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Existing land surface models (LSMs) describe physical and biologicalprocesses that occur over a wide range of spatial and temporal scales. Forexample, biogeochemical and hydrological processes responsible for carbon(CO2, CH4) exchanges with the atmosphere range from the molecular scale(pore-scale O2 consumption) to tens of kilometers (vegetationdistribution, river networks). Additionally, many processes within LSMs arenonlinearly coupled (e.g., methane production and soil moisture dynamics),and therefore simple linear upscaling techniques can result in largeprediction error. In this paper we applied a reduced-order modeling (ROM)technique known as "proper orthogonal decomposition mapping method" thatreconstructs temporally resolved fine-resolution solutions based oncoarse-resolution solutions. We developed four different methods and appliedthem to four study sites in a polygonal tundra landscape near Barrow, Alaska.Coupled surface–subsurface isothermal simulations were performed for summermonths (June–September) at fine (0.25 m) and coarse (8 m) horizontalresolutions. We used simulation results from three summer seasons(1998–2000) to build ROMs of the 4-D soil moisture field for the study sitesindividually (single-site) and aggregated (multi-site). The results indicatethat the ROM produced a significant computational speedup(> 103) with very small relative approximation error(< 0.1%) for 2 validation years not used in training theROM. We also demonstrate that our approach: (1) efficiently corrects forcoarse-resolution model bias and (2) can be used for polygonal tundra sitesnot included in the training data set with relatively good accuracy(< 1.7% relative error), thereby allowing for the possibilityof applying these ROMs across a much larger landscape. By coupling the ROMsconstructed at different scales together hierarchically, this method has thepotential to efficiently increase the resolution of land models for coupledclimate simulations to spatial scales consistent with mechanistic physicalprocess representation

geoscientific model development

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape