| چکیده انگلیسی مقاله |
Drought is an inseparable part of climatic fluctuations which caused a lot of damages to different sections annually. Prediction of drought is recognized really useful in management of crisis and reduction of damages due to its the effect on different parts of environment, agricultural sections, natural resources, and wild life. In this research, monthly drought in 12 synoptic stations located in southeast of Iran during 1980 -2014 were calculated based on SPI index, Then, using Markov chain method, monthly drought during 2015- 2020 were predicted. Based on the results, in the most synoptic stations, normal, moderate dry and severe dry classes of drought have the highest frequency of occurrence. Transition probability matrix showed that, in all synoptic stations, probability of passing from a specific drought condition to the same drought condition and probability of passing from wet conditions to dry conditions were high, but the probability of passing from the dry conditions to wet conditions were low. Results of prediction in different synoptic stations with different of accuracy level (In Iran Shahe, Zabol, Zahedan, Bam and Saravan stations accuracy of prediction were 75%, In Jask, Kerman, Bandar Abbas and Shahr Babak stations accuracy of prediction were 79.1% and In Bandar Lengeh, Chahbahar and Sirjan stations the accuracy of prediction were 83.3%,) showed that, from 2015 to 2020 the normal, moderate and severe drought classes will be the highest probability of drought occurrence. In the study area, the classes of drought (from 1 to 7) are 13.3, 25.81, 26.74, 36.11, 4.75, 2.87 respectively and 0.69 percent of predicted months will be appropriated. Extended Abstract 1-Introduction Drought is an inseparable part of climatic fluctuations which caused a lot of damages to different sections annually. Prediction of drought is recognized really useful in management of crisis and reduction of damages due to its the effect on different parts of environment, agricultural sections, natural resources, and wild life. Drought situations in the time series data of the region are evaluated to monitor the drought condition in a region. Basically, for a quantitative analysis of drought, a specific index is needed to determine wet and dry periods accurately. So far, various indices have been presented for the evaluation of drought that Standardized Precipitation Index (SPI) is one of the most important which is recommended as the indices to assess the severity of drought. 2-Materials and Methods In the present research, SPI index , as one of the most widely used indicators to evaluate drought severity, drought severity classes in monthly time scale, were evaluated. To evaluate SPI index precipitation data series of 12 synoptic stations in southwest of Iran including Hormozgan, Kerman and Sistan and Balochistan provinces from 1980 to 2014 were used. Monthly drought classes for 2015-2020 were modeled and predicted by using Markov Chain method, as an adoptable prediction method with discrete data (among the different methods of drought prediction) and usable to predict drought classes. In this study, drought classes based on Standardized Precipitation Index (SPI) and Markov Chain method in 24 months (2013 and 2014 years) were predicted to assess the accuracy of predicted data. The predicted SPI classes were compared with observed SPI classes in mentioned 24 months. 3-Results and Discussion The results showed that, in the most synoptic stations, normal, moderate dry and severe dry classes of drought have the highest frequency of occurrence. Transition probability matrix created for each stations showed that, in all synoptic stations, probability of passing from a specific drought condition to the same drought condition and probability of passing from wet conditions to dry conditions were high, but the probability of passing from the dry conditions to wet conditions were low. Results of prediction in different synoptic stations with different accuracy level (In Iran Shahr, Zabol, Zahedan, Bam and Saravan stations accuracy of prediction were 75%, In Jask, Kerman, Bandar Abbas and Shahr Babak stations accuracy of prediction were 79.1% and In Bandar Lengeh, Chahbahar and Sirjan stations accuracy of prediction were 83.3%,) showed that, from 2015 to 2020 the normal, moderate and severe drought classes will be the highest probability of drought occurrence. In the study area, the classes of drought (from 1 to 7) are 13.3, 25.81, 26.74, 36.11, 4.75, 2.87 respectively and 0.69 percent of predicted months will be appropriated, while in almost all stations extreme wet and severe wet classes have the lowest frequency of occurrence. So far, Chahbahar, Jask, Kerman, Shahr Babak, Sirjan, Zabol and Zahedan stations will not be extreme wet situation during predicted 5 years. In other stations, the months with extreme wet class will be less than 3%. Based on accuracy analysis of predicted monthly drought classes in Bam, Iran Shahr, Saravan, Zabol and Zahedan stations accuracy of prediction was 75%, in Bandar Abbas, Shahr Babak, Kerman and Jask stations accuracy of prediction was 79.1% and in Bandar Lengeh, Chahbahar and Sirjan stations accuracy of prediction was 83.3%. 4-Conclusion Drought is one of the climatic phenomena and catastrophic events that has always been damaging human societies. Based on the growing needs and demands in the community for access to surface and sub-surface water resources in different sections, water resources management in the country will face a number of challenges in different fields, but these challenges will be more severe under the influence of drought occurrence. Therefore, due to the importance of this issue, basic measures at the national level along with management solutions were taken to prepare this case. Therefore, drought prediction can be effective in managing the crisis and controlling the damages caused by drought. Results of this paper showed that in next five years, the situation of study area from the point of atmospheric precipitation is not suitable. Therefore, according to results, we need to pay more attention to future droughts and correct management of drought in order to reduce the effects and damages caused by drought. |