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This article in AJ

  1. Vol. 95 No. 6, p. 1408-1423
     
    Received: Nov 19, 2002
    Published: Nov, 2003


    * Corresponding author(s): mcanders@wisc.edu
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doi:10.2134/agronj2003.1408

Upscaling and Downscaling—A Regional View of the Soil–Plant–Atmosphere Continuum

  1. Martha C. Anderson *a,
  2. William P. Kustasb and
  3. John M. Normana
  1. a Dep. of Soil Sci., Univ. of Wisconsin, Madison, WI 53706
    b USDA-ARS, Hydrology and Remote Sensing Lab., Bldg. 007, BARC West, Beltsville, MD 20705

Abstract

The strength of interaction among soil, plants, and atmosphere depends highly on scale. As the spatial scale of organized soil–plant behavior (e.g., soil drying and/or stomatal closure) increases, so does the influence the land surface has on atmospheric properties and circulations. Counterbalancing this is a system of feedback loops that serve to reduce the sensitivity of surface fluxes to changes in surface conditions. Model upscaling involves capturing land–atmosphere feedbacks and effects of land surface heterogeneity on surface fluxes and atmospheric boundary-layer dynamics that become operative at progressively larger spatial scales. Conversely, by downscaling, we learn how to appropriately parameterize subgrid-scale phenomena within large-scale modeling frameworks. This paper discusses some of the major challenges faced today in properly describing system behavior at regional spatial scales. We focus on a suite of simple biophysical models , tied closely to remote sensing, that work synergistically from canopy to mesoscales. This suite includes a diagnostic regional-scale model used for routine mapping of flux and moisture conditions across the United States at 10-km resolution. A related approach disaggregates regional flux estimates to local scales (100–102 m) for comparison with ground-based measurements or for use in site-specific agricultural or resource management applications. Coupled with turbulence- and mesoscale atmospheric models, the core land surface representation provides means for assimilating remote sensing data into large-eddy simulations and improving short-range weather forecasts. This multiscale modeling framework is being utilized in a concerted research effort aimed at identifying scale-relevant land–atmosphere feedbacks and representing surface heterogeneity efficiently and robustly in regional modeling schemes.

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