Global Land Evaporation Amsterdam Model
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.
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.
Consideration of soil constraint on evaporation.
Detailed parameterization of forest interception.
Extensive use of microwave observations, which is an asset under cloudy conditions.
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:
This version is described in detail by Martens et al. (2017, GMD).
Version 3.3 datasets
Key differences between GLEAM v3.3 and the previous GLEAM v3.2 are:
The two v3.3 datasets differ only in their forcing and temporal coverage:
For more detailed information, users are directed to the readme file on the server or the FAQ.
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:
Whenever GLEAM datasets are used in a scientific publication, the following references should be cited:
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.
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.
GLEAM datasets will not be used for commercial purposes.
Recent (selected) publications using GLEAM data
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.
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.
. . . from the GLEAM front
GLEAM v3.3 datasets available
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.
New paper using GLEAM data
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 evaporation across the globe. The open-access paper can be downloaded here.
Frequently Asked Questions