U.S. Department of Energy Office of Biological and Environmental Research

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Capturing Detailed Dynamics of Tundra Polygonal Structures Using Statistical Modeling Methods
Published: February 19, 2016
Posted: May 19, 2016

High-resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional- and watershed-scale processes. Direct deterministic simulations of fine-resolution land surface variables present many challenges, including high computational cost. In a recent Department of Energy (DOE)-supported study, statistically based reduced-order modeling techniques were used to facilitate emulation of fine-resolution simulations. An emulator, a Gaussian process regression, was used to approximate fine-resolution four-dimensional soil moisture fields predicted using a three-dimensional surface-subsurface hydrological simulator (PFLOTRAN). A dimension-reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced-order model (ROM). The ROM reduces simulation computational demand to negligible levels compared to the underlying fine-resolution model. In addition, the ROM constructed was equipped with an uncertainty estimate, allowing modelers to construct a ROM consistent with uncertainty in the measured data. The ROM is also capable of constructing statistically equivalent analogues that can be used in uncertainty and sensitivity analyses. The technique was applied to four polygonal tundra sites near Barrow, Alaska, that are part of DOE’s Next-Generation Ecosystem Experiments (NGEE)-Arctic project. The ROM is trained for each site using simulated soil moisture from 1998 to 2000 and validated using the simulated data for 2002 and 2006. The average relative root-mean-square errors of the ROMs are under 1 percent. The study shows that this statistical method successfully captures detailed physics in a computationally affordable way, and may be a suitable approach for modeling complex physical systems such as evolving tundra.

Reference: Liu, Y., G. Bisht, Z. M. Subin, W. J. Riley, and G. S. H. Pau. 2016. “A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site,” Vadose Zone Journal 15(2), DOI: 10.2136/vzj2015.05.0068. (Reference link)

Contact: Dorothy Koch, SC-23.1, (301) 903-0105, Daniel Stover, SC-23.1, (301) 903-0289
Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

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