• صفحه اصلی
  • مدلسازی هیدرولوژی- اقتصادی جامع کشاورزی و منابع آب استان تهران جهت ارزیابی آثار بالقوه گرمایش جهانی

اشتراک گذاری

آدرس مقاله


کد مقاله : JAE-2101-2223 (R1) بازدید : 90 صفحه: 29 - 56

10.30495/jae.2023.27036.2223

نوع مقاله: پژوهشی

مدلسازی هیدرولوژی- اقتصادی جامع کشاورزی و منابع آب استان تهران جهت ارزیابی آثار بالقوه گرمایش جهانی

محورهای موضوعی : فصلنامه علمی -پژوهشی تحقیقات اقتصاد کشاورزی

ابوذر پرهیزکاری 1 , غلامرضا یاوری 2 , ابوالفضل محمودی 3 , غلامرضا بخشی خانیکی 4

1 - دانشجوی دکترای اقتصاد کشاورزی، دانشگاه پیام نور، تهران، ایران
2 - دانشیار اقتصاد کشاورزی، دانشگاه پیام نور، تهران، ایران
3 - دانشیار اقتصاد کشاورزی، دانشگاه پیام نور، تهران، ایران
4 - استاد گروه علوم کشاورزی (بیوتکنولوژی)، دانشگاه پیام نور، تهران، ایران

تاریخ دریافت : 1399/10/18 تاریخ پذیرش : 1402/05/15 تاریخ انتشار : 1402/05/01

کلید واژه: تهران, توسعه کشاورزی, گرمایش جهانی, الگوی هیدرواقتصادی, تولیدات زراعی,

چکیده مقاله :

در مطالعه حاضر یکپارچه‌سازی سیستم مدل‌سازی هیدرولوژیکی- اقتصادی جامع کشاورزی و منابع آب در استان تهران جهت ارزیابی آثار بالقوه گرمایش جهانی مورد کنکاش و بررسی قرار گرفت. برای این منظور، ابتدا با استفاده از مدل‌های گردش عمومی (GCM) میزان اثرات گازهای گلخانه‌ای برمیانگین متغیرهای اقلیمی دما و بارش تحت سناریوهای انتشار A1B، A2 و B1 بررسی شد. این کار به کمک سامانه دیتایی GCM/RCM و مدل ریزمقیاس LARS-WG صورت گرفت. در ادامه، با استفاده از رویکرد اقتصادسنجی و تحلیل رگرسیون اثرات متغیرهای اقلیمی دما و بارش برمیانگین عملکرد محصولات منتخب زراعی ارزیابی شد. جهت بررسی تغییرات عملکرد محصولات بر الگوهای زراعی از مدل برنامه‌ریزی ریاضی اثباتی (PMP) استفاده شد. نتایج نشان دادکه رفتار متغیرهای اقلیمی دما و بارش طی دوره‌های آتی در سطح حوضه‌های مطالعاتی استان تهران نسبت به دوره پایه به ترتیب افزایشی (26/0 تا 75/3 درجه سانتی‌گراد) و کاهشی (78/0 تا 1/41 میلی‌متر) خواهد بود.

چکیده انگلیسی:

in the present study, the integration of comprehensive Hydrological -economic modeling system of agriculture and water resources in Tehran province was investigated, in order to assess the potential effects of global warming. To achieve this goal, first using General Circulation Models (GCM) the effects of greenhouse gases on the average climatic variables of temperature and precipitation under the emission scenarios A1B, A2 and B1 were investigated. This was done with the help of GCM/RCM data system and LARS-WG microscale model. Then, using econometric approach and regression analysis, the effects of climatic variables of temperature and precipitation on the average yield of selected products were evaluated. A Positive Mathematical Programming (PMP) model was used to investigate changes in products yields on cropping patterns. The results showed that the behavior of climatic variables of temperature and precipitation during the future periods in the study basins of Tehran province compared to the base period will increase (0/26 to 3/75 °c) and decrease (0/78 to 41/1 mm) respectively.

منابع و مأخذ:

 

  1. Alipour A, Mosavi H, Khalilian S. Evaluating the cost of carbon dioxide emissions from the development of Iran's agricultural sector. Journal of Agricultural Economics, 2014; 8(1):63-81. [https://www.sid.ir/paper/124421/fa].
    2.
  2. Agovinoa M, Casaccia M, Ciommi M, Ferrara M, Marchesano K. Agriculture, climate change and sustainability: The case of EU-28. Ecological Indicators, Available online 8 May 2018, In Press, Corrected Proof. [DOI:10.1016/j.ecolind.2018.04.064].
    3.
  3. Ahmad I, Tang D, Wang T, Wang M, Wagan B. Precipitation trends over time using Mann-Kendall and Spearman’s rho tests in Swat River Basin, Pakistan. Advances in Meteorology, 2015. [http://dx.doi.org/10.1155/2015/431860].
    4.
  4. Akbari M, Najafi Alamdarlo H, Mosavi H. Impacts of climate change and drought on income risk and crop pattern in Qazvin plain irrigation network. Water Research in Agriculture, 2019; 33(2): 265-281. [https://www.sid.ir/paper/196864/fa].
    5.
  5. Amirnejad H, Asadpour Kordi M. Effects of Climate Change on Wheat Production in Iran. Journal of Agricultural Economics Research, 2017; 35(9): 163-182. [https://jae.marvdasht.iau.ir/article_2520.html].
    6.
  6. Baharvand F, Mesbah Bevani A, Mahdavi M, Motamed Vaziri B, Godarzi M. Empowerment test of LARS-WG model for reproduction of daily climatic data of Kermanshah. Iranian Journal of Watershed Management Science and Engineering, 2019; 45 (13): 76-70. [https://jwmsei.ir/article-1-626-fa.html].
    7.
  7. Balali H, Viaggi D. Applying a system dynamics approach for modeling groundwater dynamics to depletion under different economical and climate change scenarios, Water Journal, 2015; 7(1): 5258- 5271. [https://jwmsei.ir/article-1-626-fa.html].
    8.
  8. Cortignani R, Dono G. Agricultural policy and climate change: An integrated assessment of the impacts on an agricultural area of Southern Italy. Environmental Science & Policy, 2018; 81: 26-35. [https://doi.org/10.1016/j.envsci.2017.12.003].
    9.
  9. Daneshgar H, Bagheri M, Mardani Najafabadi M, Alijani F, Yavari GH. Effects of climate change on hydrological and economic conditions of Bushkan plain farmers. Journal of Agricultural Economics Research, 2021; 13(2): 259-280. [https://jae.marvdasht.iau.ir/article_4439.html].
    10.
  10. Esteban E, Albiac J. Salinity Pollution Control in the Presence of Farm Heterogeneity: An Empirical Analysis. Water Economics and Policy, 2016; 2(2):1-20. [https://doi.org/10.1142/S2382624X1650017X].
    11.
  11. Gao C.H, He Z, Pan S, Xuan W, Xu Y. Effects of climate change on peak runoff and flood levels in Qu River Basin, East China. Journal of Hydro-environment Research, 2020; 28: 34-47. [https://doi.org/10.1016/j.jher.2018.02.00].
    12.
  12. Graveline C. Economic calibrated models for water allocation in agricultural production: A review. Environmental Modelling and Software, 2016; 81: 12-25. [https://doi.org/10.1016/j.envsoft.2016.03.004].
    13.
  13. Howitt R.E, Medellin-Azuara J, MacEwan D. Lund R. Calibrating disaggregate economic models of agricultural production and water management. Science of the Environmental Modeling and Software, 2012; 38: 244-258. [https://doi.org/10.1016/j.envsoft.2012.06.013].
    14.
  14. IPCC-TGICA. General guidelines on  the  use  of  scenario  data  for  climate  impact and adaptation assessment. Eds. Carter, T.R., Version 2, 71p. Intergovernmental Panel on Climate Change, Task Group on Data and Scenario Support for Impact and Climate Assessment; 2007. [https://www.ipcc-data.org/guidelines/TGICA_guidance_sdciaa_v1_final.pdf].
    15.
  15. IPCC. Climate Change. The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press; 2018. [https://www.ipcc.ch/report/ar5/wg1/].
    16.
  16. IPCC. The scientific Basis, Contribution of working group I to the third assessment report of the intergovernmental panel on climate change, Cambridge University Press. New York, USA, 2018; No: 996. [https://www.ipcc.ch/report/ar5/wg1/].
    17.
  17. Kiani Ghalesard S, Shahraki J, Akbari A, Sardar Shahraki A. Investigating the effects of climate change on food security in Iran. Journal of Environmental Hazards, 2017; 8(2): 1-22. [https://jneh.usb.ac.ir/article_3934.html].
    18.
  18. Levin A, Lin C.F, Chu C. Unit root test in panel data: Asymptotic and finite sample properties. Journal of Econometrics, 2002; 108: 1-25. [https://doi.org/10.1016/S0304-4076(01) 00098-7].
    19.
  19. Mardani Najafabadi M, Mirzaei A. Evaluating effect of policy programs to achieve water resources stability objective in Qazvin plain. Journal of Agricultural Economics Research, 2019; 11(43): 155-176. [https://jae.marvdasht.iau.ir/article_3527.html].
    20.
  20. Meteorological Organization of Tehran Province. Detailed report on the rainfall situation in Tehran province during the last five years (2009-2013), 2018; 47 pages. [https://nivar.irimo.ir/article_55969.html].
    21.
  21. Ministry of Power. Office of Basic Studies of Water Resources, Integration and Balance Group of Tehran Regional Water Company, Tehran Province, 2019; 63 pages. [https://moe.gov.ir/].
    22.
  22. NikNazli A, Dewan Mahboob H. Climate Change and Global Warming Discourses and Disclosures in the Corporate Annual Reports: A Study on the Malaysian Companies. Social and Behavioral Sciences, 2015; 172: 246-253. [https://doi.org/10.1016/j.sbspro.2015.01.361].
    23.
  23. Parhizkari A, Sabuhi M. Economic analysis of effects of technology development and mechanization on agricultural sector production in Qazvin province using positive mathematical programming model. Agricultural Economics Researches, 2013; 5(4):1-23. [https://www.sid.ir/paper/394127/fa].
    24.
  24. Parhizkari A, Mozaffari M, Khodadadi Hoseini M, Parhizkari R. Economic Analysis of the Effects of Climate Change Due to Greenhouse Gas Emissions on Agricultural Production and Available Water Resources, Case Study: Lands Downstream of Taleghan Dam. Journal of Agricultural Economics and Development, 2015; 29(1): 67-89. [https://civilica.com/doc/664163/].
    25.
  25. Parhizkari A, Yazdani S. Assessing the economic and hydrological impacts of climate change in the Khorrood watershed. Journal of Echo Hydrology, 2017; 4(3): 724-711. [https://ije.ut.ac.ir/article_62504.html].
    26.
  26. Parhizkari A, Yavari GH, Mahmoodi A, Bakhshi Khaniki GH. Comprehensive Biophysical-Economic Modeling of Climate and Agriculture (Case Study: Roodshour Watershed). Economical Modeling, 2019; 13(2): 121-144. [https://eco.firuzkuh.iau.ir/article_667945.ht].
    27.
  27. Paul D.W, Medellin-Azuarab J, Joshua H.V, Meagan S.M. Economic and policy drivers  of  agricultural  water  desalination  in California’s  central  valley. Agricultural Water Management, 2017;194:192-203. [https://doi.org/10.1016/j.agwat..07.024].
    28.
  28. Peres D.J, Cancelliere A. Modeling impacts of climate change on return period of landslide triggering. Journal of Hydrology, 2018; 567: 420-434. [https://doi.org/10.1016/j.jhydrol.2018.10.036].
    29.
  29. Petsakos A, Rozakis S. Calibration of agricultural risk programming models. European Journal of Operational Research, 2015; 242(2): 536-545. [https://doi.org/10.1111/14678489.12368].
    30.
  30. Qureshi M.E, Mobin-ud-Din A, Stuart M.W, Mac K. A multi-period positive mathematical programming approach for assessing economic impact of drought in the Murray–Darling Basin. Australia. Economic Modelling, 2014; 39: 293-304. [https://doi.org/10.1016/j.econmod.2014.02.042].
    31.
  31. Shuai C, Chen X, Xo J. Impacts of climate change on corn and soybean yields in china. Agricultural and Applied Economics Association 2013 AAEA and CAES joint Annual Meeting, Washington, DC. 2013; No: 120-145. [https://ideas.repec.org/p/ags/aaea13/149739.html].
    32.
  32. Soleymaninejad S, Sabouhi M, Banayan A. Effects of Climate Change on Crop Pattern (Case Study: Mashhad Plain). Iranian Economic Economics and Development,2019;50(2):249-263. [https://www.sid.ir/paper/396713/fa].
    33.
  33. Sun S.K, Li G, Wu P.T, Zhao X.N, Wang Y.B. Evaluation of agricultural water demand under future climate change scenarios in the Loess Plateau of Northern Shaanxi, China. Ecological Indicators, 2018; 84(2): 811-819. [https://doi.org/10.1016/j.ecolind.2017.09.048].
    34.
  34. Taniguchi K. Future changes in precipitation and water resources for Kanto Region in Japan after application of pseudo global warming method and dynamical downscaling. Journal of Hydrology: Regional Studies, 2017; 8(1): 287-303. [https://doi.org/10.1016/j.ejrh.2016.10.00].
    35.
  35. Tehran Studies and Planning Center. Management of Studies and Planning of Management Infrastructure and Urban Development, Center for Studies and Planning of Tehran. Statistical report on annual carbon production in Tehran province, 2019; No: 5. [https://jurbangeo.ut.ac.ir/article_79784_dacf1bba2f742641f1f3f0d710021e11.pdf].
    36.
  36. Tehran Studies and Planning Center. Requirements and strategies to achieve integrated urban transportation in Tehran. Reports of Tehran City Studies and Planning Center, May 2019. [https://jurbangeo.ut.ac.ir/article_79784_dacf1bba2f742641f1f3f0d710021e11.pdf].
    37.
  37. Tehran Province Agricultural Jihad Organization. Deputy for Crop Production Improvement, Agriculture Department; 2018. [https://www.tehran-agri.ir/].
    38.
  38. Tehran Province Regional Water Company. Office of Basic Studies of Water Resources; 2018. [https://thrw.ir/].
    39.
  39. Uusitalo V, Leino M. Neutralizing global warming impacts of crop production using biochar from side flows and buffer zones: A case study of oat production in the boreal climate zone. Journal of Cleaner Production, 2019; 227(1): 48-57. [https://doi.org/10.1016/j.jclepro.2019.04.175].
    40.
  40. Wilby R.L, Harris I. A frame work for assessing uncertainties in climate change impact: low flow scenarios for the River Thames, UK. Water Resources Research, 2006; 42(2):1-10. [https://doi.org/10.1029/2005WR004065].
    41.
  41. You L, Rosegrant M.W, Wood S, Sun D. Impact of growing season temperature on wheat productivity in China. Agricultural and Forest Meteorology, 2009; 149(6): 1009-1014. [https://doi.org/10.1016/j.agrformet.2008.12.004].
    42.
  42. Zubaidi S.L, Patryk K, Khalid H, Rafid KH, Mawada A. Using LARS –WG model for prediction of temperature in Columbia City, USA. International Conference on Civil and Environmental Engineering Technologies,Published1, 2019, Published under licence by IOP Publishing Ltd. [https://iopscience.iop.org/article/10.1088/1757-899X/584/1/012026/pdf].
    43.
_||_

مقالات مرتبط

حقوق این وب‌سایت متعلق به سامانه مدیریت نشریات دانشگاه آزاد اسلامی است.
حق نشر © 1403-1400

صفحه اصلی| عضویت/ ورود| درباره سند| تماس با ما|
[English] [العربية] [en] [ar]