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Assessment of climate change using SDSM downscaling Model (A case study: West of Iran) | ||
| Water Harvesting Research | ||
| دوره 4، شماره 1، تیر 2021، صفحه 29-39 اصل مقاله (1.2 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2019.2720.1029 | ||
| نویسندگان | ||
| Mohamad Hosein Mahdian2؛ Bagher Qermezcheshmeh3؛ | ||
| 1Associate Professor in Soil Conservation and Watershed Management Research Institute (SCWMRI), AREEO, Tehran, Iran. | ||
| 2Retired Professor in AREEO Tehran, Iran. | ||
| 3Assistant professor in Soil Conservation and Watershed Management Research Institute (SCWMRI), AREEO, Tehran, Iran. | ||
| چکیده | ||
| Climate change impacts are very dependent on regional geographic features, local climate variability and socio-economic status. Therefore, impact assessment researches on climate change must be launched at the local or at the regional level so that the evaluation of consequences can take place. Climate scenarios are produced by Global Circulation Models for the entire Globe with spatial resolutions of several hundred kilometers. For this reason, downscaling methods are used to bridge the gap between the large-scale climate scenarios and the fine scale where local impacts happen. In order to overcome limited computing power and for catchments with limited data, statistical downscaling is the most feasible approach in obtaining climate data for future impact investigations. So a decision support model named SDSM was used to downscale the data. Model errors and uncertainties were estimated using non-parametric statistical methods at the 95% confidence interval for precipitation, maximum temperature and minimum temperature for the mean and variance for a single site in Kermanshah in the western part of Iran. The comparison between the observed dataset and the simulations showed that the SDSM model was able to better represent the minimum and maximum temperature while for precipitation simulations are slightly under-estimated but still acceptable according to statistical tools. It is also presented simulations for the A2 SRES scenario for the 2041-2069 periods showing that the method can produce similar general tendencies. | ||
| کلیدواژهها | ||
| AOGCMs؛ downscaling؛ greenhouse gases؛ SDSM؛ SRES scenarios | ||
| مراجع | ||
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Corte-Real J, Qian B, Xu H (1999) Circulation patterns, daily precipitation in Portugal and implications for climate change simulated by the second Hadley Centre GCM. Climate Dynamics 15:921-935.
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change IPCC, Geneva, Switzerland, 104pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution to Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change IPCC, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA 996pp.
IPCC (2001) Climate Change 2001: Synthesis Report. Contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change IPCC, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA 398 pp.
IPCC (2000) Special Report on Emissions Scenarios, Cambridge University Press, Cambridge
Goodarzi, Massoud, Chubeh, Sepideh, (2019), Assessment of Downscaling Methods in Predicting Climatic Parameters under Climate Change Status: A case study in Ardabil Synoptic Station, Iranian Journal of Watershed Management Science and Engineering, Vol. 13 No. 45, (In Persian)
Lopez, Pedro Miguel de Almeida Garrett Garca (2008) Assessment of climate change statistical downscaling methods, Msc. Thesis, Universidade Nova de Lisboa, Portugal, 51pp.
Massah Bavani, A.R., and Morid, S. (2006) Impact of climate change on the water resources of Zayandehrud basin, J. Sci. and Technol. Agric. and Natur. Resour. 9: 4. 28-34. (In Persian).
Spak S, Holloway T, Lynn B, Goldberg R (2007) A comparison of statistical and dynamical downscaling for surface temperature in North America. Journal of Geophysical Research Atmospheres 112(D8): 8101-8117.
Wilby RL, Dawson CW, Barrow EM (2002) SDSM a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software 17:147-159.
Wilby RL, S.P., Zorita E, Timbal B, Whetton P, Mearns L (2004) Guidelines for the use of climate scenarios developed from statistical down-scaling methods.
Wilby RL, Dawson CW, (2008), SDSM 4.2, a decision support tool for the assessment of regional climate change impacts.
Beheshti, Maryam, Heidari, ali and Saghafian, Bahram, Susceptibility of Hydropower Generation to Climate Change: Karun III Dam Case Study, Water 2019, 11, 1025; doi:10.3390/w11051025, www. mdpi.com/journal/water.
IPCC, (2014). Summary for Policymakers. In Climate Change 2014, Mitigation of Climate Change; Cambridge University Press: Cambridge, UK.
IPCC (2013). Summary for Policymakers. In Climate Change 2013: The Physical Science Basis; Cambridge University Press: Cambridge, UK.
Pieter de Jong et al., 2018, Hydroelectric production from Brazil's São Francisco River could cease due to climate change and inter-annual variability, Science of The Total Environment, Volume 634, Pp. 1540-1553.
Fowler, Hayley J. et. al., 2007, climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. international journal of climatologyInt. J. Climatol.27: 1547 – 1578.
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