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Assessing consistency in drought risks in India with multiple multivariate meteorological drought indices (MMDI)
Femin C Varghese1, Subhasis Mitra1
1Civil Engineering Department, Indian Institute of Technology, Palakkad, Kerala, India.
The Science of the Total Environment
|January 26, 2025
Summary
Different drought indices show agreement historically but diverge under climate change, particularly in arid regions. This highlights the need for careful index selection in climate impact assessments.
Area of Science:
- Meteorology
- Climate Science
- Hydrology
Background:
- Meteorological drought characterization involves various Multi-Model Drought Indices (MMDIs).
- Understanding MMDI consistency and uncertainty is crucial for accurate climate change impact assessments.
- The Indian subcontinent faces diverse climate zones and socioeconomic pathways (SSPs) influencing drought projections.
Purpose of the Study:
- To investigate the spatio-temporal consistency of six MMDIs under historic and future climates.
- To quantify the uncertainty in meteorological drought characterization arising from different MMDI formulations.
- To assess the influence of reference evapotranspiration (ETo) methods on MMDI consistency.
Main Methods:
- Utilized ERA5 reanalysis and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs.
- Employed six MMDIs: Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI), and Supply Demand Drought Index (SDDI).
- Applied agreement mapping, category difference analysis, and global sensitivity analysis (GSA) to evaluate MMDI consistency and uncertainty.
Main Results:
- MMDIs demonstrated strong agreement under historic climate conditions.
- Broad agreement was observed among most MMDIs under climate change, with notable disagreements in arid zones and under higher emission scenarios.
- Increased uncertainty in future drought projections was linked to specific MMDIs (SDDI, SPEI projecting drier; scPDSI projecting wetter) and varying sensitivities to precipitation and ETo.
- MMDI uncertainty was significantly influenced by the choice of ETo methods, with the Penman-Monteith method identified as a key factor.
- MMDI-GCM interactions were identified as a major source of uncertainty in drought projections.
Conclusions:
- While MMDIs generally agree historically, significant discrepancies emerge under future climate change, especially in vulnerable regions.
- The selection of an appropriate MMDI is critical, as different indices can yield contrasting drought characterizations.
- Careful evaluation and selection of MMDIs are essential for reliable climate change impact assessments and drought management strategies.
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