• Updated GLEAM datasets available

    The GLEAM v3.3 datasets are now available from Downloads.

    New paper using GLEAM

    A new study published in npj Climate and Atmospheric Science shows that dynamics in land evaporation are substantially driven by major modes of internal climate variability. Open access paper can be downloaded here.

    GLEAM @ High Resolution

    Towards estimating land evaporation at field scales using GLEAM, a new study published in Remote Sensing. Access the full paper here.

  • Method

    Global Land Evaporation Amsterdam Model

     

    AMSR-E

    General

    GLEAM (Global Land Evaporation Amsterdam Model) is a set of algorithms that separately estimate the different components of land evaporation (or 'evapotranspiration’): transpiration, bare-soil evaporation, interception loss, open-water evaporation and sublimation. Additionally, GLEAM provides surface and root-zone soil moisture, potential evaporation and evaporative stress conditions.

     

    The rationale of the method is to maximize the recovery of information on evaporation contained in current satellite observations of climatic and environmental variables.

     

    GLEAM concept

    Description

    The Priestley and Taylor equation used in GLEAM calculates potential evaporation based on observations of surface net radiation and near-surface air temperature. Estimates of potential evaporation for the land fractions of bare soil, tall canopy and short canopy are converted into actual evaporation using a multiplicative evaporative stress factor based on observations of microwave Vegetation Optical Depth (VOD) and estimates of root-zone soil moisture. The latter is calculated using a multi-layer running-water balance. To try to correct for random forcing errors, observations of surface soil moisture are also assimilated into the soil profile. Interception loss is calculated separately in GLEAM using a Gash analytical model. Finally, estimates of actual evaporation for water bodies and regions covered by ice and/or snow are obtained using an adaptation of the Priestley and Taylor equation.

     

    Key features

    1. Consideration of soil constraint on evaporation.

    2. Detailed parameterization of forest interception.

    3. Extensive use of microwave observations, which is an asset under cloudy conditions.

  • Datasets

    General

    Since its development in 2011, GLEAM has been continuously revised and updated. In 2017, a third version of the model (GLEAM v3) has been developed, and two datasets produced using this version of the model are currently available under Downloads.

     

    The GLEAM v3 includes:

    1. A new data assimilation scheme that has been validated for Australia (Martens et al., 2016) and that has been optimised to work at the global scale.
    2. An updated water balance module that describes the infiltration rates as a function of the vertical gradient in soil moisture.
    3. Updated evaporative stress functions that combine the vegetation optical depth and the root-zone soil moisture estimates.

    This version is described in detail by Martens et al. (2017, GMD).

    Evaporation Components from GLEAM

    Version 3.3 datasets

    Key differences between GLEAM v3.3 and the previous GLEAM v3.2 are:

    1. The v3.3a dataset is now produced using surface radiation and near-surface air temperature from the latest reanalysis of ECMWF, ERA5 (as opposed to ERA-Interim in v3.2a).
    2. Both GLEAM datasets are now produced using dynamic land cover information based on the MEaSUREs Vegetation Continuous Fields dataset (as opposed to a static map based on MODD44B v52 in the v3.2 datasets).
    3. All forcing datasets have been updated to their last versions and extended until the end of 2018. Due to the latency in CERES radiation data, GLEAM v3.3b only runs until September 2018.
    4. Next to the daily-resolution data, both monthly and yearly datasets are available as well.

    The two v3.3 datasets differ only in their forcing and temporal coverage:

    1. GLEAM v3.3a: a global dataset spanning the 39-year period 1980–2018. The dataset is based on reanalysis net radiation and air temperature, satellite and gauged-based precipitation, and satellite-based VOD, soil moisture, and snow water equivalent.
    2. GLEAM v3.3b: a global dataset approximately spanning the 16-year period 2003–2018 (September). The dataset is mainly based on satellite data.

    For more detailed information, users are directed to the readme file on the server or the FAQ.

  • User policy

    The datasets described in the above section are freely available and can be downloaded after submitting your email.

     

    The use of GLEAM data is subject to the following terms and conditions:

    Acknowledgements

    Whenever GLEAM datasets are used in a scientific publication, the following references should be cited:

    1. Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, N.E.C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903–1925, doi: 10.5194/gmd-10-1903-2017, 2017.

    2. Miralles, D.G., Holmes, T.R.H., de Jeu, R.A.M., Gash, J.H., Meesters, A.G.C.A., Dolman, A.J.: Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15, 453–469, doi: 10.5194/hess-15-453-2011, 2011.

    Scientific use

    GLEAM datasets will not be used for commercial purposes.

    Feedback

    Any feedback about the datasets and/or website are highly appreciated and can be sent through email to info@gleam.eu.

  • Publications

    Methodology description

    1. Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, N.E.C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903–1925, 2017.
    2. Martens, B., Miralles, D.G., Lievens, H., Fernández-Prieto, D., Verhoest, N.E.C.: Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture, International Journal of Applied Earth Observations and Geoinformation, 48, 146–162,  2016.
    3. Miralles, D.G., Holmes, T.R.H., de Jeu, R.A.M., Gash, J.H., Meesters, A.G.C.A., Dolman, A.J.: Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15, 453–469, 2011.
    4. Miralles, D.G., de Jeu, R.A.M., Gash, J.H., Holmes, T.R.H., Dolman, A.J.: Magnitude and variability of land evaporation and its components at the global scale, Hydrology and Earth System Sciences, 15, 967–981, 2011.
    5. Miralles, D.G., Gash, J.H., Holmes, T.R.H., de Jeu, R.A.M., Dolman, A.J.: Global canopy interception from satellite observations, Journal of Geophysical Research, 115, D16122, 2010.

    Recent (selected) publications using GLEAM data

    1. Martens, B., Waegeman, W., Dorigo, W.A., Verhoest, N.E.C., Miralles, D.G. Terrestrial evaporation response to modes of climate variability. npj Climate and Atmospheric Science, 43, 1, 2018.  
    2. Good, S.P., Moore, G.W., Miralles, D.G. A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts. Nature Ecology & Evolution, 2017.
    3. Forzieri, G., Alkama, R., Miralles, D.G., Cescatti, A.: Satellites reveal contrasting responses of regional climate to the widespread greening of Earth, Science, 2017.
    4. Teuling, A.J., Taylor, C.M., Meirink, J.F., Melsen, L.A., Miralles, D.G., van Heerwaarden, C.C., Vautard, R., Stegehuis, A.I., Nabuurs, G.-J., de Arellano, J.V.-G.: Observational evidence for cloud cover enhancement over western European forests, Nature Communications, 8, 14065, 2017.

    5. Guillod, B.P., Orlowsky, B, Miralles, D.G., Teuling, A.J., Seneviratne, S.I.: Reconciling spatial and temporal soil moisture effects on afternoon rainfall, Nature Communications, 6, 1–6, 2015.
    6. Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., Seneviratne S.I.: Global assessment of trends in wetting and drying over land, Nature Geoscience, 7, 716–721, 2014.
    7. Lettenmaier, D.P., Alsdorf, D., Dozier, J., Huffman, G.J., Pan, M., Wood E.F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resources Research, 51, 2015.
    8. Miralles, D.G., Teuling, A.J., van Heerwaarden, C.C., Vilà-Guerau de Arellano, J.: Mega-heatwave temperatures due to combined soil dessiccation and atmospheric heat accumulation, Nature Geoscience, 7, 2014.
    9. Miralles, D.G., van den Berg, M.J., Gash, J.H., Parinussa, R.M., de Jeu, R.A.M., Beck, H.E., Holmes, T, Jiménez, C., Verhoest, N.E.C., Dorigo, W.A., Teuling, A.J., Dolman, A.J.: El Niño–La Niña cycle and recent trends in continental evaporation, Nature Climate Change, 4, 122–126, 2014.
    10. Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang., Y., Liu, Y.Y., Miralles, D.G., Pan M.: Multi-decadal trends in global terrestrial evapotranspiration and its components, Scientific Reports, 5, 19124, 2016.

  • Highlights

    . . . from the GLEAM front

     

    GLEAM v3.3 datasets available

    08/05/2019

    Updated ersions of the GLEAM global datasets of terrestrial evaporation (v3.3a and v3.3b) are now available from our server. They span up to the end of 2018. Please register under Downloads to obtain access. Updates include the use of ERA5 atmospheric data and dynamic land cover fractions.

    npj Climate and Atmospheric Science

    New paper using GLEAM data

    19/11/2018

    In a new study using GLEAM evaporation data it is shown that modes of internal climate variability, such as the El Niño Southern Oscillation, substantially affect the month-to-month variability of terrestrial evaporation across the globe. Results were published in npj Climate and Atmospheric Science, and the open access paper can be downloaded here.

    GLEAM high resolution

    GLEAM @ High Resolution

    31/10/2018

    Towards estimating land evaporation at field scales using GLEAM, a new study published in Remote Sensing.

    The open access paper can be downloaded here.

    AGU Horton Research Grant

    13/06/2018

    Brianna Pagán is the new awardee of a Horton Research Grant, aiming to promote excellence in hydrology research. Brianna is a PhD researcher at Ghent University. In her research she aims to further develop GLEAM retrievals by utilising novel satellite observations.

  • Frequently Asked Questions

     

    1. After registration on the website, I didn’t receive the login details. What should I do?
      Login details are automatically sent to the email address submitted on the website. If you did not receive login details within one hour after registration, please check your SPAM-folder. If you did not receive any email after that time, you can send your request to info@gleam.eu.
       
    2. I am not able to connect to the server, what am I doing wrong?
      Carefully read the login details and make sure that you are using the right credentials. Also make sure that you are defining the right file transfer protocol, being SFTP (Secure File Transfer Protocol). Check your firewall settings to make sure that the access to our server through port 2225 is not blocked.
       
    3. What is terrestrial evaporation, and how does it relate to the latent heat flux and evapotranspiration?
      Terrestrial evaporation is the total flux of water from land into the atmosphere (typically expressed in mm) from soil (bare soil evaporation), plant surfaces (interception loss), water surfaces (open-water evaporation), and through plant stomata (transpiration). This flux is often referred to as “evapotranspiration”. The associated consumption of energy to change the phase of water from liquid to gas during the process, is the latent heat flux (typically expressed in W.m-2), and can be calculated by accounting for the latent heat of vaporization.
       
    4. Where can I find more information about GLEAM?
      A detailed description of the methodology is provided in different scientific articles listed under Publications.
       
    5. Are the data direct observations? What is their accuracy?
      Actual evaporation is not directly measured from space. The articles under Publications contain a subset of the validations, product inter-comparisons and error analyses undergone to date.
       
    6. Are the estimates from GLEAM directly comparable with eddy-covariance latent heat flux measurements?
      Due to several issues, both estimates cannot be directly compared, and validation studies should be carefully designed: (1) the footprint of eddy-covariance towers is typically on the order of 1 km, while GLEAM pixels cover an area that is substantially larger (~25 x 25 km). This results in a representativity error, especially in heterogeneous areas; (2) the energy balance at eddy-covariance sites is generally not closed, and the actual latent heat flux tends to be underestimated; (3) eddy-covariance measurements are unreliable during rain events. Because interception fluxes can be large in nature – and so will GLEAM estimates of this flux – we strongly recommend masking times of rain and interception fluxes when comparing to eddy-covariance measurements. This is common practice in validation studies.
       
    7. Why are there negative values in the evaporation dataset?
      Missing data is indicated with a value of “-999” for all variables. Negative values (apart from “-999”) in the evaporation data indicate a negative latent heat flux, and thus a net condensation of water vapour. This typically occurs when the net radiation at the surface is negative.
       
    8. At what spatial and temporal resolution are the data available?
      All datasets are available on a 0.25° latitude-longitude regular grid and at daily temporal resolution.
       
    9. Why is there no data available over oceans?
      GLEAM is only designed to estimate evaporation over land surfaces.
       
    10. Is the forcing data of GLEAM available on the server?
      The forcing of GLEAM is not available from the server. All data used to force GLEAM are freely available from the respective data portals. References for all datasets are provided in the README file on the server.
       
    11. How is the data structured?
      The data is provided in netcdf format, with one file per year and variable. A README file is available on the server describing the structure of the data in full detail.
       
    12. What is the difference between the GLEAM v3.3a and v3.3b datasets?
      These datasets are produced using the same methodology, but different forcing datasets. They also differ in their temporal coverage. A detailed description is provided under Datasets and in the README file available on the server.
       
    13. Why are the last months missing in the GLEAM v3.3b dataset?
      Due to the latency in CERES radiation data, GLEAM v3.3b can currently only be produced untill September 2018.
       
    14. How often are the datasets updated?
      Datasets are typically updated and extended once a year, and are generally released around May–June. All users are notified when new data is available.
       
    15. Are old versions of the dataset still available for download?
      When a new version of a dataset is released, the older version becomes obsolete and is removed from the server. However, previous versions are still available upon request.
  • Contact

    Any questions or feedback? Contact us!