Spatial and Temporal Displacements in Wet and Dry Periods in the Southeast of the Caspian Sea: Golestan Province in Iran

نوع مقاله : پژوهشی

نویسندگان

1 M.Sc. Graduated, Department of Geography, Golestan University, Gorgan, Iran

2 Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran

3 Associate Professor, Department of Geography, Golestan University, Gorgan, Iran

چکیده

The global warming phenomenon has had a great impact not only on the temperature patterns of the regions, but also on the spatial-temporal patterns of the occurrence of wet and dry days. As some areas have increased (decreased) the number of dry days, the result of these changes requires new approaches to water management in these areas. Golestan province in northern Iran is one of the provinces in south of Caspian Sea, where evidence suggests a decrease in precipitation days as well as the temporal displacement of precipitation days from the cold period to the warm period of the year. Therefore, the present study investigates the probability of occurrence of wet and dry days based on the one-time Markov chain method, as a change of decade. Thus, in this research, precipitation data from 197 precipitation stations for a period of 40 years from 1971 to 2010 was used. In this study, based on the most internal consistency of different regions in terms of the occurrence of wet and dry days, eight different spatial zones were identified. The results of this study indicate that the continuity of the wetter periods in the eight-cluster zones of Golestan province indication that the length of the wetter period has decreased in most months. The highest decrease in July was on average 0.20 days per decade. However, in August, September, and October, it reached its lowest level. In August and September, clustered zones in the eastern regions of the province show an increase in the longer period. This indicates that during the last decades throughout the second half of the summer, rainfall has increased in the province.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatial and Temporal Displacements in Wet and Dry Periods in the Southeast of the Caspian Sea: Golestan Province in Iran

نویسندگان [English]

  • Ayesheh Yelghei 1
  • Abdolazim Ghanghermeh 2
  • Gholamreza Roshan 3
1 M.Sc. Graduated, Department of Geography, Golestan University, Gorgan, Iran
2 Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran
3 Associate Professor, Department of Geography, Golestan University, Gorgan, Iran
چکیده [English]

The global warming phenomenon has had a great impact not only on the temperature patterns of the regions, but also on the spatial-temporal patterns of the occurrence of wet and dry days. As some areas have increased (decreased) the number of dry days, the result of these changes requires new approaches to water management in these areas. Golestan province in northern Iran is one of the provinces in south of Caspian Sea, where evidence suggests a decrease in precipitation days as well as the temporal displacement of precipitation days from the cold period to the warm period of the year. Therefore, the present study investigates the probability of occurrence of wet and dry days based on the one-time Markov chain method, as a change of decade. Thus, in this research, precipitation data from 197 precipitation stations for a period of 40 years from 1971 to 2010 was used. In this study, based on the most internal consistency of different regions in terms of the occurrence of wet and dry days, eight different spatial zones were identified. The results of this study indicate that the continuity of the wetter periods in the eight-cluster zones of Golestan province indication that the length of the wetter period has decreased in most months. The highest decrease in July was on average 0.20 days per decade. However, in August, September, and October, it reached its lowest level. In August and September, clustered zones in the eastern regions of the province show an increase in the longer period. This indicates that during the last decades throughout the second half of the summer, rainfall has increased in the province.

کلیدواژه‌ها [English]

  • climatic variability
  • Multidecadal variation
  • Precipitation pattern
  • Markov Chain
  • Golestan province
Alexander, L. and Arblaster, J., 2009, Assessing trends in observed and modelled climate extremes over Australia in relation to future projections, Int. J. Climatol, 29, 417–435.
Alijani, B., 1996, Climate of Iran, Payam Noor University Press, Iran.
Asakereh, H. and Razmi, R., 2012, Analysis of annual precipitation changes in northwest of Iran. Geogr. Environ. Plan J. Iran J., 47, 147–162.
Asakereh, H., 2008, Analysis of the Frequency and the Spell of Rainy Days Using Markove Chain Model for City of Tabriz Iran, Journal Iran-Water Resources Research, 4(2), 46-56.
Asakereh, H., 2017, Trends in monthly precipitation over the northwest of Iran (NWI), Theoretical and Applied Climatology, 130, (1-2), 443–451.
Asakereh, H. and Mazini, F., 2010, Investigation of dry days occurrence probability in Golestan province using markove chain model, Journal of Geography and Development, 8(17), 29 –44.
Barry, R. G. and Chorley, R. J., 1998, Atmosphere, weather and climate. Routledge, UK.
Cindrić, K., Pasarić, Z. and Gajić-Čapka, M., 2010, Spatial and temporal analysis of dry spells in Croatia, Theoretical and Applied Climatology, 102, 171–184.
Daryabari, S. J., 2006, Drought Prediction Based On Probability Transition Matrix Models In Different Regions Of Iran, Journal of Applied researches in Geographical, 5(6), 87-104.
Eyvazi, M., Mosa'di, A. and Eslami, H. R., 2012, The prediction of time and location of drought in Golestan province using the probability transition matrix. Third National Conference on Integrated Water Resources Management. University of Agricultural Sciences and Natural Resources, Sari, Iran.
Farajzadeh, M., Oji, R., Cannon, A. J., Ghavidel, Y. and MassahBavani, A. R., 2014, An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran, Theoretical and Applied Climatology, 120, 377–390.
Ghanghermeh, A. A., Roshan, Gh., Khajehshkoei, A. R., Shahkooeei, E., Mirkatooli, J., Nazarnejad, N. and Tavakloli, G., 2016, Final Report on: Review and Evaluation of the Occurrence of Climate Change or Variation Upon the Resources Water and Uses in Order to Apply Risk Management Instead of Emergency Management in Real Terms and Predictions. Water Resources Management CO. Golestan Regional Water Co. Islamic Republic of Iran. Ministry of Energy.
Roshan, Gh. and Nastos, P. T., 2018, Assessment of extreme heat stress probabilities in Iran's urban settlements, using first order Markov chain model, Sustainable Cities and Society, 36 , 302–310.
Golian, S., Mazdiyasni, O. and AghaKouchak, A., 2015, Trends in meteorological and agricultural droughts in Iran, Theoretical and Applied Climatology, 119(3-4), 679–688.
Hejazizadeh, Z. and Shirkhani, A., 2005, Analysis and Predict of Statitical Drought and Short Period Dry Spells in Khorasan Region, Journal of Geographical Research Quarterly, 37(52), 2-20.
IPCC, 2007, Climate change: synthesis report of the fourth assessment report. IPCC, Geneva.
Javan, K., 2016, Analysis of the Spell of Rainy Days in Lake Urmia Basin using Markov Chain Model, researches in Geographical Sciences, 16(43), 173-193.
Kallache, M., Vrac, M., Naveau, P. and Michelangeli, P. A., 2011, Nonstationary probabilistic downscaling of extreme precipitation, Journal of Geophysical Research, 116, 1–15 [D05113].
Khadr, M., 2015, Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia), Ain Shams Engineering Journal, http://dx.doi.org/10.1016/j.asej.2015.11.005.
Khadr, M., 2016, Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia), Ain Shams Engineering Journal, 7, 47–56.
Khorshiddoost, M. A. and Fakhari, F., 2016, Analysis of the Frequency and the Spell of Rainy Days Using Markove Chain Model in Southwest of Iran, Journal Geography And Planning,20(55), 87-104.
Mandal, K. G., Padhi, J., Kumar, A., Ghosh, S., Panda, D.K. and Mohanty, R.K., 2015, Analyses of rainfall using probability distribution and Markovchain models for crop planning in Daspalla region in Odisha, India, Theoretical and Applied Climatology, 121,517–528.
Mostafazadeh, R., Mehdi Vafakhah, M. and Zabihi, M., 2016, Analysis of Monthly Wet and Dry Spell Occurrence by using Power Laws in Golestan Province, Iran, Ecohydrology, Volume 2, 429-443.
Mostafazadeh, A., Zabihi, M. and Adhami, M., 2017, Spatial and temporal analysis of monthly precipitation variations ‎in Golestan Province using fractal dimension, Watershed Engineering and Management, Volume 9, 34-45.
Orosa, J. A., Costa, Á. M., Rodríguez-Fernández, Á. and Roshan, Gh., 2014, Effect of climate change on outdoor thermal comfort in humid climates, Journal of Environmental Health Science and Engineering, 12, 46-60.
Privault, N., 2013, Discrete-time Markov chains, in: Understanding Markov Chains. Springer, 77–94.
Roshan, Gh., Ghanghermeh, A. and Orosa, J., 2013b, Thermal comfort and forecast of energy consumption in Northwest Iran, Arabian Journal of Geosciences, 9, 3657–3674.
Roshan, Gh., Ghanghermeh, A., Nasrabadi, T. and Bahari Meimandi, J., 2013a, Effect of global warming on intensity and frequency curves of precipitation, case study of northwestern Iran, Water Resource Management, 27, 1563–1579.
Sillmann, J., Kharin, V.V., Zwiers, F.W., Zhang, X. and Bronaugh, D., 2013, Climate extremes indices in the CMIP5 multimodel ensemble: part 2. Future climate projections, Journal of Geophysical Research, 118, 2473–2493.
Sonnadara, D.U.J. and Jayewardene, D.R., 2015, A Markov chain probability model to describe wet and dry patterns of weather at Colombo, Theoretical and Applied Climatology, 119, 333–340.
Tan, W.L., Yusof, F. and Yusop, Z., 2014, Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model, Journal of Theor. Appl. Climatol., DOI 10.1007/s00704-016-1795-9.
Wilby, R.L. and Dawson, C.W., 2007, SDSM4.2–A decision support tool for the assessment of regional climate impacts. User Manual, 1–94.
Yang, T., Li, H., Wang, W., Xu, C.Y. and Yu, Z., 2012, Statistical downscaling of extreme daily precipitation, evaporation, and temperature and construction of future scenarios, Hydrological Processes, 26, 3510–3523.
Yoo, C., Lee, J. and Ro, Y., 2015, Markov Chain Decomposition of Monthly Rainfall intoDaily Rainfall: Evaluation of Climate Change Impact, Journal of Advances in Meteorology, doi.org/10.1155/2016/7957490.