Yuan et al. (2025) A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-11-24
• Authors: Mengjia Yuan, Guojing Gan, Jingyi Bu, Yanxin Su, Hongyu Ma, Xianghe Liu, Leyao Zhang, Yongqiang Zhang, Yanchun Gao
• DOI: 10.1038/s41597-025-06320-x

Research Groups

• Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
• University of Chinese Academy of Sciences, Beijing 100049, China
• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China
• Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA

Short Summary

This study developed and validated a global, high-resolution (0.1°, monthly, 1980-2019) drought dataset based on the Multivariate Composite Drought Index (MCDI) and its constituent indices, demonstrating its effectiveness in characterizing comprehensive drought dynamics and ecosystem responses by accounting for time lag and cumulative effects.

Objective

• To produce a global, high-resolution (0.1°) drought dataset (1980-2019) for the Multivariate Composite Drought Index (MCDI) and its constituent indices, which accounts for time lag and cumulative drought effects, to address the lack of comprehensive composite drought index datasets.

Study Configuration

• Spatial Scale: Global (180°W to 180°E, 90°S to 90°N) at 0.1° resolution.
• Temporal Scale: Monthly, from 1980 to 2019.

Methodology and Data

• Models used: Multivariate Composite Drought Index (MCDI), Standardized Precipitation Actual Evapotranspiration Index (SPAEI), Standardized Soil Moisture Index (SSI), Standardized Runoff Index (SRI), Water Storage Deficit Index (WSDI).
• Data sources:
  • Precipitation (P): Multi-Source Weighted-Ensemble Precipitation (MSWEP) (0.1°).
  • Actual Evapotranspiration (AET): Global Land Evaporation Amsterdam Model (GLEAM) (0.1°).
  • Soil Moisture (SM): ECMWF Reanalysis v5 (ERA5)-Land (0.1°, 0-10 cm layer).
  • Runoff (RO): Averaged data from ERA5-Land, FEWS NET Land Data Assimilation System (FLDAS), and Ghiggi et al. (resampled to 0.1°).
  • Terrestrial Water Storage Anomaly (TWSA): Averaged reconstructed datasets from Li et al. and Humphrey and Gudmundsson (resampled to 0.1°).
  • Validation data: self-calibrating Palmer Drought Severity Index (scPDSI), PKU GIMMS Normalized Difference Vegetation Index (NDVI), and Gross Primary Productivity (GPP) and Evapotranspiration (ET) from 30 FLUXNET sites.

Main Results

• The generated global dataset provides MCDI and its four constituent indices (SPAEI, SSI, SRI, WSDI) at 0.1° resolution on a monthly scale for 1980-2019.
• MCDI showed strong agreement with scPDSI, with 59.84% of global pixels having a Pearson Correlation Coefficient (PCC) greater than 0.5 (p < 0.05), and generally outperformed its constituent indices in correlation with scPDSI.
• MCDI exhibited higher positive correlations with NDVI (31.89% of pixels with PCC > 0.3, p < 0.05) compared to scPDSI (23.99%), particularly in southern/eastern Africa and Australia, indicating better capture of vegetation response to wet-dry changes.
• Validation with FLUXNET data (GPP, ET) at 30 sites confirmed that MCDI changes were consistent with ecosystem responses to droughts, demonstrating greater robustness (mean PCC with GPP: 0.33; with ET: 0.34) than scPDSI (mean PCC with GPP: 0.27; with ET: 0.31).
• MCDI effectively captured the spatial and temporal evolution of major drought events in Australia (2019), the Contiguous United States (2012), and South Africa (2015-2016), providing a more comprehensive drought characterization than single-factor indices.

Contributions

• Provides the first global, high-resolution (0.1°) composite drought index dataset (MCDI) that integrates meteorological, agricultural, and hydrological factors.
• Incorporates the critical aspects of time lag and cumulative effects of drought, enhancing the accuracy of drought characterization and ecosystem response.
• Offers a valuable and consistent data resource to support global drought monitoring, assessment, and the development of adaptive management strategies under climate change.
• Establishes a benchmark dataset for the development and rigorous evaluation of future composite drought indices.

Funding

• Key Project of the National Natural Science Foundation of China (Grant No. 42330506)
• General Program of the National Natural Science Foundation of China (Grant No. 42071054)
• International (Regional) Cooperation and Exchange Project of the National Natural Science Foundation of China (Grant No. 42361144709)

Citation

@article{Yuan2025global,
  author = {Yuan, Mengjia and Gan, Guojing and Bu, Jingyi and Su, Yanxin and Ma, Hongyu and Liu, Xianghe and Zhang, Leyao and Zhang, Yongqiang and Gao, Yanchun},
  title = {A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06320-x},
  url = {https://doi.org/10.1038/s41597-025-06320-x}
}