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Streamflow drought time series forecasting

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Abstract

Drought is considered to be an extreme climatic event causing significant damage both in the natural environment and in human lives. Due to the important role of drought forecasting in water resources planning and management and the stochastic behavior of drought, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model is applied to the monthly streamflow forecasting of the Zayandehrud River in western Isfahan province, Iran. After forecasting 12 leading month streamflow, four drought thresholds including streamflow mean, monthly streamflow mean, 2-, 5-, 10- and 20-year return period monthly drought and standardized streamflow index were chosen. Both observed and forecasted streamflow showed a drought period with different severity in the lead-time. This study also demonstrates the usefulness of SARIMA models in forecasting, water resources planning and management.

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Acknowledgements

The author is grateful to two anonymous reviewers for their comments, which helped improve presentation significantly. The comments from Z. Sen, C. Chatfield and A. Mishra are also appreciated. The author also thanks George Christakos, the Editor-in-Chief of the journal of Stochastic Environmental Research and Risk Assessment.

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  1. Natural Resources Faculty, Isfahan University of Technology, Isfahan, Iran

    Reza Modarres

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  1. Reza Modarres
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Correspondence to Reza Modarres.

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Modarres, R. Streamflow drought time series forecasting. Stoch Environ Res Ris Assess 21, 223–233 (2007). https://doi.org/10.1007/s00477-006-0058-1

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