Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2399-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2399-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 May 2021
Research article |
| 10 May 2021
| 10 May 2021
Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
Nataniel M. Holtzman
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
Alexandra G. Konings
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
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We estimate daily evapotranspiration (ET) across the United States using the ‘surface flux equilibrium’ approach, which assumes that the balance of temperature and humidity in the atmosphere reflects recent ET on land. Using triple collocation, we compare our estimates to three other ET datasets and find that the surface flux equilibrium ET method performs well. Surface flux equilibrium ET may therefore be useful for hydrologic studies where simple, parameter-free ET estimates are advantageous.
Meng Zhao, Erica L. McCormick, Geruo A, Alexandra G. Konings, and Bailing Li
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Root-zone water storage capacity (Sr) helps plants survive droughts and influences water and climate systems. Using GRACE (Gravity Recovery and Climate Experiment) satellite data, we estimated Sr globally and found that it exceeds 2 m soil storage in nearly half of the vegetated areas, far more than previously thought. Incorporating our Sr estimates into a global hydrological model improves evapotranspiration simulations, particularly during droughts, highlighting the value of our approach for advancing water resource and ecosystem modeling.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
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Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
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Short summary
The flow of water through plants varies with species-specific traits. To determine how they vary across the world, we mapped the traits that best allowed a model to match microwave satellite data. We also defined average values across a few clusters of trait behavior. These form a tractable solution for use in large-scale models. Transpiration estimates using these clusters were more accurate than if using plant functional types. We expect our maps to improve transpiration forecasts.
The flow of water through plants varies with species-specific traits. To determine how they vary...
