Find in Library

<p>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-JPL<span class="inline-formula">_SM</span> 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-JPL<span class="inline-formula">_SM</span>). 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 <span class="inline-formula"><i>R</i>²</span> ( 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: <span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.73, ubRMSD <span class="inline-formula">=</span> 1.05 g C m<span class="inline-formula">⁻²</span> d<span class="inline-formula">⁻¹</span>; ET: <span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.73, ubRMSD <span class="inline-formula">=</span> 0.61 mm d<span class="inline-formula">⁻¹</span>) than in the humid and sub-dry humid regions (GPP: <span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.61, ubRMSD <span class="inline-formula">=</span> 1.23 g C m<span class="inline-formula">⁻²</span> d<span class="inline-formula">⁻¹</span>; ET: <span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.66; ubRMSD <span class="inline-formula">=</span> 0.67 mm d<span class="inline-formula">⁻¹</span>). 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 (<span class="inline-formula"><i>μ</i></span>): 3.4 mm per month; estimated standard deviation of <span class="inline-formula"><i>μ</i></span>: 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.</p>