Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning - FENG Xin; LIU Yanju; TONG Hongfu; QIAN Shuni (Renmin Zhujiang)
Sea surface temperature (SST) is one of the main factors for drought forecasting. Conventional forecasting models mainly use SST from fixed sea areas (e.g., ENSO), without searching for available SST signals from a global large-scale perspective. Combining with the random forest algorithm, this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests. The results are as follows. ① The model can effectively forecast the temporal and spatial evolution of drought, and as the forecast period becomes longer, the forecast accuracy decreases accordingly. ② The accuracy of drought forecast is higher in non-flood season than in flood season, and the coastal area has a better forecast effect than the inland area. ③ The occurrence of droughts in the Pearl River Basin may be related to typical climate fluctuations, such as the El Niño Southern Oscillation and the North Atlantic Oscillation.