A Global Probabilistic Dataset for Monitoring Meteorological Droughts

Abstract

AbstractAccurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. A number of drought datasets are 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 realization), and input and output 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, mimicking weather/climate prediction studies, we develop Drought Probabilistic (DROP), a new global land gridded dataset, in which an ensemble of observation-based datasets is used to obtain the best near-real-time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end users. 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.