资源环境科技发展态势分析平台

Microtopography, or heterogeneities in the elevation across scales much smaller than the domain of interest, plays a critical role in surface water retention, surface/subsurface interactions, and runoff. Resolving microtopographic influences on flow requires high-resolution simulations that are computationally demanding even when considering the surface system in isolation and even more so when surface flow is one component in integrated simulations that couple surface flow with unsaturated subsurface flow. There is thus significant motivation for models that allow the effects of subgrid microtopography to be better represented. Subgrid models modify coarsened models to capture the microtopography-induced nonlinear effects on hydrologic processes. We present a subgrid model that alters the water storage and flow terms in the diffusion wave equation for surface flow. Stochastically generated microtopography with strongly contrasting spatial structure, high-resolution digital elevation maps from a polygonal tundra site on the North Slope of Alaska and a hummocky microtopography from a field site in Northern Minnesota are used to assess the accuracy and applicability of the subgrid model to disparate landscapes. Approaches for determining subgrid model parameters are tested and simulation results using the subgrid model are compared to benchmark fine-scale simulations and to coarse simulations that ignore microtopography. Our findings confirm that a properly parameterized subgrid model greatly improves the coarse-scale representation of hydrographs and total water content in the system. Using the polygonal tundra example, we propose and test a strategy for moving to application-relevant spatial scales by combining microtopography classification and a few fine-scale simulations on small subdomains.