DROP: a DROught Probabilistic near-real time monitoring tool

Accurate and timely information of evolving drought conditions is crucial to take early actions and alleviate their impacts. A number of drought datasets is already available. They cover the last three decades and provide data in near-real time (using different sources), but they are all "deterministic" (i.e. single realisation), and data partly differ between them.  Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the Standardized Precipitation Index. Then, by applying an ensemble approach, similarly to weather/climate prediction studies, we develop DROP (DROught Probabilistic; Turco et al. 2020), a new global land gridded dataset to monitor meteorological drought that gathers an ensemble of observation-based datasets providing near-real time estimates with associated uncertainty. This approach makes the most of the available information and brings it to the end-users. DROP, publicly available at https://drop.shinyapps.io/DROP/, is operationally updated every monthly and provides drought information in near-real time, i.e., up to the previous month. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging.

 

References

Turco M, Jerez S, Donat M, Toreti M, Vicente-Serrano S M, Doblas-Reyes, F J. (2020). A global probabilistic dataset for monitoring meteorological droughts. Bulletin of the American Meteorological Society. Under review.

 

Acknowledgments

M.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).