Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Adeli b.; NAFARZADEGAN A.R.; MALEKIAN A.; NOHEGAR A.

Title

Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran

Author(s)

Adeli b. | NAFARZADEGAN A.R. | MALEKIAN A. | NOHEGAR A. | Issue Writer Certificate

Keywords

droughtQ1

stochastic modelQ1

Markov chainQ2

equilibrium probabilityQ2

state transitionQ2

Abstract

drought is an unexpected reduction in rain during a certain period of time in an area which is not necessarily arid (Warren and Khogali, 1992). Conventionally, decrease in precipitation is considered as the origin of meteorological drought. This leads to a reduction of storage volumes and fluxes involved in the hydrological cycle. On the other hand, planning and decision making in the ﬁ eld of water resources requires prediction of drought events (Nafarzadegan et al. 2012). Since Markov processes was introduced, it has been widely applied in natural science especially hydrology and water resources planning. So that Markov processes also played an important role in drought prediction. In this study the occurrence probability of drought events was investigated, using the Standardized Precipitation Index (SPI) and the combination of RUN theory and a Markov chain model in Khuzestan province, Iran. Khuzestan province in southwest of Iran is located in the southern hills of Zagros Mountain range (29° 56΄ N, 50° 22΄ E to 33° 05΄ N, 47° 42 E) and covers an area of about 67000 km2. The mean annual precipitation varies from 300 mm for valleys to 800 mm for mountains (Mirakbari et al. 2010). In this study, the monthly rainfall data were collected from 45 meteorological stations, located in Khuzestan and neighboring provinces, for a 31-year statistical period that was chosen as the common time factor. The SPI is widely employed throughout the world in both research and operational applications since it is normalized to location and normalized in time (Nafarzadegan et al. 2012). The SPI is based on the probability distribution of the long-term precipitation record for a desired period. The long-term record of precipitation totals, for each ending month, is ﬁ tted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee, 1997). In this study, the SPI values were calculated for a 31-year period using the monthly rainfall data from 45 meteorological stations. In cases that the time series of SPI values pass the Markov character test, they can be predicted by the Markov chain model. The Chi-square (χ 2) statistic is usually used to carry out the discrete series of Markov character test. A Markov process is a process for which, if the present is given, the future and the past are independent of each other. In a Markov chain, the transition from one state to the next occurs at discrete time intervals. The probability of moving from state i (Si) at time t to state j (Sj) at time t+1 is the state transition probability Pij (Thompson, 1990). Therefore, if we get all the conditions of the environmental parameters as dry (D), normal (N) and wet (W) states and we take a dry condition as current state; the next step could be any of the conditions D, N or W by some percent of probability. We used the first-order Markov chain paying attention to the current condition, the conditional probability is estimated. Thus, we can show the degree of correlation between each state by the aid of the transition matrix. If a balance could be found for the transition matrix, a probability of transition by one special case would apply for all cases and have a tendency to reach a constant number, and by the aid of this equilibrium probability the conditions of a system in long term could be predicted. This resulting matrix is called an equilibrium matrix. Therefore, if all the conditions of an environmental parameter are in the form of one of the considered drought classes (i. e. D, N or W), then, when we are in a dry state, the environmental condition and the next trend in the following step by having some percent of probability (Pij) will be one of the conditions of D, N and W (Moradi et al. 2011). For the given significance level (α =0. 05), the chi-square test indicated that the first-order Markov chain was fitted to the calculated time series of SPI at all stations. Thus, the SPI time series was modeled by the Markov chain model. The outcomes showed that the mean frequency of transition from a wet year into a dry year was 0. 044 and the mean frequency of transition from the normal state into the dry condition was 0. 177, in the simulation period. The results also indicated that the equilibrium probability of wet, normal and dry states in Khuzestan province were 35%, 35% and 30%, respectively. Based on SPI time series at 45 stations, the mean duration of drought events, was about four years. It is worth noting that Darkhazineh and Cheshmeh-Shirin stations exhibited the highest and the lowest probability of drought persistence, respectively. Meanwhile, the largest and the smallest number of expected drought events were witnessed in Zeydun-Sardasht and Mal-Agha stations, respectively. In general, the equilibrium probability of dry state in the eastern part of Khuzestan province was higher than the western area of the province. The objective of this study was to investigate the occurrence probability of drought events using the standardized precipitation index (SPI) and the combination of RUN theory and the Markov chain model in Khuzestan province. Considering three states (dry, normal, and wet), equilibrium probability matrices, and transition probability matrices were computed. The probability of drought occurrence, the average number of expected drought events, and the duration of drought events were also computed according to the simulation period. The results showed that the probability of transition from the wet condition into the dry state is very low. Additionally, the probability of transition from the normal state into the dry state is much higher than the probability of transition from the wet state to the dry condition. The results from this study assist decision makers in developing management policies in order to mitigate the potential drought-related impacts in prone areas.

No record.

APA: Copy

Adeli, b., NAFARZADEGAN, A.R., MALEKIAN, A., & NOHEGAR, A.. (2018). Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran. IRANIAN WATER RESEARCH JOURNAL, 12(2 (29) ), 29-39. SID. https://sid.ir/paper/159780/en

Vancouver: Copy

Adeli b., NAFARZADEGAN A.R., MALEKIAN A., NOHEGAR A.. Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran. IRANIAN WATER RESEARCH JOURNAL[Internet]. 2018;12(2 (29) ):29-39. Available from: https://sid.ir/paper/159780/en

IEEE: Copy

b. Adeli, A.R. NAFARZADEGAN, A. MALEKIAN, and A. NOHEGAR, “Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran,” IRANIAN WATER RESEARCH JOURNAL, vol. 12, no. 2 (29) , pp. 29–39, 2018, [Online]. Available: https://sid.ir/paper/159780/en

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Assessment of meteorological drought characteristics using the RUN theory and Markov chain models in Khuzestan province, Iran