Advances in Atmospheric Sciences

, Volume 33, Issue 9, pp 1047–1060 | Cite as

Incorporation of a dynamic root distribution into CLM4.5: Evaluation of carbon and water fluxes over the Amazon

  • Yuanyuan Wang
  • Zhenghui Xie
  • Binghao Jia


Roots are responsible for the uptake of water and nutrients by plants and have the plasticity to dynamically respond to different environmental conditions. However, most land surface models currently prescribe rooting profiles as a function only of vegetation type, with no consideration of the surroundings. In this study, a dynamic rooting scheme, which describes root growth as a compromise between water and nitrogen availability, was incorporated into CLM4.5 with carbon–nitrogen (CN) interactions (CLM4.5-CN) to investigate the effects of a dynamic root distribution on eco-hydrological modeling. Two paired numerical simulations were conducted for the Tapajos National Forest km83 (BRSa3) site and the Amazon, one using CLM4.5-CN without the dynamic rooting scheme and the other including the proposed scheme. Simulations for the BRSa3 site showed that inclusion of the dynamic rooting scheme increased the amplitudes and peak values of diurnal gross primary production (GPP) and latent heat flux (LE) for the dry season, and improved the carbon (C) and water cycle modeling by reducing the RMSE of GPP by 0.4 g C m-2 d-1, net ecosystem exchange by 1.96 g C m-2 d-1, LE by 5.0 W m-2, and soil moisture by 0.03 m3 m-3, at the seasonal scale, compared with eddy flux measurements, while having little impact during the wet season. For the Amazon, regional analysis also revealed that vegetation responses (including GPP and LE) to seasonal drought and the severe drought of 2005 were better captured with the dynamic rooting scheme incorporated.

Key words

CLM4.5 dynamic root distribution carbon cycle water cycle Amazon 


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  1. Avissar, R., P. L. S. Dias, M. A. F. S. Dias, and C. Nobre, 2002: The large-scale biosphere-atmosphere experiment in Amazonia (LBA): Insights and future research needs. J. Geophys. Res., 107(D20), LBA 54-1–LBA 54-6, doi: 10.1029/2002JD002704.Google Scholar
  2. Baker, I. T., L. Prihodko, A. S. Denning, M. Goulden, S. Miller, and H. R. da Rocha, 2008: Seasonal drought stress in the Amazon: Reconciling models and observations. J. Geophys. Res., 113(G1), G00B01, doi: 10.1029/2007JG000644.Google Scholar
  3. Barlage, M., and X. B. Zeng, 2004: Impact of observed vegetation root distribution on seasonal global simulations of land surface processes. J. Geophys. Res., 109, D09101, doi: 10.1029/2003JD003847.CrossRefGoogle Scholar
  4. Bonan, G. B., 1996: Land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user’s guide. Tech. Note NCAR/TN-417-STR, National Center for Atmospheric Research, Boulder, Colo.Google Scholar
  5. Canadell, J., R. B. Jackson, J. B. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze, 1996: Maximum rooting depth of vegetation types at the global scale. Oecologia, 108(4), 583–595, doi: 10.1007/BF00329030.CrossRefGoogle Scholar
  6. Castillo, C. K. G., S. Levis, and P. Thornton, 2012. Evaluation of the new CNDV option of the Community Land Model: Effects of dynamic vegetation and interactive nitrogen on CLM4 means and variability. J. Climate, 25, 3702–3714.CrossRefGoogle Scholar
  7. Chen, J. L., C. R. Wilson, B. D. Tapley, Z. L. Yang, and G. Y. Niu, 2009: 2005 drought event in the Amazon River basin as measured by GRACE and estimated by climate models. J. Geophys. Res., 114, B05404, doi: 10.1029/2008JB006056.Google Scholar
  8. Coelho, F. E., and D. Or, 1999. A model for soil water and matric potential distribution under drip irrigation with water extraction by roots. Pesquisa Agropecuária Brasileira, 34, 225–234.Google Scholar
  9. Collins, D. B. G., and R. L. Bras, 2007: Plant rooting strategies in water-limited ecosystems. Water Resour. Res., 43, W06407, doi: 10.1029/2006WR005541.CrossRefGoogle Scholar
  10. Dickinson, R. E., M. Shaikh, R. Bryant, and L. Graumlich, 1998. Interactive canopies for a climate model. J. Climate, 11, 2823–2836.CrossRefGoogle Scholar
  11. Drewry, D. T., P. Kumar, S. Long, C. Bernacchi, X. Z. Liang, and M. Sivapalan, 2010: Ecohydrological responses of dense canopies to environmental variability: 1. Interplay between vertical structure and photosynthetic pathway. J. Geophys. Res., 115(G4), 1–25.Google Scholar
  12. El Maayar, M., and O. Sonnentag, 2009: Crop model validation and sensitivity to climate change scenarios. Climate Research, 39(1), 47–59.CrossRefGoogle Scholar
  13. El Masri, B., S. J. Shu, and A. K. Jain, 2015: Implementation of a dynamic rooting depth and phenology into a land surface model: Evaluation of carbon, water, and energy fluxes in the high latitude ecosystems. Agricultural and Forest Meteorology, 211–212, 85–99.CrossRefGoogle Scholar
  14. Fan, F. C., L. F. Zhang, Z. H. Li, S. Y. Liu, Y. F. Shi, and J. M. Jia, 2012: Response of root distribution of tomato to different irrigation methods in Greenhouse. Journal of Hebei Agricultural Sciences, 16(8), 36–40, 44. (in Chinese)Google Scholar
  15. Feddes, R. A., and Coauthors, 2001. Modeling root water uptake in hydrological and climate models. Bull. Amer. Meteor. Soc., 82, 2797–2810.CrossRefGoogle Scholar
  16. Hatzis, J. J., 2010: The development of a dynamic root distribution for the Community Land Model with carbon-nitrogen interactions. M.S. thesis, Northern Illinois University, Di Kalb, 184 pp.Google Scholar
  17. Hodge, A., 2004. The plastic plant: Root responses to heterogeneous supplies of nutrients. New Phytologist, 162, 9–24.CrossRefGoogle Scholar
  18. Hudiburg, T. W., B. E. Law, and P. E. Thornton, 2013. Evaluation and improvement of the Community Land Model (CLM4) in Oregon forests. Biogeosciences, 10, 453–470.CrossRefGoogle Scholar
  19. Hutchings, M. J., and H. de Kroon, 1994. Foraging in plants: The role of morphological plasticity in resource acquisition. Advances in Ecological Research, 25, 159–238.CrossRefGoogle Scholar
  20. Ichii, K., H. H. Hashimoto, M. A. White, C. Potter, L. R. Hutyra, A. R. Huete, R. B. Myneni, and R. R. Nemani, 2007. Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality. Global Change Biology, 13, 67–77, doi: 10.1111/j.1365-2486.2006.01277.x.CrossRefGoogle Scholar
  21. Ivanov, V. Y., R. L. Bras, and E. R. Vivoni, 2008: Vegetationhydrology dynamics in complex terrain of semiarid areas: 1. A mechanistic approach to modeling dynamic feedbacks. Water Resour. Res., 44, W03429, doi: 10.1029/2006WR005588.Google Scholar
  22. Jackson, R. B., H. A. Mooney, and E. D. Schulze, 1997. A global budget for fine root biomass, surface area, and nutrient contents. Proceedings of the National Academy of Sciences of the United States of America, 94, 7362–7366.CrossRefGoogle Scholar
  23. Jackson, R. B., J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze, 1996. A global analysis of root distributions for terrestrial biomes. Oecologia, 108, 389–411.CrossRefGoogle Scholar
  24. Jing, C. Q., L. Li, X. Chen, and G. P. Luo, 2014. Comparison of root water uptake functions to simulate surface energy fluxes within a deep-rooted desert shrub ecosystem. Hydrological Processes, 28, 5436–5449.CrossRefGoogle Scholar
  25. Jung, M., M. Reichstein, and A. Bondeau, 2009. Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2001–2013.CrossRefGoogle Scholar
  26. Jung, M., and Coauthors, 2011: Global patterns of landatmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res., 116, G00J07, doi: 10.1029/2010JG001566.Google Scholar
  27. Lai, C. T., and G. Katul, 2000. The dynamic role of root-water uptake in coupling potential to actual transpiration. Advances in Water Resources, 23, 427–439.CrossRefGoogle Scholar
  28. Lawrence, D. M., and Coauthors, 2011: Parameterization Improvements and Functional and Structural Advances in Version 4 of the Community Land Model. Journal of Advances in Modeling Earth Systems, 3, M03001.Google Scholar
  29. Lawrence, P. J., and T. N. Chase, 2007: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J. Geophys. Res., 112, G01023, doi: 10.1029/2006JG000168.Google Scholar
  30. Le, P. V. V., P. Kumar, D. T. Drewry, and J. C. Quijano, 2012. A graphical user interface for numerical modeling of acclimation responses of vegetation to climate change. Computers & Geosciences, 49, 91–101, doi: 10.1016/j.cageo.2012.07.007.CrossRefGoogle Scholar
  31. Li, F., S. Levis, and D. S. Ward, 2013. Quantifying the role of fire in the Earth system-Part 1: Improved global fire modeling in the Community Earth System Model (CESM1). Biogeosciences, 10, 2293–2314, doi: 10.5194/bg-10-2293-2013.CrossRefGoogle Scholar
  32. Li, L. H., Y. P. Wang, Q. Yu, B. Pak, D. Eamus, J. Yan, E. van Gorsel, and I. T. Baker, 2012: Improving the responses of the Australian community land surface model (CABLE) to seasonal drought. J. Geophys. Res., 117, G04002, doi: 10.1029/2012JG002038.Google Scholar
  33. Li, X. M., C. X. Xu, and S. M. Su, 1998: Affection of deep ditch manuring method to apple root system pattern in arid farming orchard. Acta Botanica Boreali-Occidentalia Sinica, 18(4), 590–594. (in Chinese)Google Scholar
  34. Marthews, T. R., C. A. Quesada, D. R. Galbraith, Y. Malhi, C. E. Mullins, M. G. Hodnett, and I. Dharssi, 2014. Highresolution hydraulic parameter maps for surface soils in tropical South America. Geoscientific Model Development, 7, 711–723.CrossRefGoogle Scholar
  35. McMurtrie, R. E., C. M. Iversen, R. C. Dewar, B. E. Medlyn, T. Näsholm, D. A. Pepper, and R. J. Norby, 2012: Plant root distributions and nitrogen uptake predicted by a hypothesis of optimal root foraging. Ecology and Evolution, 2(6), 1235–1250.CrossRefGoogle Scholar
  36. Miguez-Macho, G., and Y. Fan, 2012: The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res., 117, D15114, doi: 10.1029/2012JD017540.Google Scholar
  37. Nepstad, D. C., and Coauthors, 1994. The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature, 372, 666–669.CrossRefGoogle Scholar
  38. Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, National Center for Atmospheric Research, 257 pp.Google Scholar
  39. Oleson, K. W., and Coauthors, 2013: Technical description of version 4.5 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-503+STR, National Center for Atmospheric Research, 420 pp.Google Scholar
  40. Ryel, R., M. Caldwell, C. Yoder, D. Or, and A. Leffler, 2002: Hydraulic redistribution in a stand of Artemisia tridentata: Evaluation of benefits to transpiration assessed with a simulation model. bdOecologia, 130(2), 173–184, doi: 10.1007/s004420100794.Google Scholar
  41. Saleska, S. R., K. Didan, A. R. Huete, and H. R. da Rocha, 2007: Amazon forests green-up during 2005 drought. Science, 318, 612.CrossRefGoogle Scholar
  42. Schenk, H. J., 2008. The shallowest possible water extraction profile: A null model for global root distributions. Vadose Zone Journal, 7, 1119–1124.CrossRefGoogle Scholar
  43. Schenk, H. J. and R. B. Jackson, 2002: The global biogeography of roots. Ecological Monographs, 72(3), 311–328.CrossRefGoogle Scholar
  44. Shangguan, W., Y. J. Dai, Q. Y. Duan, B. Y. Liu, and H. Yuan, 2014. A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6, 249–263.CrossRefGoogle Scholar
  45. Sivandran, G., and R. L. Bras, 2013. Dynamic root distributions in ecohydrological modeling: A case study at Walnut Gulch ExperimentalWatershed. Water Resour. Res., 49, 3292–3305, doi: 10.1002/wrcr.20245.CrossRefGoogle Scholar
  46. Smithwick, E. A. H., M. S. Lucash, M. L. McCormack, and G. Sivandran, 2014. Improving the representation of roots in terrestrial models. Ecological Modelling, 291, 193–204.CrossRefGoogle Scholar
  47. Tomasella, J., M. G. Hodnett, L. A. Cuartas, A. D. Nobre, M. J. Waterloo, and S. M. Oliveira, 2008. The water balance of an Amazonian micro-catchment: The effect of interannual variability of rainfall on hydrological behaviour. Hydrological Processes, 22, 2133–2147, doi: 10.1002/hyp.6813.CrossRefGoogle Scholar
  48. Verhoef, A., and G. Egea, 2014. Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models. Agricultural and Forest Meteorology, 191, 22–32.CrossRefGoogle Scholar
  49. Viovy, N., 2011: CRUNCEP data set [Description available at Data available at 1901 2012/].Google Scholar
  50. Warren, J. M., P. J. Hanson, C. M. Iversen, J. Kumar, A. P. Walker, and S. D. Wullschleger, 2015. Root structural and functional dynamics in terrestrial biosphere models-evaluation and recommendations. New Phytologist, 205, 59–78.CrossRefGoogle Scholar
  51. Weaver, J. E., 1926. Root Development of Field Crops. McGraw- Hill Book Co., New York & London, 291 pp.Google Scholar
  52. White, M. A., P. E. Thornton, S. W. Running, and R. R. Nemani, 2000: Parameterization and sensitivity analysis of the Biome- BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions, 4, 1–85.CrossRefGoogle Scholar
  53. Yan, B. Y., and R. E. Dickinson, 2014. Modeling hydraulic redistribution and ecosystem response to droughts over the Amazon basin using Community Land Model 4.0 (CLM4). J. Geophys. Res., 119, 2130–2143, doi: 10.1002/2014JG002694.CrossRefGoogle Scholar
  54. Yuan, X., and X. Z. Liang, 2011. Evaluation of a Conjunctive Surface-Subsurface Process model (CSSP) over the contiguous United States at regional-local scales. Journal of Hydrometeorology, 12, 579–599, doi: 10.1175/2010JHM1302.1.CrossRefGoogle Scholar
  55. Zeng, N., J. H. Yoon, J. A. Marengo, A. Subramaniam, C. A. Nobre, A. Mariotti, and J. D. Neelin, 2008: Causes and impacts of the 2005 Amazon drought. Environmental Research Letters, 3, 014002, doi: 10.1088/1748-9326/3/1/014002.CrossRefGoogle Scholar
  56. Zeng, X. B., 2001: Global vegetation root distribution for land modeling. Journal of Hydrometeorology, 2(5), 525–530.CrossRefGoogle Scholar
  57. Zeng, X. B., M. Shaikh, Y. J. Dai, R. E. Dickinson, and R. Myneni, 2002. Coupling of the common land model to the NCAR community climate model. J. Climate, 15, 1832–1854.CrossRefGoogle Scholar
  58. Zeng, X. B., Y. J. Dai, R. E. Dickinson, and M. Shaikh, 1998. The role of root distribution for climate simulation over land. Geophys. Res. Lett., 25, 4533–4536.CrossRefGoogle Scholar
  59. Zheng, Z., and G. L. Wang, 2007: Modeling the dynamic root water uptake and its hydrological impact at the Reserva Jaru site in Amazonia. J. Geophys. Res., 112, G04012, doi: 10.1029/2007JG000413.Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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