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Development of the DayCent-Photo model and integration of variable photosynthetic capacity

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Abstract

We integrated a photosynthetic sub-model into the daily Century model (DayCent) to improve the estimations of seasonal changes in carbon fluxes at the Niwot Ridge LTER site and the Harvard forest LTER site (DayCent-Photo). The photosynthetic sub-model, adapted from the SIPNET/PnET family of models, includes solar radiation and vapor pressure deficit controls on production, as well as temperature and water stress terms. A key feature we added to the base photosynthetic equations is the addition of a variable maximum net photosynthetic rate (Amax). We optimized the parameters controlling photosynthesis using a variation of the Metropolis-Hastings algorithm along with data-assimilation techniques. The model was optimized and validated against observed net ecosystem exchange (NEE) and estimated gross primary production (GPP) and ecosystem respiration (RESP) values for AmeriFlux sites at Niwot Ridge and Harvard forest. The inclusion of a variable Amax rate greatly improved model performance (NEE RMSE = 0.63 gC·m–2, AIC = 2099) versus a version with a single Amax parameter (NEE RMSE = 0.74 gC·m–2, AIC = 3724). DayCent-Photo was able to capture the inter-annual and seasonal flux patterns for NEE, GPP, ecosystem respiration (RESP), and daily actual evapotranspiration (AET), but tended to overestimate yearly NEE uptake. The DayCent-Photo model has been successfully set up to simulate daily NEE, GPP, RESP, and AET for deciduous forest, conifer forests, and grassland systems in the US using AmeriFlux data sets and has recently been improved to include the impact of UV radiation surface litter decay (DayCent-UV). The simulated influence of a variable Amax rate suggests a need for further studies on the process controls affecting the seasonal photosynthetic rates. The results for all of the forest and grassland sites show that maximum Amax values occurs early during the growing period and taper off toward the end of the growing season.

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Acknowledgements

This work is supported by the US Department of Agriculture (USDA) UV-B Monitoring and Research Program, Colorado State University, under USDA National Institute of Food and Agriculture Grant 2016-34263-25763.

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Authors and Affiliations

  1. USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 80521, USA

    Jonathan R. Straube, Maosi Chen, William J. Parton, Shinichi Asso, Yan-An Liu & Wei Gao

  2. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 80523, USA

    Jonathan R. Straube, William J. Parton, Shinichi Asso & Dennis S. Ojima

  3. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Yan-An Liu

  4. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Yan-An Liu

  5. ECNU-CSU Joint Research Institute for New Energy and the Environment, Shanghai, 200062, China

    Yan-An Liu & Wei Gao

  6. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, 80523, USA

    Wei Gao

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  1. Jonathan R. Straube
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  2. Maosi Chen
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  3. William J. Parton
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  6. Dennis S. Ojima
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  7. Wei Gao
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Correspondence to Jonathan R. Straube or Yan-An Liu.

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Straube, J.R., Chen, M., Parton, W.J. et al. Development of the DayCent-Photo model and integration of variable photosynthetic capacity. Front. Earth Sci. 12, 765–778 (2018). https://doi.org/10.1007/s11707-018-0736-6

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