Abstract
Plant water-use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO2 concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO2. Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long-term observation-based estimates of WUE that will better inform the representation of WUE in vegetation models.
| Original language | English |
|---|---|
| Pages (from-to) | 2242-2257 |
| Number of pages | 16 |
| Journal | Global Change Biology |
| Volume | 25 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2019 |
Bibliographical note
Funding Information:A.L. was supported by a Newton International Fellowship (NF170082; Royal Society, UK). M.D.K. acknowledges the ARC Centre of Excellence for Climate Extremes (CE170100023)?and?support from the New South Wales Research Attraction and Acceleration Program. We thank all the authors that have made available their data, and who contributed to and maintained the ITRDB platform. This work used eddy-covariance data acquired and shared by the FLUXNET community, including the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy-covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Centre, and the OzFlux, ChinaFlux, and AsiaFlux offices. We thank the anonymous reviewers that have considerably helped improve this manuscript.?This work contributes to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment.
Funding Information:
A.L. was supported by a Newton International Fellowship (NF170082; Royal Society, UK). M.D.K. acknowledges the ARC Centre of Excellence for Climate Extremes (CE170100023) and support from the New South Wales Research Attraction and Acceleration Program. We thank all the authors that have made available their data, and who contributed to and maintained the ITRDB platform. This work used eddy‐covariance data acquired and shared by the FLUXNET community, including the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux‐TERN, TCOS‐Siberia, and USCCC. The ERA‐ Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy‐covariance data processing and harmo‐ nization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Centre, and the OzFlux, ChinaFlux, and AsiaFlux offices. We thank the anonymous reviewers that have considerably helped improve this manuscript. This work contributes to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment.
Publisher Copyright:
© 2019 John Wiley & Sons Ltd
Keywords
- carbon isotopic discrimination
- eddy-covariance flux
- spatial scales
- stomatal conductance
- trends in water-use efficiency
- vegetation modelling
