Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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Indian Journal of Agricultural Research, volume 51 issue 2 (april 2017) : 103-111

Forecasting of meteorological drought using ARIMA model

M. Karthika*, Krishnaveni, V.Thirunavukkarasu
1<p>Centre For Water Resources, Anna University, Chennai, 608 002, Tamil Nadu, India</p>
Cite article:- Karthika* M., Krishnaveni, V.Thirunavukkarasu (NaN). Forecasting of meteorological drought using ARIMA model . Indian Journal of Agricultural Research. 51(2): 103-111. doi: 10.18805/ijare.v0iOF.7631.

Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. The principal objective of the study is to  carryout  short-term annual forecasting of meteorological drought using Auto Regressive Integrated Moving Average (ARIMA)  model  in  Lower Thirumanimuthar Sub-basin located in semi-arid region of Tamilnadu, India is chosen as the study area which is predominantly affected by drought over few decades. Suitable linear stochastic model, non seasonal autoregressive integrated moving average (ARIMA)  was developed to predict drought. The best model was selected based on minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Parameter estimation step indicates that the estimated model parameters are significantly different from zero. The predicted data using the best ARIMA model were compared to the observed data for model validation purpose in which the predicted data show reasonably good agreement with the actual data. Hence the models were applied to forecast drought in the Lower Thirumanimuthar sub-basin region up to 2 years in advance with good accuracy.

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