مدل سازی اثرات تغییر اقلیم بر نیاز آبیاری و کارایی مصرف آب در گندم‌زارهای استان خوزستان

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه کشاورزی اکولوژیک، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

2 گروه زراعت، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

یکی از مهم‌ترین پیامدهای تغییر اقلیم آینده، تأثیر آن بر مصرف آب در بخش کشاورزی است که می‌تواند مدیریت منابع آب را با چالش‌های جدی روبرو سازد. در این مطالعه به‎منظور پیش‎بینی اثرات تغییر اقلیم بر رشد و نمو گندم در شش شهرستان استان خوزستان شامل اهواز، بهبهان، دزفول، ایذه، رامهرمز و امیدیه از مدل گردش عمومی HadCM3تحت سه سناریوی B1،A1Bو A2در دوره 65-2046 استفاده شد. برای ریزمقیاس کردن پارامترهای اقلیمی مولد آب و هوایی LARS-WG مورداستفاده قرار گرفت. پس از شبیه‎سازی اقلیم آینده و تولید پارامترهای موردنیاز، شبیه‎سازی رشد و نمو گندم با استفاده از مدل APSIM-Wheat انجام شد. نتایج ارزیابی مدل LARS-WG با استفاده از شاخص NRMSEحاکی از دقت بالای مدل در شبیه‎سازی تابش (از 63/0درصد تا 67/1 درصد)، دمای کمینه (از 63/0 درصد تا 98/1درصد) و بیشینه (از 63/0 درصد تا 05/1 درصد) بود درحالی‌که مقدار این شاخص برای بارندگی (از 42/11درصد تا 47/25 درصد) در مقایسه با دیگر متغیرها بالاتر بود. نتایج شبیه‎سازی نشان داد که در استان خوزستان عملکرد دانه گندم در شرایط تغییر اقلیم نسبت به دوره پایه به‎طور میانگین 16 درصد افزایش می‎یابد. با افزایش عملکرد دانه و همچنین کاهش تبخیر-تعرق (کاهش 5 درصدی در مقایسه با دوره پایه) در شرایط تغییر اقلیم، کارایی مصرف آب 23 درصد افزایش می‎یابد. به طور کلی نتایج این تحقیق نشان داد که با توجه به افزایش دما (7 درصد)، افزایش غلظتCO2(از 334 پی‎پی‎ام به 526 پی‎پی‎ام در سال 2050)، کاهش طول فصل رشد (74/7 روز) و کاهش تبخیر-تعرق نیاز آبیاری گندم در شرایط تغییر اقلیم آینده 9 درصد کاهش می‎یابد.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling the Effects of Climate Change on Irrigation Requirement and Water Use Efficiency of Wheat Fields of Khuzestan Province

نویسندگان [English]

  • R. Deihimfard 1
  • H. Eyni Nargeseh 2
  • Sh. Farshadi 1
1 Shahid Beheshti University
2 Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University
چکیده [English]

Introduction: One of the most important consequences of the future climate change is its impact on water use and water use efficiency (WUE) in agriculture which could challenge the water resources management. Khuzestan province is one of the most important areas of crops production in Iran particularly for wheat, so that 15.73 percent of total irrigated wheat production and 8.85 percent of total arable land is located in this province. Therefore, investigating climate change effects on irrigated wheat production, WUE and irrigation requirement will be necessary in the Khuzestan province. In this context, this study was conducted to simulate the growth and yield of irrigated wheat under climate change conditions, and to calculate WUE and irrigation requirement in this province.
Materials and Methods: The current study was done at six locations of Khuzestan province in southwestern Iran, included Ahwaz, Behbahan, Dezful, Izeh, Omidiyeh and Ramhormoz. Historical daily weather data including solar radiation (MJ m-2 d-1), precipitation (mm) and maximum and minimum temperatures (˚C) for the baseline period gathered for each study location from their established meteorological stations. To predict the climatic variables in the future, HadCM3 climate model was applied under three emission scenarios (B1, A1B and A2) for one future time period (2046-65). The observed historical daily weather data at each location was used to generate the future scenario files to be applied in LARS-WG (Long Ashton Research Station-Weather Generator) program. These parameters are necessary for future projection of weather variables. The downscaled daily weather data obtained from the LARS-WG included maximum and minimum temperatures, rainfall and solar radiation for each period of future climate. These data are required for running crop simulation model. The Agricultural Production Systems simulator (APSIM) was used to predict the impacts of climate change on wheat yield, WUE and irrigation requirement. The model requires daily weather variables (maximum and minimum temperatures, precipitation and solar radiation), soil properties, type of genotype (as cultivar-specific parameters), and crop management information as inputs to simulate crop growth and development. In order to evaluate the climate model NRMSE (Normalized Root Mean Square Error) index was used. Finally, the outputs obtained from the model simulation experiments were analyzed using excel, SAS and Sigma Plot.
Results and Discussion: Results of climate model evaluation indicated that LARS-GW well predicted radiation (NRMSE from 0.63 to 1.67%), maximum (NRMSE from 0.63% to 1.05%) and minimum (NRMSE from 0.63% to1.97%) temperatures. However, the accuracy in prediction of rainfall (NRMSE from 11.42% to 21.47%) was not as good as the other climatic variables. The simulation results in the baseline by APSIM-Wheat showed that maximum and minimum grain yield were obtained in the Izeh (6764.2 Kg.ha-1) and Omidiyeh (5230.2 Kg.ha-1), respectively. Under climate change conditions (rising temperature and elevated CO2), on average, the highest and lowest grain yield were obtained in Izeh (7755.3 Kg.ha-1) and Omidiyeh (6290.76 Kg.ha-1), respectively. The simulation results in the baseline also indicated that the highest and lowest evapotranspiration (ET) were obtained in the Izeh (441.7 mm) and Ramhormoz (401.5 mm), respectively. When averaged across all future scenarios, the maximum and minimum ET were obtained in Izeh (409.56 mm) and Ramhormoz (375.38 mm), respectively. The future rising temperature will intensify the ET, whereas reducing stomata conductance due to higher CO2 concentration in one hand, and shortening growing period due to rising temperature on the other hand, will reduce the cumulative ET in wheat. The simulation results in the baseline showed that the highest and lowest WUE were obtained in Izeh (15.32 Kg.ha-1.mm-1) and Omidiyeh (12.7 Kg.ha-1.mm-1), respectively. In climate change conditions (rising temperature and CO2 elevated), on average the highest and lowest WUE were obtained in Izeh (18.93 Kg.ha-1.mm-1) and Omidiyeh (15.76 Kg.ha-1.mm-1), respectively. Wheat crop would be benefitted under future climate change in Khozestan province as it is a C3 plant, and under optimal conditions (no water and nitrogen limitations), it will produce more grain because of reduced stomata conductance and increased photosynthesis and WUE owing to elevated CO‌2. Simulation results also indicated that under climate change conditions, on average, the highest and lowest irrigation requirement were obtained in Ahwaz (315.39 mm) and Izeh (225.96 mm), respectively. The reduced irrigation requirement of wheat under climate change conditions could be attributed to decreasing length of growing season and increasing CO2 concentration.
Conclusion: In the current study, the effects of climate change caused by rising temperature and elevating CO2 concentration on WUE, irrigation requirement, growth and yield of wheat were investigated in the Khuzestan province. The simulation results showed that, wheat grain yield under climate change conditions (averaged across all scenarios) will increase by 16 % compared to the baseline. In addition, WUE will be increased 23 percent owing to increasing grain yield (+16%) and decreasing ET (5%) under different scenarios. Overall, under climatic conditions of Khuzestan province in 2046-2065, WUE would be increased by 23% and irrigation requirement would be decreased by 9%. The reasons behind these increases and decreases are rising temperature (7%), elevating CO2 concentration (up to 526 ppm for 2046-65) and decreasing the length of growing season and ET both by 5%.

کلیدواژه‌ها [English]

  • APSIM
  • Climate Scenario
  • GCM
  • simulation
1. Aggarwal P.K. 1994. Simulating the effect of climatic factors, genotype and management on productivity of wheat in India. Agricultural Research Institute, pp. 1-11.
2. Alizadeh A., Sayari N., Hesami Kermani M.R., Bannayan Aval M., and Farid Hossaini A. 2010. Assessment of Climate Change Potential Impacts on Agricultural Water Use and Water Resources of Kashaf rood basin. Journal of water and soil, 24(4): 815-835. (in Persian with English abstract).
3. Angstrom A. 1924. Solar and terrestrial radiation. Quarterly Journal of the RoyalMeteorological Society, 50:121–5.
4. Anonymous. 2014. Agricultural statistics, 2013-2014, volume 1. Available at:http://www.maj.ir/Portal/Home/.
5. Ashraf B., Mousavi-Baygi M., Kamali G.A., and Davari K. 2012. Evaluation of wheat and Sugar beet water use Variation due to Climate Change Effects in two Coming Decades in the selected plains of Khorasan Razavi. Iranian Journal of Irrigation and drainage, 2(6): 105-117. (in Persian with English abstract).
6. Bannayan M. 2009. Crop models efficiency and performance under elevatedatmospheric CO2. Journal of Water and Soil, 23(4): 115-126. (in Persian with English abstract).
7. Bannayan M., Lotfabadi S., Sanjani S., Mohammadian A., and Agaalikhani M. 2011. Effects of precipitation andtemperature on cereal yield variability in northeast of Iran. International Journal of Biometeorology, 55: 387-401.
8. Bos M.G. 1985. Summary of ICID definitions of irrigation efficiency ICID Bull, 34: 28–31.
9. de Boer HJ., Lammertsma E.I., Wagner-Cremer F., Dilcher D.L., Wassen M.J., and Dekker S.C. 2011. Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2. Proceeding national academy sciences, 108:4041-40466.
10. Deihimfard R., Eyni Nargeseh H., and Haghighat M. 2016. Zoning of drought incident in Fars province under climate change conditions using standardized precipitation index. Journal of Agrecology, 7(4): 528-546. (In Persian with English abstract).
11. Deihimfard R., Nassiri Mahallati M., and Koocheki A. 2015. Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modelling. Field Crops Research, 184:28–38.
12. Drake B.G., and Gonzàlez-Meler M.A. 1997. More efficient plants: a consequence of rising atmospheric CO2?Annual Review of Plant Physiology and Plant Molecular Biology, 48:609-639.
13. Eyni Nargeseh H., Deihimfard R., Soufizadeh S., Haghighat M., and Nouri O. 2016. Predicting the impacts of climate change on irrigated wheat yield in Fars province using APSIM model. Electronic Journal of Crop Production, 8(4): 203-224. (In Persian with English abstract).
14. Farhanfar S., Bannayan M., Khazaei H.R., and Mousavi Baygi M. 2015. Vulnerability assessment of wheat andmaize production affected by drought and climate change. International Journal of Disaster Risk Reduction, 13: 37- 51.
15. Ghorbani K., Zakerinia M., and Hezarjaribi A. 2013. The effect of climate change on water requirement of soybean in Gorgan. Journal of Agricultural Meteorology, 2(1): 60-72. (in Persian with English abstract).
16. Gohari A., Eslamian S., Abedi-Koupaei J., Massah Bavani A., Wang D., and Madani K. 2013. Climate changeimpacts on crop production in Iran's Zayandeh-Rud River Basin. Science of the Total Environmental, 442:405-419.
17. Hajarpour A., Soltani A., Zeinali E., and Sayyedi F. 2013. Simulating the impact of climate change on productionof Chickpea inrainfed and irrigated condition of Kermanshah. Journal of Plant Production, 20 (2):235-252. (inPersian with English abstract)
18. Hoogenboom G., Jones J.W., Porter C.H., Wilkens P.W., Boote K.J., Batchelor W.D., Hunt L.A., and Tsuji G.Y. (Editors). 2003. Decision Support System for Agrotechnology Transfer Version 4.0. Vol. 1: Overview. University of Hawaii, Honolulu, HI.
19. IPCC. 2014 Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp 151.
20. Keating B.A., Carberry P.S., Hammer G.L., Probert M.E., Robertson M.J., Holzworth D., Huth N.I., Hargreaves J.N.G., Meinke H., Hochman Z., McLean G., Verburg K., Snow V., Dimes J.P., Silburn M., Wang E., Brown S., Bristow K.L., Asseng S., Chapman S., McCown R.L., Freebairn D.M., and Smith C.J. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18:267– 288.
21. Koocheki A., Nassiri M., Soltani A., Sharif H, and Ghorbani R. 2006. Effects of climate change on growth criteriaand yield of sunflower and chickpea crops in Iran. Climate Research, 30: 247-253.
22. Koocheki A., Nassiri M., Sharifi H., and Zand E. 2001. Simulation of growth, phenology and production of Wheat cultivars in effect of climate change under Mashhad conditions. Journal of Desert, 6(2): 117-127. (In Persian with English abstract).
23. Ludwig F., and Asseng S. 2006. Climate change impacts on wheat productionin a Mediterranean environment in Western Australia. Agricultural Systems, 90: 159-179.
24. Lv Z., Lio X., Cao W., and Zhu Y. 2013. Climate change impacts on regional winter wheat production in main wheat production regions of China. Agricultural of Forest Meteorology, 171-172: 234-248.
25. Mo X., Liu S., Lin Z., and Guo R. 2009. Regional crop yield, water consumption and water use efficiency and their responses to climate changein the North China Plain. Agriculture Ecosystems Environment, 134:67–78.
26. Nakicenovic N., and Swart R. 2000. Emissions scenarios. Special Report oftheIntergovernmental Panel on Climate Change. Cambridge University Press,Cambridge.
27. Nehbandani A.R., and Soltani A. 2016. Simulate the Effect of Climate Change on Development, Irrigation Requirements and Soybean Yield in Gorgan. Journal of water and soil, 30(1): 77-87. (in Persian with English abstract).
28. Olesen J.E., Trnka M., Kersebaum K.C., Skjelvag A.O., Seguin B., Peltonen-Sainio P., Rossi F., Kozyra J., and Micale F. 2011.Impacts and adaptation of European crop production systems toclimate change. European Journal of Agronomy, 34:96-112.
29. Prudhomme C., Wilby R.L., Crooks S., Kay A.L., and Reynard N.S. 2010. Scenario-neutral approach to climate change impact studies: application to flood risk. Journal of Hydrology, 390:198-209.
30. Rahimi D., and Salahshour F. 2014. Estimation of Water Requirement, Evaporation and Potential Transpiration ofBrassica Napus L Plant in Ahwaz Town Using CROWPWAT Model. International journal of Advanced Biological and Biomedical Research, 2(4):1377-1387.
31. Rahmani M., Jami Al-Ahmadi M., Shahidi A., and Hadizadeh Azghandi M. 2016. Effects of climate change on length of growth stages and water requirement of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) (Case study: Birjand plain). Journal of Agroecology, 7(4):443-460. (in Persian with English abstract).
32. Reidsma P., Ewert F., Lansink AO., and Leemans R. 2010. Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses. European Journal of Agronomy, 32:91–102.
33. SAS Institute. 2001. SAS System, eighth ed. SAS Inst., Cary, NC.
34. Semenov M.A., and Barrow E.M. 2002. LARS-WG: A Stochastic Weather Generator for Use in Climate Impact Studies, Version 3.0, User’s Manual.
35. Sigmaplot. 2003. Published by systat software. SigmaPlot 12.5 User’s Guid.
36. Wall G.W., Garcia R.L., Wechsung F., and Kimball B.A. 2011. Elevated atmospheric CO2 and drought effects onleaf gas exchange properties of barley. Agriculture Ecosystems and Environment, 144(2):390-404.
37. Wall B.H. "TAMET". 1977: Computer program for processing meteorological data." CSIRO Australia. Division of Tropical Crops and Pastures.Tropical Agronomy Technical Memorandum, 4, 13p.
38. Wallach D., and Goffinet B. 1987. Mean squared error of prediction in models for studying economic and agricultural systems. Biometrics, 43:561–576.
39. Wang E., Robertson M.J., Hammer G.L., Carberry P.S., Holzworth D., Meinke H., Chapmesan S.C., Hargreaves J.N.G., Huth N.I., and Mclean G. 2002. Development of generic crop model template in the cropping system model APSIM. European journal of Agronomy, 18:121-140.
40. Wang J., Wang E., and Liu D.L. 2011. Modelling the impact of climate change on wheat yield and field water balance over the Murry-Darling Basin in Australia. Theoretical and Applied Climatology, 104:285–300.
41. Wetterhall F., Bardossy A., Chen D., Halldin S., and Xu C. 2009. Statistical downscaling of daily precipitation over Sweden using GCM output. Theoretical and Applied Climatology, 96: 95-103.
42. Wilby R.L., and Wigley T.M.L. 1997. Downscaling general circulation model output: A review of methodsand limitations. Progress in Physical Geography, 21: 530-548.
43. Wilcox J., and Makowski D. 2014. A meta-analysis of the predicted effects of climate change on wheat yields48-using simulation studies. Field Crops Research, 156(2):180-190.
44. Yang Y., Liu D.L., Rajin Anwar M., Leary G., Macadam I., and Yang Y. 2016. Water use efficiency and crop water balance of rainfed wheat in a semi-arid environment: sensitivity of future changes to projected climate changes and soil type. Theoretical and Applied Climatology, 123:565-579.