Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data
Metadatos
Mostrar el registro completo del ítemAutor
Strebel, Lukas; Bogena, Heye; Vereecken, Harry; Andreasen, Mie; Aranda Barranco, Sergio; Franssen, Harrie-Jan HendricksEditorial
Copernicus Publications
Fecha
2204-02-28Referencia bibliográfica
Strebel, L., Bogena, H., Vereecken, H., Andreasen, M., Aranda-Barranco, S., and Hendricks Franssen, H.-J.: Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data, Hydrol. Earth Syst. Sci., 28, 1001–1026, https://doi.org/10.5194/hess-28-1001-2024, 2024
Patrocinador
LIFE programme of the European Union under contract number LIFE 17 CCA/ES/000063, with additional funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1502/1-2022 – project number 450058266Resumen
Land surface models (LSMs) are an important tool
for advancing our knowledge of the Earth system. LSMs
are constantly improved to represent the various terrestrial
processes in more detail. High-quality data, freely available
from various observation networks, are being used to improve
the prediction of terrestrial states and fluxes of water
and energy. To optimize LSMs with observations, data
assimilation methods and tools have been developed in the
past decades.We apply the coupled Community Land Model
version 5 (CLM5) and Parallel Data Assimilation Framework
(PDAF) system (CLM5-PDAF) for 13 forest field sites
throughout Europe covering different climate zones. The
goal of this study is to assimilate in situ soil moisture measurements
into CLM5 to improve the modeled evapotranspiration
fluxes. The modeled fluxes will be evaluated using
the predicted evapotranspiration fluxes with eddy covariance
(EC) systems. Most of the sites use point-scale measurements
from sensors placed in the ground; however, for
three of the forest sites we use soil water content data from
cosmic-ray neutron sensors, which have a measurement scale
closer to the typical land surface model grid scale and EC
footprint. Our results show that while data assimilation reduced
the root-mean-square error for soil water content on
average by 56% to 64 %, the root-mean-square error for
the evapotranspiration estimation is increased by 4 %. This
finding indicates that only improving the soil water content
(SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest
sites. To improve evapotranspiration estimates, it is also
necessary to consider the representation of leaf area index
(LAI) in magnitude and timing, as well as uncertainties in
water uptake by roots and vegetation parameters.