Global high-resolution drought indices for 1981–2022

蒸散量 环境科学 降水 气候学 高分辨率 植被(病理学) 气候变化 气象学 遥感 地理 地质学 生态学 医学 海洋学 病理 生物
作者
Solomon H. Gebrechorkos,Jian Peng,Ellen Dyer,Diego G. Miralles,Sergio M. Vicente‐Serrano,Chris Funk,Hylke E. Beck,Dagmawi Asfaw,Michael Bliss Singer,Simon Dadson
出处
期刊:Earth System Science Data [Copernicus Publications]
卷期号:15 (12): 5449-5466 被引量:31
标识
DOI:10.5194/essd-15-5449-2023
摘要

Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).
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