GLObal Production Efficiency Model(GLO-PEM)

A new model of global primary production (GLObal Production Efficiency Model, GLO-PEM), based on the production efficiency concept, is described. GLO-PEM is the first attempt to model both global net and gross primary production using the production efficiency approach and is unique in that it uses satellite data to measure both absorption of photosynthetically active radiation (APAR) and also the environmental variables that affect the utilization of APAR in primary production.

Primary productionmodelremote sensingglobalvegetation.

Contributor(s)

Initial contribute: 2019-04-25

Classification(s)

Application-focused categoriesIntegrated-perspectiveGlobal scale
Method-focused categoriesProcess-perspectiveBiological process calculation

Detailed Description

English {{currentDetailLanguage}} English

    The current version of GLO-PEM (GLObal Production Efficiency Model) was designed on the basis of the pro- duction efficiency concept (Prince, 1991; Prince, Justice & Moore, 1994) in which gross primary production (Pg photosynthesis-photorespiration) is calculated from the products of the photosynthetically active radiation (PAR) absorbed by the vegetation (APAR) and a potential conver- sion 'efficiency' or carbon yield of absorbed energy in terms of Pg, (Kumar & Monteith, 1982; Landsberg et al., 1995). The model is summarized in equation 1.

whereis the reductioncaused by environmental factors in time interval t (a proportion); is the potentialin terms of gross production (g C MJ -1);Nt is the proportion of incident PAR (St) absorbed by the vegetation (sometimes calledfPAR). YgYm are measures of respiration.

     The APAR in time interval t is obtained from incident PAR (St) multiplied by Nt, the proportion of the incident PAR absorbed by the vegetation, estimated from the nor- malized difference vegetation index (NDVI = IR - R/ IR + R, where IR is near infrared and R red reflectance).

    When first introduced, s was regarded as an empirical constant. Here we propose to identify it with the quantum yield of a leaf, a well-known variable with defined values and dependancy on the particular bio- chemical carbon fixation pathway and temperature.  for C3 and C4 photosynthesis was calculated separately and then the value of  for a specific pixel was obtained from the estimated proportion of each carbon fixation pathway present in each 10-day period.

    The utilization of APAR in assimilation of carbon diox- ide in Pg is strongly affected by environmental variables such as air temperature, soil moisture and vapour pressure deficits (Runyon et al., 1993). The combined effects of environmental factors in reducing the efficiency from its potential value  to its actual value  were specified by the three components of , by analogy with stomatal models:

The first component()accounts for the reduction in assimilation as a result of low air temperature(Ta)since photosynthesis is known to be sensitive to low tempera- tures, and stomata may remain closed for several days following freezing temperatures;the second()accounts for the reduced stomatal conductance caused by high atmospheric water vapour pressure deficits; and the thirdaccounts for the effect of soil moisture on assimilation. Each component can vary between time intervals (t). Although originally developed for a leaf scale, this model was shown to account for stand scale behaviour in a range of sites in OTTER .

    Seasonal Pg was obtained from the time sum of the products of Nt,St, and .eq. 1). Seasonal Pn was calculated by multiplication of Pg by two factors, Yg and Ym, representing the proportion of Pg remaining after growth and maintenance respiration, respectively. Y. was modelled from the above-ground biomass of the vegetation using an algorithm by Hunt (1994). Above-ground biomass was obtained from a new algorithm based on minimized reflectance in the visible channel of the AVHRR.

    Unlike apparently similar models (Potter et al., 1993; Running & Hunt, 1993; Ruimy et al., 1994), at no point in GLO-PEM were field observations of primary production or used. These are reserved for validation.

 

模型元数据

{{htmlJSON.HowtoCite}}

Zhiyi Zhu (2019). GLObal Production Efficiency Model(GLO-PEM), Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/65fe6791-78a4-4b74-a2d4-db95ac91f1ca
{{htmlJSON.Copy}}

Contributor(s)

Initial contribute : 2019-04-25

{{htmlJSON.CoContributor}}

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









{{htmlJSON.RelatedItems}}

{{htmlJSON.LinkResourceFromRepositoryOrCreate}}{{htmlJSON.create}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

{{htmlJSON.authorshipSubmitted}}

Cancel Submit
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Localizations}} + {{htmlJSON.Add}}
{{ item.label }} {{ item.value }}
{{htmlJSON.ModelName}}:
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
名称 别名 {{tag}} +
系列名 版本号 目的 修改内容 创建/修改日期 作者
摘要 详细描述
{{tag}} + 添加关键字
* 时间参考系
* 空间参考系类型 * 空间参考系名称

起始日期 终止日期 进展 开发者
* 是否开源 * 访问方式 * 使用方式 开源协议 * 传输方式 * 获取地址 * 发布日期 * 发布者



编号 目的 修改内容 创建/修改日期 作者





时间分辨率 时间尺度 时间步长 时间范围 空间维度 格网类型 空间分辨率 空间尺度 空间范围
{{tag}} +
* 类型
图例


* 名称 * 描述
示例描述 * 名称 * 类型 * 值/链接 上传


{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Add}} {{htmlJSON.Cancel}}

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
{{htmlJSON.Cancel}} {{htmlJSON.Confirm}}
9.ORbeXvi8W3