توسعه یک مدل پیش‌بینی ریسک خشکسالی هواشناسی (مطالعه موردی: زیرحوضه آبریز افین)

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه فردوسی مشهد

2 فردوسی مشهد

3 دانشگاه فردوسی مشهد، گروه آمار

چکیده

خشکسالی به عنوان پیچیده­ترین، اما کمتر شناخته شده­ترین خطر در میان تمام خطرات طبیعی است که نسبت به هر خطر طبیعی دیگر، درصد بیشتری از مردم را تحت تأثیر قرار می‌دهد. خشکسالی یکی از پدیده­های طبیعی و مکرر اقلیمی است؛ تجزیه و تحلیل ریسک خشکسالی ترکیبی از تجزیه و تحلیل خطر خشکسالی و تجزیه و تحلیل آسیب­پذیری خشکسالی است. در این مطالعه سعی شده است چشم­انداری از تغییرات ریسک خشکسالی هواشناسی در آینده نشان داده شود. مطالعه به­صورت موردی برای زیرحوضه افین (واقع در استان خراسان جنوبی) انجام شده است. دوره آماری استفاده شده برای دوره پایه 33 سال (2015-1983) می­باشد. داده­های آینده براساس سه مدل از پروژه CORDEX بدست آمده است. دوره آتی، به سه دوره 27 ساله شامل، آینده نزدیک (2046-2020)، آینده میانی (2073-2047) و آینده دور (2100-2074) تقسیم شده است. به منظور محاسبه ریسک خشکسالی، مخاطره خشکسالی براساس سه شاخص خشکسالی SPI، SPEI  و eRDI برای دوره پایه و دوره­های آتی و پس از آن آسیب­پذیری تعیین شد. افزایش شدت خشکسالی­ها در دوره­های آتی از دیگر نتایج حاصل از این مطالعه است. خروجی­های ریسک بدست آمده از روش مستقیم محاسبه ریسک که با داده­های CORDEX و نیز روش استفاده از مدل پیش­بینی ریسک که در این مطالعه بدست آمد، نشان از افزایش تعداد وقایع خشکسالی و بدنبال آن افزایش وقایع ریسک خشکسالی در منطقه دارد. همچنین، مشاهده شد شدت ریسک خشکسالی­ها براساس سناریوی انتشار RCP8.5 بیشتر از RCP4.5 می­باشد

کلیدواژه‌ها


عنوان مقاله [English]

Development of Weather Meteorological Drought Forecast Model (Case Study: Sub-basin Afin Watershed)

نویسندگان [English]

  • M. Mousavi Baygi 2
  • Amin Alizadeh 2
  • Aboalfazl Mosaedi 2
  • Mehdi Jabbari Nooghabi 3
2 Ferdowsi University of Mashhad
3 Department of Statistics,, Ferdowsi University of Mashhad
چکیده [English]

Introduction: Drought is the most complex, but less well-known risk among all natural hazards, which affects more people than any other natural hazard. Meteorological and seasonal hydrological drought is a common phenomenon in tropical countries and is expected to increase further in the future. Drought is one of the natural and frequent climate phenomena; Drought risk analysis is a combination of drought risk analysis and drought vulnerability analysis. Drought risk assessment methods can be calculated either by remote sensing methods or by statistical methods or by combining both methods. Drought risk assessment shows a more Suitable and accurate view of the drought because, in addition to drought severity  is  simultaneously Includes the probability of occurrence of drought and the impact this phenomenon on the environment and the region. In this study, has been made to illustrate Visionary of Changes in future meteorological drought risk.
Materials and methods: The study was conducted as a case study for the Afin sub-basin The average of minimum temperature, mean of maximum temperature, average temperature at 2 meters above ground level and rainfall data in this research have been used. The statistical period used for the base period is 33 years (1983-2015). Future data is derived from three models of the cordex project. The upcoming period is divided into three 27-year periods including the near future (2020-2046), the middle term (2047-2073) and the distant future (2074-2100). In order to investigate the drought in future periods was prepared a combination model of three climatic models  using the Bayesian method. Then, the future values of the meteorological parameters were calculated. Drought risk for the upcoming periods was calculated by direct method and modeling method. Finally, a comparison was made between the two methods in order to determine the appropriateness of the predicted model.
Results and discussion: In the survey of the intensity of SPI and SPEI drought indices during the base time period for time scales studied, the SPEI and SPI drought indices showed that both, drought events were the same during the studied period, while the indicator SPEI drought shows more mild and moderate droughts, and the SPI index has shown intense intensity on some scales. In future periods, according to the RCP8.5 scenario, the number of  drought events  in each period does not differ from the RCP4.5 scenario, but the intensities are higher than RCP4.5. By completing the questionnaire and using exploratory and confirmatory factor analysis methods, the drought vulnerability was determinated 53%. ARIMA (0,0,0) , The appropriate time series model was used to predict the level of risk. In the drought risk prediction section, the results showed that according to the SPI drought index in the upcoming periods, the number of drought events relative to the base period is relatively higher, thus the number of drought events (including four drought conditions) will increase in the far future than the two upcoming middle and nearer periods. According to prediction models of risk, rainfall parameter  for all time scales of SPI index and for four time scales of spring, autumn, winter and annual drought index SPEI,  is an effective parameter in drought estimation and effect on drought occurrence in the study area.
Conclusion :The results of this study indicate an increase in temperature in future periods based on both RCP emission scenarios. Increasing the severity of droughts in future periods is another result of this study. The risk outcomes obtained from the direct risk-measurement method, which was obtained with CORDEX data as well as the method of using the risk-predictive model obtained in this study,Showed strong correlation and no significant difference in mean, which indicates the model's appropriateness for risk prediction (hazard and after that risk) in the future.Also,The risk outcomes obtained from the direct Risk calculation method, which is based on CORDEX data with the method of using the risk prediction model obtained in this study, indicates an increase in the number of drought events followed by an increase in drought risk events in the region. also, it was observed that Severity of drought risk according to the RCP8.5 release scenario is higher than RCP4.5. For more more accurate results, it is suggested that more models (more than three models) be used from the sixth report of the Intergovernmental Panel on Climate Change.

کلیدواژه‌ها [English]

  • Drought Vulnerability
  • Drought risk
  • Drought Risk ARIMA
  • ARIMA
  • CORDEX
1- Alizade Chopari O., and Najafi S. 2016. Changes in temperature and precipitation in different regions of Iran. Physics of Earth and Space 43: 569-584.
2- Bachmair S., Svensson C., Prosdocimi I., Hannaford J., and Stahl K. 2017. Developing drought impact functions for drought risk management. Natural Hazards and Earth System Sciences 17: 1947-1960.
3- Chopra P. Drought risk assessment using remote sensing and GIS: a case study of Gujarat. 2006. ITC.
4- Ghaseminezhad S., Soltani S., and Safiyanian A.R. 2013. Drought risk assessment in Isfahan province. Journal of Agricultural Science and Technology, Water and Soil Science 18(68): 225-213.
5- Ghaznavi M., Mosaedi A., and Ghabaie Sogh M. 2018. Investigation of the effect of climatic conditions on drought status persistence in six stations Synoptic Selection of the Country. The 7th National Conference on Rainfall Rainfall Systems - Tehran- 1st and 2nd of March, 2018,Soil and Watershed Management Institute - Rainwater Leveling Systems Association.
6- Godarzi L., and Rozbahani A. 2016. Evaluation of the Efficiency of Arima & Halt Winters Time Series Models in Monthly Temperature and Precipitation Estimation (Case Study: Lotyan Station). Irrigation Sciences and Engineering 40: 137-149.
7- Hay L. E., Wilby R.L., and Leavesley G.H. 2000. A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. JAWRA Journal of the American Water Resources Association 36: 387-397.
8- Hashemi-ana S.K., Khosravi M., and Tavousi T. 2015. Validation of AOGCMs capabilities for simulation length of dry spells under the climate change in Southwestern area of Iran. Open J Air Pollut 4: 76-85
9- Hoshyar M., Sobhani B., and Hoseyni S.A. 2017. Uranium Maximum Temperature Variation Vision Using the CanESM2 Model Output. Geography and Planning Journal 22: 305-325.
10- Javidi Sabaghian R., and Sharifi M.B. 2009. Using random models in river flow simulation and forecasting annual average annual discharge of the river by time series analysis. Iran Water Resources Management Conference. number 1.
11- Khalil A. 2015. Quantitative Investigation and Modeling of Agricultural Damage Risk of Flood Precipitation in Iran. Agricultural Meteorology Journal 3(2): 33-24.
12- Khalili A., and Bazrafshan J. 2006. Drought persistence and survival assessment using annual precipitation data at Iranian stations. Proceedings of the Second Conference on Water Resources Management. 23 and 24 January . Esfahan.
13- Khalil N., Rezaee Pazhand H., Derakhshan H., and Davari K. 2017. Development of a framework for assessing agricultural drought risk on wheat. Iranian Water Resources Research 14: 59-70
14- Kavakebi GH., Mousavi Baygi M., Mosaedi A., and Jabbari Noghabi M. 2013. Determination of effective factors on drought occurrence by analyzing panel data (Case study: Khorasan Razavi province). Water and Soil 6: 1298-1310.
15- Lehner F., Coats S., Stocker T.F., Pendergrass A.G., Sanderson B.M., Raible C.C., and Smerdon J.E. 2017. Projected drought risk in 1.5 C and 2 C warmer climates. Geophysical Research Letters 44: 7419-7428.
16- Mckee T B., Doesken N J., and Kleist J. 1993. The relationship of drought frequency and duration to time scales. Paper presented at the Proceedings of the 8th Conference on Applied Climatology.
17- Mosaedi A., Kavakebi GH., and Abdollahzade S. 2010. Detection of climate change based on Mann-Whitney statistical test in Mashhad. First National Conference on Meteorology and Water Management.
18- Mostafazade R., and Zabihi M. 2015. Analysis and comparison of SPI and SPEI indices in meteorological drought evaluation using R software (Case study: Kurdistan province). Physics of Earth and Space 42: 633-643.
19- Pandey S., Pandey A., Nathawat M., Kumar M., and Mahanti N. 2012. Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, India. Natural Hazards 63: 279-303.
20- PEI W., FU Q., LIU D., LI T.-X., CHENG K., and CUI S. 2017. Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China. Theoretical and Applied Climatology 1-14.
21- Pittock A.B. 2003. Climate change: an Australian guide to the science and potential impacts.
22- Portahermi M., Roknabadieftekhari A., and Kazemi N. 2013. The Role of Drought Risk Management Approach in Reducing Economic-Social Vulnerability of Rural Farmers (From the Perspectives of Officials and Experts) Case Study: Sulduz Village, West Azarbaijan. Rural Research Quarterly, 1.
23- Prathumchai K., Honda K., and Nualchawee K. 2001. Drought risk evaluation using remote sensing and GIS: a case study in Lop Buri Province. 22nd Asian conference on remote sensing. 9.
24- Sabziparvar A.A., Saghaie S., and Nozari H. 2012. Comparison of the Hargreaves-Somoni Reference Flow Evaporation and the FAO Governorate 56 on the Scale of the Karkheh Basin.
25- Shahid S., and Behrawan H. 2008. Drought risk assessment in the western part of Bangladesh. Natural Hazards 46: 391-413.
26- Solomon S., Qin D., Manning M., Chen Z., Marquis M., Averyt K.B., Tignor M., and Miller H.L. 2007. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, 2007. Cambridge University Press, Cambridge.
27- Soltani Gerdfaramarzi S., Saberi A., and Gheysori M. 2016. Determine the best time series model for predicting annual precipitation of selected stations in West Azarbaijan province. Journal of Applied Geosciences Research.
28- Tigkas D., Vangelis H., and Tsakiris G. 2017. An Enhanced Effective Reconnaissance Drought Index for the Characterisation of Agricultural Drought. Environmental Processes. 4(1): 137–148.
29- Tsakiris G. 2007. Practical application of risk and hazard concepts in proactive planning. European Water 19: 47-56.
30- Vicent-serrano S.M., Begueria S., and Lopez-moreno J.I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate 23: 1696-1718.
31- Wentz F.J., Ricciardulli L., Hilburn K., and Mears C. 2007. How much more rain will global warming bring? Science 317: 233-235.
32- Wilhite D. 1992. Drought management and climate change. Contractors’ report prepared for the Office of Technology Assessment, Washington, DC.
33- Wilhite D.A., Hayes M.J., Knutson C., and Smith K.H. 2000. Planning for Drought: Moving From Crisis to Risk Management 1. JAWRA Journal of the American Water Resources Association 36: 697-710.
34- Wu H., and Wilhite D.A. 2004. An operational agricultural drought risk assessment model for Nebraska, USA. Natural Hazards 33: 1-21.
35- Yuan X.-C., Zhou Y.L., Jin J.L., and Wei Y.-M. 2013. Risk analysis for drought hazard in China: a case study in Huaibei Plain. Natural Hazards 67: 879-900.
CAPTCHA Image