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.
Highlights
Water stress during drought restricts photosynthesis, weakening the strength of the terrestrial carbon sink (Ma et al, 2012; Wolf et al, 2016; Konings et al, 2017) and possibly causing plant mortality under severe conditions (McDowell et al, 2016; Adams et al, 2017; Choat et al, 2018)
Having derived spatial maps of variations in plant hydraulic traits, we explore whether simple alternatives to plant functional types (PFTs) can be built to facilitate parameterizing land surface models
Even in areas where vegetation optical depth (VOD) has been shown to be less correlated with LAI, including central Australia, central Asia, southern Africa, and the western US (Momen et al, 2017), the estimated VOD accounting for the signature of leaf water potential is able to capture observed VOD
Summary
Water stress during drought restricts photosynthesis, weakening the strength of the terrestrial carbon sink (Ma et al, 2012; Wolf et al, 2016; Konings et al, 2017) and possibly causing plant mortality under severe conditions (McDowell et al, 2016; Adams et al, 2017; Choat et al, 2018). The plant response to water stress directly controls regional water resources and drought propagation by modulating water flux and energy partitioning between the land surface and the atmosphere (Goulden and Bales, 2014; Manoli et al, 2016; Anderegg et al, 2019). How plants regulate water, carbon, and energy fluxes and plant mortality under drought could vary considerably depending on plant properties, plant hydraulic traits (Sack et al, 2016; Hartmann et al, 2018; McDowell et al, 2019). Understanding this variation is crucial for the accurate prediction of ecosystem dynamics under changing climate.
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