Global transpiration data from sap flow measurements: the SAPFLUXNET database

Abstract

Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

One Stomatal Model to Rule Them All? Toward Improved Representation of Carbon and Water Exchange in Global Models

“…Ideally, models should link Ψ l to g s through hydraulic reduction factors/cost terms that rely on realistic k max . Newly available sap flow measurements from the SAPFLUXNET database (Poyatos et al., 2021), together with existing hydraulic trait databases and aligned with leaf‐level observations, offer an opportunity to infer relevant parameterizations of k max and xylem failure thresholds (i.e., Ψ crit , k crit ) that could reduce model bias in Ψ l estimates. Explicit rhizosphere (Venturas et al., 2018; Wang et al., 2019) and/or symplastic limitations (De Cáceres et al., 2021) under dry conditions could also be explored, noting associated parameterization uncertainties (Xu & Trugman, 2021).…”

Stressors Reveal Ecosystems' Hidden Characteristics

“…Large-scale, long-term and paired ecosystem studies produce datasets of such high temporal density to be statistically independent at sufficient time scales (Baldocchi & Ma, 2013) and allow the substitution of space with time (Panofsky & Dutton, 1984). Notably, these results also reveal how crucial it is to thoroughly report metadata regarding the stand history along with present and past environmental context of measurements when reporting observations to global observational databases such as TRY (Kattge et al, 2011), SAPFLUXNET (Poyatos et al, 2020), the Ameriflux and other Fluxnet networks (Baldocchi et al, 2001;Novick et al, 2018), hydraulic traits databases (e.g., Chave et al, 2009;Choat et al, 2012), or GLOPNET (Reich et al, 2007;Wright et al, 2004), for example. Capturing the variability of plant functional traits and the ensuing observable response metrics is of central importance to moving towards new, increasingly "trait"-focused frameworks for ecophysiological and specifically ecohydrological classifications of vegetation, such as hydraulic strategies.…”

A deep learning-based hybrid model of global terrestrial evaporation

“…To mitigate the effects of the uncertainty in E t estimates arising from the choice of the partitioning model used in this study, we supplement the estimates of tall vegetation E t partitioned from E at the flux towers with a more direct estimate of E t from sap flow measurements. These in situ measurements are sourced from SAPFLUXNET, a global database of tree-level sap flow measurements 58 . It contains sub-daily time series of sap flow accompanied by in situ-measured hydrometeorological variables and ancillary site, stand and plant metadata.…”