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

Abstract

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 parameterizing 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 data sets 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 Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; collectively 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 (ALEXI) model 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 plant functional types (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.

Parameterizing Vegetation Traits With a Process‐Based Ecohydrological Model and Xylem Water Isotopic Observations

“…Our finding that constraining a process-based model with hydrologic flux data can yield ecologically consistent vegetation parameter values is in agreement with other recent work (Knighton, Kuppel, et al, 2020;Knighton, Singh, & Evaristo, 2020;Kuppel et al, 2018a;Liu et al, 2021;Peaucelle et al, 2019). Model sensitivity analysis further indicated that δ XYLEM data can support estimation of plant trait values related to rooting, stomatal conductance, growth temperatures, and possibly tree water storage (Figure 3).…”

Detecting forest response to droughts with global observations of vegetation water content

“…Ultimately, the accuracy of such inferences likely inherently depends on the relative sensitivity and information content of different observations, including the VWC estimates. Nevertheless, some early applications of data assimilation with remotely sensed VWC estimates show this approach has promise (Liu et al, 2021 ; Liu et al, 2021 ). In each of these studies, observations at two times a day were used, but a more complete diel cycle may act as an even stronger constraint.…”

Intra‐Specific Variability in Plant Hydraulic Parameters Inferred From Model Inversion of Sap Flux Data

“…Among common model inversion techniques (e.g., frequentist methods or Kalman filter [Mo et al., 2008]), we select Markov Chain Monte Carlo (MCMC) for parameter estimation, primarily because it quantifies the epistemic uncertainty associated with each model parameter in addition to its best estimates (Wu et al., 2014). It has also been successfully used for inversion of PHMs in eddy covariance (Liu et al., 2020) and remote sensing (Liu et al., 2021) settings. In our case, we use MCMC to estimate the whole‐plant effective values of plant hydraulic traits as parameters in a physiologically informed sap flow model using local soil water potential and atmospheric conditions as inputs to the model.…”