Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison
• Productivity of a coniferous forest was adversely affected by short-term rises in temperature. • These rises caused these forests to change from carbon sinks to sources at a daily time scale. • Consequently warmer summers were found to reduce annual forest productivity. • This reduction was simula...
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| creator | Wang, Z. Grant, R.F. Arain, M.A. Chen, B.N. Coops, N. Hember, R. Kurz, W.A. Price, D.T. Stinson, G. Trofymow, J.A. Yeluripati, J. Chen, Z. |
| description | • Productivity of a coniferous forest was adversely affected by short-term rises in temperature. • These rises caused these forests to change from carbon sinks to sources at a daily time scale. • Consequently warmer summers were found to reduce annual forest productivity. • This reduction was simulated by four different process models, and so appeared to be robust. • A simple equation was developed to estimate weather effects on productivity in inventory models.
Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as
CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among
CBM-CFS3 and four process models (
ecosys,
CN-CLASS,
Can-IBIS and
3PG) over a 2500
ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (
NPP), net ecosystem productivity (
NEP) and net biome productivity (
NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated
NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled
NBP among models was much smaller, and attributed mainly to differences in simulated
NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual
NPP and
NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July–September (
T
amax
) in all process models (
R
2
=
0.4–0.6), indicating that these correlations were robust. The negative correlation between
T
amax
and
NEP was attributed to different processes in different models, which were tested by comparing CO
2 fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (
T
a
). The general agreement in sensitivity of annual
NPP to
T
amax
among the process models led to th |
| doi_str_mv | 10.1016/j.ecolmodel.2011.06.005 |
| format | Article |
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Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as
CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among
CBM-CFS3 and four process models (
ecosys,
CN-CLASS,
Can-IBIS and
3PG) over a 2500
ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (
NPP), net ecosystem productivity (
NEP) and net biome productivity (
NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated
NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled
NBP among models was much smaller, and attributed mainly to differences in simulated
NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual
NPP and
NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July–September (
T
amax
) in all process models (
R
2
=
0.4–0.6), indicating that these correlations were robust. The negative correlation between
T
amax
and
NEP was attributed to different processes in different models, which were tested by comparing CO
2 fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (
T
a
). The general agreement in sensitivity of annual
NPP to
T
amax
among the process models led to the development of a generalized algorithm for weather effects on
NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3:
NPP′
=
NPP
−
57.1 (
T
amax
−
18.6), where
NPP and
NPP′ are the current and temperature-adjusted annual
NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July–September, and
T
amax
is the mean value for the current year. Our analysis indicated that the sensitivity of
NPP to
T
amax
was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on
NPP and
NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.</description><identifier>ISSN: 0304-3800</identifier><identifier>EISSN: 1872-7026</identifier><identifier>DOI: 10.1016/j.ecolmodel.2011.06.005</identifier><identifier>CODEN: ECMODT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>3PG ; air temperature ; Animal and plant ecology ; Animal, plant and microbial ecology ; anthropogenic activities ; biogeochemical cycles ; Biological and medical sciences ; Can-IBIS ; Carbon budget ; carbon dioxide ; Carbon flux ; carbon sinks ; CBM-CFS3 ; CN-CLASS ; coniferous forests ; correlation ; Disturbance ; Ecosys ; Ecosystem models ; eddy covariance ; forest canopy ; Forest productivity ; forest stands ; Fundamental and applied biological sciences. Psychology ; General aspects ; General aspects. Techniques ; humus ; inventories ; land cover ; meteorological data ; Methods and techniques (sampling, tagging, trapping, modelling...) ; net ecosystem production ; primary productivity ; rivers ; Synecology ; weathering</subject><ispartof>Ecological modelling, 2011-09, Vol.222 (17), p.3236-3249</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c467t-d34d10e5dfd754960a9e76aa3c89671600bb2653fcace8bd151d63466771b12f3</citedby><cites>FETCH-LOGICAL-c467t-d34d10e5dfd754960a9e76aa3c89671600bb2653fcace8bd151d63466771b12f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0304380011003383$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24493782$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Z.</creatorcontrib><creatorcontrib>Grant, R.F.</creatorcontrib><creatorcontrib>Arain, M.A.</creatorcontrib><creatorcontrib>Chen, B.N.</creatorcontrib><creatorcontrib>Coops, N.</creatorcontrib><creatorcontrib>Hember, R.</creatorcontrib><creatorcontrib>Kurz, W.A.</creatorcontrib><creatorcontrib>Price, D.T.</creatorcontrib><creatorcontrib>Stinson, G.</creatorcontrib><creatorcontrib>Trofymow, J.A.</creatorcontrib><creatorcontrib>Yeluripati, J.</creatorcontrib><creatorcontrib>Chen, Z.</creatorcontrib><title>Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison</title><title>Ecological modelling</title><description>• Productivity of a coniferous forest was adversely affected by short-term rises in temperature. • These rises caused these forests to change from carbon sinks to sources at a daily time scale. • Consequently warmer summers were found to reduce annual forest productivity. • This reduction was simulated by four different process models, and so appeared to be robust. • A simple equation was developed to estimate weather effects on productivity in inventory models.
Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as
CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among
CBM-CFS3 and four process models (
ecosys,
CN-CLASS,
Can-IBIS and
3PG) over a 2500
ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (
NPP), net ecosystem productivity (
NEP) and net biome productivity (
NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated
NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled
NBP among models was much smaller, and attributed mainly to differences in simulated
NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual
NPP and
NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July–September (
T
amax
) in all process models (
R
2
=
0.4–0.6), indicating that these correlations were robust. The negative correlation between
T
amax
and
NEP was attributed to different processes in different models, which were tested by comparing CO
2 fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (
T
a
). The general agreement in sensitivity of annual
NPP to
T
amax
among the process models led to the development of a generalized algorithm for weather effects on
NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3:
NPP′
=
NPP
−
57.1 (
T
amax
−
18.6), where
NPP and
NPP′ are the current and temperature-adjusted annual
NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July–September, and
T
amax
is the mean value for the current year. Our analysis indicated that the sensitivity of
NPP to
T
amax
was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on
NPP and
NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.</description><subject>3PG</subject><subject>air temperature</subject><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>anthropogenic activities</subject><subject>biogeochemical cycles</subject><subject>Biological and medical sciences</subject><subject>Can-IBIS</subject><subject>Carbon budget</subject><subject>carbon dioxide</subject><subject>Carbon flux</subject><subject>carbon sinks</subject><subject>CBM-CFS3</subject><subject>CN-CLASS</subject><subject>coniferous forests</subject><subject>correlation</subject><subject>Disturbance</subject><subject>Ecosys</subject><subject>Ecosystem models</subject><subject>eddy covariance</subject><subject>forest canopy</subject><subject>Forest productivity</subject><subject>forest stands</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>General aspects. Techniques</subject><subject>humus</subject><subject>inventories</subject><subject>land cover</subject><subject>meteorological data</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><subject>net ecosystem production</subject><subject>primary productivity</subject><subject>rivers</subject><subject>Synecology</subject><subject>weathering</subject><issn>0304-3800</issn><issn>1872-7026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFUctu1DAUjRBIDIVvqDeIVcJ1HnbCblSVh1SJBXRteezr4lESB9sZNL_BF3M7qbplZck-L59TFNccKg5cfDxWaMI4BYtjVQPnFYgKoHtR7Hgv61JCLV4WO2igLZse4HXxJqUjAPC6r3fF39uTHled_fzA_qDOvzAydA5NTizMzM8Zo57nVY_spKMn4OWWzZgZ-aZzyjixJQa7muxPPp9ZcEwzE3TKRKLXhRQyMhcipsxGPdtk9IKf2J5dUm8mJkwLGaQwvy1eOT0mfPd0XhX3n29_3nwt775_-XazvytNK2QubdNaDthZZ2XXDgL0gFJo3Zh-EJILgMOhFl3jjDbYHyzvuBVNK4SU_MBr11wVHzZdSv97pWxq8sngSAkxrEn1w0AqtQBCyg1pYkgpolNL9JOOZ8VBPY6gjup5BPU4ggKhaARivn_y0PTp0VGXxqdnet22QyP7mnDXG87poPQD9aDuf5BQSzv1Db8o7TcEUiUnj1El43E2aH2ktZQN_r9p_gG3o66A</recordid><startdate>20110910</startdate><enddate>20110910</enddate><creator>Wang, Z.</creator><creator>Grant, R.F.</creator><creator>Arain, M.A.</creator><creator>Chen, B.N.</creator><creator>Coops, N.</creator><creator>Hember, R.</creator><creator>Kurz, W.A.</creator><creator>Price, D.T.</creator><creator>Stinson, G.</creator><creator>Trofymow, J.A.</creator><creator>Yeluripati, J.</creator><creator>Chen, Z.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>20110910</creationdate><title>Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison</title><author>Wang, Z. ; Grant, R.F. ; Arain, M.A. ; Chen, B.N. ; Coops, N. ; Hember, R. ; Kurz, W.A. ; Price, D.T. ; Stinson, G. ; Trofymow, J.A. ; Yeluripati, J. ; Chen, Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c467t-d34d10e5dfd754960a9e76aa3c89671600bb2653fcace8bd151d63466771b12f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>3PG</topic><topic>air temperature</topic><topic>Animal and plant ecology</topic><topic>Animal, plant and microbial ecology</topic><topic>anthropogenic activities</topic><topic>biogeochemical cycles</topic><topic>Biological and medical sciences</topic><topic>Can-IBIS</topic><topic>Carbon budget</topic><topic>carbon dioxide</topic><topic>Carbon flux</topic><topic>carbon sinks</topic><topic>CBM-CFS3</topic><topic>CN-CLASS</topic><topic>coniferous forests</topic><topic>correlation</topic><topic>Disturbance</topic><topic>Ecosys</topic><topic>Ecosystem models</topic><topic>eddy covariance</topic><topic>forest canopy</topic><topic>Forest productivity</topic><topic>forest stands</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>General aspects. Techniques</topic><topic>humus</topic><topic>inventories</topic><topic>land cover</topic><topic>meteorological data</topic><topic>Methods and techniques (sampling, tagging, trapping, modelling...)</topic><topic>net ecosystem production</topic><topic>primary productivity</topic><topic>rivers</topic><topic>Synecology</topic><topic>weathering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Z.</creatorcontrib><creatorcontrib>Grant, R.F.</creatorcontrib><creatorcontrib>Arain, M.A.</creatorcontrib><creatorcontrib>Chen, B.N.</creatorcontrib><creatorcontrib>Coops, N.</creatorcontrib><creatorcontrib>Hember, R.</creatorcontrib><creatorcontrib>Kurz, W.A.</creatorcontrib><creatorcontrib>Price, D.T.</creatorcontrib><creatorcontrib>Stinson, G.</creatorcontrib><creatorcontrib>Trofymow, J.A.</creatorcontrib><creatorcontrib>Yeluripati, J.</creatorcontrib><creatorcontrib>Chen, Z.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Ecological modelling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Z.</au><au>Grant, R.F.</au><au>Arain, M.A.</au><au>Chen, B.N.</au><au>Coops, N.</au><au>Hember, R.</au><au>Kurz, W.A.</au><au>Price, D.T.</au><au>Stinson, G.</au><au>Trofymow, J.A.</au><au>Yeluripati, J.</au><au>Chen, Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison</atitle><jtitle>Ecological modelling</jtitle><date>2011-09-10</date><risdate>2011</risdate><volume>222</volume><issue>17</issue><spage>3236</spage><epage>3249</epage><pages>3236-3249</pages><issn>0304-3800</issn><eissn>1872-7026</eissn><coden>ECMODT</coden><abstract>• Productivity of a coniferous forest was adversely affected by short-term rises in temperature. • These rises caused these forests to change from carbon sinks to sources at a daily time scale. • Consequently warmer summers were found to reduce annual forest productivity. • This reduction was simulated by four different process models, and so appeared to be robust. • A simple equation was developed to estimate weather effects on productivity in inventory models.
Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as
CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among
CBM-CFS3 and four process models (
ecosys,
CN-CLASS,
Can-IBIS and
3PG) over a 2500
ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (
NPP), net ecosystem productivity (
NEP) and net biome productivity (
NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated
NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled
NBP among models was much smaller, and attributed mainly to differences in simulated
NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual
NPP and
NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July–September (
T
amax
) in all process models (
R
2
=
0.4–0.6), indicating that these correlations were robust. The negative correlation between
T
amax
and
NEP was attributed to different processes in different models, which were tested by comparing CO
2 fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (
T
a
). The general agreement in sensitivity of annual
NPP to
T
amax
among the process models led to the development of a generalized algorithm for weather effects on
NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3:
NPP′
=
NPP
−
57.1 (
T
amax
−
18.6), where
NPP and
NPP′ are the current and temperature-adjusted annual
NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July–September, and
T
amax
is the mean value for the current year. Our analysis indicated that the sensitivity of
NPP to
T
amax
was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on
NPP and
NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ecolmodel.2011.06.005</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0304-3800 |
| ispartof | Ecological modelling, 2011-09, Vol.222 (17), p.3236-3249 |
| issn | 0304-3800 1872-7026 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_899160260 |
| source | Elsevier ScienceDirect Journals |
| subjects | 3PG air temperature Animal and plant ecology Animal, plant and microbial ecology anthropogenic activities biogeochemical cycles Biological and medical sciences Can-IBIS Carbon budget carbon dioxide Carbon flux carbon sinks CBM-CFS3 CN-CLASS coniferous forests correlation Disturbance Ecosys Ecosystem models eddy covariance forest canopy Forest productivity forest stands Fundamental and applied biological sciences. Psychology General aspects General aspects. Techniques humus inventories land cover meteorological data Methods and techniques (sampling, tagging, trapping, modelling...) net ecosystem production primary productivity rivers Synecology weathering |
| title | Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison |
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