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. We therefore propose the use of reduced-order modeling techniques to facilitate emulation of fine-resolution simulations. We use an emulator, Gaussian process regression, 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 that we constructed is 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 analogs that can be used in uncertainty and sensitivity analyses. We apply the technique to four polygonal tundra sites near Barrow, Alaska that are part of the Department of Energy’s Next-Generation Ecosystem Experiments (NGEE)–Arctic project. The ROM is trained for each site using simulated soil moisture from 1998–2000 and validated using the simulated data for 2002 and 2006. The average relative RMSEs of the ROMs are under 1%.

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