Modelling dynamic wood density in LPJ-GUESS
(2024) In Student thesis series INES NGEM01 20241Dept of Physical Geography and Ecosystem Science
- Abstract
- Wood density is a crucial trait in trees influencing carbon storage in forests, yet dynamic global vegetation models (DGVMs) neglect its variability under different environmental conditions. For this thesis, I integrated environmentally-dependent wood density into the DGVM LPJ-GUESS by simulating mean annual tree ring density based on temperature variables. The model considers earlywood and latewood as crucial structural parts in temperate tree rings, deriving mean tree ring density from the width and density of these components. Validation at sites along a hill slope in Lötschental, Switzerland on European larch (Larix decidua), showed that while the model captured mean wood density trends across elevations, it did not reproduce annual... (More)
- Wood density is a crucial trait in trees influencing carbon storage in forests, yet dynamic global vegetation models (DGVMs) neglect its variability under different environmental conditions. For this thesis, I integrated environmentally-dependent wood density into the DGVM LPJ-GUESS by simulating mean annual tree ring density based on temperature variables. The model considers earlywood and latewood as crucial structural parts in temperate tree rings, deriving mean tree ring density from the width and density of these components. Validation at sites along a hill slope in Lötschental, Switzerland on European larch (Larix decidua), showed that while the model captured mean wood density trends across elevations, it did not reproduce annual mean tree ring density.
I compared the versions of LPJ-GUESS, with and without dynamic wood density. Results indicate that dynamic wood density impacts trees’ annual net primary productivity (ANPP) and carbon storage in leaves and sapwood. However, these changes could not be consistently associated with increases in wood density relative to the wood density parameter of LPJ-GUESS without dynamic wood density, suggesting complex effects on tree allocation dynamics. Average tree height per age group was consistently lower in LPJ-GUESS-WD across elevations, regardless of ANPP variations, with a negative correlation observed between mean height difference and stem density. These findings underscore the importance of incorporating dynamic wood density in DGVMs due to its impact on predictions of forest carbon dynamics and tree growth patterns. (Less) - Popular Abstract
- Understanding how trees grow, and store carbon is essential for predicting the growth of forests and their role in tackling climate change. Wood density, which varies with environmental conditions like temperature, is a key factor in this process. However, many current models used to predict forest behavior do not account for these changes in wood density.
This study proposes a way to include wood density in such a model, LPJ-GUESS. The focus was on European larch, and the model was tested in the Lötschental valley in Switzerland. The improved model considered how tree rings form each year, factoring in the density of latewood and the width of earlywood and latewood which form under different conditions.
Results showed that the wood... (More) - Understanding how trees grow, and store carbon is essential for predicting the growth of forests and their role in tackling climate change. Wood density, which varies with environmental conditions like temperature, is a key factor in this process. However, many current models used to predict forest behavior do not account for these changes in wood density.
This study proposes a way to include wood density in such a model, LPJ-GUESS. The focus was on European larch, and the model was tested in the Lötschental valley in Switzerland. The improved model considered how tree rings form each year, factoring in the density of latewood and the width of earlywood and latewood which form under different conditions.
Results showed that the wood density model accurately predicted general trends in wood density across different elevations but struggled with the fluctuating density from year to year. However, including dynamic wood density in LPJ-GUESS had noticeable effects on how trees grow and store carbon. It impacted the annual net primary productivity (ANPP), which measures how much carbon trees capture and use for growth, as well as the amount of carbon stored in leaves and sapwood. Trees also tended to be shorter on average in the modified model, and this decrease in height was linked to the density of stems in the forest. However, besides in height, changes in wood density did not always lead to straightforward results. For example, an increased wood density at two different elevations was once associated with increased ANPP and once with decreased ANPP.
These findings highlight the need to include dynamic wood density in forest prediction models to improve the accuracy of forecasts related to carbon storage and tree growth patterns. This can help better understand and manage forests in the face of climate change. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9172582
- author
- Voss, Anna-Kristina LU
- supervisor
- organization
- course
- NGEM01 20241
- year
- 2024
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography, Ecosystem Analysis, Wood formation, Wood density, DGVM, LPJ-GUESS
- publication/series
- Student thesis series INES
- report number
- 677
- language
- English
- id
- 9172582
- date added to LUP
- 2024-08-26 16:19:22
- date last changed
- 2024-08-26 16:19:22
@misc{9172582,
abstract = {{Wood density is a crucial trait in trees influencing carbon storage in forests, yet dynamic global vegetation models (DGVMs) neglect its variability under different environmental conditions. For this thesis, I integrated environmentally-dependent wood density into the DGVM LPJ-GUESS by simulating mean annual tree ring density based on temperature variables. The model considers earlywood and latewood as crucial structural parts in temperate tree rings, deriving mean tree ring density from the width and density of these components. Validation at sites along a hill slope in Lötschental, Switzerland on European larch (Larix decidua), showed that while the model captured mean wood density trends across elevations, it did not reproduce annual mean tree ring density.
I compared the versions of LPJ-GUESS, with and without dynamic wood density. Results indicate that dynamic wood density impacts trees’ annual net primary productivity (ANPP) and carbon storage in leaves and sapwood. However, these changes could not be consistently associated with increases in wood density relative to the wood density parameter of LPJ-GUESS without dynamic wood density, suggesting complex effects on tree allocation dynamics. Average tree height per age group was consistently lower in LPJ-GUESS-WD across elevations, regardless of ANPP variations, with a negative correlation observed between mean height difference and stem density. These findings underscore the importance of incorporating dynamic wood density in DGVMs due to its impact on predictions of forest carbon dynamics and tree growth patterns.}},
author = {{Voss, Anna-Kristina}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Modelling dynamic wood density in LPJ-GUESS}},
year = {{2024}},
}