Exploring and anticipation of famine during pasture covering growth periodCase study: Ghouri chay aquiferous zone (Pars abad Moghan city)
Subject Areas :saeide eini 1 , meysam toulabi nejad 2 , 3
1 -
2 -
3 -
Keywords: Exploring Anticipation Famine CanEMS2 model Pasture,
Abstract :
In order to famine exploring; temperature mean and daily data values , total of Pars abad station precipitation during 196-2016 statistical period for implementation of SPI Index were used in 1,3 and 6 months intervals and also TM Landset satellite images was applied for NDVI famine investigation. In anticipation part, CanESM2 was applied under RCP4.5 diffusion scenario in 5th series and also anticipating variables such as NCEP-NCAR 1961-2005 were applied. According to famine indices implementation (SPI and NDVI), temperature values anticipation and Pars abad station precipitation (CanESM2 model under RCP4.5 scenario), it was recognized that pasture growth situation in current conditions during flowering and seeding stages in some of main species of Ghouri chay aquiferous zone were improper. Mentioned main case was obtained through Pars abad Moghan station Ambrotermic curve and 1 month SPI famine index during 1375-1395 statistical periods, but, at the basis of anticipation temperature and precipitation values by using CanESM2 Model with 5th series model reports ; mentioned conditions will be drier during 1385-1477 and also pasture covering growth period will be reduced 6 months to 3 months. This case needs to management plans and aqueferous implementation in studied region as a natural reservoirs according to pasture high importance.
1. Asiaei, m. (2006). Drought Analysis Using the Standardized Precipitation Index (SPI) Case Study: Khorasan Province, Journal of Geosciences, No. 2, pp. 145- 122.
2. Akbari, M. (2004). Improvement of Irrigation Management of Farms Using Combination of Satellite, Farm and Simulation Modeling, SWAP, Ph.D., Tehran, Tarbiat Modares University.
3. Caccamo, J,. Chisholm, LA,. Bradstock, RA and Puotinen, ML,. (2011), Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing Environment. 115: 2626-2639.
4. Hadian, F. Hosseini, S. Z and Seyed Hosseini, M. (2014). Monitoring of vegetation changes using rainfall data and satellite images in northwest of Iran. Quarterly Journal of Iranian Pasture and Desert Research, Vol. 21, No. 4, pp. 768-756.
5. Jafarpour, Sh and Kanooni, A. (2015). Climate change scenarios in the fifth report of the Intergovernmental Panel on Climate Change and its comparison with the previous report, the 2nd National Conference on Natural Resources and Environment, Mohaghegh Ardebil University, Ardebil.
6. Jahangir, M. Honestly, Mrs. M and Yousefi, H. (2015). Monitoring and prediction of drought status using the standard rainfall index (SPI) and multi-layer perceptron neural network (Case study: Tehran and Alborz provinces), Ecohydrology, No. 4, pp. 428-417.
7. Ji, L and Peters, AJ,. (2003), Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment. 87: 85-98.
8. Khazaei, M. R and Khazaee, h. (2016). Uncertainty analysis of GCM models and emission scenarios in evaluating the effect of climate change on monthly runoff of Bashar basin, Journal of Environmental Science and Technology, No. 3, pp. 16-1.
9. Li, Z., Li, X., Wei, D., Xu, Ȳ and Wang, H .,(2010), An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences. 2: 964-969.
10. Mozazenzadeh, R., Senior, P., Hero, B and Judgment, K. (2012). Drought monitoring and non-aqueous cultures using remote sensing techniques. Journal of Water and Irrigation Management, No. 2, pp. 52-39.
11. Potop, V., Mozny, M and Soukup, J., (2012), Drought evolution at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic, Agricultural and Forest Meteorology. 156: 121-133.
12. Rajabi, A. (2011). Climate Change Uncertainty Analysis by SDSM Model in Kermanshah, Fourth Iranian Water Resources Management Conference, Amir Kabir University of Technology, Tehran.
13. Sari Sarraf, B., Mahmoudi, S., Zanganeh, S and Pashaei, Z. (2015). Monitoring and prediction of drought and drought in Tabriz using CLIMGEN model and SPI index, Hydrogeomorphology, No. 2, pp. 78-61.
14. Thompson, S. A., (1999), Hydrology for water management, Balkema, Rotterdam Netherlands, 362 pp.
15. Touma, D., Ashfaq, M., Nayak., M., Kao, S-C and Diffenbaugh, N., (2015), A multi-model and multi index evaluation of drought characteristics in the 21st century. Journal of Hydrology, 526: 196-207.
16. Wang, Q,. Adiku, S,. Tenhunen, J and Granier, A,. (2005), On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment. 94: 244-255.
