Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landsc...

Flow based upscaling of absolute permeability has become an important step in practical simulations of flow through heterogeneous formations. The central idea is to compute upscaled, grid-block permeability from fine scale solutions of the flow equation. Such solutions can be either local in each gridblock or global in the whole domain. It is well-known that the grid-block permeability may be s...

Partly motivated by algebraic multigrid and renormalization group methods, systematic multiscale procedures are described for solving or simulating very large systems, with computations at each fine scale being confined to just small parts of the entire domain. Applicable to nonlinear, deterministic or stochastic, autonomous systems with strong interscale interactions. Examples from molecular d...

Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based o...

Some educational interventions successfully “scale up.” Others do not. Little— arguably, almost nothing—is known about the factors that lead to successful scaling up. The goal of this chapter is to identify a number of these factors through a disciplined and methodologically rigorous approach. The difficulties associated with scaling up can broadly be summarized as falling into two classes: (1)...

A new efficient multilevel upscaling procedure for single-phase saturated flow in porous media is presented. While traditional approaches to this problem have focused on the computation of an upscaled hydraulic conductivity, here the coarsescale model is created explicitly from the fine-scale model through the application of operator-induced variational coarsening. This technique, which origina...

A new methodology is presented that allows the upscaling of land surface parameters of a Soil-VegetationAtmosphere-Transfer (SVAT) Model. Focus is set on the proper representation of latent and sensible heat fluxes on grid scale at underlying subgrid-scale heterogeneity. The objective is to derive effective land surface parameters in the sense that they are able to yield the same heat fluxes on...

Complexity of failure is reflected from sensitivity of strength to small defects and wide scatter of macroscopic behaviors. In engineering practices, spatial information of materials at fine scales can only be partially measurable. Random field (RF) models are important to address the uncertainty in spatial distribution. To transform a RF of microcracks into failure probability at full structur...

Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current...