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6 results for “Evapotranspiration Flux”
Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset
<p><strong>Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset</strong><br> </p> <p>We curated a dataset of post-processed <em>in situ</em> evapotranspiration (ET) measurements, primarily from eddy covariance flux towers, from stations located within the contiguous United States. The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data that were post-processed from 148 flux towers, 4 weighing lysimters, and 8 Bowen Ration stations. Original data was retrieved from the <a href="https://ameriflux.lbl.gov/">AmeriFlux</a> network and other networks and partners. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. Energy balance components (latent and sensble heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and latent energy (ET) was subject to additional visual quality control. Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and more.</p> <p><br><strong>Description of the Data and file structure</strong></p> <p>The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within: </p> <p>1. A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.<br>2. A similar collection of time series files for monthly aggregated data in "monthly_data_files". <br>3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory. <br>4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, land cover type, principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units. </p> <p>Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file. </p> <p>Here is a visual of the file structure:</p> <blockquote> <p><br>flux_ET_dataset<br>│ README.md<br>│ variable_explanation.xlsx<br>│ station_metadata.xlsx<br>│<br>└───daily_data_files<br>│ │ [site ID]_daily_data.csv<br>│ │ ...<br>└───monthly_data_files<br>│ │ [site ID]_monthly_data.csv<br>│ │ ...<br>└───graphical_files<br>│ │ [site ID]_plots.html<br>│ │ ...<br>```</p> </blockquote> <p>The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m<sup>2</sup>. </p> <p><br><strong>Sharing/access Information</strong></p> <p>Currently, this repository is the only location where the data are hosted. Original data, prior to post-processing, were retrieved from multiple providers listed below:</p> <p>* AmeriFlux network (https://ameriflux.lbl.gov/) </p> <p>* California State University, Monterey Bay, Seaside, CA, USA </p> <p>* Desert Research Institute, Reno, NV, USA </p> <p>* gridMET, Northwest Knowledge Network at the University of Idaho (https://thredds.northwestknowledge.net/) </p> <p>* United States Geological Survey Nevada Water Science Center, Carson City, NV, USA </p> <p>* Delta-Flux network, Arkansas, Louisiana, MS, USA </p> <p>* United States Department of Agriculture Agricultural Research Service (USDS-ARS): </p> <p> * Sustainable Water Management Research Unit, Stoneville, MS, USA </p> <p> * US Salinity Laboratory, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, USA </p> <p> * Conservation & Production Research Laboratory, Bushland, TX, USA </p> <p> * US Arid-Land Agricultural Research Center, Maricopa, AZ, USA </p> <p> * Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA </p> <p>Further contact information for each station as well as DOI's for original AmeriFlux data are included in the "station_metadata.xlsx" file. </p> <p><br><strong>Code/Software</strong></p> <p>All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.1.6. The package is hosted on <a href="https://github.com/Open-ET/flux-data-qaqc">GitHub</a> and <a href="https://pypi.org/project/fluxdataqaqc/">PyPI</a>, it also has <a href="https://flux-data-qaqc.readthedocs.io/en/latest/">online documentation</a> including an in depth user tutorial. </p>
Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest
<p>Dataset</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table> <p> </p> <p>Codes for Analysis</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table> <p> </p> <p>ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl)</p> <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> <p>To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this:</p> <p>wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95}</p> </li> <li> <p>Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart)</p> </li> </ul> <p> </p> <p> </p>
Evapotranspiration data from eddy-covariance flux-tower measurements and Landsat imagery in California’s Sierra Nevada from 1985 to 2019
Open the record for dataset details and reuse information.
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): Soil Thermal Flux, Temperature and Moisture Data
This study originated with the objective of parameterizing riparian evapotranspiration (ET) in the water budget of the Middle Rio Grande. We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis, Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows. This data set includes the soil temperature and moisture data collected during this study.
An Excel spreadsheet including 7-year eddy covariance-based evapotranspiration and auxilary data at Yunxiao mangrove flux tower
<p>An Excel spreadsheet including 7-year eddy covariance-based evapotranspiration and auxilary data at Yunxiao mangrove flux tower required to reproduce key results in the main text of a manuscript (each figure corresponds to a single sheet). Contact Xudong Zhu at Xiamen University (xdzhu@xmu.edu.cn) if you have any question.</p>
Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): CO2 Concentration and Flux data
This study originated with the objective of parameterizing riparian evapotranspiration (ET) in the water budget of the Middle Rio Grande. We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis, Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows. These data are CO2 concentration at canopy and CO2 flux from canopy collected as part of this study.
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