نوع مقاله : مقاله پژوهشی
نویسندگان
1 استاد گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل
2 دانشجوی دکتری آب و هواشناسی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل
3 استادیار گروه جغرافیا، دانشکده علوم انسانی، دانشگاه فردوسی مشهد، مشهد
چکیده
در این پژوهش، دورنمای تبخیر-تعرق مرجع (ETo) بخش جنوبی حوزه آبخیز رودخانه ارس تحت شرایط تغییر اقلیم با استفاده از ریزگردان SDSM ترسیم شد. برای این منظور، از دادههای هواشناسی ایستگاههای سینوپتیک منتخب واقع در این حوضه استفاده شد. پس از دریافت خروجی ریزگردانی شده برای پارامترهای مورد نیاز برای برآورد ETo به روش پنمن-مانتیث فائو شماره 56 برای آینده نزدیک (2021 تا 2050 میلادی) اقدام به محاسبه آن شد. در این راستا، از دادههای روزانه بازتحلیل NCEP و دادههای ایستگاهی کمینه و بیشینه دما، سرعت باد، رطوبت نسبی و ساعات آفتابی در مقیاس روزانه و نیز دادههای خروجی مدل CanESM2 تحت سناریوهای RCPs برای تولید دادههای ایستگاهی آینده برای تخمین ETo حوضه ارس استفاده شد. ایستگاههای مورد مطالعه شامل اهر، اردبیل، پارسآباد، جلفا، خوی و ماکو بود و دوره پایه برای دادههای مورد نظر 2005-1985 در نظر گرفته شد. ابتدا، کارایی SDSM در شبیهسازی پارامترهای مورد نیاز برای تخمین ETo از طریق مقایسه دادههای شبیهسازی شده NCEP با دادههای ایستگاهی ارزیابی شد. مقایسه آنها نشاندهنده کارایی مناسب مدل در شبیهسازی دادهها بود. لذا، پارامترهای اقلیمی با استفاده از مدل CanESM2 تحت سناریوهای RCP برای آینده شبیهسازی شده، پس از محاسبه مقادیر ماهانه آنها، برای تخمین ETo حوضه به CROPWAT وارد شده و مقدار و روند متغیر برای سه دهه آتی محاسبه شد. نتایج نشان داد، ETo حوضه در دوره آتی نسبت به دوره پایه بهطور متوسط حدود هفت میلیمتر در سال افزایش خواهد یافت. برحسب ایستگاهی نیز ETo در پارسآباد (102 میلیمتر) و جلفا (66 میلیمتر) افزایشی خواهد بود که این افزایش به معنی افزایش نیاز آبی گیاهان در آینده نیز است. همچنین، روند آتی ETo در خوی، ماکو، اهر و اردبیل کاهشی خواهد بود.
کلیدواژهها
عنوان مقاله [English]
Evaluation perspective of the Aras Basin reference crop evapotranspiration in future climatic condition under RCPs scenarios
نویسندگان [English]
- Bromand Salahi 1
- Mahnaz Saber 2
- Abbas Mofidi 3
1 Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil
2 PhD. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil
3 Assistant professor of Climatology, Department of Geography, Faculty of Humanities, Ferdowsi University of Mashhad, Mashhad
چکیده [English]
In this study, the perspective of reference crop evapotranspiration (ETo) in the southern part of the Aras River Basin under climate change condition was drawn using SDSM downscaling. For this purpose, meteorological data of selected synoptic stations located in this basin were used and after receiving the downscaling outputs for the parameters required for estimating ETo by Penman-Monteith FAO-56, basin ETo was calculated for the near future (2021-2050). In this regard, daily reanalysis NCEP data and station daily data include: maximum and minimum temperature, wind speed, relative humidity and sunshine hours as well as output data on CanESM2 model under RCPs scenarios were used to generate future station data for estimate Aras Basin ETo. The studied stations included: Ahar, Ardabil, Parsabad, Jolfa, Khoy and Makoo and the base period for the data was considered 1985-2005. First, the efficiency of SDSM in simulating the parameters required for ETo estimation was evaluated by comparing NCEP simulated data with station data. Their comparison indicated the appropriate performance of the model in simulating data. Therefore, climatic parameters were simulated using the CanESM2 model under RCPs scenarios for the future. After calculating their monthly values, in CROPWAT was entered to estimate the basin ETo and trend of the variable for the next three decades were calculated. The results showed that the basin ETo in the future period compared to the base period will increase by an average of about 7 mm per year. In terms of stations, there will be an increase in Parsabad (102 mm) and Jolfa (66 mm). This increase also means an increase in the water needs of plants. Also, the future trends of ETo in Khoy, Makoo, Ahar and Ardabil will be decreasing.
کلیدواژهها [English]
- Climate change
- Climatic models
- Downscaling
- Penman-Monteith FAO
- SDSM
- Ahmadi, A., A. Khoramian and H.R. Safavi. 2015. Assessment of climate change impacts on snow-runoff processes a case study: Zayandehroud River Basin. Iran-Water Resources Research, 2(33): 70-82 (in Persian).#2.Chen, H., C.Y. Xu and S. Guo. 2012. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, 434– 435: 36–45.#3.Dibike, Y.B. and P. Coulibaly. 2005. Hydrologic impact of climate change in the Saguenay Watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology, 307: 145-163.#4.Farrokhzadeh, B., S. Choobeh and O.B. Bazrafshan. 2021. Assessing the climate change effects on Standardized Precipitation Evapotrancpiration Index (SPEI), case study: Latian Dam. Journal of Rainwater Catchment Systems, 8(26): 56-72.#5.Gagnon, S., B. Singh, J. Rousselle and L. Roy. 2005. An application of the Statistical Down Scaling Model (SDSM) to simulate climatic data for streamflow modelling in Quebec. Canadian Water Resources Journal, 30(4): 297-314.#6.Gebremeskel, G. and A. Kebedeb. 2018. Estimating the effect of climate change on water resources: integrated use of climate and hydrological models in the Werii Watershed of the Tekeze River Basin, Northern Ethiopia. Agriculture and Natural Resources, 52(2): 195-207.#7.Germezcheshmeh, B., A. Rasuli, M. Rezaei Banafsheh, A. Massah Bavani and A. Khorshiddust. 2014. Impact assessment of morpho-climatic parameters in accuracy of SDSM. Watershed Engineering and Management, 6(2): 155-164 (in Persian).#8.Goudarzi, M., B. Salahi and A. Hosseini. 2016. Performance assessment of LARS-WG and SDSM downscaling models in simulation of climate changes in Urmia Lake Basin. Iranian Journal of Watershed Management Science and Engineering, 9(31): 11-22 (in Persian).#9.Graham, P., S. Hagemann, S. Juan and M. Beniston. 2007. On interpreting hydrological change from regional climate models. Journal of Climate Change, 81: 97-122.#10.Hashmi, M.Z., A.Y. Shamseldin and W. Melville. 2011. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, 25: 475-484.#11.Heshmati, F. and N. Sayari. 2021. Projected changes of potential evapotranspiration under RCP climate change scenarios, case study: Bandar Anzali. Journal of Agricultural Meteorology, 9(1): 63-76 (in Persian).#12.Javadizadeh, F., P. Kardavani, B. Alijani and F. Asadian. 2018. Efficiency of SDSM statistical downscaling models in predicting temperature parameters. Physical Geography, 11(42): 47-66 (in Persian).#13.Lio, L., Z.H. Lio, X. Ren, T. Fischer and Y. Xu. 2011. Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model. Quaternary International, 244: 211-220.#14.Meenu, R., S. Rehana and P.P. Mujumdar. 2012. Assessment of hydrologic impacts of climate change in Tunga–Bhadra River Basin, India with HEC-HMS and SDSM. Hydrological Processes, 27(11): 1572-1589.#15.Pervez, M., H. Shahriar and M. Geoffrey. 2014. Projections of the Ganges–Brahmaputra precipitation-downscaled from GCM predictors. Journal of Hydrology, 517: 120-134.#16.Rezaei, M., M. Nohtani, A. Moghaddamnia, A. Abkar and M. Rezaei. 2014. Performance Evaluation of Statistical Downscaling Model (SDSM) in forecasting precipitation in two arid and hyper arid regions. Journal of Water and Soil (Agricultural Sciences and Technology), 28(4): 836-845 (in Persian).#17.Roohipanah, F. 2013. Capability assessment of SDSM Model in downscaling of temperature and precipitation in hot and dry climate, case study: synoptic stations of Yazd and Tabass. MSc Thesis. (in Persian).#18.Shamsipour, A. 2013. Climatic modeling (theory and method). University of Tehran Press (in Persian).#19.Sobhani, B., M. Eslahi and I. Babaeian. 2016. Efficiency of statistical downscaling models of SDSM and LARS-WG in the simulation of meteorological parameters in Urmia Lake Basin. Physical Geography Research Quarterly, 47(4): 499-516 (in Persian).#20.Subbarao, P., R. Rajendra Prasad, M. Rajib and K. Harald. 2017. Development of a method to identify change in the pattern ofextreme streamflow events in future climate: application on the Bhadra Reservoir inflow in India. Journal of Hydrology: Regional Studies, 9: 236-246.#21.Timbal, B., E. Fernandez and Z. Li. 2009. Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environmental Modelling and Software, 24: 341–358.#22.Toews, M.W. and D.M. Allen. 2009. Evaluating different GCMs for predicting spatial recharge in an irrigated arid region. Journal of Hydrology, 374(3-4): 265-281.#23.Weilby, L.R. and C.W. Dawson. 2007. User manual SDSM: version 2.2-A decision support tool for the assessment of climate impacts, 1-94.#24.Wilby, R.L., C.W. Dawson and E.M. Barrow. 2002. SDSM- a decision suport tool for the assessment of regional climate change impacts. Journal of Environmental Modeling and Software, 17: 147-159.#25.Zoheyri, Z., R. Ghazavi, E. Omidvar and A. Davudirad. 2020. Comarison of LARS-WG and SDSM downscaling models for prediction temperature and precipitation changes under RCP scenarios. Arid Region Geographic Stdudies, 10(40): 39-52 (in Persian).#26.Zorrati-Poor, E., A. Soltani Mahammadi and F. Baradaran. 2017. The effect of climate change on the trend of increasing temperature and evapotranspiration potential using SDSM Model in Ahvaz City. Journal of Water Science and Engineering, 7(18): 47-56 (in Persian).#27.Zulkarnain, , S. Supiah and H. Sabri. 2014. Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and Applied Climatology, 116: 243-257.