A remote sensing‐based three‐source energy balance model to improve global estimations of evapotranspiration in semi‐arid tree‐grass ecosystems. (2nd December 2021)

• Record Type: Journal Article
• Authors: Burchard‐Levine, Vicente; Nieto, Héctor; Riaño, David; Kustas, Wiliam P.; Migliavacca, Mirco; El‐Madany, Tarek S.; Nelson, Jacob A.; Andreu, Ana; Carrara, Arnaud; Beringer, Jason; Baldocchi, Dennis; Martín, M. Pilar
• Is Part Of: Global change biology. Volume 28:Number 4(2022)
• Page Start: 1493
• Page End: 1515
• Publication Date: 2021-12-02
• Subjects: 3SEB -- ecohydrology -- evapotranspiration -- phenology -- remote sensing -- surface energy balance -- transpiration -- tree‐grass ecosystem -- TSEB; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.16002
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: It is well documented that energy balance and other remote sensing‐based evapotranspiration (ET) models face greater uncertainty over water‐limited tree‐grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass‐dominated understory and tree‐dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy‐limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration ( T ), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal‐based three‐source energy balance (3SEB) model, accommodating an additional vegetation source within the well‐known two‐source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy‐covariance (EC) TGE sites, with variable tree canopy cover and rainfall ( P ) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m 2 ; MSG: LE RMSD ~90 W/m 2 ), improving over both TSEB and seasonally changing TSEB (TSEB‐2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates ( r > .76), derived from a machine learning ET partitioning method. The T /ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T /ET. However, trees and grasses had contrasting relations with respect to monthly P . These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks. Abstract : Remote sensing modelling is fundamental to improve our monitoring and understanding of the Earth system and its response to global change. However, these models often have higher uncertainty in complex ecosystems such as savannas or tree‐grass ecosystems, despite their important role in the global biogeochemical cycles and their sensitivity to climate change feedbacks. This work proposed the three‐source energy balance (3SEB) model to improve evapotranspiration monitoring, including its partitioning between evaporation and transpiration, in such landscapes with multiple vegetation layers.