He et al. (2026) Contrasting trends in climatic and ecohydrological aridity over one-fifth of global drylands

Identification

• Journal: International Journal of Applied Earth Observation and Geoinformation
• Year: 2026
• Date: 2026-03-10
• Authors: Lei He, Alexis Berg, Kailiang Yu, Jian-Sheng Ye, Josep Peñuelas, Philippe Ciais, Jingfeng Xiao, Xu Lian, Jianping Huang, Jing Li, Wei Li, Jian Peng, Songhan Wang, Ning Ma, Zecheng Guo, Thomas W. Crowther, Jiangpeng Cui, Chenghu Zhou, Yaowen Xie, Zhao-Liang Li
• DOI: 10.1016/j.jag.2026.105229

Research Groups

• State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
• National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
• College of Earth and Environment Sciences, Lanzhou University, Lanzhou, China
• Department of Geography, Université de Montréal, Montréal, QC, Canada
• Department of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA
• State Key Laboratory of Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
• CSIC, Global Ecology CREAF-CSIC-UAB, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
• CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
• Laboratoire des Sciences du Climat et de l′Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France
• Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
• Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
• Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
• Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China
• Department of Remote Sensing, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
• Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, College of Agriculture, Nanjing Agricultural University, Nanjing, China
• Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland
• State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
• Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China
• Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou, China
• The Data Intelligence Laboratory of Tibetan Plateau Humanistic Environment, Lanzhou University, Lanzhou, China
• State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Agricultural Sciences, Beijing, China

Short Summary

This study reveals that nearly one-fifth (22.3%) of global vegetated drylands exhibited contrasting trends in climatic and ecohydrological aridity over the past four decades, primarily driven by the opposing effects of elevated atmospheric CO2 on vegetation structure and stomatal conductance.

Objective

• To determine the spatial distribution of two types of contrasting climatic and ecohydrological aridity changes (i.e., increased climatic aridity with decreased ecohydrological aridity, and decreased climatic aridity with increased ecohydrological aridity) across global drylands in recent decades.
• To identify the underlying mechanisms driving these contrasting patterns of aridity change.

Study Configuration

• Spatial Scale: Global drylands, defined by Bailey's four global ecoregion domains. All data were resampled to a 0.5 degree resolution for final analyses.
• Temporal Scale: 1982–2018 (37 years). The ecohydrological aridity index (EI) was calculated using a 15-year moving window to represent long-term mean conditions.

Methodology and Data

• Models used:
  • Climatic Aridity Index (AI): Ratio of annual precipitation (P) to annual potential evapotranspiration (PET).
  • Ecohydrological Aridity Index (EI): Defined as LAI - (a × cor(SM, Tran) + b), where LAI is leaf area index, cor(SM, Tran) is the correlation between soil moisture and vegetation transpiration, and 'a' and 'b' are fitting parameters.
  • Revised Penman-Monteith method: Used for PET estimation, incorporating variations in stomatal resistance driven by atmospheric CO2 levels.
  • Canopy stomatal conductance (gs): Estimated from transpiration or evapotranspiration, atmospheric pressure, and vapor pressure deficit.
  • Water Use Efficiency (WUE): Ratio of gross primary productivity (GPP) to evapotranspiration (ET) or vegetation transpiration (Tran).
  • Land-Atmosphere Coupling (LAC): A two-legged index linking correlations for soil moisture vs. evapotranspiration, and evapotranspiration vs. precipitation, scaled by the standard deviation of precipitation.
  • Least-squares linear regression: Applied to estimate temporal trends in AI and EI.
  • Ridge regression: Used to assess the relative importance of atmospheric CO2 concentration, energy factors, and water factors in driving EI changes.
  • Structural Equation Modeling (SEM): Employed to quantify pathways linking EI to climate variables through mediating factors (LAI, gs, LAC).
• Data sources:
  • Precipitation: Climatic Research Unit gridded Time Series (CRU TS v4.07), Global Precipitation Climatology Centre (GPCC) v2020, Climate Prediction Center (CPC), Multi-Source Weighted-Ensemble Precipitation (MSWEP).
  • Potential Evapotranspiration (PET): CRU PET, Global Land Evaporation Amsterdam Model (GLEAM) v3.5a, hourly PET (hPET), custom PET dataset derived from MSWX using revised Penman-Monteith.
  • Leaf Area Index (LAI): Long-term Global Mapping (GLOBMAP), Global Land Surface Satellite (GLASS), Global Inventory Modeling and Mapping Studies (GIMMS).
  • Soil Moisture (SM): ERA5-Land, GLEAM, CPC.
  • Vegetation Transpiration (Tran) and Actual Evapotranspiration (ET): GLEAM, ERA5-Land.
  • Gross Primary Productivity (GPP): GLASS, Breathing Earth System Simulator (BESS), NIRv-derived GPP, Multiscale Satellite Remote Sensing (MUSES).
  • Climate Variables: MSWX (air temperature, air pressure, relative humidity).
  • Atmospheric CO2 Concentration: NOAA/GML globally averaged monthly data.
  • Biotic Data: Bailey (1995) for dryland regions, HILDA+ for land use/cover, MODIS MOD44B for low-vegetation cover.

Main Results

• Approximately 22.3% ± 2.5% of global vegetated drylands exhibited contrasting trends in climatic and ecohydrological aridity during 1982–2018.
• Of these, 13.4% ± 2.7% experienced increased climatic aridity (AI↓) but decreased ecohydrological aridity (EI↑), primarily in the western United States and southern Australia.
• The remaining 8.9% ± 3.6% showed decreased climatic aridity (AI↑) but increased ecohydrological aridity (EI↓), predominantly in Kazakhstan, southern Africa, and northeastern Australia.
• Elevated atmospheric CO2 concentration (eCO2) was identified as the dominant predictor of EI variability, exerting opposite effects: positive in AI↓ & EI↑ regions and negative in AI↑ & EI↓ regions.
• In AI↓ & EI↑ regions, eCO2 led to a significant decline in canopy stomatal conductance (–15.5 ± 7.1 units per year) and a rise in water use efficiency (0.010 ± 0.012 grams of carbon per millimeter per year), indicating enhanced water-saving capacity that mitigated climatic drying.
• In AI↑ & EI↓ regions, weak eCO2-induced water savings (gs trend: 1.7 ± 6.8 units per year; WUE trend: 0.003 ± 0.012 grams of carbon per millimeter per year) combined with increased vegetation structure (LAI) led to increased transpiration, likely exceeding the ecosystem's water-supply capacity and intensifying ecohydrological stress.
• Land cover changes accounted for a minor fraction of contrasting aridity regions (1.2% for AI↓ & EI↑ and 0.7% for AI↑ & EI↓), suggesting they were not the primary drivers.

Contributions

• Provides the first comprehensive quantification of the spatial patterns and underlying mechanisms of contrasting climatic and ecohydrological aridity changes across global drylands.
• Highlights the nonlinear and internally regulated nature of ecosystem responses to climatic variability, demonstrating that vegetation dynamics are not always synchronized with climatic aridity.
• Offers new insights into the complex interactions between vegetation, water, and carbon cycling in drylands under climate change, particularly the critical role of eCO2-induced physiological and structural changes.
• Establishes an important benchmark for evaluating and refining Earth system models, which can lead to more reliable projections of future carbon cycle and climate dynamics in dryland ecosystems.

Funding

• National Natural Science Foundation of China (41921001)
• Data Intelligence Laboratory of Tibetan Plateau Humanistic Environment (Lanzhou University)

Citation

@article{He2026Contrasting,
  author = {He, Lei and Berg, Alexis and Yu, Kailiang and Ye, Jian-Sheng and Peñuelas, Josep and Ciais, Philippe and Xiao, Jingfeng and Lian, Xu and Huang, Jianping and Li, Jing and Li, Wei and Peng, Jian and Wang, Songhan and Ma, Ning and Guo, Zecheng and Crowther, Thomas W. and Cui, Jiangpeng and Zhou, Chenghu and Xie, Yaowen and Li, Zhao-Liang},
  title = {Contrasting trends in climatic and ecohydrological aridity over one-fifth of global drylands},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2026},
  doi = {10.1016/j.jag.2026.105229},
  url = {https://doi.org/10.1016/j.jag.2026.105229}
}