Shalwee et al. (2025) Multi-dimensional assessment of drought vulnerability with composite drought index and principal component analysis for socioeconomic development in Semi-Arid regions

Identification

• Journal: Discover Applied Sciences
• Year: 2025
• Date: 2025-11-22
• Authors: Shalwee Shalwee, Renu Dhupper, Maya Kumari, Anil K. Gupta, Deepak Kumar
• DOI: 10.1007/s42452-025-07994-z

Research Groups

• Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh, Noida, Uttar Pradesh, India
• Amity School of Natural Resources & Sustainable Development (ASNRD), Amity University Uttar Pradesh, Noida, Uttar Pradesh, India
• Integrated Center for Adaptation, Climate Change, Disaster Risk Reduction and Sustainability (ICARS), Greater Noida Extension Centre (GNEC) IIT Roorkee, Greater Noida, Uttar Pradesh, India
• National Institute of Disaster Management (NIDM), Rohini, Delhi, India
• Atmospheric Sciences Group, Texas Tech University, Department of Geosciences, College of Arts & Sciences, Lubbock, Texas, USA

Short Summary

This study developed a Composite Drought Index (CDI) using Principal Component Analysis (PCA) to integrate meteorological and agricultural drought indices, providing a more accurate and multi-dimensional assessment of drought vulnerability in semi-arid regions. The CDI revealed significantly higher instances of extreme and severe drought compared to individual indices, offering enhanced insights for drought management.

Objective

• To develop a novel Composite Drought Index (CDI) by integrating meteorological (Standardised Precipitation Index - SPI, Standardised Precipitation Evapotranspiration Index - SPEI) and agricultural (Vegetation Condition Index - VCI) drought parameters using Principal Component Analysis (PCA) for a multi-dimensional assessment of drought vulnerability.
• To apply the proposed CDI in a representative semi-arid region (Ranchi, India) to identify key factors influencing community resilience and vulnerability, and to provide actionable insights for effective drought mitigation and adaptive management strategies.

Study Configuration

• Spatial Scale: Ranchi district, Jharkhand, India (22°52′–23°45′ N latitude and 84°45′–85°50′ E longitude, 500 to 700 meters above mean sea level). Data resolutions include 0.25° × 0.25° for rainfall, 1° × 1° for temperature, and 250 meters for NDVI.
• Temporal Scale: 2001 to 2020 for daily precipitation, temperature, and NDVI data.

Methodology and Data

• Models used:
  • Composite Drought Index (CDI)
  • Principal Component Analysis (PCA)
  • Standardised Precipitation Index (SPI)
  • Standardised Precipitation Evapotranspiration Index (SPEI)
  • Vegetation Condition Index (VCI)
  • Hargreaves method for Potential Evapotranspiration (PET) calculation.
• Data sources:
  • India Meteorological Department (IMD): Gridded daily rainfall (0.25° × 0.25°) and temperature (1° × 1°) datasets for 2001–2020.
  • MODIS (MOD13Q): NDVI data at 250 meters resolution, at 16-day intervals for 2001–2020.

Main Results

• The Composite Drought Index (CDI) identified 4.6% extreme drought and 7.9% severe drought in the study area (Ranchi, India) from 2001 to 2020, which are significantly higher percentages than those estimated by individual SPI, SPEI, or VCI indices.
• The CDI effectively captured both meteorological and agricultural drought conditions, identifying drought events in years such as 2002, 2003, 2004, 2005, 2009, 2010, 2016, 2018, and 2019, including dry periods during monsoon and winter droughts.
• Correlation analysis revealed weak positive correlations between VCI and SPI/SPEI indices (e.g., VCI and SPEI3: 0.114), suggesting that vegetation health is influenced by factors beyond just precipitation and evapotranspiration.
• Strong positive correlations were observed among SPI indices (e.g., SPI9 and SPI12: 0.809) and SPEI indices (e.g., SPEI9 and SPEI12: 0.901) across different timescales, indicating the persistence and accumulation of drought conditions.
• PCA extracted two principal components, with Component 1 explaining approximately 70% (69.76%) of the data variance and being dominated by SPI 6, 9, and SPEI 6, 9, 12, which were used to construct the CDI.

Contributions

• Development of a novel Composite Drought Index (CDI) that integrates both meteorological (SPI, SPEI) and remote sensing (VCI) data, enhancing the precision and multi-dimensional assessment of drought monitoring.
• Application of the CDI in a semi-arid region (Ranchi, India) to demonstrate its applicability and effectiveness in identifying high-risk zones and complex interactions between climatic and vegetative factors.
• Provision of a robust and replicable framework for early warning systems and rapid assessment of drought impacts on agricultural systems, offering valuable insights for policymakers, resource managers, and development organizations in formulating targeted mitigation strategies and adaptive management practices.

Funding

No funding was obtained for this study.

Citation

@article{Shalwee2025Multidimensional,
  author = {Shalwee, Shalwee and Dhupper, Renu and Kumari, Maya and Gupta, Anil K. and Kumar, Deepak},
  title = {Multi-dimensional assessment of drought vulnerability with composite drought index and principal component analysis for socioeconomic development in Semi-Arid regions},
  journal = {Discover Applied Sciences},
  year = {2025},
  doi = {10.1007/s42452-025-07994-z},
  url = {https://doi.org/10.1007/s42452-025-07994-z}
}