Prediction of Drought in the Khorasan Razavi Province During 2011-2030 by Using Statistical Downscaling of HADCM3 Model Output

Document Type : Research Article


Ferdowsi University of Mashhad


Because of the vital role of water in human life, examining the phenomenon of climate change effects on drought severity and frequency is important for any area of interest. Nowadays, climate Researchers considers the effects of climate change and climate simulations by using the atmospheric-ocean general circulation models. To achieve the prediction of climatologically parameters, various statistical and dynamical models have been developed to simulate and downscaling of GCM output models. The statistical model of LARS-WG is such a model, which is very powerful for this aspect.
Study Area
The province of Khorasan Razavi with an area of 144802 Km2 is located in the northeast of Iran. Based on De Martonne’s climate the study area included indicator, Khorasan Razavi classified in arid and semiarid climate zones. The highest point of province is Binalud Mountain with an elevation of 3420 meter and the lowest point in Sarakhs plain with an elevation of 299 meter above the sea level. The LARS-WG model is one of the stochastic weather data generators, which is using to generate data for daily precipitation, radiation, maximum and minimum temperature for present and future times.
Material and Methods
To run the model of LARS-WG, daily precipitation, minimum and maximum temperature, and sunshine hours of 10 synoptic stations of Khorasan Razavi for 20-year duration were Selected for Model inputs (1991–2010). All data obtained from the data center of Iran meteorology office.
The aim of this study is assessing the effects of climate change on drought occurrences in Khorasan Razavi by using drought index such as decile (DI) and the standardized precipitation Index (SPI) for the next two decades. The daily data from the output of general circulation model HADCM3 under scenario A2 is downscaled by LARS-WG statistical model version 5, and the ability LARS-WG5 model is validated in simulations of past climate (1991-2010), in 10 synoptic stations. Then the climatic variables of the minimum temperature, maximum temperature, precipitation, and sunshine hours are simulate for 2011-2030. Then, rainfall and drought conditions are monitored to extraction of inter annually list of drought indicators.
Results and Discussion
The results showed that the LARS-WG model has high ability to simulate climatic variables. The most error in simulation of climatic parameters is related to rainfall. While the model shows higher accuracy for estimation of minimum and maximum values but for average amount, the rainfall has increased in the 75 % to 77% of months the first and second decades of forecasting period. The noteworthy result of calculated index deciles is that the number of months, with average, severe and very severe drought conditions in the twenty next years is reduced considerably compared with the base period.
Furthered Results showed a very good agreement between deciles (DI) and the standardized precipitation Index (SPI) for assessment of drought for next two decades. Torbat-jam station is an exaction point Due its difference results compare with other stations. The differences results of Torbat-jam station is by increasing of sunshine hours and it’s consequent rainfall based on our analysis more than 90% of the study area will face to increasing the drought intensity over the next twenty years.
Check the status of drought in Khorasan Razavi province during the next two decades, shows most of the stations in most years of study period, the drought decrease and the number of wet month’s increases. Comparing the results of two different (first and second) decades implies that the number of wet months in the second decade increases respect to the first decade. The results also show the climate of Khorasan Razavi will be quiet difference with the current situation. The result of drought situation in this study is agreement with the results of some other studies and of course is not agree with the results showed by few researchers. The reason of the similarity of the results can be confirm the ability of the model and thus as error reduction in output of climate parameters. Also the reason of disagreement of the results could be due to run time error of the model in climatic parameters simulation in other studies.


Abbasi, F., S.H. Malbusi., I. Babaeian., M. Asmari., R. Borhani., 2010. Climate Change Prediction of South Khorasan Province During 2010–2039 by Using Statistical Downscaling of ECHO-G Data. Journal of Water and Soil 24, 2, 218-233.
Alizadeh. A., 2009. Principles of Applied Hydrology. Twenty-Sixth Edition, Imam Reza University Press, 870 PP.
Babaeian, I., Z. Najafi nik., A. zaki zadeh., 2005. The Preliminary study and evaluation of the Weather Generator Models. A Case Study: The evaluation of LARS-WG model on selected stations Khorasan. Climatology Center. Project Report.
Babaeian, I., Z. Najafi nik., 2010. The Analysis of Climate Change in Khorasan Razavi During 2010-2039 by Using Downscaling of GCM model output. Journal of Geography and Regional Development 15, 1-19.
Barrow, E., G. Yu., 2005. Climate Scenario for Alberta. A report prepared for the priarie Adaptation Research Climate Research Services.
Edward, D.C., T.B. Mckee., 1997. Characteristics of 20th century drought in the United States and multiple time scales. pp. 155. In: Climatology Report. Colorado state University.
Elshamy, M.E., H.S. Wheater., N. Gedney., C. Huntingford., 2005. Evaluation of the rainfall component of weather generator for climate change studies. Journal of Hydrology 326, 1-24.
Gibbs, w.J., J.V. Maher., 1967. Rainfall deciles as drought indicators, 37-48. In: Australian Bureau of Meteorology.
Hadley center., 2006. Effect of climate change in the developing countries.UK Meteorological Office.
Haltiner G., R.Williams. 1980. Numirical Prediction and Dynamic Meteorology, John Wiley & Sons. pp. 115-120.
Hidalgo, H., M. Dettinger., D. Cayan., 2008. Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. Pier Final Project Report, Prepared for: California Energy Commission Public Interest Energy Research Program.
IPCC., 2007. Summary for policy makers climate change: The physical science basis. Contribution of working group I to the forth assessment report. Cambridge University Press, 881 pp.
Kamal, A.R., M. Bavani. 2010. Climate Change and Variability Impact in Basin’s Runoff with Interference of Two Hydrology Models Uncertainty. Journal of Water and Soil 24, )5(, 920-931.
Khazaneh Dari, L., F. Zabol Abbasi., S.H. Ghandhari., M. Kohi., 2009. The Perspective of Drought Conditions over the Next Thirty Years. Journal of Geography and Regional Development 12, 83-98.
Loukas, A., L. Vasiliades., J.Tzabiras., 2008. Climate change effects on drought severity. Department of Civil engineering, University of Thessaly, 38334 Volos, Greece, Adv. Geosci. 17,PP. 23-29,
Mishra, A. K., V. P. Singh., 2010. A review of drought concepts. Journal of Hydrology, 391, 202–216.
Mosaedi, A., M. Ghabaei Sough., 2011.Modification of Standardized Precipitation Index (SPI) based on relevant probability distribution function. Journal of Water and Soil 25, ) 5), 1206-1216.
Rajabi, A., 2010. Modeling Of Kermanshah Climate by means of Downscaling Model of LARS-WG. 2nd Nationam Conference on Utilization Integrated Management of Water Resources. 29-30 January. Kerman.
Rezaei Pazhand, H., 2001. Application of Statistics and Probability in Water Resources. Sokhan Gostar Press. Mashhad.
Rezaei Pazhand, H., A. Bozurg Nia., 2002. Non-Linear Regression. Mashhad University Press.
Salehnia N. 2010. Drought Prediction With GCMs Atmosphere and Statistical Downscaling Outputs (Case Study Neyshabour Basin). MSc Thesis, Ferdowsi University of Mashhad.
Semenov, M.A., 2008. Simulation of extreme weather events by a stochastic weather generator. Climate Research 35, 203-212.
Shah Karami, N., A.R. Massah Bavani., S. Morid., H. Fahmi., 2008. The Uncertainty Analysis of Coupled Ocean – Atmosphere – General Circulation Models on Climate Change Scenarios of Temperature and Rainfall Zayandeh Roud Basin. Training Workshop on Impact of Climate Change on Water Resources. 13 February.
Stuart, G., M. Frey., 2005. Drought Detection And Quantification Using Field-Based Spectral Measurements Of Vegetation In Semi-Arid Regions. Submitted in Partial Fulfillment of the Requirment for the Masters of Science in Hydrology New Mexico.
Velayati, S., K. Davari., H. Ansari., H. Teimori., M. Shahedi., F. Talebi., S. Tamasoki., 2010.The final report of the project preparation Khorasan Razavi Province.
Wilby, R.L., I. Harris., 2006. A frame work for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resources Research 42, 121-134.