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.

Abstract. Deep soil recharge (DSR) (at depth greater than 200 cm) is an important part of water circulation in arid and semi-arid regions. Quantitative monitoring of DSR is of great importance to assess water resources and to study water balance in arid and semi-arid regions. This study used a typical bare land on the eastern margin of Mu Us Sandy Land in the Ordos Basin of China as an example to illustrate a new lysimeter method of measuring DSR to examine if the annual recharge coefficient is valid or not in the study site, where the annual recharge efficient is the ratio of annual DSR over annual total precipitation. Positioning monitoring was done on precipitation and DSR measurements underneath mobile sand dunes from 2013 to 2015 in the study area. Results showed that use of an annual recharge coefficient for estimating DSR in bare sand land in arid and semi-arid regions is questionable and could lead to considerable errors. It appeared that DSR in those regions was influenced by precipitation pattern and was closely correlated with spontaneous strong precipitation events (with precipitation greater than 10 mm) other than the total precipitation. This study showed that as much as 42 % of precipitation in a single strong precipitation event can be transformed into DSR. During the observation period, the maximum annual DSR could make up 24.33 % of the annual precipitation. This study provided a reliable method of estimating DSR in sandy areas of arid and semi-arid regions, which is valuable for managing groundwater resources and ecological restoration in those regions. It also provided strong evidence that the annual recharge coefficient was invalid for calculating DSR in arid and semi-arid regions. This study shows that DSR is closely related to the strong precipitation events, rather than to the average annual precipitation, as well as the precipitation patterns.

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/). We harmonised 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, visualise and process SAPFLUXNET data is available from CRAN.

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