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<article language="en">
	<journal>
		<journal_title>Hydrology and Earth System Sciences</journal_title>
		<journal_url>www.hydrol-earth-syst-sci.net</journal_url>
		<issn>1027-5606</issn>
		<eissn>1607-7938</eissn>
		<volume_number>11</volume_number>
		<issue_number>6</issue_number>
		<publication_year>2007</publication_year>
	</journal>
	<doi>10.5194/hess-11-1857-2007</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/11/1857/2007/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/11/1857/2007/hess-11-1857-2007.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/11/1857/2007/hess-11-1857-2007.pdf</fulltext_pdf>
	<start_page>1857</start_page>
	<end_page>1868</end_page>
	<publication_date>2007-11-29</publication_date>
	<article_title content_type="html">Uncertainties in land use data</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>G. Castilla</name>
			<email>gcastill@ucalgary.ca</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>G. J. Hay</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Institute for Regional Development, University of Castilla La Mancha, Albacete, Spain</affiliation>
		<affiliation numeration="2" content_type="html">Department of Geography, University of Calgary, Alberta, Canada</affiliation>
	</affiliations>
	<abstract content_type="html">This paper deals with the description and assessment of uncertainties in
land use data derived from Remote Sensing observations, in the context of
hydrological studies. Land use is a categorical regionalised variable
reporting the main socio-economic role each location has, where the role is
inferred from the pattern of occupation of land. The properties of this
pattern that are relevant to hydrological processes have to be known with
some accuracy in order to obtain reliable results; hence, uncertainty in
land use data may lead to uncertainty in model predictions. There are two
main uncertainties surrounding land use data, positional and categorical. The
first one is briefly addressed and the second one is explored in more depth,
including the factors that influence it. We (1) argue that the conventional
method used to assess categorical uncertainty, the confusion matrix, is
insufficient to propagate uncertainty through distributed hydrologic models;
(2) report some alternative methods to tackle this and other
insufficiencies; (3) stress the role of metadata as a more reliable means to
assess the degree of distrust with which these data should be used; and (4)
suggest some practical recommendations.</abstract>
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</article>

