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.
Highlights
Accurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management
A few drought-monitoring tools are available, including the global drought-monitoring system based on the standardized precipitation–evapotranspiration index (Beguería et al 2014), the Global Integrated Drought Monitoring and Prediction System (GIDMaPS; Hao et al 2014), and the Global Precipitation Climatology Centre (GPCC) drought index (Ziese et al 2014)
In this study we focus on precipitation deficits through the standardized precipitation index (SPI; McKee et al 1993), suggested by the World Meteorological Organization as a starting point for meteorological drought monitoring (WMO 2012)
Summary
Accurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. 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. Drought is a complex phenomenon that involves different natural and eventually human drivers Based on both physical and socioeconomic contributing factors, drought is usually classified into four types: meteorological, agricultural, hydrologic, and socioeconomic (Wilhite and Glantz 1985). In this study we focus on precipitation deficits through the standardized precipitation index (SPI; McKee et al 1993), suggested by the World Meteorological Organization as a starting point for meteorological drought monitoring (WMO 2012)
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