Format:
Online-Ressource
ISSN:
1607-7938
Content:
Abstract SM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R 2 R 2 = = - 2 - 1 R 2 = = - 1 R 2 = = - 2 - 1 R 2 = = - 1 μ μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.
In:
volume:26
In:
number:24
In:
year:2022
In:
pages:6311-6337
In:
extent:27
In:
Hydrology and earth system sciences, Munich : EGU, 1997-, 26, Heft 24 (2022), 6311-6337 (gesamt 27), 1607-7938
Language:
English
DOI:
10.5194/hess-26-6311-2022
URN:
urn:nbn:de:101:1-2022122204325378837318
URL:
https://doi.org/10.5194/hess-26-6311-2022
URL:
https://nbn-resolving.org/urn:nbn:de:101:1-2022122204325378837318
URL:
https://d-nb.info/1276335431/34
URL:
https://hess.copernicus.org/articles/26/6311/2022/hess-26-6311-2022.pdf
URL:
https://hess.copernicus.org/articles/26/6311/2022/
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