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Combining new advances in modeling dynamic vegetation and nutrient competition to improve boreal forest predictions to changing climate

Presentation Date
Wednesday, December 15, 2021 at 2:50pm
Convention Center - eLightning Theater IV

Boreal forests are experiencing disproportionately higher rates of temperature increase due to climate change and snow/albedo feedbacks. Changes in this large biome have strong implications for regional carbon, water, and energy cycling, and shifts in plant functional types will alter albedo, ecosystem productivity, and surface and canopy water fluxes. For example, warming induces permafrost loss, altering the hydrology, and soil biological and physical processes, and deepening of the soil active layer that then may increase nutrient availability and favor vegetation growth. These climate-related interactions will affect plant competitive interactions, survival, and carbon storage. In this study we explore how vegetation dynamics will be affected by increases in permafrost thaw depth, plant and microbial nitrogen competition, and differences in nutrient limitation due to shifts in PFTs (e.g. faster resource acquisition in deciduous plants). To be able to accurately predict these complex ecological processes we are using a new demographic vegetation model (FATES; Functionally-Assembled Terrestrial Ecosystem Simulator) that is coupled to ELM, the land surface model of E3SM. We present here a newly implemented nutrient competition, acquisition, and an extensible approach of nutrient allocation and transport within plants in the ELM-FATES model. This work has successfully coupled the interactions of nutrients between soil biogeochemistry in ELM and plant productivity and carbon in FATES, with improved model hypothesis testing for plant’s nutrient storage capacity. With the inclusion of nutrient cycling in the previously ‘carbon-only’ ELM-FATES where the largest competition was light driven, the productivity and biomass was significantly reduced. An uncertainty quantification experiment (via large ensembles and surrogate models) revealed that model parameters related to carbon storage and leaf economics had the largest impact on plant processes. We applied a Bayesian inference approach using neural networks to calibrate the model parameters against observational datasets, and greatly improved model predictions to match field inventory data. These newly represented ecological-based processes have helped to improve the representation of these vulnerable forests in an Earth System Model.

Funding Program Area(s)