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Development of the Biome-BGC model for simulation of managed herbaceous ecosystems

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  • Hidy, D.
  • Barcza, Z.
  • Haszpra, L.
  • Churkina, G.
  • Pintér, K.
  • Nagy, Z.

Abstract

Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18–60% using the original model and 10–18% using the developed model; squares of the correlation coefficients were between 0.02–0.49 using the original model and 0.50–0.81 using the developed model.

Suggested Citation

  • Hidy, D. & Barcza, Z. & Haszpra, L. & Churkina, G. & Pintér, K. & Nagy, Z., 2012. "Development of the Biome-BGC model for simulation of managed herbaceous ecosystems," Ecological Modelling, Elsevier, vol. 226(C), pages 99-119.
    • Handle: RePEc:eee:ecomod:v:226:y:2012:i:c:p:99-119
    DOI: 10.1016/j.ecolmodel.2011.11.008
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    References listed on IDEAS

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    1. Ma, Shaoxiu & Churkina, Galina & Wieland, Ralf & Gessler, Arthur, 2011. "Optimization and evaluation of the ANTHRO-BGC model for winter crops in Europe," Ecological Modelling, Elsevier, vol. 222(20), pages 3662-3679.
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    4. Di Vittorio, Alan V. & Anderson, Ryan S. & White, Joseph D. & Miller, Norman L. & Running, Steven W., 2010. "Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses," Ecological Modelling, Elsevier, vol. 221(17), pages 2038-2053.
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    6. Wang, Weile & Ichii, Kazuhito & Hashimoto, Hirofumi & Michaelis, Andrew R. & Thornton, Peter E. & Law, Beverly E. & Nemani, Ramakrishna R., 2009. "A hierarchical analysis of terrestrial ecosystem model Biome-BGC: Equilibrium analysis and model calibration," Ecological Modelling, Elsevier, vol. 220(17), pages 2009-2023.
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    1. Sun, Qingling & Li, Baolin & Zhang, Tao & Yuan, Yecheng & Gao, Xizhang & Ge, Jinsong & Li, Fei & Zhang, Zhijun, 2017. "An improved Biome-BGC model for estimating net primary productivity of alpine meadow on the Qinghai-Tibet Plateau," Ecological Modelling, Elsevier, vol. 350(C), pages 55-68.
    2. Han, Qifei & Li, Chaofan & Zhao, Chengyi & Zhang, Yaoqi & Li, Shoubo, 2018. "Grazing decreased water use efficiency in Central Asia from 1979 to 2011," Ecological Modelling, Elsevier, vol. 388(C), pages 72-79.
    3. Chaobin Zhang & Ying Zhang & Jianlong Li, 2019. "Grassland Productivity Response to Climate Change in the Hulunbuir Steppes of China," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    4. Xiaotao Huang & Yongsheng Yang & Chunbo Chen & Hongfei Zhao & Buqing Yao & Zhen Ma & Li Ma & Huakun Zhou, 2022. "Quantifying and Mapping Human Appropriation of Net Primary Productivity in Qinghai Grasslands in China," Agriculture, MDPI, vol. 12(4), pages 1-13, March.
    5. Qifei Han & Geping Luo & Chaofan Li & Shoubo Li, 2018. "Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    6. Maša Zorana Ostrogović Sever & Zoltán Barcza & Dóra Hidy & Anikó Kern & Doroteja Dimoski & Slobodan Miko & Ozren Hasan & Branka Grahovac & Hrvoje Marjanović, 2021. "Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses," Land, MDPI, vol. 10(9), pages 1-23, September.
    7. Kipling, Richard P. & Bannink, André & Bellocchi, Gianni & Dalgaard, Tommy & Fox, Naomi J. & Hutchings, Nicholas J. & Kjeldsen, Chris & Lacetera, Nicola & Sinabell, Franz & Topp, Cairistiona F.E. & va, 2016. "Modeling European ruminant production systems: Facing the challenges of climate change," Agricultural Systems, Elsevier, vol. 147(C), pages 24-37.

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