Publication:
Modeling evapotranspiration during SMACEX: Comparing two approaches for local- and regional-scale prediction

dc.contributor.author Su, H en_US
dc.contributor.author McCabe, Matthew Francis en_US
dc.contributor.author Wood, E.F. en_US
dc.contributor.author Su, Z en_US
dc.contributor.author Prueger, J. en_US
dc.date.accessioned 2021-11-25T13:35:53Z
dc.date.available 2021-11-25T13:35:53Z
dc.date.issued 2005 en_US
dc.description.abstract he Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture-Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%-15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology. © 2005 American Meteorological Society. en_US
dc.identifier.uri http://hdl.handle.net/1959.4/40070
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.source Legacy MARC en_US
dc.subject.other Evapotranspiration en_US
dc.title Modeling evapotranspiration during SMACEX: Comparing two approaches for local- and regional-scale prediction en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.accessRights.uri http://purl.org/coar/access_right/c_14cb
unsw.identifier.doiPublisher http://dx.doi.org/10.1175/JHM466.1 en_US
unsw.relation.faculty Other UNSW
unsw.relation.faculty Engineering
unsw.relation.ispartofissue 6 en_US
unsw.relation.ispartofjournal Journal of Hydrometeorology en_US
unsw.relation.ispartofpagefrompageto 910-922 en_US
unsw.relation.ispartofvolume 6 en_US
unsw.relation.originalPublicationAffiliation Su, H, UNSW en_US
unsw.relation.originalPublicationAffiliation McCabe, Matthew Francis, Civil & Environmental Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.originalPublicationAffiliation Wood, E.F. en_US
unsw.relation.originalPublicationAffiliation Su, Z en_US
unsw.relation.originalPublicationAffiliation Prueger, J. en_US
unsw.relation.school School of Civil and Environmental Engineering *
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