How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing. (February 2022)

• Record Type: Journal Article
• Authors: Duarte, Henrique F.; Raczka, Brett M.; Bowling, David R.; Wang, Aihui; Buotte, Polly C.; Lin, John C.
• Is Part Of: Environmental modelling & software. Volume 148(2022)
• Publication Date: 2022-02
• Subjects: Community land model -- Meteorological forcing -- Complex terrain -- Vegetation biomass; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105288
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Most carbon stocks and fluxes in the western United States are found in mountainous terrain, where observations and modeling are difficult. Terrestrial biosphere models generally underestimate above-ground biomass (AGB) over this region. Here, we identify methods to reduce this underestimation by focusing upon 1) biases in meteorological datasets, 2) model representation of water stress, and 3) spatial resolution. We adopted the widely-used Community Land Model version 4.5 (CLM 4.5) with six different meteorological datasets and found a 6-fold variation in simulated AGB across Utah/Colorado. Simulations underestimated AGB because of warm and dry biases within the meteorological datasets that reduced water availability and restricted plant growth. To eliminate the AGB underestimation we adopted a meteorological dataset designed for complex terrain (gridMET), combined with a representation of plant hydraulic stress (CLM 5.0). Conversely, changes in spatial resolution (meteorological variables and land surface description) had negligible impact on simulated AGB. Highlights: Default CLM 4.5 underestimated above-ground biomass (AGB) across Central Rockies. Modeled AGB varied 6-fold with bias-corrected meteorological datasets. GridMET meteorology and representing plant hydraulics removed the low bias in AGB. Simulations implemented at fine and coarse spatial resolution provided the same AGB.