Short Term Drought Prediction Based on Stable States Between the Land and the Atmosphere

Prolonged drought has devastating societal impacts ranging from recreation to water and food supply and can ultimately result in significant financial losses. In a warming world, the frequency of seasonal- and multi-year drought episodes is likely to increase. Those in the poorest countries who rely on domestic ecosystem services (including food production) are the most vulnerable. Accurate drought prediction has the potential to reduce negative impacts and support societal resiliency by informing sustainable water-management and planning practices. Such predictions require a thorough understanding of the physical mechanisms that lead to the onset, persistence, and recovery of drought and the ability to replicate these processes in forecast models at appropriate time scales (weeks to months). While synoptic drivers play a key role in drought development, there is increasing evidence that the land surface plays an important role in the skill of sub-seasonal to seasonal forecasts. However, there is still a lot unknown about the direct impact of local processes, such as Land-Atmosphere (L-A) interactions, on drought evolution due to low signals relative to the noise of natural variability and insufficient observational data sets. Here we present our work on using a statistical based prediction of drought evolution based on the stable states between the land and the atmosphere. This includes producing short-term (30 day) predictions of the Coupling Drought Index (CDI) and precipitation and comparing them with observations from reanalysis over a period of 2015-2022 in order to make use of global measurements of soil moisture from SMAP. The statistical model is based on the Coupling Stochastic Model (CSM; Roundy et al. 2014), which is a Markov-Chain model based on the persistence in L-A coupling regime but was recently updated to include a simple soil moisture model. The strengths and limitation of this prediction method will be discussed as well as future directions for improving global drought forecasting through remotely sensed observations.