Conditioning a multiple-patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely sensed data

Stewart Franks*, Keith J. Beven

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

It has been shown that the calibration of soil vegetation-atmosphere transfer (SVAT) models is inherently uncertain, even when data are available over a relatively limited homogeneous area. The representation of subgrid-scale variability of fluxes is not easily achieved because of the lack of information available about appropriate parameter distributions and their covariance. However, remote sensing of thermal surface responses offers the possibility of obtaining distributed estimates of surface fluxes. In this paper, multiple Landsat-Thematic Mapper (TM) images of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site are used to derive uncertain estimates of the land surface-atmosphere sensible and latent fluxes over a period of time. Employing a framework based on fuzzy set theory, the parameter space representing all feasible parameterizations of a SVAT model are examined with respect to these image estimates. Areal weightings for a number of functional types of flux behavior are then derived through which the temporal evolution of surface fluxes can be estimated.

Original languageEnglish
Pages (from-to)2751-2761
Number of pages11
JournalWater Resources Research
Volume35
Issue number9
DOIs
Publication statusPublished - 1 Jan 1999

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