Development of probabilistic drought intensification forecasts using the GOES-BASED Evaporative Stress Index

We propose to develop a drought early warning system based on satellite-derived maps of evapotranspiration (ET) and forecast output from the National Multi Model Ensemble (NMME) that will provide probabilistic drought intensification forecasts over weekly to monthly time scales. Recent examples of rapid drought development have clearly demonstrated the need for a reliable drought early warning system capable of providing vulnerable stakeholders additional time to prepare for worsening drought conditions.

The proposed study will use the Evaporative Stress Index (ESI) dataset generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model using GOES thermal infrared imagery. The ESI represents standardized anomalies in the ratio of actual-topotential ET, and has been shown to agree well with standard precipitation-based drought indices and with the U.S. Drought Monitor (USDM). Because ALEXI computes ET using remotely sensed land surface temperatures, which respond quickly to changes in soil moisture content, the ESI is often able to detect increasing water stress sooner than other drought metrics, thereby making it a useful drought early warning tool. Temporal changes in the ESI (referred to as ΔESI) have been shown to provide valuable information about the rate of drought intensification, thus, a Rapid Change Index (RCI) product encapsulating the cumulative magnitude of ΔESI anomalies has also been developed. Preliminary work has revealed a strong relationship between the magnitude of the RCI and subsequent changes in the USDM drought depiction.

In this work, probabilistic drought intensification forecasts will be generated each week across the contiguous U.S. based on the RCI and NMME forecast output. New insight into the causes of rapid drought development will be gained through detailed analyses of soil moisture, rainfall, and atmospheric anomalies both preceding and accompanying notable flash drought events in recent years. Refinements will be made to the RCI-based probabilities through development of synergistic methods that combine drought early warning signals from multiple data sources, such as the ESI, Standardized Precipitation Index, and the North American Land Data Assimilation System, and through evaluation of alternative forms of RCI computation. After evaluating the efficacy of the RCI-derived drought intensification probabilities, new methods will be devised to incorporate ensemble forecasts of temperature and rainfall from the NMME as a means of further enhancing their forecast skill. The drought forecast products will be relevant to multiple end users, including authors of the Climate Prediction Center Seasonal and Monthly Drought Outlook products.

The proposed project will benefit the MAPP initiative through the development of an innovative probabilistic drought early warning and forecasting tool that will support decision-making and risk characterization by vulnerable stakeholders. The development of robust drought intensification forecasts with high spatial resolution addresses the NOAA Next-Generation Strategic Plan by providing early warning of worsening drought conditions that will support regional drought preparation and mitigation activities. Timely delivery of the probabilistic forecasts through the NIDIS user portal will help inform the public about the possibility of rapidly worsening drought conditions.