LAUSR

Droughts of greater severity are expected to occur more frequently at larger space-time scales under global warming and climate change. Intensified drought and increased rainfall intermittency will heighten tree mortality. To mitigate drought-driven societal and environmental hazards, reliable long-term drought forecasting is critical. This review examines causative mechanisms for drought and tree mortality, and synthesizes stochastic, statistical, dynamical, and hybrid statistical-dynamical drought forecasting models as well as theoretical, empirical, and mechanistic tree mortality forecasting models. Since an increase in global mean temperature changes the strength of sea surface temperature (SST) teleconnections, forecasting models should have the flexibility to incorporate the varying causality of drought. Some of the statistical drought forecasting models, which have nonlinear and nonstationary natures, can be merged with dynamical models to compensate for their lack of stochastic structure in order to improve forecasting skills. Since tree mortality is mainly affected by a hydraulic failure under drought conditions, mechanistic forecasting models, due to their capacity to track the percentage of embolisms against available soil water, are adequate to forecast tree mortality. This study also elucidates approaches to improve long-term drought forecasting and regional tree mortality forecasting as a future outlook for drought studies.