ارزیابی شاخص خشکسالی فیزیک مبنای تقاضای تبخیری در اقلیم‌های مختلف ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب-دانشکده کشاورزی- دانشگاه فردوسی مشهد-ایران

2 استاد گروه علوم و مهندسی آب- دانشکده کشاورزی- دانشگاه فردوسی مشهد

3 استادیار گروه جغرافیا، دانشکده ادبیات، دانشگاه فردوسی مشهد

چکیده

دسترسی به ابزاری که بتواند بر اساس پویایی روابط بین سطح زمین و جو و نه لزوما بارش و دمای هوا، خشکسالی­های کشاورزی و هیدرولوژیکی را اندازه­گیری کند و آگاهی­های زودهنگام را جهت اخذ تصمیم­گیری­های مدیریتی ارائه دهد، ضروری است. در این راستا، نیاز به انواع داده­های شبکه­بندی شده­ای است که بتواند کمبودهای شبکه غیر یکنواخت داده­های زمینی یا محدودیت­های داده­های ماهواره­ای را جبران نماید. مفهوم تقاضای تبخیری جو (EDDI) نشانگر تشنگی جو است و بر اساس محرک­های اقلیمی فیزیکی دمای هوا، سرعت باد، تابش خورشیدی و رطوبت به راحتی و در زمان نزدیک به واقعی قابل دسترس است. در این تحقیق به منظور برآورد شاخص خشکسالی تقاضای تبخیری در شرایط مختلف اقلیمی ایران، از داده های شبکه بندی شده تحلیل مجدد مدل ERA-Interim از پایگاه ECMWF  طی سال­های 2017-1979استفاده شد و توانایی این شاخص در پایش خشکسالی هیدرولوژیکی در برابر شاخص­های رایج خشکسالی SPI و SPEI مورد ارزیابی قرار گرفت. ضرایب همبستگی قوی و معنی­دار در مقیاس­های ماهانه، فصلی و سالانه بین شاخص EDDI با SPEI مبین نقش مهم تبخیر تعرق در پایش خشکسالی در مناطق خشک و نیمه خشک است و می­تواند ضعف شاخصSPI  در مناطق کم بارش را جبران کند. این شاخص توانایی پایش خشکسالی­های کوتاه مدت و ماندگار را زودتر از دیگر شاخص­های رایج نظیر SPI و SPEI دارد که این شاخص را پیشرو می­کند. شاخص EDDI ابزاری آسان برای اجرای هشدار زودهنگام عملیاتی و کنترل طولانی مدت خشکسالی هیدرولوژیکی است. همچنین با پایش در هر مقیاس زمانی( به طور مثال فصلی) می­توان از نتایج آن برای پیش­آگاهی دوره­های طولانی­تر( سالانه یا چند ساله) استفاده نمود و شکاف بین پیش­ بینی­های کوتاه مدت و فصلی را جبران می­کند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Physical Evaporative Demand Drought Index in Different Climates of Iran

نویسندگان [English]

  • Azam Arabi Yazdi 1
  • Seyed Hossein Sanaei Nejad 2
  • Abas Mofidi 3
1 water engineering Department, Ferdowsi University of Mashhad, Iran
2 Professor, Water Engineering, College of Agriculture, Ferdowsi University of Mashhad
3 Assistant Prof. Ferdowsi University of Mashad
چکیده [English]

Introduction: Drought is a creeping and gradual phenomenon that can cause irreparable damage in many fields such as agriculture, food security, water resources management and the economy. It is essential to accessing a tool that can measure agricultural and hydrological droughts based on the dynamics of the relationship between land surface-atmosphere -not necessarily precipitation and air temperature- and provide early awareness for managerial decision-making. What makes drought monitoring important to us is the impacts on the agricultural sector (agricultural productivity, access to food security, agricultural and livestock insurance), water supply management and economic and social impacts. The concept of evaporative demand drought index (EDDI) reflects thirst for the atmosphere and is easily and realistically available in near-real-time based on physical climatic drivers of air temperature, wind speed, solar radiation and humidity, providing comprehensive information on drought dynamics.  The use of an indicator that can indicate the dynamics of drought in the shortest possible time will help to make managerial decisions and different levels of policy making to announce early operational warnings in the field of agriculture and reduce the social and economic consequences of this phenomenon. In this regard, there is a need for gridded data that can provide the required data set and compensate for non-uniform network data gaps or satellite data limitations. near-real-time networked data such as ERA-Interim is also useful in regional and extensive drought monitoring.
Material and methods: In this study, the ERA-Interim reanalysis data from ECMWF database was used to estimate the evaporative demand drought index in different climatic conditions of Iran and its ability in drought monitoring was investigated. Using probabilistic methods, the ASCI-PM method to estimate atmospheric evaporation demand, the EDDI index was calculated as the index of hydrological drought in Iran in different time scales during 1979- 2017. Also, the EDDI index was evaluated against the SPI and SPEI common drought indices.
Result and discussion: The results of evaporation demand estimates in the country show that seasonal variation and climate variability are factors that change the rate of evaporation demand. As a result of the interaction of the governing climatic factors, different climatic zones are created throughout the country and each region experiences different evaporation rates throughout the year. The EDDI index compensates for the gap between the theory of drought and operational drought management in determining and monitoring persistent drought as soon as possible between the occurrence of the phenomenon and the available data available. Significant correlation coefficients at monthly, seasonal and annual scales between EDDI and SPEI index indicate the important role of evapotranspiration in drought monitoring at arid and semi-arid regions so can offset the weakness of SPI in low rainfall areas. It has the ability to monitor short, medium and long term droughts earlier than other common indices, such as SPI and SPEI. The EDDI is capable of reporting a variety of persistent droughts without the need for precipitation data. Rapid response to environmental drying and humidification, processes caused by interactions between the atmosphere and the Earth's surface, making this index more flexible and advanced than other common indices. The longer the cumulative period of drought, the greater the time the indicator progresses.
Conclusion: The EDDI indicator is an easy tool for operational early warning, fire hazards, seasonal to seasonal drought prediction and long-term hydrological drought Monitoring at any time scale (e.g. seasonally). It can also be used to predict longer periods (annual or multi-year), so compensates the gap between sub-seasonal to seasonal forecasts. An important advantage of using networked data in calculating the EDDI index is that applicable at all times of the year - on cloudy days or for areas with snow cover, to complete the data due to satellite transit times and Delay in data access - no restrictions. Since atmospheric evaporation demand in the EDDI index is considered to be the cause of the drought, when combined with satellite data, EDDI and ESI (Green Water Index) combine to show real drought stress. Give. It is also recommended that MODIS, LANDSAT and ALEXI products be used to evaluate transpiration values and compare the EDDI index with satellite data and compare the results. The EDDI index is capable of decomposing to atmospheric governing factors such as radiation and advection components. By sensitivity analysis, it is possible to determine the main governing factors such as temperature, wind, short wavelength radiation and specific humidity on drought in each region, Determining and provided additional understanding of the dynamics and assessment of drought.

کلیدواژه‌ها [English]

  • Sustainable Drought
  • Evaporative Demand Drought Index
  • Flash Drought
  • gridded data
  • ECMWF
  1.  

    1. انصاری، ح.، داوری، ک.، و ثنایی نژاد، س.ح.، 1389، ﭘﺎﻳﺶ ﺧﺸﻜﺴﺎﻟﻲ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﺑﺎرﻧﺪﮔﻲ و تبخیر و تعرق استاندارد ﺷﺪه(SEPI) ﺗﻮﺳﻌﻪ ﻳﺎفته بر اساس منطق فازی، نشریه آب و خاک، جلد 24، شماره 1، فروردین-اردیبهشت 1389، ص. 52-38.
    2. حجازی زاده، ز.، جوی زاده، س.، و م، موسوی.، 1389، بررسی سیر خشکسالی اقلیمی و اثرات آن بر کشت گندم دراستان بوشهر جغرافیا، فصلنامه علمی پژوهشی انجمن جغرافیای ایران دوره جدید، سال هشتم، شماره 24.
    3. رضیئی، ط..، ستوده، ف.، 1396، بررسی دقت مرکز اروپایی پیش­بینی­های میان مدت جوی(ECMWF) در پیش­بینی بارش مناطق مختلف اقلیمی ایران، مجله فیزیک زمین و فضا، دوره 43، شماره 1، ص 133-147
    4. یوسفی، م.، انصاری، ح.، مساعدی، ا.، صمدی، س. ز، 1396، بررسی همبستگی بین سه شاخص خشکسالی با تعدادی از پارامترهای آب و هوایی در چند نمونه اقلیمی ایران، تحقیقات منابع آب ایران، سال سیزدهم، شماره 3، 197-194.
      1. Abramowitz, M., Stegun, I. A., 1965, Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables. New York: Dover Publication; 1965.
      2. Anderson, M. C., Hain, C., Wardlow, B., Pimstein, A., Mecikalski, J. R., Kustas, W. P., 2011, Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. Journal of Climate, 24 (8), 2025–2044.
      3. Bayissa, Y., Maskey, S., Tadesse, T., Van Andel, SJ., Moges, S., Van Griensven, A., Solomatine, D., 2018, Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin Ethiopia, Geosciences, 8(3):81, doi:10.3390/geosciences8030081.
      4. Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I., McVicar, T.R., Adler, R. F, 2019, MSWEP V2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment, Bulletin of the American Meteorological Society, 100(3), 473-500, doi:10.1175/BAMS-D-17-0138.1.
      5. Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G.P., Wood, E. F, 2017a, Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modelling, Hydrology and Earth System Sciences, 21(12), 6201-6217.
      6. Budyko, M. I., 1974, Climate and life, International Geophysics Series, 18: 508.
      7. Burn, D. H., Hannaford, J., Hodgkins, G. A., Whitfield, P. H., Thorne, R., Marsh, T, 2012, Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow, Hydrological Sciences Journal, 57(8), 1580-1593., doi:10.1080/02626667.2012.728705.
      8. Burton, I., Kates, R. W. and White, G. F., 1978, the Environment as Hazard, Oxford University Press, 240 pp.
      9. Chaudhuri, A. H., Ponte, R. M., Forget, G., Heimbach, P., 2013, A comparison of atmospheric reanalysis surface products over the ocean and implications for uncertainties in air–sea boundary forcing, Journal of Climate, 26(1), 153-170, doi:10.1175/JCLI-D-12-00090.1.
      10. Chen, H., Sun, J., 2015, Changes in Drought Characteristics over China Using the Standardized Precipitation Evapotranspiration Index, Journal of Climate, 28:5430-5447, doi:10.1175/jcli-d-14-00707.1.
      11. Chen, H., and J. Sun, 2015: Changes in drought characteristics over China using the standardized precipitation evapotranspira tion index. J. Climate, 28, 5430–5447, https://doi.org/10.1175/JCLI-D-14-00707.1
      12. Chen, H., and J. Sun, 2015: Changes in drought characteristics over China using the standardized precipitation evapotranspira-Tion index. J. Climate,28, 5430–5447, https://doi.org/10.1175/JCLI-D-14-00707.1
      13. Chen, H., and J. Sun, 2015: Changes in drought characteristics over China using the standardized precipitation evapotranspira tion index. J. Climate,28, 5430–5447, https://doi.org/10.1175/JCLI-D-14-00707.1
      14. Chen, H., and J. Sun, 2015: Changes in drought characteristics over China using the standardized precipitation evapotranspira tion index. J. Climate,28, 5430–5447, https://doi.org/10.1175/JCLI-D-14-00707.1
      15. Chen, J., Brissette, F. P., & Chen, H., 2018, Using reanalysis-driven regional climate model outputs for hydrology modelling, Hydrological processes, 32(19), 3019-3031, doi:10.1002/hyp.13251.
      16. Dee, D. P., Uppala S.M., Simmons, A. J., Berrisford, P., Poli. P., Kobayashi, S., Andrae, U.,  Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,  Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J.,  Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,  Matricardi, M., McNally, A. P., Monge‐Sanz, B. M., Morcrette, J.‐J., Park, B.‐K., Peubey, C., de Rosnay, P., Tavolato,C., Thépaut, J.‐N., Vitart, F., 2011, The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Quarterly Journal of the Royal Meteorological Society, 137(656), 553-597, doi:10.1002/qj.828.
      17. Dewes, C. F., Rangwala, I., Barsugli, J.J., Hobbins, M.T., Kumar, S., 2017, Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand, PLoS One 12(3):e0174045, doi:10.1371/journal.pone.0174045.
      18. Du pisani, L. G., Fouche´, H. J., Venter, J. C., 1998, assessing rangeland drought in South Africa, Agricultural Systems, 57(3), 367-380.
      19. Emerton, R., Cloke, H. L., Stephens, E. M., Zsoter, E., Woolnough, S. J., Pappenberger, F., 2017, Complex picture for likelihood of ENSO-driven flood hazard, Nature communications, and 8(14796), doi: 10.1038/ncomms14796.
      20. Essou, G. R., Brissette, F., & Lucas-Picher, P., 2017, The use of reanalysis and gridded observations as weather input data for a hydrological model: Comparison of performances of simulated river flows based on the density of weather stations, Journal of Hydrometeorology, 18(2), 497-513, doi: 10.1175/JHM-D-16-0088.1.
      21. Farahmand, A., AghaKouchak, A., 2015, A generalized framework for deriving nonparametric standardized drought indicators, Advances in Water Resources, 76:140-145, doi:10.1016/j.advwatres. 2014. 11.012.
      22. González, J., Valdés, JB., 2006, new drought frequency index: Definition and comparative performance analysis, Water Resources Research, 42(11), doi: 10.1029/2005 wr004308.
      23. Groisman, P., et al, 1999, Changes in the Probability of Heavy Precipitation: Important Indicators of Climatic Change, Climatic Change, 42:243-283, doi: 10.1023/a: 1005432803188.
      24. Heim, R., 2002, A Review of Twentieth‒Century Drought Indices Used in the United States, Bulletin of the American Meteorological Society, 83, doi: 10.1175/1520-0477(2002)0832.3.CO; 2.
      25. Hobbins, M. T., Wood, A., McEvoy, D. J., Huntington, J. L., Morton, C., Anderson, M., Hain, C., 2016, The Evaporative Demand Drought Index, Part I: Linking Drought Evolution to Variations in Evaporative Demand, Journal of Hydrometeorology, 17:1745-1761, doi:10.1175/JHM-D-15-0121.1.
      26. Hosking, J. R. M., 1990, L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics, Journal of the Royal Statistical Society Series B (Methodological), 52(1):105-124, https://www.jstor.org/ stable/ 2345653.
      27. Kao, S-C., Govindaraju, R., 2008, Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas, Water Resources Research, 44(2), doi: 10.1029/2007WR006261.
      28. Keyantash, J. A., Dracup, J. A., 2002, The quantification of drought: an evaluation of drought indices, Bulletin of the American Meteorological Society, 83(8), 1167-1180, doi:10.1175/1520-0477-83.8.1167.
      29. Lindsay, R., Wensnahan, M., Schweiger, A., Zhang, J., 2014, Evaluation of seven different atmospheric reanalysis products in the Arctic, Journal of Climate, 27(7), 2588-2606, doi:10.1175/JCLI-D-13-00014.1.
      30. McEvoy, D.J., Huntington, J.L., Hobbins, M.T., Wood, A., Morton, C., Anderson, M., Hain, C., 2016, The Evaporative Demand Drought Index, Part II: CONUS-Wide Assessment against Common Drought Indicators, Journal of Hydrometeorology, 17:1763-1779, doi:10.1175/jhm-d-15-0122.1.
      31. McKee, T.B., Doesken, N. J., Kleist, J., 1993, the relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, MA, pp. 179–183.
      32. Mostafa, I., François, B., Richard, A., 2019, Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modeling over North-America. Hydrology and Earth System Sciences Discussions. 1-35. doi: 10.5194/hess-2019-316.
      33. Otkin, J,A., Shafer, M., Svoboda, M., Wardlow, B., Anderson, M. C., Hain, C., Basara, J., 2015, Facilitating the Use of Drought Early Warning Information through Interactions with Agricultural Stakeholders, Bulletin of the American Meteorological Society, 96(7), 1073-1078 doi:10.1175/ bams-d-14-00219.1.
      34. Ruffault, J., Moron, V., Trigo, R. M., Curt, T., 2017, Daily synoptic conditions associated with large fire occurrence in Mediterranean France: evidence for a wind-driven fire regime, International Journal of Climatology, 37(1), 524-533, doi:10.1002/ joc.4680.
      35. Song, X., Lu, F., Xiao, W., Zhu, K., Zhou, Y., Xie, Z., 2019, Performance of 12 reference evapotranspiration estimation methods compared with the Penman–Monteith method and the potential influences in northeast China, Meteorological Applications 26(1), 83-96, doi:10.1002/met.1739.
      36. Tsakiris, G., Pangalou, D., Vangelis, H., 2007, Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI), Water Resources Management, 21, 821-833, doi:10.1007/s11269-006-9105-4.
      37. Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010, A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23(7), 1696-1718 doi:10.1175/2009jcli2909.1.
      38. Wang, H., Vicente-serrano, S.M., Tao, F., Zhang, X., Wang, P., Zhang, C., Chen, Y., Zhu, D., Kenawy, A.E., 2016, Monitoring winter wheat drought threat in Northern China using multiple climate-based drought indices and soil moisture during 2000–2013, Agricultural and Forest Meteorology, 228-229, doi:10.1016/j.agrformet.2016.06.004
      39. Wang, Q., Shi, P., Lei, T., Geng, G., Liu, J., Mo, X., Li, X., Zhou, H., Wu, J., 2015, The alleviating trend of drought in the Huang-Huai-Hai Plain of China based on the daily SPEI, International Journal of Climatology, 35(13), 3760-3769, doi:10.1002/joc.4244.
      40. Whitfield, P. H., Burn, D. H., Hannaford, J., Higgins, H., Hodgkins, G. A., Marsh, T., Looser, U., 2012, Reference hydrologic networks I, The status and potential future directions of national reference hydrologic networks for detecting trends, Hydrological Sciences Journal, 57(8), 1562-1579, doi:10.1080/02626667.2012.728706.
      41. Wilhite, D. A., Glantz, M. H., 1985, Understanding: the Drought Phenomenon: The Role of Definitions, Water International, 10(3), 111-120, doi: 10.1080/02508068508686328.
      42. Wilhite, D. A., 1993, Drought assessment, management, and planning: theory and case studies, Natural Resource Management and Policy, Series, 2, Kluwer, 293 pp.
      43. Wu, J., Chen, X., Gao, L., Yao, H., Chen, Y., Liu, M., 2016, Response of Hydrological Drought to Meteorological Drought under the Influence of Large Reservoir, Advances in Meteorology, 2016, 1-11, doi:10.1155/2016/2197142.
      44. Yao, N., Li, Y., Lei, T., Peng, L., 2018, Drought evolution, severity and trends in mainland China over 1961-2013, The Science of the Total Environment, 616-617, 73-89, doi:10.1016/j.scitotenv.2017.10.327.
      45. Zargar, A., Sadiq, R., Naser, B., Khan, F.I., 2011, a review of drought indices, Environmental Reviews, 19, 333-349, doi: 10.1139/a11-013.
      46. https://climatology.ir/wp-content/uploads/2014/07/1s.jpg