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Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models

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Abstract

The prediction of drought is critically important for early warning and mitigation of its impacts. Selecting the most appropriate prediction model provides opportunities for reducing the drought’s adverse effects on different sectors. So, in this study, the capability of several prediction models to predict drought (based on the Reconnaissance Drought Index (RDI) in 1 and 3-month time scales) was compared. The models included stationary time series models (ST), cyclostationary time series (CST) models, autoregressive fractionally integrated moving average (ARFIMA), periodic autoregressive fractionally integrated moving average (PARFIMA), first-order Markov chain (FOMC), and second-order Markov chain (SOMC). For choosing the best-fitted model, the correlation coefficient (R2) and absolute values of T-Statistics (AVTS) between predicted (using each model) and observed RDI in 1 and 3-month time scales at 15 stations during the period 2017–2021 in Iran were used. For this purpose, the 1967 to 2016 data series was used. Then the best prediction model (with the highest performance level) was used to predict 1 and 3-month RDI in the investigated stations from 2022 to 2031. For this purpose, 1967 to 2021 data series was used. The results showed that CST models with the highest R2 values (significantly at the 5% level in both time scales in all stations) and the lowest AVTS values (significantly at the 1% level in both time scales in all stations) for the best-fitted models in 1 and 3-month time scales had the best performance in predicting monthly and seasonal RDI. To predict monthly and seasonal RDI in all stations, the PARIMA (24, 0, 0), 12 and PARIMA (20, 0, 0), 4 models were used as the best models, respectively. The predictions indicated that normal (No) and moderately (Mod) dry classes would be more frequent in both time scales. This study demonstrates that CST models can be useful tools for drought prediction and management.

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Data Availability

The data used in this research are available by the corresponding author upon reasonable request.

Abbreviations

ST:

Stationary time series models

AR:

Autoregressive models

MA:

Moving average models

ARMA:

Autoregressive moving average models

ARIMA:

Autoregressive integrated moving average models

CST:

Cyclostationary time series

PAR:

Periodic autoregressive models

PMA:

Periodic moving average models

PARMA:

Periodic autoregressive moving average models

PARIMA:

Periodic autoregressive integrated moving average models

ARFIMA:

Autoregressive fractionally integrated moving average

PARFIMA:

Periodic autoregressive fractionally integrated moving average

RDI:

Reconnaissance drought index

FOMC:

First-order Markov chain

SOMC:

Second-order Markov chain

R2 :

Correlation coefficient

AVTS:

Absolute values of T-Statistics

ACF:

Autocorrelation function

PACF:

Partial autocorrelation function

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Acknowledgements

The authors would like to thank the Iran Meteorological Organization for providing the climatic data.

Funding

No funds, grants, or other support was received.

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Authors and Affiliations

  1. Department of Range and Watershed Management (Nature Engineering), Faculty of Agriculture, Fasa University, Fasa, Iran

    Abdol Rassoul Zarei

  2. Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran

    Mohammad Reza Mahmoudi

  3. Department of Civil Engineering, Faculty of Engineering, Fasa University, Fasa, Fars, Iran

    Alireza Pourbagheri

Authors
  1. Abdol Rassoul Zarei
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  2. Mohammad Reza Mahmoudi
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  3. Alireza Pourbagheri
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The participation of Abdol Rassoul Zarei and Mohammad Reza Mahmoudi includes the data collection, analyzing the results and writing the article and the participation of Alireza Pourbagheri includes the analyzing the results.

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Correspondence to Abdol Rassoul Zarei.

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Zarei, A.R., Mahmoudi, M.R. & Pourbagheri, A. Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models. Water Resour Manage 38, 2601–2625 (2024). https://doi.org/10.1007/s11269-024-03789-w

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