Moradian et al. (2026) Projecting global soil moisture droughts under climate change: characteristics, agricultural exposure, and adaptation insights

Identification

• Journal: npj Sustainable Agriculture
• Year: 2026
• Date: 2026-09-24
• Authors: Sogol Moradian, Salem Gharbia, Fatimatuj Sonny, Ali Torabi Haghighi, Agnieszka I. Olbert
• DOI: 10.1038/s44264-026-00144-x

Research Groups

• Atlantic Technological University, Sligo, Ireland
• University of Galway, Galway, Ireland
• University of Oulu, Oulu, Finland

Short Summary

This study introduces a framework to project global agricultural drought impacts under climate change by integrating bias-corrected soil moisture data from 17 CMIP6 models. It reveals a clear intensification of soil moisture drought characteristics and increased agricultural vulnerability by mid-century, particularly under the high-emission SSP5-8.5 scenario.

Objective

• To assess the historical and projected trends of soil moisture droughts on a global scale under different climate change scenarios using climate model simulations.
• To evaluate the spatial and temporal variations of soil moisture droughts and their implications for agriculture.
• To bridge the gap between hazard-based drought assessments and impact-driven approaches by integrating drought severity with regional vulnerabilities for improved climate resilience strategies.

Study Configuration

• Spatial Scale: Global, with all models reclassified to a 0.25° × 0.25° grid. Continental analysis for North America, South America, Europe, Africa, Asia, and Australia.
• Temporal Scale: Baseline period: 2000–2014 (GLDAS). Projection period: 2015–2050 (CMIP6). Monthly soil moisture data.

Methodology and Data

• Models used: 17 CMIP6 (Coupled Model Intercomparison Project Phase 6) models (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CanESM5, CESM2, CMCC-CM2-SR5, CNRM-ESM2-1, EC-Earth3-Veg-LR, KACE-1-0-G, MIROC6, MIROC-ES2L, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM, TaiESM1, UKESM1-0-LL). GLDAS-Noah25-M.2.1 (Global Land Data Assimilation System) for reference.
• Data sources: CMIP6 climate model simulations (soil moisture), GLDAS-Noah v2.1 (monthly 0.25° soil moisture data), Land Cover Climate Research Data Package (CRDP) for global land cover information.
• Key techniques:
  • Bias correction: Linear scaling method applied to CMIP6 soil moisture data using GLDAS as reference.
  • Multi-model ensemble mean (MMEM) for CMIP6 data.
  • Standardised Soil Moisture Index (SSMI) calculation using the empirical Gringorten distribution method (non-parametric).
  • Drought event characterization: Frequency (number of events), duration (months), severity (absolute integral area of SSMI), and intensity (average SSMI value).
  • Drought Exposure Index (DEI): Calculated as the cube root of (normalised frequency × normalised total duration × normalised mean intensity).
  • Climate scenarios: Shared Socioeconomic Pathways (SSPs) SSP2-4.5 ("middle-of-the-road") and SSP5-8.5 ("high-emission").
  • Spatial interpolation: Bicubic interpolation to unify spatial resolution to 0.25° × 0.25°.
  • Statistical tests: Kolmogorov-Smirnov (K-S) test for SSMI suitability. Performance metrics (Correlation Coefficient, Kling-Gupta Efficiency, Mean Absolute Error, Mean Error, Relative bias, Root Mean Square Error, Index of Agreement) for bias correction evaluation.

Main Results

• A clear intensification of soil moisture drought characteristics is projected towards mid-century (2050), particularly under the SSP5-8.5 scenario, with longer drought durations and increased spatial extent across major agricultural regions.
• Under SSP5-8.5, drought events tend to last longer (higher duration) but occur less frequently, resulting in fewer but more prolonged drought episodes.
• Continental analysis highlights pronounced drying trends in South America, southern Europe, South Asia, and parts of North America. Australia exhibits considerable interannual fluctuations.
• Agricultural vulnerability assessments identify cropland areas in these regions as most at risk.
• The global mean Drought Exposure Index (DEI) is projected to increase from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5 by 2050, reflecting a significant rise in exposure under the more extreme emissions scenario.
• The percentage of agricultural land affected by drought conditions shows a general upward trend towards 2050 across continents, with Australia, Asia, North America, and South America showing the greatest projected variability and exposure.

Contributions

• Introduces a novel framework that applies a consistent bias-correction and multi-model ensemble approach directly to soil moisture outputs from climate models, rather than their climatic drivers.
• Utilizes a non-parametric Standardised Soil Moisture Index (SSMI) formulation, which is robust under non-stationary climate conditions.
• Develops a new Drought Exposure Index (DEI) that integrates drought characteristics (frequency, duration, intensity) with global cropland distribution to quantify agricultural drought exposure spatially.
• Enables a shift from conventional hazard-based drought assessments to a spatially explicit, soil-moisture-driven agricultural drought exposure framework at the global scale.
• Provides actionable insights and critical recommendations for long-term soil moisture drought adaptation, sustainable water management, and climate resilience by identifying the most vulnerable drought-prone regions.

Funding

• University of Galway, College of Science and Engineering 2022–23 Research Seed Fund Award.

Citation

@article{Moradian2026Projecting,
  author = {Moradian, Sogol and Gharbia, Salem and Sonny, Fatimatuj and Haghighi, Ali Torabi and Olbert, Agnieszka I.},
  title = {Projecting global soil moisture droughts under climate change: characteristics, agricultural exposure, and adaptation insights},
  journal = {npj Sustainable Agriculture},
  year = {2026},
  doi = {10.1038/s44264-026-00144-x},
  url = {https://doi.org/10.1038/s44264-026-00144-x}
}