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PDSI_CMIP6: an ensemble CMIP6-projected self-calibrated Palmer drought severity index dataset
1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China.
A new dataset offers reliable drought severity estimates from 1850-2094. This self-calibrated Palmer Drought Severity Index (PDSI) dataset accounts for climate change, improving drought monitoring and water resource management.
Area of Science:
- Climate Science
- Hydrology
- Environmental Science
Background:
- Drought significantly impacts water resources and ecosystem services.
- The Palmer Drought Severity Index (PDSI) is crucial for drought monitoring but has limitations under changing climate conditions.
Purpose of the Study:
- To develop a comprehensive, reliable, and climate-change-aware dataset of drought severity.
- To provide monthly, multi-model, and multi-scenario self-calibrated PDSI estimates from 1850 to 2094.
Main Methods:
- Developed a self-calibrated PDSI dataset (PDSI_CMIP6) using 11 Coupled Model Intercomparison Project 6 (CMIP6) climate model outputs.
- Replaced the traditional bucket model with direct CMIP6 hydrological outputs for enhanced accuracy.
- Validated PDSI estimates against soil moisture simulations using correlation and regression analyses.
Main Results:
- The dataset reveals significant spatial heterogeneity in long-term drought trends across continents.
- While global-mean drought change is limited, regional intensification of drought is projected under climate change.
- The findings highlight regional variations in terrestrial moisture conditions influenced by CMIP6-projected hydrological changes.
Conclusions:
- The PDSI_CMIP6 dataset offers uncertainty-constrained quantifications of terrestrial moisture conditions.
- This dataset improves drought monitoring and climate change assessment by providing reliable, climate-aligned estimates.
- It serves as a valuable resource for understanding and managing water resources in a changing climate.
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