Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture
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Published:2022-12-16
Issue:24
Volume:26
Page:6311-6337
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ISSN:1607-7938
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Container-title:Hydrology and Earth System Sciences
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language:en
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Short-container-title:Hydrol. Earth Syst. Sci.
Author:
Li Sinan,Zhang Li,Xiao Jingfeng,Ma Rui,Tian Xiangjun,Yan Min
Abstract
Abstract. Reliable modeling of carbon and water fluxes is essential for understanding
the terrestrial carbon and water cycles and informing policy strategies
aimed at constraining carbon emissions and improving water use efficiency.
We designed an assimilation framework (LPJ-Vegetation and soil moisture
Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP)
and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM 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 R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better
performance in the arid and semi-arid regions (GPP: R2 = 0.73,
ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than
SMOS data. Our global simulation modeled by LPJ-VSJA was compared
with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS
ET, and GLEAM ET) using the triple collocation (TC) method. Our products,
especially ET, exhibited advantages in the overall error distribution
(estimated error (μ): 3.4 mm per month; estimated standard deviation
of 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.
Funder
National Natural Science Foundation of China
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference114 articles.
1. Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008. 2. Albergel, C., Calvet, J.-C., Mahfouf, J.-F., Rüdiger, C., Barbu, A. L., Lafont, S., Roujean, J.-L., Walker, J. P., Crapeau, M., and Wigneron, J.-P.: Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France, Hydrol. Earth Syst. Sci., 14, 1109–1124, https://doi.org/10.5194/hess-14-1109-2010, 2010. 3. Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., and Calvet, J.-C.: Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces, Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, 2020. 4. AmeriFlux: AmeriFlux Eddy Covariance Data [data set], https://ameriflux.lbl.gov/login/?redirect_to=/data/download-data/, last access: 4 October 2021. 5. Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C.,
Murray-Tortarolo, G., Papale, D., Parazoo, N. C., and Peylin,
P.: Spatiotemporal patterns of terrestrial gross primary production: A
review, Rev. Geophys., 53, 785–818, 2015.
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