Stochastic and time series drought forecast using rainfall oscillations in arid and Semi-arid environments

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Research Paper 01/07/2016
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Stochastic and time series drought forecast using rainfall oscillations in arid and Semi-arid environments

Abbasali Vali, Mostafa Dastorani, Adel sepehr, Chooghi Bairam Komaki
J. Bio. Env. Sci.9( 1), 245-256, July 2016.
Certificate: JBES 2016 [Generate Certificate]

Abstract

The importance of water supplies in the world, underscores the need for estimating and forecasting the trend of meteorological phenomena, understanding atmospheric phenomena and its trend in economic management. This includes optimization of profitability and productivity impact, especially in arid and semi-arid schedules. Conversely, climate and rainfall are highly non-linear and complicated phenomena, which require non-linear mathematical modeling and simulation for trusted accurate prediction. In this study, monthly rainfall data were obtained from 10 synoptic stations from 1985 to 2014. Thereafter, R software was employed in predicting the height of rainfall in 10 synoptic stations (2003 to 2014) using monthly height of rainfall data (1985 to 2014). In this research, five models (AR, MA, ARMA, ARIMA, and SARIMA) with 12 different structures were tested. After deciding on the optimal model to be used for each station, rainfall was forecast for 120 months (2014 to 2024) and then for the years 2014 and 2024 iso-rainfall maps were outlined. From the findings of this research, it was observed that in 80% of data, ARMA (2,1) had better results than the other models and according to the simulated and predicted rainfall by time series models, the drought situation was evaluated using standardized precipitation index (SPI). The result thus revealed that in comparison to 2014, severe drought will have decreased by the end of 2024.

VIEWS 2

Bartolini P, Salas JD, Obeysekera J. 1988. “Multivariate Periodic ARMA (1, 1) Processes, “Water Resource Researches, 24, 1237- 1246.

Box GEP, Jenkins GM, Reinsel GC. 1994. Time series analysis: forecasting and control. 3rd ed. Prentice Hall, Englewood Clifs, NJ Box.

Bras RL, Rodriguez-Iturbe I. 1985. Random Functions and Hydrology. Addison-Wesley Publishing Co.

Brockwell PJ, Davis RA. 2010. “Introduction to time series and forecasting. New York”: Springer.

Burlando P, Rosso R, Cadavid LG, Salas JD. 1993. Forecasting of short-term rainfall using ARMA models. Journal of Hydrology 144, 193–211.

Chang TJ, Teoh CB. 1991. Use of the kriging method for studying characteristics of ground water droughts, Journal of Water Resource Association 31, 1001–1007, 1995.

De Silva MAP. 2006. A time series model to predict the runoff ratio of catchments of the Kalu ganga basin Journal of the National Science Foundation of Sri Lanka 34, 103-105. DOI:10.4038/jnsfsr.v34i2.2089.

Firouzi F, Negaresh H, Khosravi M. 2012. “Modeling, prediction and assessment of selected precipitation in Fars province” Journal of Geography and Regional Planning 2, 77 -91.

French MN, Krajewski WF, Cuykendall RR. 1992. “Rainfall forecasting in space and time using neural network”, Journal of Hydrology 137, 1–31.

Gwangseob K, Ana PB. 2001. “Quantitative flood forecasting using multi sensor data and neural networks”, Journal of Hydrology 246, 45–62.

Haltiner JP, Salas JD. 1988, “Development and Testing of a Multivariate Seasonal ARMA (1, 1) Model,” Journal of Hydrology 104, 247-272.

Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV. 1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society 80, 429–43.

Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV. 1999. Monitoring the 1996 drought using the standardized precipitation index. Bull. Bulletin of the American Meteorological Society 80, 429–438.

Hipel KW, McLeod AE. 1994. “Time Series Modeling of Water Resources and Environmental Systems”. Elsevier: Amsterdam.

Javidi Sabbaghian R, Sharifi MB. 2009. Random Modeling Application in River Flow Simulation and Estimation of Mean Annual River Discharge by Time Series Analysis. International Conference on Water Resources (ICWR). Shahrood, Iran, August 15–17.

Khadar Babu SK, Karthikeyan K, Ramanaiah MV, Ramanah D. 2011. Prediction of Rain-fall flow Time Series using Auto-Regressive Models. Advances in Applied Science Research 2, 128-133.

Kumar Adhikary S, Mahidur Rahman MD, Das Gupta A. 2012. “A number of stochastic time series models such as the Markov, Box-Jenkins “International journal of Applied Sciences and Engineering Research 1, 238- 249. DOI: 10.6088/ijaser.0020101024.

Mansour MM, Barkwith A, Hughes AGN. 2011. A simple overland flow calculation method for distributed groundwater recharge models. Hydrological Processes 25, 3462-3471. DOI: 10.1002/hyp.8074.

McKee TB, Doesken NJ, Kleist J. 1993. The relation of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology. American Meteorological Society. Boston 179–184.

Mirzavand M, Ghazavi R. 2015. A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods. Water resources management 29. DOI 10.1007/s11269-014-0875-9.

Morid S, Smakhtin V, Moghaddasi M. 2006. Comparison of seven meteorological indexes for drought monitoring in Iran. International Journal of Climatology 26, 971-985. DOI: 10.1002/joc.1264.

Nazemosadat M.J, Haghighi G, Sharifzadeh M, Ahmadvand M. 2006. “Acceptance of the long-term forecasts of rainfall”. Journal of promote teach Iranian Agriculture 22, 1-15.

Nirmala M, Sundaram SM. 2010. Modeling and predicting the monthly rainfall in Tamilnadu as a seasonal multivariate ARIMA process. International Journal of Computer Engineering & Technology (IJCET) 1, 103-111.

Poormohammadi S, Malekinezhad H, Poorshareyati R. 2013. Comparison of ANN and time series appropriately in prediction of ground water table (Case Study: Bakhtegan basin). Water and Soil Conservation 20, 251-262.

Priento-Gonzalez R, Cortes-Hernandez VE, Montero-Martinez MJ. 2011. Variability of the sta-ndardized precipitation index over Mexico under the A2 climate change scenario. Atmosfera 24, 243-250.

Saeidian Y, Ebadi H. 2004. Determine the time series of data flow (case study: Vanyar station in the river basin Ajichai). 2th Students Conference on Soil and Water Resources, Shiraz, Iran, May, 12-13.

Said SM, Manjang S, Wihardi Tjaronge M, Arsyad TM. 2013. Arima Application as an Alternative Method of Rainfall Forecasts in Watershed of Hydrology Power Plant. International Journal of Computational Engineering Research 3, 68-73.

Santos M. A. 1983. Regional droughts: a stochastic characterization, Journal of hydrology 66, 183–211.

Saplioglu K, Cimen M, Akman B. 2010. “Daily Precipitation Prediction in Isparta Station by Artificial Neural Network”, Ohrid, Republic of Macedonia, 25-29.

Schaars F, Von Asmuth DC. 2012. Software for hydrogeologic time series analysis, interfacing data with physical insight. Environmental Modelling & Software 38, 178-190. DOI: 10.1016/ j.envsoft. 2012.06.003.

Seed AW, Draper C, Srikanthan R, Menabde M. 2000. A multiplicative broken-line model for time series of mean areal rainfall. Water Resource Research 36(8), 2395-2399.

Shirmohammadi B, Vafakhah M, Moosavi V, Moghaddamnia A. 2013. “Application of several data-driven techniques for predicting groundwater level”. Water Resource Management 27, 419–432. DOI: 10.1007/s11269-012-0194-y.

Soltani S, Modarres R, Eslamian SS. 2007. The use of time series modeling for the determination of rainfall climates of Iran. International Journal of Climatology 27, 819–829. DOI: 10.1002/joc.1427.

Tokar AS, Santon PA. 1999. “Rainfall-Run off modeling using artificial neural networks”, journal of Hydrologic Engineering 3, 232-233.

Toth E, Brath A, Montanari A. 2000. Com-parison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 239, 132–147.

Wu H, Hayes MJ, Wilhite DA, Svoboda MD. 2005. The effect of the length of record on the Standardized Precipitation Index calculation. International Journal of Climatology 25, 505-520. DOI: 10.1002/joc.1142.