Incorporation of needleleaf traits improves estimation of light absorption and gross primary production of evergreen needleleaf forests

Abstract

The seasonal dynamics and interannual variation of gross primary production (GPP, g C/m²/day) of evergreen needleleaf forest (ENF) are important but most of models underestimate ENF GPP. In this work, we selected three ENF sites with 10+ years of data from the eddy flux towers and investigated temporal dynamics of GPP, climate, and vegetation greenness (as measured by vegetation indices from MODIS surface reflectance data) during 2000–2020. We found that the seasonal dynamics of GPP and vegetation indices were correlated highly at two sites (US-Ho2, US-NR1) under Warm Summer Continental climate (Dfb) and Subarctic climate (Dfc), where the seasonality of air temperature, radiation and rainfall are synchronized, but weakly at the site (US-Me2) under Mediterranean climate (Csb), where the seasonality of air temperature and radiation is asynchronized with that of rainfall and trees have deep roots for access to deep soil water in a year. We incorporated the needleleaf traits and modified the equation that estimates light absorption by chlorophyll of needleleaf in the data-driven Vegetation Photosynthesis Model (VPM), which resulted in substantial improvement of GPP estimates. Daily GPP estimates over 2000–2020 from the VPM (v3.0) agreed well with the GPP estimates provided by AmeriFlux. As ENF at the US-Me2 site under the Mediterranean climate has deep roots for access water in the deep soils, we incorporated no-water stress in the wet season for the VPM (v3.0) simulations, which further improved GPP estimates of ENF at the site. This study highlights the importance of needleleaf traits and plant root traits in the VPM v3.0 for estimating GPP of evergreen needleleaf forests under different types of climate systems.