Climate model; Downscaling; Fars; GIS; Interpolation

Document Type : Scientific - Research

Authors

1 Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, Iran

2 Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

3 Department of Agricultural Meteorology, Iran Meteorological Organization, Tehran, Iran

Abstract

Introduction
Today, Climate change issue is one of the main challenges for scientists and due to the critical role of water in human life, the study of climate change impacts on severity and frequency of drought in each region seems to be indispensable (Hulme et al., 1999). Drought is usually occurred over a period of water shortage owing to less rainfall, high evapotranspiration and pumping a huge amount of water from water tables. This issue could have extensive consequences on agriculture, ecosystems and communities. The objectives of this study were to predict meteorological parameters, calculation of drought index and its zoning under the changing climate in Fars province.

Materials and methods
In order to predict the future climate in nine districts of Fars province (including Shiraz, Eghlid, Fasa, Lar, Lamerd, Darab, Zarghan, Neiriz and Abadeh), two climate models (HadCM3 and IPCM4) was applied under three scenarios (B1, A1B and A2). LARS-WG software was applied to downscale climate parameters (Semenov and Barrow, 2002). To predict incident probability of drought in the all study locations, a drought index (Standardize Precipitation Index, SPI) was calculated at a time scale of 12 months. SPI is the most commonly used drought index. SPI is calculated based upon the differences between monthly rainfall and average rainfall for a certain period of time according to the time scale (Mckee et al., 1995). In this study the SPI time series have been estimated for the historical base period 1980-1990 and for three future periods (2011-2030, 2046-2065, 2080-2099). Finally, drought maps and zoning were conducted in the whole province using GIS and based on IDW interpolation method.

Results and discussion
Results of climate models evaluation indicated that LARS-GW well predicted radiation, and maximum and minimum temperatures (RMSE of 0.51, 0.46 and 1.02%, respectively). However, the accuracy in prediction of rainfall was not as good as the other climatic variables (RMSE of 11.48%). This is mainly due to the fact that there is a high variability in rainfall under arid and semi-arid conditions. Other studies also showed that LARS-WG often over- or underestimate rainfall compared with other climatic variables. According to the simulated aridity index in the baseline period, Abadeh and Lar classified into extreme drought class (-2.48 and -2.09) while under future climate change Lamerd categorized in the extreme drought class. The most severe drought occurred in Neyriz (1.33) using HadCM3 model under A2 scenario in 2080-2099. While, the lowest drought severity obtained in Lamerd (-2.58) using IPCM4 model under A1B scenario in 2046-2065. According to the zoning maps, a vast majority of Fars province had normal climate in the baseline which, are mainly located in southern part of Fars including Neyriz, Darab, Fasa, Lamerd and Eghlid. In contrast, only a limited part of the study locations classified as drought included Abadeh, Zarghan and Lar. Results of t-test also showed that there is no difference between HadCM3 and IPCM4 climate models in terms of future climate prediction (p≥0.05). Results also revealed that for most of study locations, SPI would be in normal class for the all three periods compared with the baseline.
Drought zoning in the baseline in 12 month time scale indicated that the lowest drought was occurred in southern part of Fars while the most severe was observed in both northern areas and some limited part of the south. It was generally concluded that the major part of the Fars province was in normal (the southern half of the province) and moderate class (the northern half of the province) for baseline period according to SPI. However, for projected period, major part of regions would be in normal class. As the Fars province is one of the major producers of cereals in the country, it is estimated the area will benefit from climate change in the future particularly under rainfed conditions.

Conclusion
The results of the current study showed that drought would be intensified under climate change in Fars province and most of the area will benefit from changing climate in the future. However, it is necessary for the authorities to take the results into account, and have applicable water resources management strategies to be able to deal with possible problems in the future decades. Decision makings also should be accomplished with especial considerations to the uncertainties that almost appear in the results.

Acknowledgements
The authors acknowledge the financial support of the project (No. 600/4330 on March 2015) by Vice President for Research and Technology, Shahid Beheshti University, G.C., Iran.

Keywords


1- Abarghouei, B.H., Asadizarch, M.A., Dastorani, M.T., Kousari, M.R., and Safari Zarch, M. 2011. The survey of climatic drought trend in iran. Stochastic Environmental Research and Risk Assessment 25: 851-863.
2- Aggarwal, P.K. 1994. Simulating the effect of climatic factors, genotype and management on productivity of wheat in India. Agricultural Research Institute p. 1-11.
3- Almorox, J., Benito, M., and Hontoria, C. 2005. Estimation of monthly Angstrom–Prescott equation coefficients from measured daily data in Toledo, Spain. Renewable Energy 30: 931-936.
4- Ansari, H., and Davari, K. 2007. Zoning drought period using standardized precipitation index in GIS (case study: Khorasan province). Journal of Geographical researches 60: 97-108. (In Persian)
5- Babaeian, I., Najafi Nik, Z., Zabol Abasi, F., Habibi Nokhandan, M., Adab, H., and Malbousi, S. 2010. Assessment of climate change of country in 2010-2039 period using General Circulation Model data of ECHO-G. Quarterly of Geography and Development 16: 135-152. (In Persian)
6- Bannayan, M., and Eyshi Rezaei, E. 2014. Future production of rainfall wheat in Iran (Khorasan province): Climate change scenario analysis. Mitigation Adaptation Strategy Global Change 19: 211-227.
7- Bannayan, M., Kobayashi, K., Kim, H.Y., Liffering, M., Okada, M., and Miura, S. 2005. Modeling the interactive effects of atmospheric CO2 and N on rice growth and yield. Field Crops Research 93: 237- 251.
8- Bannayan, M., Lotfabadi, S., Sanjani, S., Mohammadian, A., and Agaalikhani, M. 2011. Effects of precipitation and temperature on cereal yield variability in northeast of Iran. International Journal of Biometeorology 55: 387- 401.
9- Bhalme, H.N., and Mooley, D.A. 1980. Large-scale droughts-floods and monsoon circulation. Monthly Weather Review 108: 1197-1211.
10- Confalonieri, U., Menne, B., Akhtar, R., Ebi, K.L., Hauengue, M., Kovats, R.S., Revich, B., and Woodward, A. 2007. Human Health, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., and Hanson, C.E. Cambridge University Press, Cambridge, UK. 391-431.
11- Dastorani, M.T., Massah Bavani, A.R., Poormohammadi, S., and Rahimian, M.H. 2011. Assessment of potential climate change impacts on drought indicators (case study: Yazd station, central Iran). Desert 16: 159-167.
12- Dibike, Y.B., and Coulibaly, P. 2005. Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models. Journal of Hydrology 307: 145-163.
13- Dinar, A., Mendelsohn, R., Evenson, R.E., Parikh, J., Sanghi, A., Kumar, K., McKinsey, J., and Lonergan, S. 1998. Measuring the Impact of Climate Change on Indian Agriculture. World Bank Technical Paper 402, Washington, D.C. p. 281.
14- Edwards, D.C., and McKee, T.B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report Number 97-2. p. 155. Colorado State University, Fort Collins, Colorado.
15- Eyni Nargeseh, H. 2014. Predicting the possible impacts of climate change on wheat yield in Fars province using APSIM-Wheat. Master Dissertation, Department of Agroecology, Shahid Beheshti University of Tehran, Iran. (In Persian with English Summary)
16- Eyni Nargeseh, H., Deihim Fard, R., Soufizadeh, S., Haghighat, M., and Nouri, O. 2014. Predicting impacts of climate change on irrigated Wheat yield in Fars province. In the conference of new finding in environmental and agricultural ecosystems, Tehran university, Iran, 20 December 2014. (In Persian with English Summary)
17- Eyshi Rezaie, E., and Bannayan, M. 2012. Rainfed wheat yields under climate change in northeastern Iran. Meteorological Application 19: 346-354.
18- Eyvazi, M., and Mosaedi, A. 2011. Monitoring and analysis of meteorological drought in Golestan province using geostatistical methods. Journal of Range and Watershed 1: 65-78.
19- Farhanfar, S., Bannayan, M., Khazaei, H.R., and Mousavi Baygi, M. 2015. Vulnerability assessment of wheat and maize production affected by drought and climate change. International Journal of Disaster Risk Reduction 13: 37-51.
20- Gandomkar, A., Hoseini Laghab, G.H.H., and Tirband, M. 2010. Zoning climate of Fars province by De Martonne method using GIS. National Conference of Human, Environment and Sustainable Development. 10-11 March. Islamic Azad University of Hamedan, Iran. (In Persian)
21- Hajarpoor, A., Yousefi, M., and Kamkar, B. 2012. Accuracy Assessment of Weather Assimilators of CLIMGEN, LARS-WG and Weather Man in Assimilation of three Different Climatic Parameters of three Different Climates (Gorgan, Gonbad and Mashhad). Geography and Development Iranian Journal 12(35): 201-216. (In Persian with English Summary)
22- Hammer, G.L., and Nicholls, N. 1996. Managing for climate variability: The role of seasonal climate forecasting in improving agricultural systems, In: Proc. Second Australian Conference on Agricultural Meteorology. Bureau of Meteorology, Commonwealth of Australia, Melbourne, Australia p. 19-27.
23- Hashmi, M.Z., Shamseldin, A.Y., and Melville, B.W. 2009. Downscaling of future rainfall extreme events: a weather generator based approach. The 18th World IMACS Congress and MODSIM09 International Congress on modelling and Simulation. Cairns, Australia.
24- Hoogenboom, G., Jones, J.W., Porter, C.H., Wilkens, P.W., Boote, K.J., Batchelor, W.D., Hunt, L.A., and Tsuji, G.Y. 2003. Decision Support System for Agrotechnology Transfer Version 4.0. Vol. I: Overview. University of Hawaii, Honolulu, HI p. 60.
25- Huang, Y., Yu, Y., Zhang, W., Sun, W., Liu, S., Jiang, J., Wu, J., Yu, W., and Yang, Z. 2009. Agro-C: A biogeophysical model for simulating the carbon budget of agroecosystems. Agriculture and Forest Meteorology 149: 106-129.
26- Hulme, M., Barrow, E.M., Arnell, N.W., Harisson, P.A., Jones, T.C., and Dowing, T.E. 1999. Relative impacts of human-induced climate change and natural climate variability. Nature 397: 688-691.
27- IPCC. 2007. Climate change (2007): The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, UK, and New York, NY, USA. 996 pp.
28- IPCC. 2013. Summary for policymakers. In: Climate Change (2013): Fifth assessment report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York.
29- IPCC. 2014. Summary for policymakers. In: Climate Change (2014): Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., and White, L.L. (Eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA p. 1-32.
30- Jamieson, P.D., Porter, J.R., and Wilson, D.R. 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research 27: 337- 350.
31- Jiang, R., Xie, J., He, H., and Lou, J. 2014. Use of four drought indices for evaluating drought characteristics under climate change in Shaanxi, China: 1951-2012, Nat Hazards.
32- Khaliliaqdam, N., Mosaedi, A., Soltani, A., and Kamkar, B. 2013. Evaluation of ability of LARS-WG model for simulation some weather parameters in Sanandaj. Journal of Water and Soil Conservation 19(4): 85-102. (In Persian with English Summary)
33- Khosravi, M., Movagheri, A.R., and Mansoori Daneshvar, M.R. 2011. Assessment of PNI, RAI, SIP and SPI for zoning drought severity of Iran by comparing two interpolation method IDW and digital elevation model DEM. Geography and Environmental Sustainability 5: 53-70. (In Persian with English Summary)
34- Kilsby, C.G., and Jones, P.D. 2007. A daily weather generator for use in climate change studies. Environmental Modeling and Software 22: 1705-1719.
35- Koocheki, A., Nassiri, M., Kamali, G.A., and Shahandeh, H. 2006. Potential impacts of climate change on agroclimatic indicators in Iran. Arid Lands Research and Management 20: 245-259.
36- Li, B., Su, H., Chen, F., Wu, J., and Qi, J. 2013. The changing characteristics of drought in China from 1982 to 2005. Nat Hazards 68: 723-743.
37- Loukas, A., Vasidiales, L., and Tzabiras, J. 2008. Climate change effects on drought severity. Advances in Geosciences 17: 23-29.
38- McKee, N.J., Doesken, T.B., and Kleist, J. 1995. Drought monitoring with multiple time scales. Proceedings of the Ninth Conference on Applied Climatology Boston, MA. American Meteorological Society p. 233-236.
39- McKee, T.B., Doesken, N.J., and Kleist, J. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology. Boston, MA: American Meteorological Society 179-184.
40- Moafi Madani, F., Mousavi Baygi, M., and Ansari, H. 2012. Predicting of drought in the Khorasan Razavi province during 2011-2030 by using statistical downscaling of HadCM3 model output. Geography and Environmental Hazards 3: 3-4.
41- Mohamadian, A., Kouhi, M., Adineh Baigi, A., Rasouli, S.J., and Bazrafshan, B. 2010. Comparison of monitoring of drought using SPI, DI and PNI and zoning them (case study: northern Khorasan province). Journal of Water and Soil Conservation 17(1): 177-184. (In Persian with English Summary)
42- Moradi, H.R., Rajabi, M., and Faraj Zadeh, M. 2007. Analysis of trend and spatial characteristics of drought severities in Fars province. Journal of Pasture and Desert researches 14(1): 97-109. (In Persian with English Summary)
43- Mosaedi, A., and Ghobadi Sogh, M. 2011. Modification of Standardized Precipitation Index (SPI) based on relevant probability distribution function. Journal of Water and Soil 25(5): 1206-1216. (In Persian with English Summary)
44- Nakicenovic, N., and Swart, R. 2000. Emissions scenarios. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
45- Nassiri, M., Koocheki, A., Kamali, G.A., and Shahandeh, H. 2006. Potential impact of climate change on rainfed wheat production in Iran. Archives of Agronomy and Soil Science 52: 113-124.
46- 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 to climate change. European Journal of Agronomy 34: 96-112.
47- Palmer, W.C. 1965. Meteorological drought. US Department of Commerce, Weather Bureau, Washington, DC. No, 45. p. 65.
48- Palmer, W.C. 1968. Keeping track of crop moisture conditions, nationwide: the new crop moisture index, Weatherwise 21(4): 156-161.
49- Prieto-Gonzalez, R., Cortes-Hernandez, V.E., and Montero-Martinez, M.J. 2011. Variability of the standardized precipitation index over Mexico under the A2 climate change scenario. Atmosfera 24(3): 243-250.
50- 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.
51- Racsko, P., Szeidl, L., and Semenov, M.A. 1991. A serial approach to local stochastic weather models. Ecological Modeling l(57): 27-41.
52- Sanaiee Nezhad, H., Ansari, H., Davari, K., and Morid, S. 2003. Monitoring and assessment of severity of drought periods of Mashhad in Different time scales using Standardized Precipitation Index. Journal of Soil and Water Sciences 17(2): 201-209.
53- 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.
54- Semenov, M.A., Brooks, R.J., Barrow, E.M., and Richardson, C.W. 1998. Comparison of the WGEN and LARSWG stochastic weather generators for diverse climates. Climate Research 10: 95-107.
55- Shahian, R., Jame, A., Arianfar, R., Haghighat, M., and Dehghan, M. 2009. Zoning threshold drought cisis of Fars province by application rainfall criterion index SPI and GIS. Journal of Water Resources Engineering 2(4): 33-42. (In Persian)
56- Sheffield, J., and Wood, E.F. 2011. Drought: past problems and future scenarios, Earthscan, London, UK and Washington, DC, USA. p. 184.
57- Valizadeh, J., Ziaei, S.M., and Mazloumzadeh, S.M. 2013. Assessing climate change impacts on wheat production (a case study). Journal of the Saudi of Agricultural Sciences 78: 2-9.
58- Vicente-Serrano, S.M., Begueria, S., and Lopez-Moreno, J.I. 2010. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate 23: 1696-1718.
59- 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.
60- Wilby, R.L., and Wigley, T.M.L. 1997. Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography 21: 530-548.
61- Wilhite, D.A., and Glantz, M.H. 1985. Understanding: The drought phenomenon: The role of definitions. Water International 10: 111-120.
62- Yan-Jun, L., Xiao-dong, Z., Fun, L., and Jing, M.A. 2012. Analysis of drought Evolvement characteristics based on Standardized Precipitation Index in the Huaihi river basin. Procedia Engineering 28: 434-437.
63- Zamaniyan, M.T., Behyar, M.B., Karimi Hosseini, A., and Vazifedoust, M. 2012. Agricultural drought monitoring and analysis using remotely sensed data from NOAA-AVHRR. Journal of Climate Research 3(9): 102-103.
CAPTCHA Image