Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture
Identifieur interne : 000838 ( Istex/Corpus ); précédent : 000837; suivant : 000839Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture
Auteurs : R. K. Munro ; W. F. Lyons ; Y. Shao ; M. S. Wood ; L. M. Hood ; L. M. LeslieSource :
- Environmental Modelling and Software [ 1364-8152 ] ; 1998.
Abstract
Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.
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DOI: 10.1016/S1364-8152(98)00038-3
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Shao</name><affiliation><mods:affiliation>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</mods:affiliation></affiliation></author><author><name sortKey="Wood, M S" sort="Wood, M S" uniqKey="Wood M" first="M. S." last="Wood">M. S. Wood</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation></author><author><name sortKey="Hood, L M" sort="Hood, L M" uniqKey="Hood L" first="L. M." last="Hood">L. M. Hood</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation></author><author><name sortKey="Leslie, L M" sort="Leslie, L M" uniqKey="Leslie L" first="L. M." last="Leslie">L. M. Leslie</name><affiliation><mods:affiliation>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0</idno><date when="1998" year="1998">1998</date><idno type="doi">10.1016/S1364-8152(98)00038-3</idno><idno type="url">https://api.istex.fr/document/29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000838</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000838</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title><author><name sortKey="Munro, R K" sort="Munro, R K" uniqKey="Munro R" first="R. K." last="Munro">R. K. Munro</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation><affiliation><mods:affiliation>Corresponding author.</mods:affiliation></affiliation></author><author><name sortKey="Lyons, W F" sort="Lyons, W F" uniqKey="Lyons W" first="W. F." last="Lyons">W. F. Lyons</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation></author><author><name sortKey="Shao, Y" sort="Shao, Y" uniqKey="Shao Y" first="Y." last="Shao">Y. Shao</name><affiliation><mods:affiliation>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</mods:affiliation></affiliation></author><author><name sortKey="Wood, M S" sort="Wood, M S" uniqKey="Wood M" first="M. S." last="Wood">M. S. Wood</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation></author><author><name sortKey="Hood, L M" sort="Hood, L M" uniqKey="Hood L" first="L. M." last="Hood">L. M. Hood</name><affiliation><mods:affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</mods:affiliation></affiliation></author><author><name sortKey="Leslie, L M" sort="Leslie, L M" uniqKey="Leslie L" first="L. M." last="Leslie">L. M. Leslie</name><affiliation><mods:affiliation>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Environmental Modelling and Software</title><title level="j" type="abbrev">ENSO</title><idno type="ISSN">1364-8152</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1998">1998</date><biblScope unit="volume">13</biblScope><biblScope unit="issue">3–4</biblScope><biblScope unit="page" from="333">333</biblScope><biblScope unit="page" to="339">339</biblScope></imprint><idno type="ISSN">1364-8152</idno></series><idno type="istex">29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0</idno><idno type="DOI">10.1016/S1364-8152(98)00038-3</idno><idno type="PII">S1364-8152(98)00038-3</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1364-8152</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>R.K. Munro</name><affiliations><json:string>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</json:string><json:string>Corresponding author.</json:string></affiliations></json:item><json:item><name>W.F. Lyons</name><affiliations><json:string>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</json:string></affiliations></json:item><json:item><name>Y. Shao</name><affiliations><json:string>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</json:string></affiliations></json:item><json:item><name>M.S. Wood</name><affiliations><json:string>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</json:string></affiliations></json:item><json:item><name>L.M. Hood</name><affiliations><json:string>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</json:string></affiliations></json:item><json:item><name>L.M. Leslie</name><affiliations><json:string>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Australia</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Soil moisture</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Simulation</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Numerical weather prediction</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.</abstract><qualityIndicators><score>7.036</score><pdfVersion>1.2</pdfVersion><pdfPageSize>598 x 795 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>4</keywordCount><abstractCharCount>1966</abstractCharCount><pdfWordCount>4036</pdfWordCount><pdfCharCount>25319</pdfCharCount><pdfPageCount>7</pdfPageCount><abstractWordCount>279</abstractWordCount></qualityIndicators><title>Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title><pii><json:string>S1364-8152(98)00038-3</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>13</volume><pii><json:string>S1364-8152(00)X0006-0</json:string></pii><pages><last>339</last><first>333</first></pages><issn><json:string>1364-8152</json:string></issn><issue>3–4</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Environmental Modelling and Software</title><publicationDate>1998</publicationDate></host><publicationDate>1998</publicationDate><copyrightDate>1998</copyrightDate><doi><json:string>10.1016/S1364-8152(98)00038-3</json:string></doi><id>29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0</id><score>0.042136</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©1998 Elsevier Science Ltd</p></availability><date>1998</date></publicationStmt><notesStmt><note type="content">Fig. 1: Soil moisture distribution over the Australian continent for 15 February 1996. (a) Predicted soil moisture in m3 m−3 for layer 0–0.05 m; (b) for layer 0.05–0.20 m; (c) for layer 0.5–1.0 m; and (d) total soil water in mm for the top 1 m soil.</note><note type="content">Fig. 2: Available soil moisture for 15 February 1996 (left column); 17 February 1996 (middle column) and 18 February 1996 (right column). In each column, available soil moisture for soil layer 0–0.05 m, layer 0.20–0.50 m and depth average over the top 1 m soil are shown.</note><note type="content">Table 1: List of parameters required by the ALSIS land surface schema</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title><author xml:id="author-1"><persName><forename type="first">R.K.</forename><surname>Munro</surname></persName><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation><affiliation>Corresponding author.</affiliation></author><author xml:id="author-2"><persName><forename type="first">W.F.</forename><surname>Lyons</surname></persName><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation></author><author xml:id="author-3"><persName><forename type="first">Y.</forename><surname>Shao</surname></persName><affiliation>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</affiliation></author><author xml:id="author-4"><persName><forename type="first">M.S.</forename><surname>Wood</surname></persName><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation></author><author xml:id="author-5"><persName><forename type="first">L.M.</forename><surname>Hood</surname></persName><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation></author><author xml:id="author-6"><persName><forename type="first">L.M.</forename><surname>Leslie</surname></persName><affiliation>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</affiliation></author></analytic><monogr><title level="j">Environmental Modelling and Software</title><title level="j" type="abbrev">ENSO</title><idno type="pISSN">1364-8152</idno><idno type="PII">S1364-8152(00)X0006-0</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1998"></date><biblScope unit="volume">13</biblScope><biblScope unit="issue">3–4</biblScope><biblScope unit="page" from="333">333</biblScope><biblScope unit="page" to="339">339</biblScope></imprint></monogr><idno type="istex">29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0</idno><idno type="DOI">10.1016/S1364-8152(98)00038-3</idno><idno type="PII">S1364-8152(98)00038-3</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1998</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>Australia</term></item><item><term>Soil moisture</term></item><item><term>Simulation</term></item><item><term>Numerical weather prediction</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1998">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>ENSO</jid><aid>979</aid><ce:pii>S1364-8152(98)00038-3</ce:pii><ce:doi>10.1016/S1364-8152(98)00038-3</ce:doi><ce:copyright year="1998" type="full-transfer">Elsevier Science Ltd</ce:copyright></item-info><head><ce:title>Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</ce:title><ce:author-group><ce:author><ce:given-name>R.K.</ce:given-name><ce:surname>Munro</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref></ce:author><ce:author><ce:given-name>W.F.</ce:given-name><ce:surname>Lyons</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref></ce:author><ce:author><ce:given-name>Y.</ce:given-name><ce:surname>Shao</ce:surname><ce:cross-ref refid="AFF2">b</ce:cross-ref></ce:author><ce:author><ce:given-name>M.S.</ce:given-name><ce:surname>Wood</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref></ce:author><ce:author><ce:given-name>L.M.</ce:given-name><ce:surname>Hood</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref></ce:author><ce:author><ce:given-name>L.M.</ce:given-name><ce:surname>Leslie</ce:surname><ce:cross-ref refid="AFF3">c</ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</ce:textfn></ce:affiliation><ce:affiliation id="AFF3"><ce:label>c</ce:label><ce:textfn>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author.</ce:text></ce:correspondence></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Australia</ce:text></ce:keyword><ce:keyword><ce:text>Soil moisture</ce:text></ce:keyword><ce:keyword><ce:text>Simulation</ce:text></ce:keyword><ce:keyword><ce:text>Numerical weather prediction</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture</title></titleInfo><name type="personal"><namePart type="given">R.K.</namePart><namePart type="family">Munro</namePart><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation><affiliation>Corresponding author.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">W.F.</namePart><namePart type="family">Lyons</namePart><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Y.</namePart><namePart type="family">Shao</namePart><affiliation>Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales, Sydney, NSW 2052, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M.S.</namePart><namePart type="family">Wood</namePart><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">L.M.</namePart><namePart type="family">Hood</namePart><affiliation>Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">L.M.</namePart><namePart type="family">Leslie</namePart><affiliation>School of Mathematics, University of New South Wales, Sydney, NSW 2052, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1998</dateIssued><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.</abstract><note type="content">Fig. 1: Soil moisture distribution over the Australian continent for 15 February 1996. (a) Predicted soil moisture in m3 m−3 for layer 0–0.05 m; (b) for layer 0.05–0.20 m; (c) for layer 0.5–1.0 m; and (d) total soil water in mm for the top 1 m soil.</note><note type="content">Fig. 2: Available soil moisture for 15 February 1996 (left column); 17 February 1996 (middle column) and 18 February 1996 (right column). In each column, available soil moisture for soil layer 0–0.05 m, layer 0.20–0.50 m and depth average over the top 1 m soil are shown.</note><note type="content">Table 1: List of parameters required by the ALSIS land surface schema</note><subject><genre>Keywords</genre><topic>Australia</topic><topic>Soil moisture</topic><topic>Simulation</topic><topic>Numerical weather prediction</topic></subject><relatedItem type="host"><titleInfo><title>Environmental Modelling and Software</title></titleInfo><titleInfo type="abbreviated"><title>ENSO</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199810</dateIssued></originInfo><identifier type="ISSN">1364-8152</identifier><identifier type="PII">S1364-8152(00)X0006-0</identifier><part><date>199810</date><detail type="volume"><number>13</number><caption>vol.</caption></detail><detail type="issue"><number>3–4</number><caption>no.</caption></detail><extent unit="issue pages"><start>233</start><end>404</end></extent><extent unit="pages"><start>333</start><end>339</end></extent></part></relatedItem><identifier type="istex">29CEBFF0682A44BF7F2E7AF154B8BAC2DC30E7B0</identifier><identifier type="DOI">10.1016/S1364-8152(98)00038-3</identifier><identifier type="PII">S1364-8152(98)00038-3</identifier><accessCondition type="use and reproduction" contentType="copyright">©1998 Elsevier Science Ltd</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Ltd, ©1998</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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