Advanced

Modeling the evolution of wildfire : an analysis of short term wildfire events and their relationship to meteorological variables

Demet, Sean LU (2014) In Student thesis series INES NGEM01 20121
Dept of Physical Geography and Ecosystem Science
Abstract
Wildfire events are expected to increase with the changing climate; thereby increasing atmospheric, economic, and anthropogenic impacts. Gaining a real-time understanding of the evolution of wildfire events can benefit regional meteorology models, global atmospheric models, and hazard warning systems. As a result, an attempt at modeling the evolution of wildfire was undertaken utilizing 8 years of daily meteorological variables and fire radiative power (FRP) provided by the European Centre for Medium-Range Weather Forecasting from 2003 to 2010.

Fire Radiative Power (FRP) measures the rate of radiative energy emitted by active wildfire events across the globe. FRP is observed from MODIS sensors aboard the sun-synchronous Terra and Aqua... (More)
Wildfire events are expected to increase with the changing climate; thereby increasing atmospheric, economic, and anthropogenic impacts. Gaining a real-time understanding of the evolution of wildfire events can benefit regional meteorology models, global atmospheric models, and hazard warning systems. As a result, an attempt at modeling the evolution of wildfire was undertaken utilizing 8 years of daily meteorological variables and fire radiative power (FRP) provided by the European Centre for Medium-Range Weather Forecasting from 2003 to 2010.

Fire Radiative Power (FRP) measures the rate of radiative energy emitted by active wildfire events across the globe. FRP is observed from MODIS sensors aboard the sun-synchronous Terra and Aqua satellites and provides a unique way to incorporate active wildfire information into climate models and weather forecasting. Observations have shown that the amount of FRP is related to the rate at which fuel is being consumed, linking it directly to the fuel load of ecosystems. The impact of meteorological variables on the behavior of FRP, on a daily basis, is expected to show an observable relationship. Modeling this relationship is the primary objective of the thesis.

Meteorological variables and a time-delayed FRP value were established as independent variables for linear regression modeling. The relative change in FRP (ΔFRP) functioned as the dependent variable. Three distinct ecosystems (Equatorial, Warm Temperate, and Boreal) were included to account for vegetation structure and fuel load. Ecosystem selection was performed using the climate based Köppen-Geiger Climate Classification which created approximately homogeneous ecosystems based off of observed temperature and precipitation values.

Covariate analysis showed no significant correlation between the independent variables and ΔFRP. Mann-Whitney U Tests identified ecosystems where statistically significant trends were observed and suggested opportunities for successful linear regression modeling. Both linear and non-linear relationships were accounted for in the application of a Bayesian Information Criteria to the linear regression modeling. The linear regression results did not produce a successful model and the impact of meteorological variables on FRP was not observable.

This body of work can be improved by incorporating the magnitude of FRP in the calculation of ΔFRP. Additionally, identifying threshold behavior of meteorological variables can improve the identification of significant relationships. Finally, focusing on smaller spatial scales and including actual fuel load values along with anthropogenic mitigation practices is poised to improve the linear regression modeling of the evolution of wildfire. (Less)
Abstract
Popular Summary

Wildfire events are expected to increase with the changing climate; thereby increasing atmospheric, economic, and human health impacts. A better understanding of the evolution of wildfire events can benefit regional meteorology models and global atmospheric models. Because of this, an attempt at modeling the evolution of wildfire was undertaken using 8 years of daily meteorological variables and fire radiative power (FRP) provided by the European Centre for Medium-Range Weather Forecasting for the years 2003 to 2010.

Fire Radiative Power (FRP) measures the rate of radiative energy emitted by active wildfire events across the globe. FRP is an observation from the MODIS sensors aboard the sun-synchronous Terra and Aqua... (More)
Popular Summary

Wildfire events are expected to increase with the changing climate; thereby increasing atmospheric, economic, and human health impacts. A better understanding of the evolution of wildfire events can benefit regional meteorology models and global atmospheric models. Because of this, an attempt at modeling the evolution of wildfire was undertaken using 8 years of daily meteorological variables and fire radiative power (FRP) provided by the European Centre for Medium-Range Weather Forecasting for the years 2003 to 2010.

Fire Radiative Power (FRP) measures the rate of radiative energy emitted by active wildfire events across the globe. FRP is an observation from the MODIS sensors aboard the sun-synchronous Terra and Aqua satellites. They provide a new way to incorporate active wildfire information into climate models and weather forecasting. Observations have shown that the amount of FRP is related to the rate at which fuel is being consumed; this links FRP directly to the fuel load of ecosystems, which is a critical component that dictates fire behavior. The relationship between meteorological variables and FRP, on a daily basis, should be observable. Modeling the behavior of this relationship is the goal of the thesis.

Meteorological variables and the preceding day’s FRP value were used to predict the relative change in FRP (ΔFRP) using linear regression modeling. Three distinct ecosystems (Equatorial, Warm Temperate, and Boreal) were focused on to account for vegetation structure and fuel load, which dictate fire behavior. Ecosystems were selected with the Köppen-Geiger Climate Classification, a classification system based off of temperature and precipitation observations.

During the analysis, meteorological variables and ΔFRP showed no significant relationship. A more detailed test, the Mann-Whitney U Test, identified where significant relationships were observed. Linear regression modeling, ideally, provides a predictive model that optimally explains observed behaviors. To do this, a Bayesian Information Criteria selected which meteorological variable was the best, while limiting the amount of variables used. The linear regression did not produce a successful predictive model and the impact of meteorological variables on FRP was not observable.

This predictive model can be improved by changing the ΔFRP calculation to incorporating the size of fires. Looking on a smaller map (rather than globally) and using fuel load observations can improve the model. Furthermore, including a variable that represents wild fire suppression techniques from municipalities can assist in the linear regression modeling of the evolution of wildfire. (Less)
Please use this url to cite or link to this publication:
author
Demet, Sean LU
supervisor
organization
course
NGEM01 20121
year
type
H2 - Master's Degree (Two Years)
subject
keywords
evolution of wildfire, fire, wildfire, fire radiative power, Ecosystem Analysis, Physical Geography and Ecosystem Sciences, Physical Geography, linear regression modeling, Bayesian Information Criteria, Köppen-Geiger Climate Classification
publication/series
Student thesis series INES
report number
301
language
English
id
4286230
date added to LUP
2014-02-24 12:28:49
date last changed
2014-02-24 12:28:49
@misc{4286230,
  abstract     = {Popular Summary

Wildfire events are expected to increase with the changing climate; thereby increasing atmospheric, economic, and human health impacts. A better understanding of the evolution of wildfire events can benefit regional meteorology models and global atmospheric models. Because of this, an attempt at modeling the evolution of wildfire was undertaken using 8 years of daily meteorological variables and fire radiative power (FRP) provided by the European Centre for Medium-Range Weather Forecasting for the years 2003 to 2010.

Fire Radiative Power (FRP) measures the rate of radiative energy emitted by active wildfire events across the globe. FRP is an observation from the MODIS sensors aboard the sun-synchronous Terra and Aqua satellites. They provide a new way to incorporate active wildfire information into climate models and weather forecasting. Observations have shown that the amount of FRP is related to the rate at which fuel is being consumed; this links FRP directly to the fuel load of ecosystems, which is a critical component that dictates fire behavior. The relationship between meteorological variables and FRP, on a daily basis, should be observable. Modeling the behavior of this relationship is the goal of the thesis. 

Meteorological variables and the preceding day’s FRP value were used to predict the relative change in FRP (ΔFRP) using linear regression modeling. Three distinct ecosystems (Equatorial, Warm Temperate, and Boreal) were focused on to account for vegetation structure and fuel load, which dictate fire behavior. Ecosystems were selected with the Köppen-Geiger Climate Classification, a classification system based off of temperature and precipitation observations.

During the analysis, meteorological variables and ΔFRP showed no significant relationship. A more detailed test, the Mann-Whitney U Test, identified where significant relationships were observed. Linear regression modeling, ideally, provides a predictive model that optimally explains observed behaviors. To do this, a Bayesian Information Criteria selected which meteorological variable was the best, while limiting the amount of variables used. The linear regression did not produce a successful predictive model and the impact of meteorological variables on FRP was not observable.

This predictive model can be improved by changing the ΔFRP calculation to incorporating the size of fires. Looking on a smaller map (rather than globally) and using fuel load observations can improve the model. Furthermore, including a variable that represents wild fire suppression techniques from municipalities can assist in the linear regression modeling of the evolution of wildfire.},
  author       = {Demet, Sean},
  keyword      = {evolution of wildfire,fire,wildfire,fire radiative power,Ecosystem Analysis,Physical Geography and Ecosystem Sciences,Physical Geography,linear regression modeling,Bayesian Information Criteria,Köppen-Geiger Climate Classification},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Modeling the evolution of wildfire : an analysis of short term wildfire events and their relationship to meteorological variables},
  year         = {2014},
}