Articles | Volume 14, issue 6
https://doi.org/10.5194/esd-14-1239-2023
https://doi.org/10.5194/esd-14-1239-2023
Research article
 | 
29 Nov 2023
Research article |  | 29 Nov 2023

Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model: implementation and effects on simulated water dynamics

Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri

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Cited articles

Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. a, b, c
Albergel, C., Munier, S., Bocher, A., Bonan, B., Zheng, Y., Draper, C., Leroux, D. J., and Calvet, J.-C.: LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables, Remote Sens., 10, 2072–4292, https://doi.org/10.3390/rs10101627, 2018. a
Alessandri, A., Gualdi, S., Polcher, J., and Navarra, A.: Effects of land surface–vegetation on the boreal summer surface climate of a GCM, J. Climate, 20, 255–278, https://doi.org/10.1175/JCLI3983.1, 2007. a
Alessandri, A., Catalano, F., De Felice, M., Van Den Hurk, B., Doblas Reyes, F., Boussetta, S., Balsamo, G., and Miller, P. A.: Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth, Clim. Dynam., 49, 1215–1237, https://doi.org/10.1007/s00382-016-3372-4, 2017. a, b, c, d, e, f, g, h
Anderson, M. C., Norman, J. M., Kustas, W. P., Li, F., Prueger, J. H., and Mecikalski, J. R.: Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX, J. Hydrometeorol., 6, 892–909, https://doi.org/10.1175/JHM465.1, 2005. a, b
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Short summary
Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
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