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تحلیل احتمالاتی اثرات خشکسالی بر عملکرد گندم دیم با کاربرد توابع مفصل (مطالعه موردی: دشت تبریز) | ||
دانش آب و خاک | ||
مقاله 5، دوره 34، شماره 1، فروردین 1403، صفحه 75-89 اصل مقاله (828.82 K) | ||
شناسه دیجیتال (DOI): 10.22034/ws.2023.54324.2506 | ||
نویسندگان | ||
محمد خالدی علمداری1؛ ابوالفضل مجنونی هریس* 2؛ احمد فاخری فرد3 | ||
1دانشجوی دکتری تخصصی آبیاری و زهکشی، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز. تبریز، ایران. | ||
2دانشیار گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز. | ||
3استاد گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز | ||
چکیده | ||
دستیابی به راهکارهای استفاده از اطلاعات آبوهوا در راستای پیادهسازی استراتژیهای مدیریت ریسک و بهتبع آن افزایش آمادگی و کاهش آسیبپذیری در برابر تغییرات آبوهوایی تحت عنوان مدیریت ریسک یکی از چالشهایی است که جامعه کشاورزی با آن مواجه است. در این میان، خشکسالی از عمده منابع مخاطرهآمیز برای سیستمهای کشاورزی به شمار میآید و این تهدید غالباً در شرایط کشت دیم خود را بهصورت ویژهای نشان میدهد. در این پژوهش شاخص خشکسالی در طول دوره رشد گندم دیم و عملکرد آن در منطقه تبریز واقع در شرق دریاچه ارومیه با هدف توسعه مدل مبتنی بر توابع مفصل بهمنظور تعیین احتمالات توأم ریسک عملکرد گندم دیم و وضعیتهای مختلف خشکسالی مورد بررسی قرار گرفت. براساس نتایج حاصل، مناسبترین توزیع آماری برای شاخص خشکسالی و عملکرد به ترتیب نرمال و لوجستیک میباشد. این توزیعها بهصورت مشترک در تابع مفصل منتخب کلایتون با شاخصهای ارزیابی AIC و RMSE که مقادیر آنها به ترتیب -11.10 و 0.036 میباشد لحاظ و احتمالات توأم شرایط مورد نظر را ارائه میکنند. نتایج نشان داد که احتمال تجمعی رویداد ریسک عملکرد و وقوع خشکسالی به طور کلی در حدود 33 درصد برآورد میگردد که با تفکیک احتمال وقوع توأم، منوط به وقوع خشکسالی ملایم، متوسط، شدید و بسیار شدید، مقادیر احتمال رویداد به ترتیب برابر با 18.43، 7.82، 4.26 و 2.32 درصد میباشد؛ لذا احتمال وقوع توأم ریسک عملکرد در شرایط مختلف خشکسالی متفاوت بوده و به عنوان رویداد حاد، با شدت یافتن وضعیت خشکسالی، احتمال وقوع توأم نیز کاهش مییابد. | ||
کلیدواژهها | ||
آنالیز ریسک؛ کاپولای ارشمیدسی؛ کاپولای بیضوی؛ ریسک خشکسالی؛ SPI | ||
مراجع | ||
Angelidis P, Maris F, Kotsovinos N and Hrissanthou V, 2012. Computation of drought index SPI with alternative distribution functions. Water Resource Management 26(9): 2453–2473.
Arbenz P, 2013. Bayesian copulae distributions, with application to operational risk management—some comments. Methodology and Computing in Applied Probability 15:105–108.
Bahremand A, Alvandi E, Bahrami M, Dashti Marvili M, Heravi H.,Khosravi GR, Kornejady A,Samadi Arghini H,Tajiki M and Teimouri M, 2016. Copula functions and their application in stochastic hydrology. Journal of Conservation and Utilization of Natural Resources 4(2):1-20. ( in Persian with English abstract)
Ben-Ari T, Adrian J, Klein T, Calanca P, Van der Velde M and Makowski D, 2016. Identifying indicators for extreme wheat and maize yield losses. Agricultural and Forest Meteorology 220: 130-140.
Bokusheva R, Kogan F, Vitkovskaya I, Conradt S and Batyrbayeva M, 2016. Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses. Agricultural and Forest Meteorology 220:200-6.
Challinor AJ, Ewert F, Arnold S, Simelton E and Fraser E, 2009. Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. Journal of Experimental Botany 60: 2775-2789.
Chiew FH, Piechota TC, Dracup JA and McMahon TA, 1998. El Nino/Southern Oscillation and Australian rainfall, streamflow and drought: Links and potential for forecasting. Journal of Hydrology 204(1-4):138-49.
Edwards DC and McKee TB, 1997. Characteristics of 20th century drought in the United States at multiple time scales. Atmospheric Science Paper 634:1-30.
Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T and Izaurralde RC, 2015. The global gridded crop model intercomparison: Data and Modeling Protocols for Phase 1 (V1. 0). Geoscientific Model Development 8(2):261-77.
Fang HB, Fang KT and Kotz S, 2002. The meta-elliptical distributions with given marginals. Journal of Multivariate Analysis 82:1-16.
Folberth C, Elliott J, Müller C, Balkovic J, Chryssanthacopoulos J, Izaurralde RC, Jones CD, Khabarov N, Liu W, Reddy A and Schmid E, 2016. Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates. Biogeosciences Discussions [preprint] 1-30.
Frank MJ, 1979. On the simultaneous associativity of F (x, y) and x + y − F (x, y). Aequationes Mathematicae 19:194–226.
Genest C, Favre AC, Béliveau J and Jacques C, 2007. Metaelliptical copulas and their use in frequency analysis of multivariate hydrological data. Water Resources Research 43(9):1-12.
Genest C and Rivest LP, 1993. Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association 88(423):1034-43.
Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM and Toulmin C, 2010. Food security: the challenge of feeding 9 billion people. Science 327(5967):812-818.
Gumbel EJ, 1960. Distributions of extreme values in several dimensions. Paris Institute of Statistics 9:171-173.
Hernandez-Barrera S, Rodriguez-Puebla C and Challinor AJ, 2017. Effects of diurnal temperature range and drought on wheat yield in Spain. Theoretical and Applied Climatology 129(1):503-19.
Hlavinka P, Trnka M, Semeradova D, Dubrovský M, Žalud Z and Možný M, 2009. Effect of drought on yield variability of key crops in Czech Republic. Agricultural and Forest Meteorology 149 (3–4):431–442.
Huang J, Zhuo W, Li Y, Huang R, Sedano F, Su W, Dong J, Tian L, Huang Y and Zhu D, 2020. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. International Journal of Digital Earth 13: 504-526.
Huang X and Wang Z, 2018. Probabilistic spatial prediction of categorical data using elliptical copulas. Stochastic Environmental Research and Risk Assessment 32:1631-1644.
Lee T, Modarres R and Ouarda TB, 2013. Data‐based analysis of bivariate copula tail dependence for drought duration and severity. Hydrological Processes 27:1454-1463.
Leng G and Hall J, 2019. Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Science of the Total Environment, 654:811-21.
Lesk C, Rowhani P and Ramankutty N, 2016. Influence of extreme weather disasters on global crop production. Nature 529 (7584): 84–87.
Li Y, Gu W, Cui W, Chang Z and Xu Y, 2015. Exploration of copula function use in crop meteorological drought risk analysis: a case study of winter wheat in Beijing, China. Natural Hazards 77:1289-1303.
Li C, Singh VP and Mishra AK, 2013. A bivariate mixed distribution with a heavy-tailed component and its application to single-site daily rainfall simulation. Water Resources Research, 49:767–789.
Lobell DB, Roberts MJ, Schlenker W, Braun N, Little BB, Rejesus RM and Hammer GL, 2014. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344(6183):516-9.
Madadgar S, AghaKouchak A, Farahmand A and Davis SJ, 2017. Probabilistic estimates of drought impacts on agricultural production. Geophysical Research Letters 44 (15): 7799–7807.
Mann HB, 1945. Non-parametric tests against trend. Econometrica 13:163–171.
Matiu M, Ankerst DP and Menzel A, 2017. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS One 12 (5): e0178339.
McKee TB, Doeskin NJ and Kleist J, 1993. The relationship of drought frequency and duration to time scales. Pp.179-184. Proceedings of the 8th Conference on Applied Climatology. 17-22 January, Anaheim, California.
Mirabbasi R, Fakheri-Fard A and Dinpashoh Y, 2012. Bivariate drought frequency analysis using the copula method. Theoretical and Applied Climatology 108:191-206.
Mpelasoka F, Hennessy K, Jones R and Bates B, 2008. Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management. International Journal of Climatology: A Journal of the Royal Meteorological Society 28(10):1283-92.
Nelsen RB, 2006. An Introduction to Copulas. Springer Science & Business Media. Springer New York, NY.
Páscoa P, Gouveia CM, Russo A and Trigo RM, 2017. The role of drought on wheat yield interannual variability in the Iberian Peninsula from 1929 to 2012. International Journal of Biometeorology 61(3):439-51.
Potopova V, Boroneant C, Boincean B and Soukup J, 2016. Impact of agricultural drought on main crop yields in the Republic of Moldova. International Journal of Climatology 36(4): 2063–2082.
Quiring SM, 2009. Developing objective operational definitions for monitoring drought. Journal of Applied Meteorology and Climatology 48: 1217-1229.
Ribeiro AF, Russo A, Gouveia CM and Páscoa P, 2019a. Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theoretical and Applied Climatology 136(1):203-20.
Ribeiro AF, Russo A, Gouveia CM, Páscoa P and Pires CA, 2019b. Probabilistic modelling of the dependence between rainfed crops and drought hazard. Natural Hazards and Earth System Sciences 19(12): 2795-2809.
Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N and Neumann K, 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences 111(9):3268-73.
Salvadori G and De Michele C, 2004. Frequency analysis via copulas: Theoretical aspects and applications to hydrological events. Water Resources Research 40(12): 1-17.
Sanainejad SH, Ansari H, Davari K and Morid S, 2003. Monitoring and assessment of drought severity in mashhad at different time scales using standardized precipitationindex (SPI). Iranian Journal of Soil Research 17(2): 201-209. ( in Persian with English abstract)
Shi W and Tao F, 2014. Vulnerability of African maize yield to climate change and variability during 1961–2010. Food Security 6 (4): 471–481.
Sklar M, 1959. N-dimensional distribution functions and their margins. Publications of the Statistical Institute of the University of Paris 8:229-231.
Srinivas S, Menon D and Meher Prasad A, 2006. Multivariate simulation and multimodal dependence modeling of vehicle axle weights with copulas. Journal of Transportation Engineering 132(12):945-55.
Tao F, Zhang Z, Liu J and Yokozawa M, 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new super-ensemblebased probabilistic projection. Agricultural and Forest Meteorology 149 (8):1266–1278.
Tebaldi C and Lobell D, 2008. Towards probabilistic projections of climate change impacts on global crop yields. Geophysical Research Letters 35 (8):1-6.
Tilman D, Balzer C, Hill J and Befort BL, 2011. Global food demand and the sustainable intensification of agriculture. Biological Sciences 108 (50): 20260–20264.
Troy T, Kipgen C and Pal I, 2015. The impact of climate extremes and irrigation on US crop yields. Environmental Research Letters 10(5): 1-10.
Van Dijk AI, Beck HE, Crosbie RS, De Jeu RA, Liu YY, Podger GM, Timbal B and Viney NR, 2013. The millennium drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resources Research 49(2):1040-1057.
Yu C, Li C, Xin Q, Chen H, Zhang J, Zhang F, Li X, Clinton N, Huang X, Yue Y and Gong P, 2014. Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions. Environmental Modelling & Software 62:454-464.
Zampieri M, Ceglar A, Dentener F and Toreti A, 2017. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environmental Research Letters 12 (6): 1-11
Zipper SC, Qiu J and Kucharik CJ, 2016. Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes. Environmental Research Letters 11 (9): 1-11 | ||
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