Preprints
https://doi.org/10.5194/gmd-2022-87
https://doi.org/10.5194/gmd-2022-87
Submitted as: model description paper
02 May 2022
Submitted as: model description paper | 02 May 2022
Status: this preprint is currently under review for the journal GMD.

Developing a Parsimonious Canopy Model (PCM v1.0) to Predict Forest Gross Primary Productivity and Leaf Area Index

Bahar Bahrami1, Anke Hildebrandt1,2, Stephan Thober1, Corinna Rebmann1, Rico Fischer3, Luis Samaniego1, Oldrich Rakovec1,4, and Rohini Kumar1 Bahar Bahrami et al.
  • 1Department of Computational Hydro-system, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
  • 2Friedrich Schiller University Jena, Institute of Geoscience, Terrestrial Ecohydrology, Burgweg 11, 07745 Jena, Germany
  • 3Department of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
  • 4Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic

Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming poses fundamental alterations to forest ecological functions (e.g. carbon uptake) and forest biophysical variables (e.g. leaf area index). Quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is of critical significance in the terrestrial water and carbon cycles. There already exist a number of models to simulate dynamics of LAI and GPP, however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanism associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes such as the role of temporal dynamic of LAI in affecting water balance. Therefore, in this study, we propose a parsimonious forest canopy model (PCM) to predict daily dynamics of LAI and GPP with few required input at a medium level of complexity which is also suitable for integration into state-of-the-art large scale hydrologic models. The light use efficiency (LUE) concept is central to PCM (v1.0), coupled with a phenology submodel. PCM estimates total assimilated carbon based on conversion efficiency of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass from which prognostic and temperature-driven LAI is simulated. The model combines modules for estimation of soil hydraulic parameters based on the so-called pedotransfer functions and vertically weighted soil moisture considering the underground root distribution, when soil moisture data is available. We test the model on deciduous broad-leaved forest sites in Europe and North America selected from the FLUXNET network. We analyze the model parameter sensitivity on the resulting GPP and LAI and identified on average 10 common sensitive parameters at each study site (e.g., LUE, SLA, etc). Model performance is evaluated in a verification period using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling-Gupta-Efficiency; KGE varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ± 8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models. Thereby, it facilitates the seamless representation of coupled water and carbon cycle components, i.e. prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (e.g., evapotranspiration), while GPP predictions would benefit from simulated soil water storage from a hydrologic model.

Bahar Bahrami et al.

Status: open (until 27 Jun 2022)

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Bahar Bahrami et al.

Bahar Bahrami et al.

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Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.