Document Type : Research Article

Authors

Urmia University

Abstract

Introduction: The water footprint index as a complete indicator represents the actual used water in agriculture based on the climate condition, the amount of crop production, the people consumption pattern, the agriculture practices and water efficiency in any region. The water footprint in agricultural products is divided to three components, including green, blue and gray water footprint. Green water footprint is rainwater stored in soil profile and on vegetation. Blue water refers to water in rivers, lakes and aquifers which is used for irrigation purposes. Gray water footprint refers to define as the volume of contaminated water. The water footprint in arid and semiarid regions with high water requirement for plants and limited fresh water resources has considerable importance and key role in the planning and utilization of limited water resources in these regions. On the other hand, increasing the temperature and decreasing the rainfall due to climate change, are two agents which affect arid and semiarid regions. Therefore, in this research the water footprint of agriculturalcrop production in Urmia Lake basin, with application of climate change for planning, stable operating and crop pattern optimizing, was evaluated to reduce agricultural water consumption and help supplying water rights of Urmia Lake.
Materials and Methods:Urmia Lake basin, as one of the main sextet basins in Iran, is located in the North West of Iran and includes large sections of West Azerbaijan, East Azerbaijan and Kurdistan areas. Thirteen major rivers are responsible to drain surface streams in Urmia Lake basin and these rivers after supplying agriculture and drinking water and residential areas in the flow path, are evacuated to the Lake. Today because of non-observance of sustainable development concept, increasing water use in different parts and climate change phenomena in Urmia Lake basin the hydrologic balance was perturbed, and Urmia Lake has been lost 90% of its volume and has a critical condition. Therefore, planning, managing and optimizing utilization of water resources in the basin have a high research priority and this requires the concentration on the consumption of water resources. In this study five major products including, wheat, sugar beet, tomato, alfalfa and corn, were studied. For this purpose, seven synoptic meteorological station data including,Salmas, Urmia, Mahabad, Takab, Tabriz, Maragheh and Sarab were used to calibrate the downscaling atmospheric-ocean general circulation model LARS.WG5 and forecast meteorological data in the future periods time (2011-2030) and (2046-2065) with the A2 scenario.The reason to selectA2 scenario was the most critical situation for the mentioned scenario. Then the obtained data were used to estimate the water requirement and water footprint of mentioned plants separately blue and green water footprint in the future periods.
Results and Discussion:The resultsof themeteorological data prediction showed thatall synoptic stations except for Tabriz station the average annual predicted rainfallvalues had the deviationfromhistoricalvalues.The mentioneddeviation in the south (Tekab, Mahabad) and West (Urmia, Salmas) ofUrmia lake basin will showincreaseanddecrease in theannual rainfallin the future, respectively.Moreover,the average annual of predicted temperature values for all studied stations showed that the temperature will increase about1°Cduring2011-2030 period and 2°C during 2046-2065 period. Potential evapotranspiration, as another important meteorological parameter has essentialrole in the estimation of crop water requirements which will be slightly affected by climate change phenomena and it will increase in the summer. The results of agricultural products water footprint show that the maximum amount of green water footprint in all studied stations was related to wheat and alfalfa, and this water footprint depend on the time and growth period. For corn, tomatoandsugar beetproducts the ratio of blue and green water footprint is greater 9. By comparing the water footprint of products it can be seen that in Urmia, Salmas and Tekab stations water footprint is decreased with decreasing rainfall and this decrease during 2065 – 2045 periods is higher than 2030 – 2011 periods.
Conclusions: According to the results, annual precipitation in southern and western regions of the Lake Urmia basin will be increased and decreased, respectively in the future periods. However, increasing approximately one Celsius degree in temperature is expected for each of the periods all over the basin. In addition,the results showed that the amount of potential evapotranspiration will be increased in the warm months (June to September) in the future periods, and agricultural water consumption pattern will be changed affected by evapotranspiration variations. In the future periods, the blue and green water footprint of most agricultural products will be increased and decreased, respectively.

Keywords

1- 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. Vol. 24, No. 4, Sep-Oct 2010, p. 815-835. (in Persian with English abstract)
2- Allen R.G., Pereira L.S., Raes D., and Smith M. 1998. Crop Evapotranspiration– Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, FAO, 1998, ISBN 92-5-104219-5.
3- 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 province. Iranian journal of irrigation and drainage No. 2, Vol. 6, Summer 2012, p. 105-117. (in Persian with English abstract)
4- Babaeian I., Karimian M., Modirian R., and Habibi Nokhandan M. 2007. Simulating rainfall of cold months in 1376 and 1379 by using RegCM3climate model. Geography and Development. No. 10, Autumn and Winter 2007, p. 55-73. (in Persian)
5- Babaeian I., Najafi Nik Z., Zaboli Abbasi F., Habibi Nokhandan M. Adab H., and Malbousi SH. 2009. Evaluating of climate change in the period 2039- 2010 AD using downscaling data of the general circulation model ECHO-G. No. 16, Winter 2009, p. 135-152. (in Persian)
6- Bruinsma J. (ed.). 2003. World agriculture: towards 2015/2030: An FAO perspective, Earthscan, London, UK.
7- Bruinsma J. 2009. The resource outlook to 2050: By how much do land, water and crop yields need to increase by 2050, Expert Meeting on How to Feed the World by 2050, 24–26 June 2009, FAO, Rome, Italy.
8- Chapagain A.K., Hoekstra A.Y., Savenije H.H.G., and Gautam R. 2006. The water footprint of cotton consumption: an assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecol. Econ. 60, 186-203.
9- Chapagain A. K., and Hoekstra A. Y. 2011. The blue, green and grey water footprint of rice from production and consumption perspectives. Ecological Economics 70: 749–758.
10- Ercin A. E., and Hoekstra A. Y. 2014. Water footprint scenarios for 2050: A global analysis. Environment International 64. 71–82.
11- FAO, 2006. FAOSTAT Data. FAO Statistical Databases. Available from: http://faostat.fao.org/default.jsp (accessed 15.12.13.).
12- FAO, 2009. CropWat 8.0, Edited, Land and Water Development Division. Food and Agriculture Organization of the United Nations, Rome.
13- FAO, 2010a ‘CROPWAT 8.0 model’, FAO, Rome, www.fao.org/nr/water/infores_ databases_cropwat.html.
14- Falkenmark M., Rockström J., and Karlberg L. 2009. Present and future water requirements for feeding humanity, Food Security, 1(1): 59-69.
15- Gholamhossien pour jafari nejad A., Alizadeh A., and Neshat A. 2010. Study on Ecological Water Footprint and indicators of virtual water in Agricultural Section of Kerman Province. Journal of Irrigation and Water Engineering. 4th year. No. 13, Autumn 2013, p. 80-89. (in Persian with English abstract)
16- Hoekstra A.Y. (ed.). 2002. Virtual water trade: Proceedings of the International Expert Meeting on Virtual Water Trade, Delft, The Netherlands, 12-13 December 2002, Value of Water Research Report Series No.12, UNESCO-IHE, Delft, The Netherlands, www.waterfootprint.org/Reports/Report12.pdf.
17- Hoekstra A.Y., Chapagain A.K., Aldaya M.M., and Mekonnen M.M. 2011. The water footprint assessment manual: Setting the global standard, Earthscan, London, UK.
18- http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf.
19- IPCC. 2007. Summary for policy makers Climate change: The physical science basis. Contribution of working group I to the forth assessment report. Cambridge University Press, 881 PP
20- McGhee, J.W., “Introductory statistics”. West Publishing Co., New York, USA, 1985
21- Milly P. C. D., Dunne K. A., and Vecchia A. V. (2005) Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438(7066): 347-350.
22- Morid S., Massah Bovani A., Mohammad Zadeh M., and Gouds K. 2006. Assessing the risks of climate change and its impact on water resources in Zayandeh Rood river basin case study, the final report (thesis). Iran Water Resources Management Company.
23- Postel S. 2000. Entering an era of water scarcity: the challenges ahead. Ecological Applications 10, 941– 948.
24- Rosegrant M. W., Cai X., and Cline S. A. 2002. Global water outlook to 2025, International Food Policy Research Institute Washington, D.C., USA.
25- Rosegrant M. W., Ringler C., and Zhu T. 2009. Water for agriculture: Maintaining food security under growing scarcity, Annual Review of Environment and Resources, 34(1): 205-222.
26- Sajjad Khan M., Coulibaly P. and Dibike Y. 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319: 357–382.
27- Semenov M.A., and Barrow E.M. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climate Change, 35: 397–414.
28- Semenov M.A., and Brooks R.J. 1999.Spatial interpolation of the LARS-WG stochasticweather generator in Great Britain. Climate Research, 11: 137–148.
29- Shen Y., Oki T., Utsumi N., Kanae S., and Hanasaki N. 2008. Projection of future world water resources under SRES scenarios: Water withdrawal / Projection des ressources en eau mondiales futures selon les scenarios du RSSE: prelèvement d'eau, Hydrological Sciences Journal, 53(1): 11-33.
30- Strzepek K., and Boehlert B. 2010. Competition for water for the food system, Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554): 2927-2940.
31- Van Oel. P.R., Mekonnen. M.M., and Hoekstra. A.Y. 2009. The external water footprint of the Netherlands: Geographically-explicit quantification and impact assessment. Ecological Economics 69: 82–92.
32- Wang Y.B., Wu P.T., Engel B.A., and Sun S.K. 2015. Comparison of volumetric and stress-weighted water footprint of grain products in China, Journal of Ecological Indicators. Ecological Indicators 48 (2015) 324–333.
33- WWAP. 2009. The United Nations World Water Development Report 3: Water in a changing world, World Water Assessment Programme, UNESCO Publishing, Paris / Earthscan, London.
34- Zarghami M., Hasan Zadeh Y., Babaeian I., Kanani R., and Abdi A. 2010. Study of climate change and its impacts on watershed runoff in the East Azerbyjan. East Azerbyjan Regional Water Company.
CAPTCHA Image