GLEAM-HR: current state and future prospects

Reliable, high-resolution evaporation data are needed for large-scale agricultural and hydrological management applications. However, field observations are too sparse to monitor large regions continuously, and satellite-based datasets are often too coarse or restricted to specific regions. An example of such satellite-based datasets is the Global Land Evaporation Amsterdam Model (GLEAM)¹. GLEAM is a state-of-the-art, global evaporation product which has been widely applied over the past decade in climate studies. However, due to its coarse original resolution (0.25 degree), it has not been used in hydrological and agricultural applications until recently². Ongoing developments have culminated in a high-resolution (HR, i.e. ~1 km) GLEAM version covering the Mediterranean region, over the 2015–2021 period. The Mediterranean region is characterised by intense human activities, different hydroclimatic conditions ranging from temperate cold to tropical, and intense seasonal rainfall at irregular spatial distributions. As a result, the region is prone to droughts, floods and landslides, making it an ideal testbed for GLEAM-HR. Here, we present current activities and future plans regarding this new dataset. Prospective plans include the extension from the Mediterranean domain to the entire European and African continents, by adopting a series of developments that have so far been confined to the coarse-scale application of the model. These include modifications in the interception module³, the incorporation of groundwater effects⁴, and the use of deep learning for the estimation of transpirational stress⁵.

 

 

¹ Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
² Martens, B., De Jeu, R. A. M., Verhoest, N. E. C., Schuurmans, H., Kleijer, J., and Miralles, D. G.: Towards estimating land evaporation at field scales using GLEAM. Remote Sens., 10, 1720, https://doi.org/10.3390/rs10111720, 2018.
³ Zhong, F., Jiang, S., van Dijk, A. I. J. M., Ren, L., Schellekens, J., and Miralles, D. G.: Revisiting large-scale interception patterns constrained by a synthesis of global experimental data, Hydrol. Earth Syst. Sci., 26, 5647–5667, https://doi.org/10.5194/hess-26-5647-2022, 2022.
⁴ Hulsman, P., Keune, J., Koppa, A., Schellekens, J., and Miralles, D. G: Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates, ESS Open Archive, https://doi.org/10.1002/essoar.10512478.1, in review, 2022.
⁵ Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G.: A deep learning-based hybrid model of global terrestrial evaporation, Nat. Commun., 13, 1912, https://doi.org/10.1038/s41467-022-29543-7, 2022.