Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States
Identifieur interne : 001246 ( Istex/Corpus ); précédent : 001245; suivant : 001247Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States
Auteurs : Matthew Garcia ; Christa D. Peters-Lidard ; David C. GoodrichSource :
- Water Resources Research [ 0043-1397 ] ; 2008-05.
Abstract
Inaccuracy in spatially distributed precipitation fields can contribute significantly to the uncertainty of hydrological states and fluxes estimated from land surface models. This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor CF is formulated as a new, scale‐independent measure of network organization. We consider that CF < 0 (a more distributed gauge network) will produce interpolation errors by reduced resolution of the precipitation field and that CF > 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse‐distance‐weighted (IDW) and multiquadric‐biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse‐distance‐squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID‐cubed method, which is also more computationally efficient than the MQB method in studies of large networks.
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DOI: 10.1029/2006WR005788
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This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor CF is formulated as a new, scale‐independent measure of network organization. We consider that CF < 0 (a more distributed gauge network) will produce interpolation errors by reduced resolution of the precipitation field and that CF > 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse‐distance‐weighted (IDW) and multiquadric‐biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse‐distance‐squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID‐cubed method, which is also more computationally efficient than the MQB method in studies of large networks.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Matthew Garcia</name><affiliations><json:string>Goddard Earth Science and Technology Center, University of Maryland Baltimore County, Baltimore, Maryland, USA</json:string><json:string>Hydrological Sciences Branch, NASA Goddard Space Flight Center, Maryland, Greenbelt, USA</json:string><json:string>E-mail: matthew.garcia@nasa.gov</json:string></affiliations></json:item><json:item><name>Christa D. Peters‐Lidard</name><affiliations><json:string>Hydrological Sciences Branch, NASA Goddard Space Flight Center, Maryland, Greenbelt, USA</json:string></affiliations></json:item><json:item><name>David C. 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This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor CF is formulated as a new, scale‐independent measure of network organization. We consider that CF > 0 (a more distributed gauge network) will produce interpolation errors by reduced resolution of the precipitation field and that CF > 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse‐distance‐weighted (IDW) and multiquadric‐biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. 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We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse‐distance‐squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID‐cubed method, which is also more computationally efficient than the MQB method in studies of large networks.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>North American monsoon</term></item><item><term>spatial interpolation</term></item><item><term>inverse‐distance</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>index-terms</head><item><term>Fifty Years of Research and Data Collection: U.S. Department of Agriculture Walnut Gulch Experimental Watershed</term></item><item><term>COMPUTATIONAL GEOPHYSICS</term></item><item><term>Computational Geophysics</term></item><item><term>HYDROLOGY</term></item><item><term>Precipitation</term></item><item><term>Monitoring networks</term></item><item><term>Hydrometeorology</term></item><item><term>INFORMATICS</term></item><item><term>Spatial analysis and representation</term></item><item><term>MATHEMATICAL GEOPHYSICS</term></item><item><term>Spatial analysis</term></item><item><term>ATMOSPHERIC PROCESSES</term></item><item><term>Precipitation</term></item><item><term>NATURAL HAZARDS</term></item><item><term>Spatial modeling</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Regular Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2006-11-30">Received</change><change when="2007-09-11">Registration</change><change when="2008-05">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/1D0591A2956C432D31911A314CE9BFCEE3049B81/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component type="serialArticle" version="2.0" xml:lang="en" xml:id="wrcr11224"><header><publicationMeta level="product"><doi>10.1002/(ISSN)1944-7973</doi><issn type="print">0043-1397</issn><issn type="electronic">1944-7973</issn><idGroup><id type="product" value="WRCR"></id><id type="coden" value="WRERAQ"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="WATER RESOURCES RESEARCH">Water Resources Research</title><title type="short">Water Resour. 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D.</givenNames> <familyName>Peters‐Lidard</familyName></author>, and <author><givenNames>D. C.</givenNames> <familyName>Goodrich</familyName></author> (<pubYear year="2008">2008</pubYear>), <articleTitle>Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States</articleTitle>, <journalTitle>Water Resour. Res.</journalTitle>, <vol>44</vol>, W05S13, doi:<accessionId ref="info:doi/10.1029/2006WR005788">10.1029/2006WR005788</accessionId>.</citation></selfCitationGroup><linkGroup><link type="toTypesetVersion" href="file:WRCR.WRCR11224.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="9"></count><count type="tableTotal" number="1"></count></countGroup><titleGroup><title type="main">Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States</title><title type="short">MONSOON RAINFALL INTERPOLATION</title><title type="shortAuthors">Garcia <i>et al</i>.</title></titleGroup><creators><creator creatorRole="author" xml:id="wrcr11224-cr-0001" affiliationRef="#wrcr11224-aff-0001 #wrcr11224-aff-0002"><personName><givenNames>Matthew</givenNames> <familyName>Garcia</familyName></personName><contactDetails><email normalForm="matthew.garcia@nasa.gov">matthew.garcia@nasa.gov</email></contactDetails></creator><creator creatorRole="author" xml:id="wrcr11224-cr-0002" affiliationRef="#wrcr11224-aff-0002"><personName><givenNames>Christa D.</givenNames> <familyName>Peters‐Lidard</familyName></personName></creator><creator creatorRole="author" xml:id="wrcr11224-cr-0003" affiliationRef="#wrcr11224-aff-0003"><personName><givenNames>David C.</givenNames> <familyName>Goodrich</familyName></personName></creator></creators><affiliationGroup><affiliation countryCode="US" type="organization" xml:id="wrcr11224-aff-0001"><orgDiv>Goddard Earth Science and Technology Center</orgDiv><orgName>University of Maryland Baltimore County</orgName><address><city>Baltimore</city><countryPart>Maryland</countryPart><country>USA</country></address></affiliation><affiliation countryCode="US" type="organization" xml:id="wrcr11224-aff-0002"><orgDiv>Hydrological Sciences Branch</orgDiv><orgName>NASA Goddard Space Flight Center</orgName><address><city>Greenbelt</city><countryPart>Maryland</countryPart><country>USA</country></address></affiliation><affiliation countryCode="US" type="organization" xml:id="wrcr11224-aff-0003"><orgDiv>Southwest Watershed Research Center, Agricultural Research Service</orgDiv><orgName>U.S. Department of Agriculture</orgName><address><city>Tucson</city><countryPart>Arizona</countryPart><country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="wrcr11224-kwd-0001">North American monsoon</keyword><keyword xml:id="wrcr11224-kwd-0002">spatial interpolation</keyword><keyword xml:id="wrcr11224-kwd-0003">inverse‐distance</keyword></keywordGroup><supportingInformation><supportingInfoItem><mediaResource alt="supplementary data" mimeType="text/plain" href="urn-x:wiley:00431397:media:wrcr11224:wrcr11224-sup-0001-t01"></mediaResource><caption>Tab‐delimited Table 1.</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main"><p xml:id="wrcr11224-para-0001" label="1">Inaccuracy in spatially distributed precipitation fields can contribute significantly to the uncertainty of hydrological states and fluxes estimated from land surface models. This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor <i>CF</i> is formulated as a new, scale‐independent measure of network organization. We consider that <i>CF</i> < 0 (a more distributed gauge network) will produce interpolation errors by reduced resolution of the precipitation field and that <i>CF</i> > 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse‐distance‐weighted (IDW) and multiquadric‐biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse‐distance‐squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID‐cubed method, which is also more computationally efficient than the MQB method in studies of large networks.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>MONSOON RAINFALL INTERPOLATION</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States</title></titleInfo><name type="personal"><namePart type="given">Matthew</namePart><namePart type="family">Garcia</namePart><affiliation>Goddard Earth Science and Technology Center, University of Maryland Baltimore County, Baltimore, Maryland, USA</affiliation><affiliation>Hydrological Sciences Branch, NASA Goddard Space Flight Center, Maryland, Greenbelt, USA</affiliation><affiliation>E-mail: matthew.garcia@nasa.gov</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Christa D.</namePart><namePart type="family">Peters‐Lidard</namePart><affiliation>Hydrological Sciences Branch, NASA Goddard Space Flight Center, Maryland, Greenbelt, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">David C.</namePart><namePart type="family">Goodrich</namePart><affiliation>Southwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Arizona, Tucson, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2008-05</dateIssued><dateCaptured encoding="w3cdtf">2006-11-30</dateCaptured><dateValid encoding="w3cdtf">2007-09-11</dateValid><edition>Garcia, M., C. D. Peters‐Lidard, and D. C. Goodrich (2008), Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States, Water Resour. Res., 44, W05S13, doi:10.1029/2006WR005788.</edition><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">9</extent><extent unit="tables">1</extent></physicalDescription><abstract>Inaccuracy in spatially distributed precipitation fields can contribute significantly to the uncertainty of hydrological states and fluxes estimated from land surface models. This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor CF is formulated as a new, scale‐independent measure of network organization. We consider that CF < 0 (a more distributed gauge network) will produce interpolation errors by reduced resolution of the precipitation field and that CF > 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse‐distance‐weighted (IDW) and multiquadric‐biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse‐distance‐squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID‐cubed method, which is also more computationally efficient than the MQB method in studies of large networks.</abstract><note type="additional physical form">Tab‐delimited Table 1.</note><subject><genre>keywords</genre><topic>North American monsoon</topic><topic>spatial interpolation</topic><topic>inverse‐distance</topic></subject><relatedItem type="host"><titleInfo><title>Water Resources Research</title></titleInfo><titleInfo type="abbreviated"><title>Water Resour. Res.</title></titleInfo><genre type="journal">journal</genre><subject><genre>index-terms</genre><topic authorityURI="http://psi.agu.org/specialSection/GULCH1">Fifty Years of Research and Data Collection: U.S. Department of Agriculture Walnut Gulch Experimental Watershed</topic><topic authorityURI="http://psi.agu.org/taxonomy5/0500">COMPUTATIONAL GEOPHYSICS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/0500">Computational Geophysics</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1800">HYDROLOGY</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1854">Precipitation</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1848">Monitoring networks</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1840">Hydrometeorology</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1900">INFORMATICS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1980">Spatial analysis and representation</topic><topic authorityURI="http://psi.agu.org/taxonomy5/3200">MATHEMATICAL GEOPHYSICS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/3252">Spatial analysis</topic><topic authorityURI="http://psi.agu.org/taxonomy5/3300">ATMOSPHERIC PROCESSES</topic><topic authorityURI="http://psi.agu.org/taxonomy5/3354">Precipitation</topic><topic authorityURI="http://psi.agu.org/taxonomy5/4300">NATURAL HAZARDS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/4319">Spatial modeling</topic></subject><subject><genre>article-category</genre><topic>Regular Article</topic></subject><identifier type="ISSN">0043-1397</identifier><identifier type="eISSN">1944-7973</identifier><identifier type="DOI">10.1002/(ISSN)1944-7973</identifier><identifier type="CODEN">WRERAQ</identifier><identifier type="PublisherID">WRCR</identifier><part><date>2008</date><detail type="volume"><caption>vol.</caption><number>44</number></detail><detail type="issue"><caption>no.</caption><number>5</number></detail><extent unit="pages"><start>n/a</start><end>n/a</end><total>14</total></extent></part></relatedItem><identifier type="istex">1D0591A2956C432D31911A314CE9BFCEE3049B81</identifier><identifier type="DOI">10.1029/2006WR005788</identifier><identifier type="ArticleID">2006WR005788</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright 2008 by the American Geophysical Union.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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