پهنه‌بندی آسیب‌پذیری از خشکسالی در ایران با استفاده از مدل AHP و منطق فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت و کنترل بیابان، گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

2 دانشجوی دکتری بیابان‌زدایی، گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

3 دانشیار، گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران

4 دانشجوی دکتری بیابان‌زدایی، دانشکده منابع طبیعی،‌ دانشگاه کاشان

چکیده

در مطالعه حاضر، به منظور ارزیابی آسیب‌پذیری به خشکسالی از روش تحلیل سلسله مراتبی متشکل از پنج شاخص اقلیم، توپوگرافی، تراکم آبراهه، کاربری اراضی و منابع آب زیرزمینی استفاده شد. پس از تعیین وزن هر یک از شاخص‌ها و زیر شاخص‌ها، با استفاده از توابع منطق فازی در نرم‌افزار ArcGIS، نقشه عضویت فازی هر یک از شاخص‌ها محاسبه و با استفاده از عملگر فازی امگا (9/0=γ) هم‌پوشانی آن‌ها ترسیم و طبقه‌بندی شد. بررسی نقشه آسیب‌پذیری خشکسالی ایران نشان داد نواحی مرکزی، شرق، جنوب، شمال شرق و جنوب شرق کشور به طور عمده در دو کلاس آسیب‌پذیری خیلی کم یا خیلی زیاد قرارگرفته‌اند. همچنین نواحی رشته‌کوه‌های زاگرس و البرز در کلاس زیاد قرار می‌گیرند. اکثر مناطق شمال غرب و غرب کشور همچنین سواحل شمالی در کلاس‌های آسیب‌پذیری متوسط تا خیلی کم قرار دارند. این عامل در ناحیه اطراف دریاچه ارومیه در محدوده زیاد تا متوسط متغیر بود.

کلیدواژه‌ها


عنوان مقاله [English]

Drought vulnerability mapping using AHP and Fuzzy Logic in Iran

نویسندگان [English]

  • S. Nasabpour 1
  • E. Haydari Alamdarloo 2
  • H. Khosravi 3
  • A. Vesali 4
1 Ph. D. Student of Desert Management and Control, Faculty of Natural resources, University of Tehran,karaj, Iran
2 Ph. D. Student of Combating Desertification, Faculty of Natural resources, University of Tehran,karaj, Iran
3 Associate Professor, Faculty of Natural Resources, University of Tehran
4 Ph. D. Student of Combating Desertification, Faculty of Natural Resources, Kashan University
چکیده [English]

In this study, Analytic Hierarchy Process method was used to assess drought vulnerability in different regions of Iran, using five indices including climate, topography, drainage density, land use and groundwater resources. After determining the weight of each index and sub-index, the fuzzy membership maps were calculated using Fuzzy logic functions in ArcGIS software. Then, by use of omega fuzzy operator (γ=0.9) the maps over laps were drawn and classified. Drought vulnerability map of Iran showed that central, Eastern, Southern, North East and South East regions are mainly located in two vulnerability classes of very low and very high. Also, Zagros and Alborz mountains region classified as highly vulnerable. Most areas in north and northwest of the country as well as northern coastal region are located in medium to very low vulnerable classes. Lake Urmia region is mostly occupied by high and medium vulnerability classes.

کلیدواژه‌ها [English]

  • AHP
  • Drought vulnerability
  • Iran
  • Risk management
Adalat Gostar, M, D., Farzadian, A., Amiri, S. N. 2010. Presentation of Stochastic Model for Drought Forecasting in Shiraz . The National Conference on Water Crisis Managemen. Islamic Azad University of Marvdasht. (In Farsi)
Ashok, K, R., Sasikala, C. 2012. Farmers’ vulnerability to rainfall variability and technology adoption in rain-fed tank irrigated agriculture. Agricultural economics Research Review, 25(2): 267-278
Bazza, M. O. 2002. Water Resources Planning and Management for Drought Mitigation. Regional Workshop on Capacity Building on Drought Mitigation in the Near East.
Bella, S. Z., Nemath, A. D., Szalai, S. 2005. Application of gis tools: drougth vulnerability in Somogy County, Hungary. Geophysical Research Abstracts. 7(02530):1076-1083.
Bonham-Carter, G. F. 1991. Geographic Information System for Geoscientists: Modeling with GIS. Computer Methods in the Geosciences, 398 Pages.
Deressa, T. 2010. Assessing of vulnerability in Ethiopian agriculture to the climate change and adaption strategies. Ph. D thesis. Environmental economics, university of Pretoria.
FAO. 2010. Global forest resources assessment, FRA 2010-Country report, Iran. [Online]. Available athttp:// www.fao.org/forestry/fra.
 Farajzadeh, M., Ahmadian, K. 2014. Temporal and Spatial Analysis of Drought with use of SPI Index in Iran. Natural Environmental Hazards.3(4): 1-16 (In Farsi)
Fathabadi, A., Gholami, H., Salajeghe, A., Azanivand, H., Khosravi, H. 2009. Drought forecasting using neural network and stochastic models. Advances in Natural and Applied Sciences, 3(2), 137-147.
Ghaseminejad, S., Soltani, S., Soffianian, A. 2014. Drought Risk Assessment in Isfahan Province. Journal of Water and Soil Science, 18 (68): 213-226 (In Farsi)
Ghodsipour, S, H. 2016. Analytical Hierarchy Process. Amirkabir University of Technology Press Publishing House. 222. (In Farsi)
Han, P., Wang, P. X., Zhang, S. Y., Zhu, D. H. 2010. Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and Computer Modeling, 51(11–12): 1398-1403
He, B., Wu, J., Lu, A., Cui, X., Zhou, L., Liu, M., Zhao, L. 2013. Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China. Natural Hazards, 66(2): 155–166.
Hewitt, K. 1997. Regions of  risk:  A  geographical introduction to disasters. England: Addison Wesley Longman Harlow, 23 (4): 365–382.
Hill, M. J., Braaten, R., Veitch, S. M., Lees, B. G., Sharma, S. 2005. Multi-criteria decision analysis in spatial decision support: the ASSESS analytic hierarchy process and the role of quantitative methods and spatially explicit analysis. Environmental Modeling and Software, 20(7) 955-976.
Khoshnodifar, Z., Sookhtanlo, M., Gholami, H. 2012. Identification and measurement of indicators of drought vulnerability among wheat farmers in Mashhad County Iran. Annals of Biological Research, 3(9): 4593-4600.
Lee, M., Pham, H., Zhang, X. 1999. A methodology for priority settingwith application to software development process. European Journal of Operational Research, 118(2):375–89.
Leichenko, R. M., O-Brien., K. L. 2001. The Dynamics of Rural Vulnerability to Global Change: The Case of southern Africa. Mitigation and Adaptation Strategies for Global Change, 7 (1):1-18.
Malczewski, J. 1999. GIS and Multi Criteria Decision Analysis. John Wiley and Sons INC., 408 pages.
Marinoni, O. 2004. Implementation of the analytical hierarchy process with VBA in ArcGIS. Computers and Geosciences., 30(6):637-646.
Me-Bar, Y., Valdez, J. 2005. On the vulnerability of the ancient Maya society to natural threats. Journal of Archaeological Science, 32(6): 813–825.
Mishra, A. K., Desai, V. R. 2005. Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment., 19(5): 326-339.
Montaseri, M ., Amirataee, B ., Khalili, K. 2015. Identification of Trend in Spatial and Temporal Dry and Wet Periods in Northwest of Iran Based on SPI and RAI Indices. Journal of Water and Soil, 30(2): 655-671. (In Farsi)
Moss, R., Brenkert A., Malone, E. 2001. Vulnerability to climate change: aMulti-criteria decision analysis. Global environmental change, 18 (1):112-127.
Nasimi, A., Mohammadi, Z. 2014. Vulnerability of Yazd Province in Drought Using Standardized Precipitation Index and Geostatistical Methods. Journal Management System. 7(20): 79-90. (In Farsi)
Nasrnia, F., Zibaee, M. 2016. Vulnerability assessment to Drought in Various Provinces, approach towards risk management in the country. Agricultural Economics and Development, 29(4): 359-373. (In Farsi)
Rossi, G., Benedini, M., Tsakiris, G., Giakoumakis, S. 1992. On regional drought estimation and analysis. Water Resources Management, 6(4): 249-277.
Saaty, T. L. 1990. How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1): 9-26,
Sadeghravesh, M., Khosravi, H., Ghasemian, S. 2015. Application of fuzzy analytical hierarchy process for assessment of combating-desertification alternatives in central Iran. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 75(1): 653-667.
Santos, M. J. J., Verıssimo, R., Rodrigues, R. 2001. Hydrological drought computation and its comparison with meteorological drought. In: Demuth, S., Stahl, K. (Eds.), ARIDE—Assessment of the Regional Impact of Droughts in Europe. Final Report to the European Union. ENV4- CT97-0553. Institute of Hydrology, Freiburg, Germany, pp. 78–79.
Shahid, S., Behravan, H. 2008. Drought risk assessment in the western part of Bangladesh. Journal of Natural Hazard Review, 46(3): 391-413.
Sivakumar, M., Wilhite, D. A. 2002. Drought preparedness    and    drought    management. Drought Mitigation and Prevention of Land Desertification, Drought Mitigation and Prevention of Land Desertification (Proc. Intern. Conf., Bled, Slovenia), UNESCO/Slov. Nat. Com. ICID, Ljubljana, paper 2.
Song, L. C., Deng, Z. Y., Dong, A. X. 2003. Drought. China Meteorological Press, Beijing. 162 pages.
Statistical Yearbook of Road Maintenance and Road Transport. 2012. Planning, Office of the Information and Communication Technology. Available at http://www.rmto.ir.
 Sui, D. Z. 1992. A Fuzzy GIS Modeling Approach for Urban Land Evaluation.  Journal of computers. Environment and Urban Systems, 16(2):101-115.
Tigkas, D. 2008. Drought Characterisation and Monitoring in Regions of Greece. European Water, 24(24): 29-39
Vincent, K. 2004. Creating an index of social vulnerability to climate change for Africa. Technical Report 56, Center Tyndall Climate Change Research, University of East Anglia, Norwich.
Walter, J. 2004. World disasters report 2004: focus on community resilience. Kumarian, Bloomfield.
Wilhite, D. A. 2000. Drought as a natural hazard: concepts and definitions, chapter 1. In: Wilhite DA (ed) Drought: a global assessment. Natural hazards and disasters series, Routledge Publishers, UK.
Xiao-Chen, Y., Yu-Liang, Z., Ju-Liang, J., Yi-Ming, W. 2013. Risk analysis for drought hazard in China: a case study in Huaibei Plain. Natural Hazards, 67(2): 879–900.
Zehtabian, G., Karimi, K., Mirdashtvan, M., Khosravi, H. 2013. Comparability Analyses of the SPI and RDI Meteorological Drought Indices in South Khorasan Province in Iran. International Journal of Advanced Biological and Biomedical Research, 1(9): 981-992.