پیش‌نگری بارش‌های فرین در حوضه دریاچه ارومیه تحت شرایط تغییر اقلیم

آب و خاک

مقاله 8، دوره 37، شماره 5 - شماره پیاپی 91، آذر و دی 1402، صفحه 769-785   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jsw.2023.81891.1270

نویسندگان

نسرین ابراهیمی؛ آذر زرین^* ؛ عباس مفیدی؛ عباسعلی داداشی رودباری

گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

سطح آب و وسعت دریاچه ارومیه در طی سال­های اخیر نسبت به میانگین بلند مدت، کاهش چشمگیری داشته و ادامه حیات آن را با تهدید جدی مواجه کرده است. لذا بررسی دقیق وضعیت بارش حوضه و پیش­نگری آن در آینده به‌عنوان یکی از مهم‌ترین متغیرهای اقلیمی اثرگذار در برنامه­ریزی­های آینده ضروری است. این پژوهش با هدف بررسی وضعیت بارش­های فرین حوضه دریاچه ارومیه در آینده نزدیک انجام شده است. برای این منظور از داده­های بارش پنج مدل از پروژه مقایسه مدل‌های جفت‌شده فاز ششم (CMIP6) تحت سه سناریو SSP1-2.6، SSP3-7.0 و SSP5-8.5 طی دوره تاریخی (2014-1990) و آینده نزدیک (2050-2026) با تفکیک افقی 5/0 درجه قوسی استفاده شده است. برای کاهش خطای مدل­های منفرد، یک مدل همادی (CMIP6-MME) بر اساس روش میانگین­گیری بیزین (BMA) از مدل‌های منفرد تولید شد. درستی مدل­های منفرد CMIP6 و مدل CMIP6-MME با دو سنجه میانگین اریبی خطا (MBE) و مجذور میانگین مربعات خطای بهنجار شده (NRMSE) مورد بررسی قرار گرفت. نتایج نشان داد مدل­های منفرد در برآورد بارش در حوضه دریاچه ارومیه کم­برآوردی دارند. مدل همادی تولید شده مقدار دو سنجه MBE و NRMSE را در سطح حوضه به مقدار قابل توجهی کاهش داد که بر این اساس نسبت به مدل­های منفرد از کارایی بالاتری برخوردار است. یافته­ها بیانگر آن است که، حوضه دریاچه ارومیه روزهای همراه با بارش سنگین و خیلی سنگین بیش­تری را در آینده نزدیک تجربه خواهد نمود. شدت بارش روزانه در بخش­های بزرگی از حوضه، بخصوص در مناطق غربی و شمالی، روند افزایشی خواهد داشت. بطور کلی ریسک ناشی از بارش­های سیل‌آسا در حوضه دریاچه ارومیه در دوره آینده نزدیک بسیار محتمل است که لازم است برنامه­های اقدام اقلیمی و پیش­گیرانه همانند مدیریت ریسک اقلیمی در اولویت برنامه‌ریزی­های مرتبط با این منطقه باشد.

کلیدواژه‌ها

تغییر اقلیم؛ حوضه دریاچه ارومیه؛ فرین‌های بارشی؛ مدل‌های CMIP6

مراجع

1. Afrouzeh, M. (2018). Analysis of the activity of Rossby waves in the Northern Hemisphere and its relationship with the temporal and spatial variability of precipitation in the Middle East. M.S Thesis in climatology, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran. (In Persian)
2. Alavinia, S.H., & Zarei, M. (2021). Analysis of spatial changes of extreme precipitation and temperature in Iran over a 50‐year period. International Journal of Climatology, 41, E2269-E2289. https://doi.org/10.1002/joc.6845
3. Alexander, L.V., & Arblaster, J.M. (2017). Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Weather and Climate Extremes, 15, 34-56. https://doi.org/10.1016/j.wace.2017.02.001.
4. Asadi Rahim-Begi, N., Zarrin, A., Modfidi, A., & Dadashi-Roudbari, A. (2022). Seasonal Distribution Analysis of Extreme Precipitation in Iran using AgERA5 dataset. Iranian Journal of Soil and Water Research, 52(11), 2723-2737. (In Persian). https://doi.org/10.22059/ijswr.2021.333263.669118.
5. Azarm, K., Mofidi, A., & Khorshiddoust, A.M. (2019). Investigation of the mesoscale mehcanisms for the occurrence of convective precipitation in the North West of Iran. Journal of the Erath and Space Physics, 45(3), 553-573. (In Persian). https://doi.org/10.22059/JESPHYS.2019.261456.1007023.
6. Bai, H., Xiao, D., Wang, B., Liu, D.L., Feng, P., & Tang, J. (2021). Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain. International Journal of Climatology, 41, E171-E186. https://doi.org/10.1002/joc.6674.
7. Chamanehfar, S., Mousavi Baygi, M., Babaeian, I., & Modaresi, F. (2022). Future projection for extreme indices of precipitation and temperature over the period 2026-2100 based on the output of CMIP6 models (Case study: Mashhad). Iranian Journal of Irrigation & Drainage, 16(5), 963-976. (In Persian). https://dorl.net/dor/20.1001.1.20087942.1401.16.5.7.2.
8. Cohen, J., Screen, J.A., Furtado, J.C., Barlow, M., Whittleston, D., & Comou, D. (2014). Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7, 627–637. https://doi.org/10.1038/ngeo2234
9. Cohen, J., Zhang, X., Francis, J., Jung, T., Kwok, R., Overland, J., & Yoon, J. (2020). Divergent consensuses on Arctic amplification influence on mid-latitude severe winter weather. Nature Climate Change, 10(1), 20-29. https://doi.org/10.1038/s41558-019-0662-y
10. Coulibaly, P., Dibike, Y.B., & Anctil, F. (2005). Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology, 6(4), 483-496. https://doi.org/10.1175/JHM409.1
11. Deng, X., Perkins‐Kirkpatrick, S.E., Lewis, S.C., & Ritchie, E.A. (2021). Evaluation of extreme temperatures over Australia in the historical simulations of CMIP5 and CMIP6 models. Earth's Future, 9(7), e2020EF001902. https://doi.org/10.1029/2020EF001902
12. Dey, A., Sahoo, D.P., Kumar, R., & Remesan, R. (2022). A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin. International Journal of Climatology, 42(16), 9215-9236. https://doi.org/10.1002/joc.7813
13. Dike, V.N., Lin, Z., Fei, K., Langendijk, G.S., & Nath, D. (2022). Evaluation and multimodel projection of seasonal precipitation extremes over central Asia based on CMIP6 simulations. International Journal of Climatology, 42(14), 7228-7251. https://doi.org/10.1002/joc.7641
14. Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., & Taylor, K.E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
15. Faye, A., & Akinsanola, A.A. (2022). Evaluation of extreme precipitation indices over West Africa in CMIP6 models. Climate Dynamics, 58(3-4), 925-939. https://doi.org/10.1007/s00382-021-05942-2
16. Fischer, E.M., & Knutti, R. (2015). Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560-564. http://dx.doi.org/10.1038/NCLIMATE2617.
17. Fowler, H.J., Lenderink, G., Prein, A.F., Westra, S., Allan, R.P., Ban, N., & Zhang, X. (2021). Anthropogenic intensification of short-duration rainfall extremes. Nature Reviews Earth & Environment, 2(2), 107-122. https://doi.org/10.1038/s43017-020-00128-6.
18. Francis, J.A., & Vavrus, S.J. (2012). Evidence linking Arctic amplification to extreme weather in mid‐ Geophysical Research Letters, 39(6), L06801.https://doi.org/10.1029/2012GL051000
19. Francis, J.A., & Vavrus, S.J. (2015). Evidence for a wavier jet stream in response to rapid Arctic warming. Environmental Research Letters, 10, https://doi.org/10.1088/1748-9326/10/1/014005
20. Goodman, S. J., Blakeslee, R., Christian, H., Koshak, W., Bailey, J., Hall, J., & Gatlin, P. (2005). The North Alabama lightning mapping array: Recent severe storm observations and future prospects. Atmospheric Research, 76(1-4), 423-437. https://doi.org/10.1016/j.atmosres.2004.11.035.
21. Guilbert, J., Betts, A.K., Rizzo, D.M., Beckage, B., & Bomblies, A. (2015). Characterization of increased persistence and intensity of precipitation in the northeastern United States. Geophysical Research Letters 42(6), 1888-1893. https://doi.org/10.1002/2015GL063124.
22. Hasemi, M. (2011). A socio-technical assessment framework for integrated water resources management (IWRM) in Lake Urmia Basin, Iran (Doctoral dissertation, Newcastle University).
23. IPCC (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and 248 Sectoral Aspects. In Contribution of working group II to the fifth assessment report of the 249 intergovernmental panel on climate change (pp. 1–1132). Cambridge, UK, New York, NY: Cambridge University Press.
24. IPCC, A. (2013). Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, 1535.
25. (2021). Summary for policymakers. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, et al. (Eds.), Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change (pp. 3–32). Cambridge University Press. https://doi.org/10.1017/9781009157896.001
26. Katiraie‐Boroujerdy, P.S., Akbari Asanjan, A., Chavoshian, A., Hsu, K.L., & Sorooshian, S. (2019). Assessment of seven CMIP5 model precipitation extremes over Iran based on a satellite‐based climate data set. International Journal of Climatology, 39(8), 3505-3522. https://doi.org/10.1002/joc.6035.
27. Khorshiddoust, A.M., Mofidi, A., Rasouli, A.A., & Azarm, K. (2016). A Synoptic analysis for the occurrence of springtime heavy rainfall in the Northwest of Iran. Journal of Natural Environmental Hazards, 5(8), 53-82. (In Persian)
28. Li, Z., Liu, T., Huang, Y., Peng, J., & Ling, Y. (2022). Evaluation of the CMIP6 precipitation simulations over global land. Earth's Future, 10(8): e2021EF002500. https://doi.org/10.1029/2021EF002500
29. Liu, J., Hertel, T.W., Diffenbaugh, N.S., Delgado, M.S., & Ashfaq, M. (2015). Future property damage from flooding: sensitivities to economy and climate change. Climatic Change, 132, 741-749. https://doi.org/10.1007/s10584-015-1478-z.
30. Malaekeh, S., Safaie, A., Shiva, L., & Tabari, H. (2022). Spatio-temporal variation of hydro-climatic variables and extreme indices over Iran based on reanalysis data. Stochastic Environmental Research and Risk Assessment, 36(11), 3725-3752. https://doi.org/10.1007/s00477-022-02223-0
31. Masoudian, S.A. (2005). Recognition of precipitation regimes of Iran using cluster analysis. Geographical Research Quarterly, 37(52), 47-59. (In Persian)
32. Meresa, H., Tischbein, B., & Mekonnen, T. (2022). Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models. Natural Hazards, 111(3), 2649-2679. https://doi.org/10.1007/s11069-021-05152-3
33. Modaresi, F., & Araghi, A. (2023). Projecting future reference evapotranspiration in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 1-12. https://doi.org/10.1007/s00704-023-04465-6
34. Nie, Y., & Sun, J. (2022). Moisture sources and transport for extreme precipitation over Henan in July 2021. Geophysical Research Letters, 49(4), e2021GL097446. https://doi.org/10.1029/2021GL097446
35. Overland, J.E., Ballinger, T.J., Cohen, J., Francis, J.A., Hanna, E., Jaiser, R., & Zhang, X. (2021). How do intermittency and simultaneous processes obfuscate the Arctic influence on midlatitude winter extreme weather events? Environmental Research Letters, 16(4), 043002. https://doi.org/10.1088/1748-9326/abdb5d
36. Pangaluru, K., Velicogna, I., C Sutterley, T., Mohajerani, Y., Ciraci, E., Sompalli, J., & Saranga, V.B.R. (2018). Estimating changes of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution. Hydrology and Earth System Sciences Discussions, 1-33. https://doi.org/10.5194/hess-2018-522
37. Pimonsree, S., Kamworapan, S., Gheewala, S.H., Thongbhakdi, A., & Prueksakorn, K. (2023). Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia. Atmospheric Research, 282, https://doi.org/10.1016/j.atmosres.2022.106522
38. Raftery, A.E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133(5), 1155-1174. https://doi.org/10.1175/MWR2906.1
39. Ryan, C., Curley, M., Walsh, S., & Murphy, C. (2022). Long‐term trends in extreme precipitation indices in Ireland. International Journal of Climatology, 42(7), 4040-4061. https://doi.org/10.1002/joc.7475
40. Screen, J.A., & Simmonds, I. (2014). Amplified mid-latitude planetary waves favour particular regional weather extremes. Nature Climate Change, 4, 704–709. https://doi.org/10.1038/nclimate2271
41. Screen, J.A., Deser, C., Smith, D.M., Zhang, X., Blackport, R., Kushner, P.J., & Sun, L. (2018). Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nature Geoscience, 11(3), 155-163. https://doi.org/10.1038/s41561-018-0059-y
42. Seneviratne, S.I., & Hauser, M. (2020). Regional climate sensitivity of climate extremes in CMIP6 versus CMIP5 multimodel ensembles. Earth's Future, 8(9), e2019EF001474. https://doi.org/10.1029/2019EF001474.
43. Sorooshian, S., Duan, Q., & Gupta, V. K. (1993). Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model. Water Resources Research, 29(4), 1185-1194. https://doi.org/10.1029/92WR02617.
44. Tabari, H. (2020). Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10(1), 1-10. https://doi.org/10.1038/s41598-020-70816-2
45. Tangang, F., Juneng, L., & Aldrian, E. (2017). Observed changes in extreme temperature and precipitation over Indonesia. International Journal of Climatology, 37(4), 1979-1997. https://doi.org/10.1002/joc.4829
46. Thackeray, C.W., Hall, A., Norris, J., & Chen, D. (2022). Constraining the increased frequency of global precipitation extremes under warming. Nature Climate Change, 12(5), 441-448. https://doi.org/10.1038/s41558-022-01329-1
47. Trenberth, K. E. (2011). Changes in precipitation with climate change. Climate Research, 47(1-2), 123-138. https://doi.org/10.3354/cr00953
48. WMO (2020). WMO statement on the state of the global climate in 2019. https://library.wmo.int/doc_num.php?explnumid=10211
49. Xu, H., Chen, H., & Wang, H. (2022). Future changes in precipitation extremes across China based on CMIP6 models. International Journal of Climatology, 42(1), 635-651. https://doi.org/10.1002/joc.7264
50. Xu, P., Wang, L., & Ming, J. (2022). Central Asian precipitation extremes affected by an intraseasonal planetary wave pattern. Journal of Climate, 35(8), 2603-2616. https://doi.org/10.1175/JCLI-D-21-0657.1
51. Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250, 105369. https://doi.org/10.1016/j.atmosres.2020.105369
52. Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144, 643-660. https://doi.org/10.1007/s00704-021-03568-2
53. Zarrin, A., & Dadashi-Roudbari, A. (2022a). Spatiotemporal variability, trend, and change-point of precipitation extremes and their contribution to the total precipitation in Iran. Pure and Applied Geophysics, 179(8), 2923-2944. https://doi.org/10.1007/s00024-022-03098-6
54. Zarrin, A., & Dadashi-Roudbari, A. (2022b). Evaluation of reanalysis-based, satellite-based, and “bias-correction”-based datasets for capturing extreme precipitation in Iran. Meteorology and Atmospheric Physics, 134(4), 67. https://doi.org/10.1007/s00703-022-00903-8
55. Zarrin, A., & Dadashi-Roudbari, A. (2023). Evaluation of CMIP6 models in estimating temperature in Iran with emphasis on equilibrium climate sensitivity (ECS) and transient climate response (TCR). Iranian Journal of Geophysics, 17(1), 39-56. (In Persian). http://doi.org/10.30499/ijg.2022.344862.1430
56. Zarrin, A., Dadashi-Roudbari, A., & Hassani, S. (2022a). Future changes in precipitation extremes over Iran: Insight from a CMIP6 bias-corrected multi-model ensemble. Pure and Applied Geophysics, 179, 441-464. https://doi.org/10.1007/s00024-021-02904-x
57. Zarrin, A., Dadashi-Roudbari, A., & Kadkhoda, E. (2022b). Drought projection in the Urmia Lake basin under SSP Scenarios until the End of the 21st Century. Iranian Journal of Soil and Water Research, 53(7), 1499-1516. (In Persian). http://doi.org/10.22059/ijswr.2022.343700.669278
58. Zeder, J., & Fischer, E.M. (2020). Observed extreme precipitation trends and scaling in Central Europe. Weather and Climate Extremes, 29, 100266. https://doi.org/10.1016/j.wace.2020.100266
59. Zhang, W., Furtado, K., Wu, P., Zhou, T., Chadwick, R., Marzin, C., & Sexton, D. (2021). Increasing precipitation variability on daily-to-multiyear time scales in a warmer world. Science Advances, 7(31), eabf8021. https://doi.org/10.1126/sciadv.abf8021
60. Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., & Zwiers, F.W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), 851-870. https://doi.org/10.1002/wcc.147
61. Zhao, Y., Qian, C., Zhang, W., He, D., & Qi, Y. (2021). Extreme temperature indices in Eurasia in a CMIP6 multi‐model ensemble: Evaluation and projection. International Journal of Climatology, 41(11), 5368-5385. https://doi.org/10.1002/joc.7134