اثر همبستگی متقابل متغیرهای شاخص خشکسالی SPEI در تحلیل بلند مدت خشکسالی

نویسندگان

1 دانشجوی دکتری مهندسی منابع آب، گروه مهندسی آب، دانشگاه ارومیه

2 استاد گروه مهندسی آب، دانشگاه ارومیه

3 دانش آموخته دکتری مهندسی منابع آب، گروه مهندسی آب، دانشگاه ارومیه

چکیده

در این مطالعه بطور نوآورانه یک ارزیابی جامعی از تأثیر ضریب همبستگی متقابل دو متغیر بارندگی و تبخیر- تعرق بر عملکرد شاخص خشکسالی SPEI در پیش‏بینی رفتار بلند مدت خصوصیات خشکسالی کشاورزی بر پایه داده‏های 39 ایستگاه سینوپتیک واقع در شرایط اقلیمی و ارتفاع مختلف در سرتاسر ایران انجام پذیرفته و عملکرد آن با شاخص SPI مقایسه گردیده است. بدین منظور سری داده‏های مصنوعی بارندگی و تبخیر- تعرق (به تعداد 10000 جفت) با استفاده از مدل استوکاستیک چندگانه اتورگرسیو با تأخیر یک برای دامنه وسیع از ضریب همبستگی متقابل دو متغیر فوق تولید شد و سپس برای پایش و تعیین خصوصیات خشکسالی استفاده گردید. نتایج این مطالعه بیانگر رفتار نظام‏مند و وابسته خصوصیات مختلف خشکسالی با همبستگی متقابل دو متغیر بارندگی و تبخیر- تعرق بود. بطوریکه، عملکرد دو شاخص خشکسالی SPEI و SPI با افزایش همبستگی متقابل دو متغیر (⇒|R|) بطور غیرخطی یا توانی نزدیک به هم بوده (ضریب همبستگی R=0.85) و برای همبستگی متقابل برابر صفر حداکثر اختلاف مابین خصوصیات خشکسالی برای دو شاخص حاصل شد. نهایتاً نتایج این مطالعه به‎عنوان یک راهنمای جامع در استنباط دقیق و واقعی از رخدادها و خصوصیات خشکسالی بازای شاخص SPEI بوده و می‎تواند کمک مؤثری در تفسیر نتایج خشکسالی با شاخص SPEI داشته باشد.

کلیدواژه‌ها

عنوان مقاله [English]

The Effect of Cross-Correlation between SPEI Variables in Long-Term Drought Analysis

نویسندگان [English]

  • negar rasouli majd 1
  • Majid Montaseri 2
  • Babak Amirataee 3
1 Ph.D. candidate, Department of Water Engineering, Urmia University, Iran
2 Department of Water Engineering, Urmia University, Iran
3 Ph.D. in Water Resources Engineering, Department of Water Engineering, Urmia University, Iran
چکیده [English]

In this study, a comprehensive evaluation of the effect of cross-correlation coefficient between precipitation and evapotranspiration on the performance of SPEI in forecasting the long-term behavior of agricultural drought characteristics in 39 synoptic stations located in different climates and altitudes throughout Iran has been performed, and its performance has been compared with SPI. For this purpose, stochastic precipitation and evapotranspiration time series (10,000 pairs) were generated using the Lag-one Autoregressive Multiple-Site Model (Multi-AR(1)) for a wide range of correlation coefficients of the above two variables and then used to monitor and determine different drought characteristics. The results of this study showed the systematic and dependent behavior of different characteristics of drought with cross-correlation between precipitation and evapotranspiration. So that, the performance of the two SPEI and SPI indices was nonlinearly close to each other (R=0.85) by increasing the cross-correlation between the two variables (|R|⇒1), and for cross-correlation of zero, the maximum difference between the drought characteristics was obtained for the two indices. Finally, the results of this study have been used as a comprehensive guide in the accurate and realistic inference of drought events and characteristics and can be an effective aid in interpreting drought outcomes with the SPEI index.

کلیدواژه‌ها [English]

  • Cross-correlation
  • Data generation models
  • Drought characteristics
  • Monte Carlo simulation
  • SPI
  • SPEI

مراجع

Bazrafshan O, Mahmudzadeh F and Bazrafshan J, 2017. Evaluation of temporal trends of the SPI and SPEI drought indices in the southern coast of Iran. Desert Management 8:54-69. (In Persian with English abstract)
Beck H, Zimmermann N, McVicar T. Vergopolan N, Berg A and Wood EF, 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5, 180214.
Brissette FP, Khalili M and Leconte R, 2007. Efficient stochastic generation of multi-site synthetic precipitation data. Journal of Hydrology 345(3-4):121-133.
Dinpashoh Y and Babamiri O, 2020. Trends in reference crop evapotranspiration in Urmia Lake basin. Arabian Journal of Geosciences 13, 372.
Dinpashoh Y, Jahanbakhsh-Asl S and Rasouli AA, 2019. Impact of climate change on potential evapotranspiration (Case study: west and NW of Iran). Theoretical and Applied Climatology 136: 185–201.
Dinpashoh Y, Jhajharia D, Fakheri Fard A, Singh VP and Kahya E, 2011. Trends in reference crop evapotranspiration over Iran. Journal of Hydrology 399(3-4):422-433.
Domínguez‐Castro F, Vicente‐Serrano SM, Tomás‐Burguera M, Peña‐Gallardo M, Beguería S, El Kenawy A, Luna Y and Morata A, 2019. High spatial resolution climatology of drought events for Spain: 1961–2014. International Journal of Climatology 39(13):5046-5062.
Dracup JA, Lee KS and Paulson Jr EG, 1980. On the statistical characteristics of drought events. Water Resources Research 16(2):289-296.
Hao Z and AghaKouchak A, 2013. Multivariate Standardized Drought Index: A parametric multi-index model. Advances in Water Resources 57:12–18.
Hosseini SB, Dinpasjhoh Y and Nikbakht J, 2015.Analysis of droughts of Northwest of Iran using the Reconnaissance Drought Index. Water and Soil 29(2): 295-310. (In Persian with English abstract)
Kalyanapu AJ, Judi DR, McPherson TN and Burian SJ, 2012. Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk. Journal of Flood Risk Management 5(1):37-48.
Khalili M, Brissette F and Leconte R, 2009. Stochastic multi-site generation of daily weather data. Stochastic Environmental Research and Risk Assessment 23(6):837-849.
Li X, He B, Quan X, Liao Z and Bai X, 2015. Use of the standardized precipitation evapotranspiration index (SPEI) to characterize the drying trend in southwest China from 1982–2012. Remote Sensing 7(8):10917-10937.
Lorenzo-Lacruz J, Vicente-Serrano SM, López-Moreno JI, Beguería S, García-Ruiz JM and Cuadrat JM, 2010. The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain). Journal of Hydrology 386(1-4):13-26.
Matalas NC, 1967. Mathematical assessment of synthetic hydrology. Water Resources Research 3(4):937-945.
McKee TB, Doesken NJ and Kleist J, 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (17 (22): 179-183).
Mishra AK and Singh VP, 2010. A review of drought concepts. Journal of Hydrology 391(1-2):202-216.
Mishra AK, Singh VP and Desai VR, 2009. Drought characterization: a probabilistic approach. Stochastic Environmental Research and Risk Assessment 23(1):41-55.
Montaseri M and Amirataee B, 2017. Comprehensive stochastic assessment of meteorological drought indices. International Journal of Climatology 37(2):998-1013.
Montaseri M, Amirataee B and Yasi M. 2019. Long‐term probability of drought characteristics based on Monte Carlo simulation approach. International Journal of Climatology 39(1):544-557.
Nosrati K, 2015. Assessment of Standardized Precipitation Evapotranspiration Index (SPEI) for drought identification in different climates of Iran. Environmental Science 12(4):63-74. (In Persian with English abstract)
Potopová V, Štěpánek P, Zahradníček P, Farda A, Türkott L and Soukup J, 2018. Projected changes in the evolution of drought on various timescales over the Czech Republic according to Euro‐CORDEX models. International Journal of Climatology 38:e939-e954.
Sharafati A, Nabaei S and Shahid S, 2020. Spatial assessment of meteorological drought features over different climate regions in Iran. International Journal of Climatology 40(3):1864-1884.
Smith RE and Hebbert RHB, 1979. A Monte Carlo analysis of the hydrologic effects of spatial variability of infiltration. Water Resources Research 15(2):419-429.
Srikanthan R and Mcmahon TA, 2001. Stochastic generation of annual, monthly and daily climate data: A review. Hydrology and Earth System Sciences Discussions, European Geosciences Union 5(4):653-670.
Stedinger JR and Vogel RM, 1984. Disaggregation procedures for generating serially correlated flow vectors. Water Resources Research 20(1):47-56.
Vicente-Serrano SM, Begueria S, Lopez-Moreno JI, 2010: A multi-scalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. Journal of Climate 23:1696–1718.
Vicente-Serrano SM, Beguería S, Lorenzo-Lacruz J, Camarero JJ, López-Moreno JI, Azorin-Molina C, Revuelto J, Morán-Tejeda E and Sanchez-Lorenzo A, 2012. Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interactions 16(10):1-27.
Vicente-Serrano SM, Miralles DG, Domínguez-Castro F, Azorin-Molina C, El Kenawy A, McVicar TR, Tomás-Burguera M, Beguería S, Maneta M and Peña-Gallardo M, 2018. Global assessment of the Standardized Evapotranspiration Deficit Index (SEDI) for drought analysis and monitoring. Journal of Climate 31(14):5371-5393.
Wu M, Li Y, Hu W, Yao N, Li L and Liu DL, 2020. Spatiotemporal variability of standardized precipitation evapotranspiration index in mainland China over 1961–2016. International Journal of Climatology 40(11):4781-4799.
Yevjevich V, 1967. An Objective Approach to Definitions and Investigation of Continental Hydrological Droughts, Hydrology Paper 23, Colorado State University, Fort Collins, CO, P. 18.
Zabihi M, Mostafazadeh R and Sharari M, 2017. Analysis of wet and dry spells intensity and duration using precipitation-based and evapotranspiration influenced indices. Journal of Watershed Management Research 8(15):125-136. (In Persian with English abstract)
 
دانش آب و خاک
دوره 33، شماره 1
فروردین 1402
صفحه 129-143

فایل ها

هم رسانی

ارجاع به این مقاله

آمار