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Rainfall and temperature are most important indicators for availability of water and evaporation control. These parameters can be used as indicators of drought. The present study aims at drought analysis and prediction in the upper Mahanadi River basin. Prolonged shortage in supply of water (atmospheric, surface or groundwater) represents an event of drought. In this work meteorological drought will be analysed using SPI for 3, 6, 9 and 12 month time scales. The Standardized Precipitation Index (SPI) depends on precipitation and used for monitoring of meteorological drought and is calculated for different time scales, which is a great advantage. After data processing of the SPI, ARIMA, a time series model is applied for forecasting of drought event, such as the autoregressive (AR) model, moving average (MA). ARIMA model has 3 parameters such as p, d, q where p provides the lag parameter of the AR function of the ARIMA model, which are acquired from PAC plot of time series data. q provides lag parameter of MA and is obtained from the ACF. SPI and ARIMA models are used for drought assessment using R package. It is observed that the upper Mahanadi basin is under near normal and mild drought condition.

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Analysis and Forecasting of Meteorological Drought in Upper Mahanadi River Basin Using SPI and ARIMA in R

  • 会议论文

  • 发表日期：

    2025年

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  • Kirtisuta Bhoi,
  • Saismruti Mohapatra,
  • Prakash Chandra Swain,
  • Anil Kumar Kar

摘要

Rainfall and temperature are most important indicators for availability of water and evaporation control. These parameters can be used as indicators of drought. The present study aims at drought analysis and prediction in the upper Mahanadi River basin. Prolonged shortage in supply of water (atmospheric, surface or groundwater) represents an event of drought. In this work meteorological drought will be analysed using SPI for 3, 6, 9 and 12 month time scales. The Standardized Precipitation Index (SPI) depends on precipitation and used for monitoring of meteorological drought and is calculated for different time scales, which is a great advantage. After data processing of the SPI, ARIMA, a time series model is applied for forecasting of drought event, such as the autoregressive (AR) model, moving average (MA). ARIMA model has 3 parameters such as p, d, q where p provides the lag parameter of the AR function of the ARIMA model, which are acquired from PAC plot of time series data. q provides lag parameter of MA and is obtained from the ACF. SPI and ARIMA models are used for drought assessment using R package. It is observed that the upper Mahanadi basin is under near normal and mild drought condition.