Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems

Authors: RENWICK, KATIE - Montana State University; Fellows, Aaron; Flerchinger, Gerald; LOHSE, KATHLEEN - Idaho State University; Clark, Pat; SMITH, WILLIAM - University Of Arizona; EMMETT, KRISTEN - Montana State University; POULTER, BENJAMIN - Montana State University

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2019
Publication Date: 5/1/2019
Citation: Renwick, K.M., Fellows, A., Flerchinger, G.N., Lohse, K.A., Clark, P.E., Smith, W.K., Emmett, K., Poulter, B. 2019. Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems. Agricultural and Forest Meteorology. 274:85-94. https://doi.org/10.1016/j.agrformet.2019.04.003.
DOI: https://doi.org/10.1016/j.agrformet.2019.04.003

Interpretive Summary: Semi-arid ecosystems play an important role in the global carbon cycle, and there is widespread interest in understanding and modeling the impacts of climate change on vegetation and associated patterns of carbon flux. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to replicate seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology may hinder our ability to predict climate change impacts. This is likely to be particularly problematic in semi-arid ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a dryland vegetation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed data on LAI. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R2 = 0.75) and gross primary productivity (R2 = 0.84), and also resulted in an improved outcome between competition between grass and shrubs. These findings demonstrate the importance of improving phenology representation in DGVMs for dryland ecosystems.

Technical Abstract: Semi-arid ecosystems play an important role in the global carbon cycle, and there is widespread interest in understanding and modeling the impacts of climate change on vegetation and associated patterns of carbon flux. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to replicate seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology may hinder our ability to predict climate change impacts. This is likely to be particularly problematic in semi-arid ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a dryland vegetation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed data on LAI. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R2 = 0.75) and gross primary productivity (R2 = 0.84), and also resulted in an improved outcome between competition between grass and shrubs. These findings demonstrate the importance of improving phenology representation in DGVMs for dryland ecosystems.