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26 results for “sap flow”
Linking Xylem Diameter Variations with Sap Flow Measurements at Harvard Forest 2003-2006
Measurements of variation in the diameter of tree stems provide a rapid response, high resolution tool for detecting changes in water tension inside the xylem. Water movement inside the xylem is caused by changes in the water tension and theoretically, the sap flow rate should be directly proportional to the water tension gradient and, therefore, also linearly linked to the xylem diameter variations. The coefficient of proportionality describes the water conductivity and elasticity of the conducting tissue. Xylem diameter variation measurements could thus provide an alternative approach for estimating sap flow rates, but currently we lack means for calibration. On the other hand, xylem diameter variation measurements could also be used as a tool for studying xylem structure and function. If we knew both the water tension in the xylem and the sap flow rate, xylem conductivity and/or elasticity could be calculated from the slope of their relationship. In this study we measured diurnal xylem diameter variation simultaneously with sap flow rates (Granier-type thermal method) in six deciduous species (Acer rubrum L., Alnus glutinosa Miller, Betula lenta L., Fagus Sylvatica L. Quercus rubra L., and Tilia vulgaris L.) for 7-91 day periods during summers 2003, 2005 and 2006 and analyzed the relationship between these two measurements. We found that in all species xylem diameter variations and sap flow rate were linearly related in daily scale (daily average R 2 = 0.61-0.87) but there was a significant variation in the daily slopes of the linear regressions. The largest variance in the slopes, however, was found between species, which is encouraging for finding a species specific calibration method for measuring sap flow rates using xylem diameter variations. At a daily timescale, xylem diameter variation and sap flow rate were related to each other via a hysteresis loop. The slopes during the morning and afternoon did not differ statistically significantly from each other,
Sap Flow of Northern Red Oak Trees Under Ecosystem Warming at Harvard Forest 2011
Over the next century, air temperature increases up to 5 °C are projected for the northeastern USA. Because evapotranspiration dominates water loss from terrestrial ecosystems, tree ecophysiological response to warming will have important consequences for forest water budgets. We measured growing season sap flow rates in mature northern red oak (Quercus rubra L.) trees in a combined air (up to 5.5 °C above ambient) and soil (up to 1.85 °C above ambient at 6-cm depth) warming experiment at Harvard Forest, MA, USA. Principal components analysis found air and soil temperatures had the largest effects on sap flow. On average, each 1 °C increase in temperature increased sap flow rates by approximately 1100 kg H2O m-2 sapwood area day-1 throughout the growing season and by 1200 kg H2O m-2 sapwood area day-1 during the early growing season. Reductions in the number of cold winter days correlated positively with increased sap flow at night during the early growing season (a decrease of 100 heating-degree-days was associated with a sapflow increase of approximately 5 kg H2O m-2 sapwood area day-1). Soil moisture declined with increased treatment temperatures, and each soil moisture percentage increase resulted in an increase in sap flow of approximately 360 kg H2O m-2 sapwood area day-1. At night, soil moisture correlated positively with sap flow rate. These results demonstrate that warmer air and soil temperatures in winter and throughout the growing season lead to increased sap flow rates, which could affect forest water budgets throughout the year.
Maple Reproduction and Sap Flow at Harvard Forest since 2011
Seed production assures the persistence of tree populations and forest cover over the long-term, and so has long interested plant demographers and foresters. Many forest tree species produce seeds synchronously and at irregular intervals across large areas, a phenomenon known as masting. Initiated in spring 2011, this study addresses the mechanisms of mast seeding in sugar maple (Acer saccharum), and its impact on pollinators, seed consumers, and forest carbon dynamics at the Harvard Forest. We monitor seed production (via counts of seeds on trees), flower production, and resource status (via sap collection) on 20 trees. Pollinator dynamics and seed predation (by weevils) are also monitored. In 2015, we added sap and seed monitoring of red maple (Acer rubrum) trees to explore the hypothesis that this non-masting species would have muted dynamics compared to its masting congener.
Sap Flow in Red Maple and Red Oak in the Harvard Forest Snow Removal Study 2011
The climate is changing in mid and high latitude environments with the depth and duration of snowpack shrinking for many temperate forest ecosystems. A reduced snowpack and increased depth and duration of soil frost can injure fine roots, which are essential for plant water uptake. Water uptake is a crucial component of ecosystem functioning because this process strongly impacts other biological processes, such as primary productivity and nutrient uptake. We evaluated the effects of changing winter climate, including snow and soil frost dynamics, on rates of water uptake (i.e. sap flow) in a snow manipulation study at Harvard Forest. We had three reference and tree plots from which we removed snow and induced soil freezing.
Detailed sap flow monitoring data at Weierbach catchment, Luxembourg
<p>The current repository contains the dasometric data of monitored trees, hourly sap flow data, and tree surveys carried out at the Weierbach catchment during 2019 and 2020. The monitored trees include the following species:</p> <p>- Beech (<em>Fagus sylvatica </em>L.)</p> <p>- Douglas Fir (<em>Pseudotsuga menziesii</em> (Mirbel) Franco)</p> <p>- Spruce (<em>Picea abies </em>L.)</p> <p>- Oak (hybrids of <em>Quercus petraea</em> (Matt.) Liebl. and <em>Quercus robur </em>L.)</p> <p>The sap flow data for all the trees is presented in cm/hr.</p> <p>The bark data presented in the "dasometry_individual_trees.csv" was obtained from the literature.</p>
Datasets of sap flow, meteorological, leaf gas measurements in urban green areas in Helsinki
<p>Datasets of sap flow, meteorological, leaf gas measurements used in the manuscript “Sap flow and leaf gas exchange response to drought and heatwave in urban green spaces in a Nordic city”. Data contains cleaned half-hourly sap flow data and half-hourly meteorological datasets (Tair, Tsoil, RH, soil temperature, soil moisture) at four different urban green areas in Helsinki.</p> <p>Manual measurements of leaf gas exchanges using GFS instruments. Datasets contains mainly the Amax parameters derived from curve fitting and instantaneous values of G and E at PAR 1100 W m<sup>-2</sup>.</p> <p>Folders contain:</p> <ul> <li>Leaf gas exchange data <ul> <li>Leaf_gas_data_all_v2.csv</li> <li>Metadata_leaf gas exchange data.csv</li> </ul> </li> <li>Meteo data <ul> <li>Meteo_Forest_data_30min.csv</li> <li>Meteo_Orchard_data_30min.csv</li> <li>Meteo_Park_data_30min.csv</li> <li>Meteo_Street_data_30min.csv</li> <li>Metainfo_meteo.xlsx</li> </ul> </li> <li>Sap flow data <ul> <li>Sap_Forest_30min_cleaned.csv</li> <li>Sap_Orchard_30min_cleaned.csv</li> <li>Sap_Park_30min_cleaned.csv</li> <li>Sap_Street_30min_cleaned.csv</li> <li>Metadata_info_sap.csv</li> </ul> </li> </ul>
Sap Flow in the Soil Freezing Study at the Hubbard Brook Experimental Forest, 2010
The climate is changing in many temperate forests with the amount of forest area dominated by sugar maple experiencing an insulating snowpack expected to shrink between 49 and 95% compared to 1951-2005 values. A reduced snowpack and increased depth and duration of soil frost can injure fine roots, which are essential for plant water uptake. Water uptake is a crucial component of ecosystem functioning because this process strongly impacts other biological processes, such as primary productivity and nutrient uptake. We evaluated the effects of changing winter climate, including snow and soil frost dynamics, by measuring sap flow rates in the Soil Freezing Study plots at the Hubbard Brook Experimental Forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Analysis of these data are published in: Harrison, J.L., Reinmann, A.B., Maloney, A.S. et al. Transpiration of Dominant Tree Species Varies in Response to Projected Changes in Climate: Implications for Composition and Water Balance of Temperate Forest Ecosystems. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00490-y
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Sap Flow
Sap flow was measured on all Climate Change Across Seasons Experiment (CCASE) plots. There are six plots total (each 11 x 14m). Two are warmed 5 degrees C throughout the growing season (Plots 3 and 4). Two others are warmed 5 degrees C in the growing season and have snow removed during winter to induce soil freezing and then warming cables turn on to create thaws; Each soil freeze/thaw cycles includes 72-hours of soil freezing followed by 72-hours of thaw (Plots 5 and 6). Four kilometers (2.5 mi) of heating cable are buried in the soil to warm these four plots. Two additional plots serve as controls for our experiment (Plots 1 and 2). This data set includes sap flow measurements for 2015 and 2017 growing seasons. These sap flow data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Sap Flow Data (2006-2013)
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees.  Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for w
The asymmetric diurnal latent heat flux in Chi-Lan montane cloud-fog forest: CLM simulations and sap flow observations
<p>Chilan_30min_sap_flow_V_2020JJA.csv recorded the data of sap flow velocity during JJA 2020.</p> <p>CL_CTR.*.nc is the analyzed CTR simulations which consider fog interception as a source of canopy water.</p> <p>CL_EXP.*.nc is the analyzed EXP simulations that do not allow the canopy to hold the water.</p>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
<p>Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.</p> <p>Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)</p>
Bridging the flux gap: sap flow measurements reveal species-specific patterns of water-use in a tallgrass prairie
<p>Predicting the hydrological consequences following changes in grassland vegetation type (i.e., woody encroachment) requires an understanding of water flux dynamics at high spatiotemporal resolution for predominant species within grassland communities. However, grassland fluxes are typically measured at the leaf or landscape scale, which inhibits our ability to predict how individual species contribute to changing ecosystem fluxes. We used external heat balance sap flow sensors and a hierarchical Bayesian state-space modeling approach to bridge this "flux-gap" and estimate continuous species-level water flux in common tallgrass prairie species. Specifically, we asked: 1) How do diurnal and nocturnal water fluxes differ among woody and herbaceous plants? (2) How sensitive are woody and herbaceous species to environmental drivers of diurnal and nocturnal water flux? We highlight three results: (1) <i>Cornus drummondii</i>, the primary woody encroacher in this grassland, exhibited the greatest canopy-level water loss, (2) nocturnal transpiration was a large component of the water lost in this ecosystem and was driven primarily by C<sub>4</sub> grasses and <i>C. drummondii</i>, and (3) the sensitivity of canopy transpiration to environmental drivers varies among plant functional types and throughout a 24-hour period. Our data reveal important insights regarding the water-use strategies of woody versus herbaceous species in tallgrass prairies, and about the potential hydrological consequences of ongoing woody encroachment. We suggest that the high, static flux rates observed in woody species will likely deplete deep water stores over time, potentially creating hydrological deficits in grasslands experiencing woody encroachment and concomitantly increasing the vulnerability of these ecosystems to drought.</p>
Sap flow in Juglans mandshurica trees during the growing season 2015 (I)
<p>The primary data on the sap flow of Manchurian walnut (Juglans mandshurica) trees under the conditions of introduction are given. The walnut population is located on the territory of the Volga-Kama Reserve. Coordinates N55.883288, E48.730038. The measurements were carried out from May 31 to June 4 year 2015. Using EMS51A instruments (EMS, Brno, Czech Republic). The measurement interval is 5 minutes. The meteorological<br> observations.</p>
Data from: Contrasting sap flow characteristics between pioneer and late-successional tree species in secondary tropical montane forests of Eastern Himalaya, India
Abstract The interactive role of life-history traits and environmental forcing on plant-water relations is crucial for understanding species response to climate change but remains poorly understood in secondary tropical montane forests (TMFs). Comparing contrasting life-history traits (pioneer vs late-successional species) in a biodiverse Eastern Himalayan secondary TMF, we investigated sap flow responses in co-occurring pioneer species, Symplocos racemosa (n=5) and Eurya acuminata (n=5), and late-successional species, Castanopsis hystrix (n=3), using modified Granier's Thermal Dissipation probes. The fast-growing pioneers S. racemosa and E. acuminata) had 2.1- and 1.6-times higher sap flux density than the late-successional C. hystrix, respectively, and exhibited characteristics of long-lived pioneer species. Significant radial and azimuthal variability in sap flow (V) between species was observed and attributed to life history traits and the canopy's access to sunlight. Nocturnal V (1800-0500 hr) was 13.8 % of daily V and is attributed to stem recharge for evening V (1800-2300 hr) and to endogenous stomatal controls for pre-dawn V (0000-0500 hr). Both the shallow-rooted pioneer species exhibited midday depression in V attributed to photosensitivity and diel moisture stress response. In contrast, deep-rooted C. hystrix transpired unaffected across the dry season likely accessing groundwater. Thus, the secondary broadleaved TMFs, with the dominance of shallow-rooted pioneers, are more prone to the negative impacts of drier and warmer winters than primary forests, which are dominated by deep-rooted species. The study provides an empirical understanding of life-history traits and microclimate modulating plant-water use in widely distributed secondary TMFs in Eastern Himalaya and highlights their vulnerability against warmer winters and reduced snowfall due to climate change.
Data from: Contrasting sap flow characteristics between pioneer and late-successional tree species in secondary tropical montane forests of Eastern Himalaya, India
Open the record for dataset details and reuse information.
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
Open the record for dataset details and reuse information.
Bridging the flux gap: sap flow measurements reveal species-specific patterns of water-use in a tallgrass prairie
Open the record for dataset details and reuse information.
SAPFLUXNET: A global database of sap flow measurements
<p><strong>General description</strong></p> <p>SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels.<br> SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide. </p> <p>The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: <a href="http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/">http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/</a>. </p> <p><br> The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (<a href="http://sapfluxnet.creaf.cat/team/">http://sapfluxnet.creaf.cat/#team</a>), coordinated by Rafael Poyatos (CREAF, <a href="http://www.creaf.cat/staff/rafael-poyatos-lopez">http://www.creaf.cat/staff/rafael-poyatos-lopez</a>), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).</p> <p><strong>Changelog </strong></p> <p>Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same. </p> <ul> <li>For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.</li> <li>In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.</li> <li>In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.</li> <li>In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.</li> </ul> <p><strong>Variables and units</strong></p> <p>SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental<br> variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags. </p> <p>Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: <a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/metadata-and-data-units.html">Metadata and data units</a>.</p> <p>To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (<a href="https://github.com/sapfluxnet/sapfluxnetr">https://github.com/sapfluxnet/sapfluxnetr</a>). In particular, have a look at the package vignettes using R:</p> <pre><code># remotes::install_github( # 'sapfluxnet/sapfluxnetr', # build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes") # ) library(sapfluxnetr) # to list all vignettes vignette(package='sapfluxnetr') # variables and units vignette('metadata-and-data-units', package='sapfluxnetr') # data flags vignette('data-flags', package='sapfluxnetr')</code></pre> <p><strong>Data formats</strong></p> <p>SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the <a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sfn-data-classes.html">corresponding vignette</a>.</p> <p><strong>Working with sfn_data files</strong></p> <p>To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).</p> <p>After downloading the entire database, the sapfluxnetr package can be used to:<br> - Work with data from a single site: data access, plotting and time aggregation.<br> - Select the subset datasets to work with.<br> - Work with data from multiple sites: data access, plotting and time aggregation.</p> <p>Please check the following package vignettes to learn more about how to work with sfn_data files:</p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sapfluxnetr-quick-guide.html">Quick guide</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/metadata-and-data-units.html">Metadata and data units</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sfn-data-classes.html">sfn_data classes</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/custom-aggregation.html">Custom aggregation</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/memory-and-parallelization.html">Memory and parallelization</a></p> <p><strong>Working with text files</strong></p> <p>We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files. </p> <p><strong>Data issues and reporting</strong></p> <p>Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.</p> <p>Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').</p> <p><strong>Data access, use and citation</strong></p> <p>This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.</p> <p>When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).</p>
Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neutral network method
<p><i><span>Aims:</span></i> This study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (<i><span>PI</span></i>) for <i><span>Liquidambar formosana</span></i><span>,</span> a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction.</p> <p><i><span>Methods: </span></i>Three-layered BP models with an architecture 4-10-1 <span>(four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) </span>were trained and tested using the Levenberg-Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development.</p> <p><i><span>Results: </span></i>The BP models with eight input combinations proved a satisfactory fit: the determination coefficients (<i><span>R</span></i><sup><span>2</span></sup>) and fitting accuracies (<i><span>Acc</span></i>) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear problems involved in transpiration. In addition, the BP models showed a bit better performance by adding <i><span>PI</span></i><i> </i><span>to</span> the input family. The best BP model was achieved taking air temperature (<i><span>T</span></i><sub><span>a</span></sub>), relative humidity (<i><span>RH</span></i>), average net radiation (<i><span>ANR</span></i>) and <i><span>PI</span></i> as the input and sap flux density (<i><span>v</span></i><sub><span>s</span></sub>) as the output, with maximum <i><span>R</span></i><sup><span>2</span></sup> and <i><span>Acc</span></i> as high as 0.95 and 90%, respectively.</p> <p><i><span>Conclusions: </span></i><span>The</span><i> </i><span>BP</span><i> </i><span>models with input combination of </span><i><span>T</span></i><sub><span>a</span></sub><sub><span>, </span></sub><i><span>RH, ANR </span></i><span>and</span><i><span> PI </span></i><span>mirrored very well measured daily variations in </span><i><span>v</span></i><sub><span>s</span></sub>. The results could be used to fine-tune sap flow estimation by <i><span>Liquidambar formosana</span></i>, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.</p>
Supplementary material for Progressive enrichment in 18O and 2H in xylem water along sap flow paths in Fagus sylvatica trees
<p>Database related to the results that are presented in the manuscript Montemagno et al. that will be submitted as research paper in the journal Ecohydrology:</p> <p><strong>Progressive enrichment in <sup>18</sup>O and <sup>2</sup>H in xylem water along sap flow paths in <em>Fagus sylvatica </em></strong></p> <p>Alessandro Montemagno<sup>1,3</sup>, Richard F. Keim<sup>2</sup>, Veneranda López-Días<sup>1</sup>, Victor Bense<sup>3</sup>, Adriaan J. Teuling<sup>3</sup>, Christophe Hissler<sup>1</sup></p> <p><sup>1</sup>CATchment and ecohydrology research group (CAT/ENVISION/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg</p> <p><sup>2</sup> School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803 USA</p> <p><sup>3 </sup>Department of Environmental Sciences, Hydrology and Quantitative Water Management Group, Wageningen University and Research, Droevendaalsesteeg 4, Wageningen, 6708 PB, The Netherlands</p> <p><em>Correspondence: </em>Alessandro Montemagno (<a href="mailto:alessandro.montemagno@outlook.com">alessandro.montemagno@outlook.com</a>), Christophe Hissler (<a href="mailto:christophe.hissler@list.lu">christophe.hissler@list.lu</a>)</p>
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