Khadke et al. (2025) Vapor pressure deficit dominates sap flow variability across forest biomes

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-24
• Authors: Leena Khadke, Akash Verma, Sandipan Mukherjee, Subimal Ghosh
• DOI: 10.1016/j.jhydrol.2025.134449

Research Groups

• Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India
• ARC Centre of Excellence for the Weather of 21st Century and Climate Change Research Centre, University of New South Wales, Sydney, Australia
• Department of Life Sciences, Imperial College London, Ascot, United Kingdom
• Ladakh Regional Centre, G. B. Pant National Institute of Himalayan Environment, Leh, India
• Centre for Climate Studies, Indian Institute of Technology Bombay, Mumbai, India

Short Summary

This study investigates the causal drivers of sap flow (SAPFlow) across 15 global forest sites using information theory-based process networks and wavelet analysis. It finds that vapor pressure deficit (VPD) is the dominant causal driver of SAPFlow variability across all forest types, forming a coupled system with soil water content (SWC) mediated by land-atmosphere feedback.

Objective

• Identify key drivers of sap flow (SAPFlow) dynamics across evergreen broadleaf, evergreen needleleaf, and mixed forest types.
• Examine the temporal scales at which hydrometeorological variables influence SAPFlow.
• Assess the instantaneous and memory effects of hydrometeorological variables on SAPFlow.

Study Configuration

• Spatial Scale: 15 sites from the global SAPFLUXNET dataset, distributed across North America, Europe, and Australia, representing subtropical and temperate zones. Three forest biomes: Evergreen Broadleaf Forests (EBF), Evergreen Needleleaf Forests (ENF), and Mixed Forests (MF).
• Temporal Scale: Daily scale data aggregated from sub-daily measurements, with a minimum of 365 data points per site. Wavelet analysis used spectral periods up to 256 days. Causal analysis (TIPNets) incorporated lags up to 7 days.

Methodology and Data

• Models used:
  • Continuous Wavelet Transform (CWT), Cross Wavelet Transform (XWT), and Wavelet Coherence (WTC) using the Morlet wavelet.
  • Temporal Information Partitioning Networks (TIPNets) framework, based on Shannon's entropy, Mutual Information (MI), and Transfer Entropy (TE).
  • Correlation and Partial Correlation analysis.
• Data sources:
  • SAPFLUXNET dataset (publicly accessible via http://sapfluxnet.creaf.cat/ and Zenodo repository https://doi.org/10.5281/zenodo.3971689).
  • Variables: Sap flow (SAPFlow), Vapor Pressure Deficit (VPD), Precipitation (P), Temperature (T), and Soil Water Content (SWC).

Main Results

• Vapor Pressure Deficit (VPD) is the dominant causal driver of SAPFlow across all forest types (EBF, ENF, MF), showing a strong positive correlation and significant instantaneous and memory-based influence.
• Sap flow rates varied from 0 to 0.004 cubic meters per hour (0 to 4000 cm³/hr) across different sites.
• Soil Water Content (SWC) forms a coupled system with SAPFlow and VPD, with its influence being more delayed compared to VPD. Partial correlation analysis revealed a positive relationship between SWC and SAPFlow when VPD's effect is controlled, correcting for a confounding negative simple correlation.
• Wavelet analysis showed VPD consistently exhibits short-term variability (within 16 days) and strong coherence with SAPFlow (4-32 days bands). SWC shows both short- and longer-term variability (8-128 days) and intermittent coherence with SAPFlow (16-64 days bands).
• Information partitioning analysis indicated that VPD contributes more unique information to SAPFlow than SWC. The combined influence (synergy) of SWC and VPD is significantly higher than their redundancy, highlighting that their coupling provides additional explanatory power for SAPFlow variability.
• Evergreen Needleleaf Forests (ENF) emerged as the biome most sensitive to environmental drivers, showing strong and persistent information transfer from atmospheric and soil variables.

Contributions

• Provides a robust framework using information theory-based causal inference (TIPNets) and wavelet analysis to disentangle direct causal drivers and their interactions on SAPFlow, moving beyond simple correlation-based approaches.
• Quantifies the unique, synergistic, and redundant effects of key hydrometeorological variables (VPD, SWC) on SAPFlow dynamics across diverse forest biomes.
• Identifies the dominant role of VPD as a primary causal driver of SAPFlow variability across all forest types, emphasizing its immediate and memory-based influence.
• Highlights the distinct temporal interaction scales of VPD (immediate) and SWC (delayed) on SAPFlow.
• Offers critical insights for refining land surface models and improving predictions of ecosystem responses to climate change by incorporating causal and lagged relationships.

Funding

The authors are grateful to Prof. Allison Goodwell and Prof. Praveen Kumar for the free availability of codes at GitHub to generate the process networks. No specific funding projects or programs were listed.

Citation

@article{Khadke2025Vapor,
  author = {Khadke, Leena and Verma, Akash and Mukherjee, Sandipan and Ghosh, Subimal},
  title = {Vapor pressure deficit dominates sap flow variability across forest biomes},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134449},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134449}
}

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