Outlook for the effects of climate change on drought according to the fifth IPCC report (case study: Ilam)

Subject Areas : Drought in meteorology and agriculture

Hadi  Ramezani Etedali ¹ (Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran.)
Fariba  hodabakhshi ² (Department of Water Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.)
Elahe  Kanani ³ (Department of Water Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.)

Received: 2022-05-15 Accepted : 2022-07-29 Published : 2022-09-23

Keywords: SDSM Model, Meteorological drought, RCP scenarios,

Abstract :

Background and Aim: The process of climate change, especially the change in temperature and precipitation, and its effect on the drought phenomenon constitute the most important topic of discussion in environmental sciences. Studying climate change and its effect on the severity and frequency of droughts in the coming decades can considerably contribute to the planning for the proper use of water resources and adapting to the destructive effects of droughts. To this end, based on the SDSM microscale method, precipitation and temperature during the 2020-2100 period were predicted in the present study using the large-scale CanESM2 model, and the effects of climate change on the meteorological drought in Ilam Province were studied using the SPI and RDI indices.Method: In this study, the required climatic data are obtained through one of the data reporting sites of the IPCC AR5. Twenty one CMIP5 models have been used to predict Precipitation and temperature parameters in the future. The weighting method of observational means was used to investigate the uncertainty caused by using the studied models. Then, using the SDSM model, meteorological data are generated under three scenarios: RCP 2.6, RCP4.5 and RCP8.5 of the CanESM2 model. Model historical data from 1993 to 2005 will be used to analyze the SDSM model using NCEP re-analysis data, and data from 2006 to 2100 will be used to estimate future climate change. The SPI and RDI time series were calculated in three scales of 3, 6 and 12 months for the historical and future period. The intensity, duration and frequency of the drought, according to the run theory for the spi and RDI on scale 3, 6 and 12 months.Results: According to the results of the uncertainty analysis, the CanESM2 model had the highest weight compared to other models for both temperature and precipitation variables. Analysis of trend in precipitation and temperature data by non - parametric mann - kendall test showed that temperature in scenario rcp8.5 has a significant positive trend ( increasing ) at 0.01 and precipitation has decreasing trend. The results of drought monitoring showed that in both SPI and RDI indices, the frequency of dry period’s decreases with increasing time scale, which is accompanied by an increase in the severity and duration of drought. The RDI index shows higher drought characteristics than SPI due to considering the average temperature in calculating dry periods. The greatest drought in the historical period according to the SPI index on a 12-month scale has an intensity of 38.22 and a duration of 26 months. RDI index on a 12-month scale has an intensity of 39.14 and a duration of 26 months.The study area will experience more severe and longer droughts in the future according to all three scenarios than in the historical period. The severity of droughts produced by RCP4.5 RCP2.6 and RCP8.5 scenarios based on 12-month SPI is 11%, 52% and 65%.Conclusion: The results of the analysis with the SDSM model and the SPI and RDI drought indices indicated that in the future climatic conditions in the 2020-2100 period, an increase in temperature and a decrease in precipitation are probable. To wit, temperature rises by 3.798 C and precipitation decreases by 6.8%. The results also revealed that the study area will experience more severe and longer droughts in the future than in the historical period under all three RCP2.6, RCP4.5 and RCP8.5 scenarios. As the time scale of the drought index increases, the severity and duration of the drought increases. The RDI index has a high behavioral similarity to the SPI, but the RDI index is sensitive to environmental changes and provides better results.

References:

Ahmadvand Kahrizi M, Rouhani H. 2016. Assessing the conservation impacts of climate change based on temperature projected on 21 century (Case study: Arazkoseh and Nodeh stations). Journal of Echo Hydrology. 3 (4): 597-609. [In Persian].

Angledis, P.; F. Maris; N. Kotsovinos.: V. Hrissanthou. 2012. Computation of drought index SPIwith alternative distribution functions. Water resources management.

Ashofteh, P.S. and Massah, A.R., 2010. Impact of climate change uncertainty on temperature and precipitation of Aidoghmoush basin in 2040-2069 period. Soil and Water Science, 19.1(20), pp.85-98. [In Persian].

Ashofteh, P.S. and Massah, A.R., 2012. Investigation of AOGCM model uncertainty and emission scenarios of greenhouse gases impact on the basin runoff under climate change, case study: Gharanghu Basin, East Azerbaijan. Iran-Water Resources Research, 8(2), pp.36-47. [In Persian].

Aziziabadi Farahani M, Bakhtiari B, Ghaderi K, Rezapour M. 2016.  Investigating the Impact of Climate Change on Hardness-Drought Frequency Curves of Qarahsoo Basin Using Detailed Functions, Iranian Soil and Water Research. 4: 743-754. [In Persian].

Bahak, B. 2013. Investigating the likelihood of climate change in Kerman province using the Man Kendall method (A case study of Kerman station), Geographical Quarterly of the Territory, Year 10, No. 39. pp. 72-65.

Bazrafshan, A., and Mahmoudzadeh, F., and Asgarinejad, A., and Bazrafshan, J. 2019. Comparative comparison of SPI, RDI and SPEI indices in analyzing the trend of severity, duration and frequency of drought in arid and semi-arid regions of Iran. Engineering and Irrigation Sciences (Scientific Journal of Agriculture), 42 (3), 117-131. [In Persian].

Edossa, D.; M. Babel; A. Gupta. 2010. Drought analysis in the awash river basin, Ethiopia. Waterresources management, 24:1441-1460.

IPCC. 2013. Climate change 2013: the physical science basis. Contribution of Working Group I to the 5th Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jahangir, M., & amrai, n., & Norozi, E. (2020). Predicting Variables Climate, Temperature and Precipitation by Multiple Linear the Model SDSM (Case Study: Tehran Synoptic Station) (Technical Report). JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 11(21), 303-311. [In Persian].

Khalili A, Bazr Afshan J.2007. Evaluation of return period and drought duration risk using annual secular precipitation data in ancient stations of Iran. 2th conference on water resources management. Isfahan University of Technology. Science and natural resources. 15: 176-182. [In Persian].

Khan, M.S.; Coulibaly, P. and Dibike, Y. 2006. Uncertainty analysis of statistical downscaling methods, Journal of Hydrology, 6: 357-382.

Khorshiddoust A, sarraf B, ghermez cheshmeh B, jafarzadeh F.2017. Estimation and Analysis of Caspian Region's Future Rainfalls by Using General Atmospheric Circulation Models. Researches in Geographical Sciences. 17 (47): 213-226. [In Persian].

Koohi, M., Sanaeinejad, Sarh. And Amini, M. 2016. The effect of different methods for estimating reference evapotranspiration on the calculation of drought identification index in several climatic samples of Iran. Journal of Climatological Research, 25: 47-65. [In Persian].

Lee, S. H, Yoo, S. H., Choi, J. Y., Bae, S. 2017. Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981-2100. MDPI 9(579): 1-19.

Lopes, P.M.D.A.G. 2008. Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon (Doctoral dissertation, FCTUNL).

McKee, T.B.; N.J. Doesken; J. Kliest. 1993. The relationship of drought frequency and durationto time scales. Proc. of the 8th Conference on Applied Climatology, American MeteorologicalSociety, Boston, 179-184.

Mishra, A. K., & Singh, V. P. (2011, April 6). A review of drought concepts. Journal of Hydrology.

Mohamadian, A., M. Kuohi, A. Adineh Baigi, S. J. Rasouli and B. Bazrafshan 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].

Quiring, S. M. and T. N. Papakryiakou. 2003. An evaluation of agricultural drought indices for the Canadian prairies. Journal of Agricultural and Forest Meteorology 118(1-2): 49-62.

Raziei,T., Shokoohi, A., Ebadi, F. 2011. Identification of the best fitted probability distribution function on precipitation data in different climate regimes of Iran, to calculate Standardized precipitation index (SPI) at different time scales. The first National Conference on Drought and Climate Change [In Persian].

Rezaei M, Nehtani M, Abkar A, Rezaei M, Mirkazhi Rigi M. Performance Evaluation of Statistical Downscaling Model (SDSM) in Forecasting Temperature Indexes in Two Arid and Hyper Arid Regions (Case Study: Kerman and Bam). Journal of Watershed Management Research. 2015; 5 (10): 117-131. [In Persian].

Sheykh Rabiee, M., Peyrowan, H., Daneshkar Arasteh, P., Akbary, M., Motamedvaziri, B. (2022). Evaluating the efficiency of climate change models in simulating temperature and precipitation components (Case study of Karganrood watershed). Journal of Geography and Environmental Studies, 11(41), 123-142. [In Persian].

Shokoohi, A. (2012). Comparison of SPI and RDI in drought analysis in local scale with emphasizing on agricultural drought (Case study: Qazvin and Takestan). Irrigation and Water Engineering Volume 3, Issue 1 -Serial Number 9 Pages 111-122. [In Persian].

Stagge, J. H., Tallaksen, L. M., Gudmundsson, L. Van Loon, A. F. and Stahl, K. 2015a. Candidate distributions for climatological drought indices (SPI and SPEI). International Journal Climatology 35: 4027-4040

Tai S, Moradi S, Khodagholi M. 2014 Simulation and Prediction of Some Climatic Elements by SDSM Multiple Linear Model and Atmospheric Circulation Models (Case Study: Neishabour Watershed), Journal of Human and Environment. 28: 15-1. [In Persian].

Thilakarathne, M. and Sridhar, V. 2017. Characterization of future drought conditions in the Lower Mekong River Basin. Weather and Climate Extremes 17:47-58.

Torabi Tabatabai, F., and Shamsnia, S. 2019. Comparison of different methods for estimating potential evapotranspiration on the results of Drought Identification Index (RDI) in Tehran province. Water Resources Engineering, 12 (41), 31-48. [In Persian].

Tsakiris, G.; D. Pangalou; H. Vangelis. (2007). Regional drought assessment based onReconiassance Drought Index (RDI). Water resources management, 21:821-833.

Vrochidou, A.-E.K., Tsanis, I.K., Grillakis, M.G., Koutroulis, A.G., (2013),The impact of climate change on hydrometeorological droughts at a basin scale, Journal of Hydrology, Vol. 476 .290-301.

Wilby, R.L., Dawson, C.W., and Barrow, E.M. (2002). SDSM-A decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software 17 (2):147-159.

Yevejevich V. (1967) an objective approach to definition and investigation of continental hydrologic droughts. Hydrology paper 23. Colorado University Press. Fort Collins.

zoheyri Z, ghazavi R, omidvar E, Davudi_rad A. (2020)  Comparison of LARS-WG and SDSM Downscaling Models for Prediction Temperature and Precipitation Changes under RCP Scenarios. Arid Regions Geographic Studies. 10 (40):39-52. [In Persian].