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Abstract
Droughts cause multiple ecological and social damages. Drought indices are key tools to quantify drought severity, but they are mainly limited to timescales of monthly or longer. However, shorter-timescale (e.g., daily) drought indices enable more accurate identification of drought characteristics (e.g., onset and cessation time) and help timely potential mitigation of adverse effects. Here, we propose a dataset of a daily drought index named daily evapotranspiration deficit index (DEDI), which is produced for global land areas from 1979 to 2022 using actual and potential evapotranspiration data. Validation efforts show that the DEDI dataset can well identify dry and wet variations in terms of spatial patterns and temporal evolutions when compared with other available drought indices on a daily scale. The dataset also has the capability to capture recent drying trends and to detect ecology- or agriculture-related droughts. Overall, the DEDI dataset is a step forward in facilitating drought monitoring and early warning at higher temporal resolution than other compared existing products.
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1 Norwegian University of Science and Technology (NTNU), Industrial Ecology Programme, Department of Energy and Process Engineering, Trondheim, Norway (GRID:grid.5947.f) (ISNI:0000 0001 1516 2393); Chinese Academy of Sciences, Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309)
2 Beijing Normal University, State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing, China (GRID:grid.20513.35) (ISNI:0000 0004 1789 9964)
3 Norwegian University of Science and Technology (NTNU), Industrial Ecology Programme, Department of Energy and Process Engineering, Trondheim, Norway (GRID:grid.5947.f) (ISNI:0000 0001 1516 2393)
4 Chinese Academy of Sciences, Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419); Chinese Academy of Sciences, Xiongan Institute of Innovation, Xiongan New Area, China (GRID:grid.9227.e) (ISNI:0000 0001 1957 3309)