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- Poyatos, R., et al.
(author)
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Global transpiration data from sap flow measurements: the SAPFLUXNET database
- 2021
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In: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 13:6, s. 2607-2649
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Journal article (peer-reviewed)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.
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- Ibupoto, A.S., et al.
(author)
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Zno/Carbon nanofibers for efficient adsorption of lead from aqueous solutions
- 2020
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In: Environmental technology. - : Taylor & Francis. - 0959-3330 .- 1479-487X. ; 41:21, s. 2731-2741
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Journal article (peer-reviewed)abstract
- Hybrid nanofibers based on ZnO loaded activated carbon nanofibers (ZnO-ACNFs) are proposed here for the elimination of hazardous lead from aqueous solutions. The prepared ZnO nanoscale material was loaded into the polyacrylonitrile nanofibers (PAN NFs) which were later carbonized by using a novel method named as a plate-sandwich method. The Synthesized nanofibrous composite was characterized by SEM, TEM, EDX, FTIR and XRD techniques to analyze its chemical and morphological properties. Moreover, the nanocomposite was efficaciously applied for the lead (Pb2+) ions removal from wastewater and simulated water through continuous filtration and batch filtration. The ZnO-ACNFs membrane showed outstanding results in adsorptive removal, giving adsorption capacity of 92.59 mg/g within the contact time of 45 min. Compared to their counterparts (ZnO and CNFs), the hybrid ZnO-ACNFs showed excellent performance in removing toxic lead.
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- Zhang, Weijie, et al.
(author)
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The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation
- 2023
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In: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 330
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Journal article (peer-reviewed)abstract
- While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.
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