Stochastic model for drought forecasting in the Southern Taiwan basin

Hsin Fu Yeh, Hsin Li Hsu

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward developing such a system. In this study, we utilized the stochastic, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) in southern Taiwan. We employed data from 1967 to 2006 to train the model and data from 2007 to 2017 for model validation. The results showed that the coefficients of determination (R2) were over 0.80 at each station, and the root-mean-square error and mean absolute error were sufficiently low, indicating that the ARIMA model is effective and adequate for our stations. Finally, we employed the ARIMA model to forecast future drought conditions from 2019 to 2022. The results yielded relatively low SPI values in southern Taiwan in future summers. In summary, we successfully constructed an ARIMA model to forecast drought. The information in this study can act as a reference for water resource management.

Original languageEnglish
Article number2041
JournalWater (Switzerland)
Issue number10
Publication statusPublished - 2019 Oct 1

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology


Dive into the research topics of 'Stochastic model for drought forecasting in the Southern Taiwan basin'. Together they form a unique fingerprint.

Cite this