Weiwei Zhan

All Publications

• Reduced water loss rather than increased photosynthesis controls CO2-enhanced water-use efficiency. Nature ecology & evolution Zhan, W., Lian, X., Liu, J., Han, J., Huang, Y., Yang, H., Zhan, C., Winkler, A. J., Gentine, P. 2025; 9 (9): 1571-1584

  Abstract

  Numerous leaf-level experiments suggest that plant intrinsic water-use efficiency (iWUE) increases under elevated CO2 because of reduced stomatal conductance and enhanced photosynthesis. However, it remains elusive whether this response can be extrapolated to the ecosystem scale, because confounding factors and compensating feedbacks are often involved in ecosystem iWUE variations. Here we develop a machine learning-based framework to disentangle the ecosystem-scale CO2 effects on iWUE and its two components, canopy conductance (Gc) and gross primary productivity (GPP), based on global networks of long-term eddy covariance observations. Our results show widespread CO2-induced enhancement of iWUE across diverse ecosystems, driven predominantly by Gc reduction rather than GPP stimulation. Moreover, three divergent response types are identified across the studied ecosystems, based on the strength and significance of CO2-driven Gc reduction and GPP enhancement, indicating spatially non-uniform responses to rising CO2. Nutrient supply, water availability and biome types are found to be critical factors regulating this spatial heterogeneity. Overall, our study provides observational insights into ecosystem-scale CO2 fertilization effects. Such understandings are essential to inform terrestrial biosphere models for better projections of carbon and water cycles given the intensified changing climate in a CO2-rich future.

  View details for DOI 10.1038/s41559-025-02761-0

  View details for PubMedID 40646261

  View details for PubMedCentralID 7896309

• When and where soil dryness matters to ecosystem photosynthesis. Nature plants Liu, J., Wang, Q., Zhan, W., Lian, X., Gentine, P. 2025; 11 (7): 1390-1400

  Abstract

  Projected increases in the intensity and frequency of droughts in the twenty-first century are expected to cause a substantial negative impact on terrestrial gross primary productivity (GPP). Yet, the relative role of soil water supply (indicated by soil moisture) and atmospheric water demand (indicated by vapour pressure deficit, VPD) on GPP remains debated, primarily due to their strong covariations, the presence of confounding factors and unresolved causal relationships among the interconnected hydrometeorological drivers of GPP. Here using a causality-guided explainable artificial intelligence framework, we show that soil moisture is the dominant regulator of water stress, surpassing the role of VPD, when and where soil water supply limits ecosystem functions. Temporally, we use in situ flux tower data to demonstrate that soil moisture dominates the GPP response during periods of insufficient soil water supply. Spatially, we assess the global spatial patterns of satellite sun-induced chlorophyll fluorescence (a proxy for GPP) in water-limited regions and demonstrate that they are mostly dominated by soil moisture. Conversely, VPD plays a greater role in controlling the temporal and spatial variations in GPP than soil moisture when and where soil water supply is not limited. The relative role of soil moisture and VPD is modulated by plant adaptation to long-term climatological aridity. Our findings advance the understanding of the impacts of soil and atmospheric dryness on ecosystem photosynthesis. They provide crucial insights into how terrestrial ecosystems respond to increasing aridity and more frequent droughts, particularly given the potential ecosystem shifts from energy to water limitation.

  View details for DOI 10.1038/s41477-025-02024-7

  View details for PubMedID 40624150

  View details for PubMedCentralID 11156669

• Inappropriateness of space-for-time and variability-for-time approaches to infer future dryland productivity changes FRONTIERS IN ENVIRONMENTAL SCIENCE Zhan, W., Lian, X., Liu, J., Gentine, P. 2022; 10

  View details for DOI 10.3389/fenvs.2022.1010269

  View details for Web of Science ID 000876171800001

• Two for one: Partitioning CO₂ fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning AGRICULTURAL AND FOREST METEOROLOGY Zhan, W., Yang, X., Ryu, Y., Dechant, B., Huang, Y., Goulas, Y., Kang, M., Gentine, P. 2022; 321

  View details for DOI 10.1016/j.agrformet.2022.108980

  View details for Web of Science ID 000800107500001