 226-242.
Coskun, C. (2010). A novel approach to degree-hour calculation: indoor and outdoor reference temperature based degree-hour calculation. Energy, 35(6), 2455-2460.
Coskun, C. (2010). A novel approach to degree-hour calculation: indoor and outdoor reference temperature based degree-hour calculation. Energy, 35(6), 2455-2460.
Dahl, M., Brun, A., & Andresen, G. B. (2017). Using ensemble weather predictions in district heating operation and load forecasting. Applied Energy, 193, 455-465.
Dombaycı, Ö. A. (2010). The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli–Turkey. Advances in Engineering Software, 41(2), 141-147.
Dubin, J. A., & Gamponia, V. (2007). Mid-Range, Average, and Hourly Estimates of Heating Degree Days: Implications for Weather Normalization of Energy Demand. The Energy Journal.
Eom, J., Clarke, L., Kim, S. H., Kyle, P., & Patel, P. (2012). China's building energy demand: Long-term implications from a detailed assessment. Energy, 46(1), 405-419.
Førland, E. J., Skaugen, T. E., Benestad, R. E., Hanssen-Bauer, I., & Tveito, O. E. (2004). Variations in thermal growing, heating, and freezing indices in the Nordic Arctic, 1900–2050. Arctic, Antarctic, and Alpine Research, 36(3), 347-356.
Fumo, N. (2014). A review on the basics of building energy estimation. Renewable and Sustainable Energy Reviews, 31, 53-60.
Gertler, P., Shelef, O., Wolfram, C., & Fuchs, A. (2013). How pro-poor growth affects the demand for energy (No. w19092). National Bureau of Economic Research.
Guttman, N. B., & Lehman, R. L. (1992). Estimation of daily degree-hours. Journal of Applied Meteorology, 31(7), 797-810.
Karl, T. R. (2009). Global climate change impacts in the United States. Cambridge University Press.
Kendall, M. (1975). Multivariate analysis. Charles Griffin.
Mann, H. B. (1945) .Nonparametric tests against trend. Econometrical: Journal of the Econometric Society, 245-259.
Moustris, K. P., Nastos, P. T., Bartzokas, A., Larissi, I. K., Zacharia, P. T., & Paliatsos, A. G. (2015). Energy consumption based on heating/cooling degree days within the urban environment of Athens, Greece. Theoretical and Applied Climatology, 122(3-4), 517-529.
Radhi, H. (2009). Evaluating the potential impact of global warming on the UAE residential buildings–a contribution to reduce the CO 2 emissions. Building and Environment, 44(12), 2451-2462.
Reichler, T., & Kim, J. (2008). How well do coupled models simulate today's climate? Bulletin of the American Meteorological Society, 89(3), 303.
Roeckner E, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kornblueh L, Manzini E, Schlese U Schulzweida U .(2006) .Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J Clim 19:3771–3791.
Rosenthal, D. H., Gruenspecht, H. K., & Moran, E. A. (1995). Effects of global warming on energy use for space heating and cooling in the United States. The Energy Journal, 77-96.
Sen, P. K., (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association. 63. pp. 1379-1389.
Terzi, F., & Kaya, H. S. (2008). Analyzing urban sprawl patterns through fractal geometry: The case of Istanbul metropolitan area.
Thiel, H., (1950). A rank-invariant method of linear and polynomial regression analysis: part3. Proceeding of Koninalijke Nederland's Academies van Weinenschatpen A. 53, pp. 1397-1412.
Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20, 411-419.
Van Hateren, J. H. (2013). A fractal climate response function can simulate global average temperature trends of the modern era and the past millennium. Climate Dynamics, 40(11-12), 2651-2670.
Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592.
Modeling the temperature of the heating hour (HDH) of the dawn of Iran's dry and desert regions.
The Journal of Geographical Research on Desert Areas
Volume 6, Issue 1
December 2018
Pages 1-32

Files

Share

How to cite

Statistics