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Assessing the Potential of Embedding Vegetation Dynamics into a Fire Behaviour Model : LPJ-GUESS-FARSITE

Lopez Blanco, Efren LU (2014) In Lund University GEM thesis series NGEM01 20141
Dept of Physical Geography and Ecosystem Science
Abstract
Disturbances such as wildfires are key players involved in the shape, structure and function of the ecosystems. Fire is rarely included in Dynamic global vegetation models due to their difficulty in implementing its processes and impacts associated. Therefore, it is essential to understand the variables and processes involved in fire, and to evaluate the strengths and weaknesses before going forward in global fire modelling.

LPJ-GUESS-SPITFIRE allows the calculation of vegetation in a daily-time-step manner. However, the fire module has revealed some flaws in performance. For this reason, an alternative fire area simulator (FARSITE), a robust and semi-empirical model widely used worldwide, has been taken into account.

The aim of... (More)
Disturbances such as wildfires are key players involved in the shape, structure and function of the ecosystems. Fire is rarely included in Dynamic global vegetation models due to their difficulty in implementing its processes and impacts associated. Therefore, it is essential to understand the variables and processes involved in fire, and to evaluate the strengths and weaknesses before going forward in global fire modelling.

LPJ-GUESS-SPITFIRE allows the calculation of vegetation in a daily-time-step manner. However, the fire module has revealed some flaws in performance. For this reason, an alternative fire area simulator (FARSITE), a robust and semi-empirical model widely used worldwide, has been taken into account.

The aim of this study is to assess a potential embedment of vegetation dynamic (LPJ-GUESS-SPITFIRE) into spatial-explicit fire behaviour modelling (FARSITE): LPJ-GUESS-FARSITE. The study includes: (1) a comparison between simulated vegetation and observed vegetation in Mediterranean regions and, to what extent to fire recurrence affects vegetation; (2) the evaluation and comparison of fuel- and tree-related variables from the observed data, and (3) the comparison of fire behaviour performed by each model.

Simulations have shown that Quercus coccifera and C3 grasses are dominant at 25 years fire return interval. Besides, the fire return interval influences largely the successional stage of the vegetation. Biomass tends to increase whereas leaf area index and net primary production decrease from short to long fire recurrence periods. Dead fuel loading, fuel depth, fuel moisture 1hr and live grass, simulated in LPJ-GUESS-SPITFIRE, tend to underestimate field measurements. On contrary fuel moisture 10hr and 100hr are overestimated. Fire behaviour results from both models have underestimated field experimental results. FARSITE results, followed by LPJ-GUESS-FARSITE, have been closer related to field data than LPJ-GUESS-SPITFIRE. The results also showed evidence of more intense fires in LPJ-GUESS-FARSITE than in LPJ-GUESS-SPITFIRE, with identical input data.

This thesis concludes that both FARSITE and LPJ-GUESS-FARSITE fire behaviour’s outputs are expected to be more realistic than LPJ-GUESS-SPITFIRE. Even though results do still underestimate real observations, there is enough evidence to say that the LPJ-GUESS framework could be improved. The substitution of the SPITFIRE module by FARSITE model, together with an increase of litter and fuel loading and a decrease of fuel moisture, reflects the promising advantages in creating the meta-model LPJ-GUESS-FARSITE. (Less)
Popular Abstract
Can a simple algorithm save our planet?

There is an increasing awareness nowadays about global warming. Ongoing researches alert about an excessive and fast rise of greenhouse gases emissions. Perhaps the estimations and future projections from scientist were not that misguided and we are already matching the worst scenarios possible.

In this general perspective, many investigation projects are trying to find answers to a growing number of questions related with environmental issues. How does our planet work? How do complex mechanisms and processes perform at multiple inter-related spatio-temporal scales? How and how much does greenhouse gasses emissions affect the natural balance?

One way of contributing into a better... (More)
Can a simple algorithm save our planet?

There is an increasing awareness nowadays about global warming. Ongoing researches alert about an excessive and fast rise of greenhouse gases emissions. Perhaps the estimations and future projections from scientist were not that misguided and we are already matching the worst scenarios possible.

In this general perspective, many investigation projects are trying to find answers to a growing number of questions related with environmental issues. How does our planet work? How do complex mechanisms and processes perform at multiple inter-related spatio-temporal scales? How and how much does greenhouse gasses emissions affect the natural balance?

One way of contributing into a better understanding of these processes are the forest fires’ modelling. Fires are important natural sources in terms of carbon dioxide emissions and fire has a worldwide effect in the climate. It is not fully understood how fires behave in many conditions and different locations. There are many parameters behind forest fires. Fuel, dryness and oxygen are the key cornerstones although there are more factors.

How fast can a fire spread? Thus how much intensity can be reach by a fire? Hence how much burning emissions can be released? How do a specific type of vegetation, wind speed or terrain affect fire spread?

People started to think about this issues in the early 70s. The answer to some of these questions can be explained by a combination of basic energy conservation physics, mathematical models and computer machines ready to calculate plenty of processes at the same time!

These models were developed long time ago but still play an important role these days. These algorithms are the basis of current top research areas, which at the same time are fundamental tools predicting and modelling the rate at which the head of a specific fire will spread. Rate of spread equations can be compute in order to calculate how much emissions can be released hence the significance of fire emissions in global scale could be analysed in depth. (Less)
Please use this url to cite or link to this publication:
author
Lopez Blanco, Efren LU
supervisor
organization
alternative title
Can a simple algorithm save our planet?
course
NGEM01 20141
year
type
H2 - Master's Degree (Two Years)
subject
keywords
mediterranean ecosystem, LPJ-GUESS-FARSITE, FARSITE, fire modelling, fire behaviour prediction, dynamic fuel model, geography, fire recurrence, fuel loading, LPJ-GUESS-SPITFIRE, fuel moisture, physical geography, GEM
publication/series
Lund University GEM thesis series
report number
4
language
English
id
4587416
date added to LUP
2014-08-27 10:11:26
date last changed
2014-08-27 10:11:26
@misc{4587416,
  abstract     = {{Disturbances such as wildfires are key players involved in the shape, structure and function of the ecosystems. Fire is rarely included in Dynamic global vegetation models due to their difficulty in implementing its processes and impacts associated. Therefore, it is essential to understand the variables and processes involved in fire, and to evaluate the strengths and weaknesses before going forward in global fire modelling.

LPJ-GUESS-SPITFIRE allows the calculation of vegetation in a daily-time-step manner. However, the fire module has revealed some flaws in performance. For this reason, an alternative fire area simulator (FARSITE), a robust and semi-empirical model widely used worldwide, has been taken into account. 

The aim of this study is to assess a potential embedment of vegetation dynamic (LPJ-GUESS-SPITFIRE) into spatial-explicit fire behaviour modelling (FARSITE): LPJ-GUESS-FARSITE. The study includes: (1) a comparison between simulated vegetation and observed vegetation in Mediterranean regions and, to what extent to fire recurrence affects vegetation; (2) the evaluation and comparison of fuel- and tree-related variables from the observed data, and (3) the comparison of fire behaviour performed by each model. 

Simulations have shown that Quercus coccifera and C3 grasses are dominant at 25 years fire return interval. Besides, the fire return interval influences largely the successional stage of the vegetation. Biomass tends to increase whereas leaf area index and net primary production decrease from short to long fire recurrence periods. Dead fuel loading, fuel depth, fuel moisture 1hr and live grass, simulated in LPJ-GUESS-SPITFIRE, tend to underestimate field measurements. On contrary fuel moisture 10hr and 100hr are overestimated. Fire behaviour results from both models have underestimated field experimental results. FARSITE results, followed by LPJ-GUESS-FARSITE, have been closer related to field data than LPJ-GUESS-SPITFIRE. The results also showed evidence of more intense fires in LPJ-GUESS-FARSITE than in LPJ-GUESS-SPITFIRE, with identical input data.

This thesis concludes that both FARSITE and LPJ-GUESS-FARSITE fire behaviour’s outputs are expected to be more realistic than LPJ-GUESS-SPITFIRE. Even though results do still underestimate real observations, there is enough evidence to say that the LPJ-GUESS framework could be improved. The substitution of the SPITFIRE module by FARSITE model, together with an increase of litter and fuel loading and a decrease of fuel moisture, reflects the promising advantages in creating the meta-model LPJ-GUESS-FARSITE.}},
  author       = {{Lopez Blanco, Efren}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Lund University GEM thesis series}},
  title        = {{Assessing the Potential of Embedding Vegetation Dynamics into a Fire Behaviour Model : LPJ-GUESS-FARSITE}},
  year         = {{2014}},
}