22 May 2023
 | 22 May 2023
Status: this preprint is open for discussion.

Representing inter-annual land cover and vegetation variability based on satellite observations in the HTESSEL land surface model

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

Abstract. Vegetation largely controls land surface-atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived 5 from satellite observations on modelled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated inter-annually varying land cover from the European Space Agency Climate Change Initiative, and inter-annually varying Leaf Area Index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data of the Fraction of green vegetation Cover (FCover) from CGLS was used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analysed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The inter-annually varying land cover consistently altered the evaporation and soil moisture in regions with major land-cover changes. The inter-annually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture-evaporation feedbacks during vegetation-water-stressed periods with inter-annually varying LAI in combination with inter-annually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation-water-stress periods.

Fransje van Oorschot et al.

Status: open (until 03 Jul 2023)

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Fransje van Oorschot et al.

Fransje van Oorschot et al.


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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 modelling 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.