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Journal Articles Nature Geoscience Year : 2022

Confronting the water potential information gap

Kimberly A. Novick
  • Function : Author
Darren L. Ficklin
  • Function : Author
Dennis Baldocchi
  • Function : Author
Kenneth J. Davis
  • Function : Author
Teamrat A. Ghezzehei
  • Function : Author
Alexandra G. Konings
  • Function : Author
Natasha Macbean
  • Function : Author
Russell L. Scott
  • Function : Author
Yuning Shi
  • Function : Author
Benjamin N. Sulman
  • Function : Author
Jeffrey D. Wood
  • Function : Author

Abstract

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the `potential' to substantially reduce conceptual and modelling uncertainties.

Dates and versions

insu-03721933 , version 1 (13-07-2022)

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Kimberly A. Novick, Darren L. Ficklin, Dennis Baldocchi, Kenneth J. Davis, Teamrat A. Ghezzehei, et al.. Confronting the water potential information gap. Nature Geoscience, 2022, 15, pp.158-164. ⟨10.1038/s41561-022-00909-2⟩. ⟨insu-03721933⟩
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