Modelling global Gross Primary Production using the correlation between key leaf traits
(2017) In Student thesis series INES NGEK01 20171Dept of Physical Geography and Ecosystem Science
- Abstract
- Sophisticated ecosystem models make it possible to evaluate the potential future changes of the carbon sequestration capacity of the terrestrial biosphere, as a response to the rapid environmental and climatic changes. Accuracy of model estimates is however strongly dependent on the parametrisation of driving parameters. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between key leaf traits may be used to constrain modelled global terrestrial GPP ranges.
Access to extensive leaf trait databases (such as GLOPNET and TRY) open possibilities to develop a more mechanistic rather than empirical based methods of representing vegetation in ecosystem models. Prentice et al. (2015) suggests that a... (More) - Sophisticated ecosystem models make it possible to evaluate the potential future changes of the carbon sequestration capacity of the terrestrial biosphere, as a response to the rapid environmental and climatic changes. Accuracy of model estimates is however strongly dependent on the parametrisation of driving parameters. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between key leaf traits may be used to constrain modelled global terrestrial GPP ranges.
Access to extensive leaf trait databases (such as GLOPNET and TRY) open possibilities to develop a more mechanistic rather than empirical based methods of representing vegetation in ecosystem models. Prentice et al. (2015) suggests that a stochastic parametrisation approach – like the one applied here – should be considered as a future improvement in ecosystem model development.
This thesis discusses the effect of varying key leaf attribute values on derived GPP estimates. Leaf parameters – specifically leaf longevity, leaf nitrogen content and leaf mass per area – are varied within their potential ranges, either individually one-at-a-time or leaf longevity and leaf N traits simultaneously. The methods are applied for LPJ-GUESS DGVM and a simple idealised model (LEIA), that accounts for GPP’s dependency on leaf traits.
According to the results, adjusting leaf lifespan values for evergreen and summergreen groups, as well as leaf N yielded a substantial reduction in global annual GPP variance, along with a decrease in mean global estimates. Findings suggest that using the correlation between leaf attributes may significantly improve LPJ-GUESS’s performance. (Less) - Popular Abstract
- Sophisticated ecosystem models make it possible to evaluate how the carbon uptake capacity of the terrestrial biosphere will change, as a response to the rapid environmental and climatic changes. The accuracy of model derived estimates is however strongly dependent on the values of parameters describing the included processes. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between specific leaf traits may be used to constrain modelled global terrestrial gross primary production (GPP, defines the amount of carbon taken up by the vegetation through the process of photosynthesis) ranges. Having access to extensive leaf trait databases open possibilities to develop the representation of vegetation... (More)
- Sophisticated ecosystem models make it possible to evaluate how the carbon uptake capacity of the terrestrial biosphere will change, as a response to the rapid environmental and climatic changes. The accuracy of model derived estimates is however strongly dependent on the values of parameters describing the included processes. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between specific leaf traits may be used to constrain modelled global terrestrial gross primary production (GPP, defines the amount of carbon taken up by the vegetation through the process of photosynthesis) ranges. Having access to extensive leaf trait databases open possibilities to develop the representation of vegetation properties in ecosystem models. This thesis discusses the effect of varying key leaf attribute values – specifically leaf longevity, leaf mass per area and leaf nitrogen content - on derived GPP estimates. The described methods are applied for LPJ-GUESS dynamic global vegetation model and a simple idealised model (LEIA), that accounts for GPP’s dependency on leaf traits. Findings suggest that using the correlation between leaf attributes may decrease the uncertainty in model derived estimates and thus significantly improve models’ performance. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8917201
- author
- Pongrácz, Alexandra LU
- supervisor
- organization
- course
- NGEK01 20171
- year
- 2017
- type
- M2 - Bachelor Degree
- subject
- keywords
- ecosystem modelling, leaf longevity, leaf nitrogen content, leaf mass per area, LEIA, LPJ-GUESS, GPP, terrestrial biosphere
- publication/series
- Student thesis series INES
- report number
- 424
- language
- English
- id
- 8917201
- date added to LUP
- 2017-06-19 17:34:39
- date last changed
- 2017-06-19 17:34:39
@misc{8917201,
abstract = {{Sophisticated ecosystem models make it possible to evaluate the potential future changes of the carbon sequestration capacity of the terrestrial biosphere, as a response to the rapid environmental and climatic changes. Accuracy of model estimates is however strongly dependent on the parametrisation of driving parameters. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between key leaf traits may be used to constrain modelled global terrestrial GPP ranges.
Access to extensive leaf trait databases (such as GLOPNET and TRY) open possibilities to develop a more mechanistic rather than empirical based methods of representing vegetation in ecosystem models. Prentice et al. (2015) suggests that a stochastic parametrisation approach – like the one applied here – should be considered as a future improvement in ecosystem model development.
This thesis discusses the effect of varying key leaf attribute values on derived GPP estimates. Leaf parameters – specifically leaf longevity, leaf nitrogen content and leaf mass per area – are varied within their potential ranges, either individually one-at-a-time or leaf longevity and leaf N traits simultaneously. The methods are applied for LPJ-GUESS DGVM and a simple idealised model (LEIA), that accounts for GPP’s dependency on leaf traits.
According to the results, adjusting leaf lifespan values for evergreen and summergreen groups, as well as leaf N yielded a substantial reduction in global annual GPP variance, along with a decrease in mean global estimates. Findings suggest that using the correlation between leaf attributes may significantly improve LPJ-GUESS’s performance.}},
author = {{Pongrácz, Alexandra}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Modelling global Gross Primary Production using the correlation between key leaf traits}},
year = {{2017}},
}