Towards a continuous, multiyear, high resolution dataset of evaporation  over Europe and Africa

High resolution accurate evaporation (E) estimates are crucial for large-scale agricultural, ecological, and hydrological applications. However, field observations are sparse, traditional satellite-based datasets based on thermal and optical imagery are unavailable during cloudy times, and most continuous global records are too coarse in terms of spatial resolution.  One of the latter, the Global Land Evaporation Amsterdam Model (GLEAM)¹ dataset, has been widely used in climate studies in recent years, but the realm of hydrological and agricultural applications was prohibited until recently due to its coarse spatial resolution². Ongoing developments have led to the development of high-resolution (1-km) E estimates over the Mediterranean region covering the period 2015–2021. The Mediterranean region, characterised by diverse hydroclimatic conditions and seasonal rainfall, experiences challenges related to droughts, floods, and landslides, making it an ideal testbed for GLEAM datasets at a high spatial resolution (GLEAM-HR). This work summarises current activities and future plans for GLEAM-HR. Our ongoing efforts include extending coverage from the Mediterranean to embrace the entire Meteosat disk (including Europe and Africa). This expansion involves incorporating modifications in the interception module³, addressing groundwater effects⁴, and using deep learning for transpirational stress estimation⁵. These advancements enhance the utility of GLEAM-HR for addressing water-related challenges, supporting sustainable water management practices, and contributing to evidence-based decision-making.   ¹Miralles, 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. ²Koppa, A., Rains, D., Hulsman, P., Poyatos, R., Miralles, D. G., 2022: A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13 (1), 1912. ³Zhong, 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. ⁴Hulsman, P., Keune, J., Koppa, A., Schellekens, J., and Miralles, D. G: Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates, Water Resources Research, https://doi.org/10.1029/2022WR033731, 2023. ⁵Koppa, 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.