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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-649
https://doi.org/10.5194/hess-2020-649

  16 Dec 2020

16 Dec 2020

Review status: this preprint is currently under review for the journal HESS.

Global ecosystem-scale plant hydraulic traits retrieved using model-data fusion

Yanlan Liu, Nataniel M. Holtzman, and Alexandra G. Konings Yanlan Liu et al.
  • Department of Earth System Science, Stanford University, Stanford, CA, USA 94305

Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is therefore crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for paramaterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model-data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with datasets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band JAXA Advanced Microwave Scanning Radiometer for EOS (AMSR-E). VOD is proportional to vegetation water content and therefore closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere Land-Exchange Inverse model (ALEXI) is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by PFTs improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model-data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.

Yanlan Liu et al.

 
Status: open (until 10 Feb 2021)
Status: open (until 10 Feb 2021)
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Yanlan Liu et al.

Data sets

Datasets: Global ecosystem-scale plant hydraulic traits retrieved using model-data fusion Yanlan Liu, Nataniel Holtzmam, and Alexandra Konings https://doi.org/10.6084/m9.figshare.13350713.v1

Yanlan Liu et al.

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