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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>14</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2010</publication_year>
	</journal>
	<doi>10.5194/hess-14-325-2010</doi>
	<article_url>http://www.hydrol-earth-syst-sci.net/14/325/2010/</article_url>
	<abstract_html>http://www.hydrol-earth-syst-sci.net/14/325/2010/hess-14-325-2010.html</abstract_html>
	<fulltext_pdf>http://www.hydrol-earth-syst-sci.net/14/325/2010/hess-14-325-2010.pdf</fulltext_pdf>
	<start_page>325</start_page>
	<end_page>338</end_page>
	<publication_date>2010-02-18</publication_date>
	<article_title content_type="html">Global spatial optimization with hydrological systems simulation: application to land-use allocation and peak runoff minimization</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>I.-Y. Yeo</name>
			<email>iyeo@umd.edu</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>J.-M. Guldmann</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Department of Geography, The University of Maryland, College Park, Maryland, USA</affiliation>
		<affiliation numeration="2" content_type="html">Department of City and Regional Planning, The Ohio State University, Columbus, Ohio, USA</affiliation>
	</affiliations>
	<abstract content_type="html">A general methodology is presented to integrate complex simulation models of
hydrological systems into optimization models, as an alternative to
scenario-based approaches. A gradient-based hill climbing algorithm is
proposed to reach locally optimal solutions from distinct starting points.
The gradient of the objective function is estimated numerically with the
simulation model. A statistical procedure based on the Weibull distribution
is used to build a confidence interval for the global optimum. The
methodology is illustrated by an application to a small watershed in Ohio,
where the decision variables are related to land-use allocations and the
objective is to minimize peak runoff. The results suggest that this specific
runoff function is convex in terms of the land-use variables, and that the
global optimum has been reached. Modeling extensions and areas for further
research are discussed.</abstract>
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</article>

