Investigation of New Integrated Drought Monitoring Model Taking into Account the Effects of Climate Anomalies

Drought mechanisms vary markedly within different ecogeographical regions. Existing drought indices do not reflect the impact of climate anomalies on drought. In this study, the climate anomaly index was incorporated into the drought model based on the study of the effects of the El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) on drought in different eco-geographic zones of China. The model uses climate anomaly indices, meteorological drought indices, vegetation growth condition data, surface temperature, and biophysical attributes as characteristic variables and the Palmer Drought Severity Index (PDSI) as the dependent variable for model construction based on the Random Forest (RF) method. The results showed that the model has high accuracy for drought monitoring. The correlation coefficients between model results and observed drought condition values for all four seasons were above 0.95. The model was applied to drought monitoring in North China and the Huang-Huai-Hai region from 2006 to 2018. The statistical interpolation results of the meteorological drought indices and precipitation data were used to verify the application effect of the model. It was found that the model can accurately monitor drought caused by precipitation scarcity and reflect local variations in drought. This study provides a new model (Climate Anomaly Considering Integrated Surface Drought Index, CAC-ISDI) for drought monitoring that aims at quantitative and detailed monitoring of drought conditions and regional differences. It provides a robust method for accurate drought monitoring and evaluation in China and the rest of the world.