Future meteorological drought monitoring using CMIP5 series climate change model and Markov chain

Droughts are one of the most spatially extensive disasters that are faced by societies. Despite the urgency to define mitigation strategies, little research has been done regarding droughts related to climate change. The challenges are due to the complexity of droughts and to future precipitation uncertainty from Global Climate Models (GCMs). It is well-known that climate change will have more impact on developing countries. Among the most significant impacts of droughts to the environment are the acceleration of desertification processes, the increase in the risk of forest fires, the reduction of the availability of water resources for domestic and industrial use and the damage done to animals and vegetation These facts made the complexity of this phenomenon explicit. For instance, droughts are initiated by a meteorological drought, then they generate a hydrological drought, which may produce an agricultural drought and, in cases of prolonged occurrence, may cause a socio-economic drought. The final stage of a socio-economic drought may cause negative impacts, such as the loss of crops and livestock, a decrease in hydroelectric generation, migration, landscape degradation or social conflicts, among others. The main aims of this study were to determine drought occurrence periods and intensities in southern Iran by different drought indices (1), to compare different drought indices (2), Estimating the probability of drought occurring in the future for southern Iran.

Study area The coastal city of Bandar Abbas is the capital of Hormozgan province and is located in the south of Iran. This city is located in the form of a coastal strip in the north of the Strait of Hormuz. The coordinates of the area include 27°11' to 27° 12' 30" North 56° 20' to 56° 21' East with an area of 0.913 square kilometers. The average annual rainfall during a 57-year statistical period (1957 to 2010) in Bandar Abbas is 172.6 mm. During the wet season (November to April) the rainfall is 94% of the annual rainfall and during it. In the dry season, the rainfall is 6% of the annual rainfall.

In the research In order to monitor and evaluate drought assess the representation of droughts from statically down scaled GCMs in the present and evaluate the temporal structure and variability of future meteorological droughts in the south of Iran under RCP 4.5 and RCP 8.5 scenarios. This is done by using products (MPI-ESM-MR) from the Coupled Model Intercomparison Project 5 (CMIP5) of the Third National Communication on Climate Change. The Standardized Precipitation Index (SPI) and Markov chain for droughts Possibilities were used to characterize extreme, severe and moderate droughts in the present (period 1982–2005) and the future (period 2016–2045). This study contributes to the spatial and temporal characterization of present and future droughts, and offers a contrasting analysis between them. In order to evaluate the efficiency of the down scaled method have been used the mean relative error (MRE), root-mean-square error of RMSE and MAE.

The results of downscale methods showed that the CF-variance method has better correlation and less error than the observed data. The results of the station Markov chain showed that the highest probability is related to dry to normal in summer and dry to wet or normal with 77.8% and 42.9%, respectively. According to the SPI index, the study area will experience more severe and prolonged droughts in the future according to both scenarios of atmospheric circulation model than the historical period. According to Scenario 4.5, with increasing the timescale of SPI, the severity of drought has decreased, so that according to the 6-month SPI, the drought has an intensity of -1.83 and according to the annual SPI has an intensity of -1.66. In the 6-month period, the average dry class and in the 9- and 12-month periods, the normal to wet class have the highest frequency. According to scenario 8.5 according to the SPI classification, autumn and summer are in the near normal (mild) class. Winter and spring fluctuate between drought and non-drought. In Part B, the index ranges from 6, 9 and 12 months in the normal to non-drought grade. Markov's probability should increase from dry to wet for months with one class. In other months, such as April, May, June and July, we probably see different things. These results are similar to the 4.5 scenario, which shows more probabilities in the normal class for several months on average. Comparison of the results of Markov chain probability for the base and future period showed that the probability of wet to dry class occurring for spring, summer and winter seasons is so that the probability of this class occurring for the base and future period according to scenario 4.5 and 8.5 It is equal to 57, 60 and 60 percent for spring, respectively. It is equal to 77.8, 67 and 50 percent for summer and 66.7, 75 and 75 percent for winter, respectively. The results showed that the probability of Markov chain for autumn in the base period from wet to normal with 89% probability to dry class to normal for the next period according to scenario 4.5 and 8.5 is equal to 89 and 90%, respectively. The results from the time periods of 6, 9 and 12 months showed that the probability of occurrence of Markov chain classes for similar scenarios of 4.5 and 8.5 are slightly different with a small percentage of probability. In examining the possibility of drought with the Markov chain, it was observed that each floor tends to move to its nearest floor. A similar issue has been reported in studies (Moreira et al., 2006; Paulo and Pereira, 2007; Yeh et al., 2014) in the study of drought using the Markov chain. According to the diagrams presented for both scenarios, the most probable is related to the normal class. According to the results, with the increase of wet season and drought, the possibility of stagnation has decreased.