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Droughts are a natural phenomenon with significant impact on society and the environment. Their frequency and severity are projected to increase toward the end of the century, which makes urgent the analysis of future drought characteristics to inform stakeholders and to allow the investigation of their effects on different domains. In this study, we developed Future Global Drought Layers composed of SPI and SPEI indices for 23 GCMs of the NEX-GDDP-CMIP6 dataset, including historical (1980-2014) data and 4 future projections (2015-2100) under four climate scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The drought indices were calculated for 3-, 6- and 12-month timescales. The data is gridded in a regular latitude-longitude format, with a spatial resolution of 0.25º (~25 km) and monthly time resolution.
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Global Future Drought Layers Based on Downscaled CMIP6 Models and Multiple Socioeconomic Pathways
Abstract Droughts are a natural hazard of growing concern as they are projected to increase in frequency and severity for many regions of the world. The identification of droughts and their future characteristics is essential to building an understanding of the geography and magnitude of potential drought change trajectories, which in turn is critical information to manage drought resilience across multiple sectors and disciplines. Adding to this effort, we developed a dataset of global historical and projected future drought indices over the 1980–2100 period based on downscaled CMIP6 models across multiple shared socioeconomic pathways (SSP). The dataset is composed of two indices: the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for 23 downscaled global climate models (GCMs) (0.25-degree resolution), including historical (1980–2014) and future projections (2015–2100) under four climate scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The drought indices were calculated for 3-, 6- and 12-month accumulation timescales and are available as gridded spatial datasets in a regular latitude-longitude format at monthly time resolution.
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- PAR ID:
- 10572587
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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