An evaluation of statistical, NMME and hybrid models for drought prediction in China

The predictability of droughts in China was investigated using a series of statistical, dynamic and hybrid models. The results indicate that, statistical models exhibit better skill in forecasting the Standardized Precipitation Index in six months (SPI6) than dynamic models. Overall, the ensemble streamflow prediction (ESP) method and wavelet machine learning models outperform other statistical models in forecasting SPI6. The hybrid model can improve the performance of SPI6 forecast by combining statistical and dynamic models using Bayesian model averaging (BMA) method. As for drought onset detection, the 'low probability of detection (POD) low probability of false alarm (POF)' and 'high POD high POF' phenomena exist in statistical and dynamic models, respectively. On average, less than 20% drought onsets can be detected in statistical models while less than 40% in dynamic models, with more than 40% false alarms appearing in statistical models and more than 75% in dynamic models. The hybrid model can slightly balance them, resulting in a POD of 20% and a POF of 50%. In spite of the low predictability, some stations with high equitable threat score (ETS) can be used in early drought warning under certain requirement. These conclusions may help improving drought prediction at a local or national scale.