A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices

High-resolution drought index datasets are essential for drought monitoring and assessment. Despite numerous global/regional drought index datasets, the composite drought index datasets that comprehensively consider meteorological, agricultural, and hydrological factors are still very few, thus hindering the capture of complex drought dynamics and comprehensive risk assessment. To address this gap, this study produced a 0.1° resolution global drought dataset (1980-2019) based on the newly developed concept of Multivariate Composite Drought Index (MCDI), which considered the time lag and cumulative effects of drought and could characterize comprehensive drought characteristics effectively. The dataset contains MCDI and its four constituent indices (Standardized Precipitation Actual Evapotranspiration Index (SPAEI), Standardized Soil Moisture Index (SSI), Standardized Runoff Index (SRI), Water Storage Deficit Index (WSDI)) on a monthly scale. Verification results showed that they indicated the drought evolution and ecosystem response process well, especially the MCDI. Overall, the dataset compensated for the data deficit of the comprehensive drought index and would provide data support for global drought monitoring and adaptive management of drought under climate change.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-11-24
DOI
https://doi.org/10.1038/s41597-025-06320-x
Citations
1
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
1.11

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices

Yongqiang Zhang, Mengjia Yuan, Xianghe Liu, Leyao Zhang et al.
1 citations
Scientific Data
Hydrology and Drought Analysis
1.11
article

A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices

Yongqiang Zhang, Mengjia Yuan, Xianghe Liu, Leyao Zhang, Guojing Gan, Jingyi Bu, Yanchun Gao, Hongyu Ma, Yanxin Su
article en
1 citations

Abstract

High-resolution drought index datasets are essential for drought monitoring and assessment. Despite numerous global/regional drought index datasets, the composite drought index datasets that comprehensively consider meteorological, agricultural, and hydrological factors are still very few, thus hindering the capture of complex drought dynamics and comprehensive risk assessment. To address this gap, this study produced a 0.1° resolution global drought dataset (1980-2019) based on the newly developed concept of Multivariate Composite Drought Index (MCDI), which considered the time lag and cumulative effects of drought and could characterize comprehensive drought characteristics effectively. The dataset contains MCDI and its four constituent indices (Standardized Precipitation Actual Evapotranspiration Index (SPAEI), Standardized Soil Moisture Index (SSI), Standardized Runoff Index (SRI), Water Storage Deficit Index (WSDI)) on a monthly scale. Verification results showed that they indicated the drought evolution and ecosystem response process well, especially the MCDI. Overall, the dataset compensated for the data deficit of the comprehensive drought index and would provide data support for global drought monitoring and adaptive management of drought under climate change.

Scientific DataVol. 12(1)
University of New Hampshire (US), Chinese Academy of Sciences (CN), Nanjing Institute of Geography and Limnology (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 17%
Hydrology and Drought Analysis
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.