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.

Plant phenotyping relevance

モデル・データ融合とマイクロ波リモートセンシングにより、全球規模の植物水理形質を推定し、独立した地上測定データと比較検証しているため、植物形質の取得・推定法が中心です。

Code and data availability

The paper's retrieved global plant hydraulic trait maps are publicly available on Figshare, and the authors' plant hydraulic model and model–data fusion code are on GitHub. Both are paper-specific, public, and actionable.