Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model
Land surface models, like the Common LandModel component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is cont...
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Veröffentlicht in: | Journal of hydrometeorology 2017-03, Vol.18 (3), p.897-915 |
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description | Land surface models, like the Common LandModel component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California,Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO₂ Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models. |
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Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California,Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO₂ Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.</description><identifier>ISSN: 1525-755X</identifier><identifier>EISSN: 1525-7541</identifier><identifier>DOI: 10.1175/JHM-D-16-0053.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Applied mathematics ; Atmosphere ; Atmospheric data ; Atmospheric models ; Carbon dioxide ; Carboxylation ; Computer simulation ; Engineering ; Fluxes ; Fruits ; Geological engineering ; Heat ; Hydrologic models ; Hydrologic sciences ; Hydrology ; Land surface models ; Leaves ; Mathematical models ; Methods ; Model evaluation/performance ; Nitrogen ; Oxygenase ; Oxygenation ; Parameter identification ; Parameter sensitivity ; Parameterization ; Parameters ; Photosynthesis ; Plant cover ; Quantum efficiency ; Ribulose-bisphosphate carboxylase ; Sensitivity ; Sensitivity analysis ; Sensitivity studies ; Stomata ; Subspace methods ; Transpiration ; Vegetation</subject><ispartof>Journal of hydrometeorology, 2017-03, Vol.18 (3), p.897-915</ispartof><rights>2017 American Meteorological Society</rights><rights>Copyright American Meteorological Society Mar 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c289t-81537af70ba56ab7287c7f4257b41a07c2d2121a288ddfb322dfbf9e0fec66943</citedby><cites>FETCH-LOGICAL-c289t-81537af70ba56ab7287c7f4257b41a07c2d2121a288ddfb322dfbf9e0fec66943</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26152618$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26152618$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,780,784,803,885,3681,27924,27925,58017,58250</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/1537027$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Jefferson, Jennifer L.</creatorcontrib><creatorcontrib>Maxwell, Reed M.</creatorcontrib><creatorcontrib>Constantine, Paul G.</creatorcontrib><creatorcontrib>Colorado School of Mines, Golden, CO (United States)</creatorcontrib><title>Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model</title><title>Journal of hydrometeorology</title><description>Land surface models, like the Common LandModel component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California,Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO₂ Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.</description><subject>Applied mathematics</subject><subject>Atmosphere</subject><subject>Atmospheric data</subject><subject>Atmospheric models</subject><subject>Carbon dioxide</subject><subject>Carboxylation</subject><subject>Computer simulation</subject><subject>Engineering</subject><subject>Fluxes</subject><subject>Fruits</subject><subject>Geological engineering</subject><subject>Heat</subject><subject>Hydrologic models</subject><subject>Hydrologic sciences</subject><subject>Hydrology</subject><subject>Land surface models</subject><subject>Leaves</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Model evaluation/performance</subject><subject>Nitrogen</subject><subject>Oxygenase</subject><subject>Oxygenation</subject><subject>Parameter identification</subject><subject>Parameter sensitivity</subject><subject>Parameterization</subject><subject>Parameters</subject><subject>Photosynthesis</subject><subject>Plant cover</subject><subject>Quantum efficiency</subject><subject>Ribulose-bisphosphate carboxylase</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Sensitivity studies</subject><subject>Stomata</subject><subject>Subspace methods</subject><subject>Transpiration</subject><subject>Vegetation</subject><issn>1525-755X</issn><issn>1525-7541</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNo9kMtLAzEQxhdR8Hn2JAQ9b81kN5vdo2h90WLxAd5CNpvYlG1Sk1Tsf2_WipeZYb7ffAxflp0CHgEwevl4P81vcqhyjGkxgp3sACihOaMl7P7P9H0_OwxhgTEuG6gPMj3-XvXOG_uB4lyhF2WDiebLxA1yGs3mLrqwsUkKJiBhO_QS3VJE0aPnYRWFlQrNhBdLFZUPyFgk0OQXXHstkjh1neqPsz0t-qBO_vpR9nY7fr2-zydPdw_XV5NckrqJeQ20YEIz3ApaiZaRmkmmS0JZW4LATJKOAAFB6rrrdFsQkqpuFNZKVlVTFkfZ-dbXhWh4kCYqOZfOWiUjH8wxYQm62EIr7z7XKkS-cGtv018cGlLWFJdlk6jLLSW9C8ErzVfeLIXfcMB8SJynxPkNh4oPiXNIF2fbi0WIzv_jpErhV1AXP7kTfeM</recordid><startdate>20170301</startdate><enddate>20170301</enddate><creator>Jefferson, Jennifer L.</creator><creator>Maxwell, Reed M.</creator><creator>Constantine, Paul G.</creator><general>American Meteorological Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>OIOZB</scope><scope>OTOTI</scope></search><sort><creationdate>20170301</creationdate><title>Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model</title><author>Jefferson, Jennifer L. ; Maxwell, Reed M. ; Constantine, Paul G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-81537af70ba56ab7287c7f4257b41a07c2d2121a288ddfb322dfbf9e0fec66943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Applied mathematics</topic><topic>Atmosphere</topic><topic>Atmospheric data</topic><topic>Atmospheric models</topic><topic>Carbon dioxide</topic><topic>Carboxylation</topic><topic>Computer simulation</topic><topic>Engineering</topic><topic>Fluxes</topic><topic>Fruits</topic><topic>Geological engineering</topic><topic>Heat</topic><topic>Hydrologic models</topic><topic>Hydrologic sciences</topic><topic>Hydrology</topic><topic>Land surface models</topic><topic>Leaves</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Model evaluation/performance</topic><topic>Nitrogen</topic><topic>Oxygenase</topic><topic>Oxygenation</topic><topic>Parameter identification</topic><topic>Parameter sensitivity</topic><topic>Parameterization</topic><topic>Parameters</topic><topic>Photosynthesis</topic><topic>Plant cover</topic><topic>Quantum efficiency</topic><topic>Ribulose-bisphosphate carboxylase</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>Sensitivity studies</topic><topic>Stomata</topic><topic>Subspace methods</topic><topic>Transpiration</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jefferson, Jennifer L.</creatorcontrib><creatorcontrib>Maxwell, Reed M.</creatorcontrib><creatorcontrib>Constantine, Paul G.</creatorcontrib><creatorcontrib>Colorado School of Mines, Golden, CO (United States)</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Journal of hydrometeorology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jefferson, Jennifer L.</au><au>Maxwell, Reed M.</au><au>Constantine, Paul G.</au><aucorp>Colorado School of Mines, Golden, CO (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model</atitle><jtitle>Journal of hydrometeorology</jtitle><date>2017-03-01</date><risdate>2017</risdate><volume>18</volume><issue>3</issue><spage>897</spage><epage>915</epage><pages>897-915</pages><issn>1525-755X</issn><eissn>1525-7541</eissn><abstract>Land surface models, like the Common LandModel component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California,Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO₂ Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/JHM-D-16-0053.1</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Applied mathematics Atmosphere Atmospheric data Atmospheric models Carbon dioxide Carboxylation Computer simulation Engineering Fluxes Fruits Geological engineering Heat Hydrologic models Hydrologic sciences Hydrology Land surface models Leaves Mathematical models Methods Model evaluation/performance Nitrogen Oxygenase Oxygenation Parameter identification Parameter sensitivity Parameterization Parameters Photosynthesis Plant cover Quantum efficiency Ribulose-bisphosphate carboxylase Sensitivity Sensitivity analysis Sensitivity studies Stomata Subspace methods Transpiration Vegetation |
title | Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model |
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