Effects of Land Hydrology Representation on Plant Carbon Productivity


A growing area of investigation in land and climate modeling is the linkage between land hydrology and terrestrial carbon systems. How soil moisture influences climate through interactions in the coupled water, energy, and carbon cycles is a key, but not well understood, challenge. A team of scientists, led by Department of Energy researchers at Pacific Northwest National Laboratory, studied how uncertainty associated with using two very different representations of soil hydrology affect model plant productivity and the carbon cycle. Using the Community Land Model (CLM) version 4 with two widely adopted runoff generation parameterizations, the team found that the global water balance is sensitive to runoff parameterizations, which caused a relative difference of about 35% in global mean total runoff and soil moisture, as well as substantial differences in their spatial distribution and seasonal variability. Consequently, the simulated global mean gross primary production differs by 20.4% as differences in soil moisture simulated between the two models directly influence leaf photosynthesis through soil moisture availability, and indirectly alter vegetation phenology through the impacts of soil moisture on soil temperature. The study highlights the significant interactions among the water, energy, and carbon cycles and the need for reducing uncertainty in the hydrologic parameterization of land surface models to better constrain carbon cycle modeling.


Lei, H., M. Huang, L. R. Leung, D. Yang, X. Shi, J. Mao, D. J. Hayes, C. R. Schwalm, Y. Wei, and S. Liu. 2014. “Sensitivity of Global Terrestrial Gross Primary Production to Hydrologic States Simulated by the Community Land Model Using Two Runoff Parameterizations,” Journal of Advances in Modeling Earth Systems, DOI:10.1002/2013MS000252.