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Archive for the ‘Wildfire’ Category
Wednesday, April 18th, 2007
I’m not attending the European Geophysics Union General Assembly this year as I have done the past couple. However, I do have a poster there (today, thanks to Bruce Malamud for posting it) on some work I have been doing with Raul Romero Calcerrada at Universidad Rey Juan Carlos in Madrid, Spain. We have been using various spatial statistical modelling techniques to examine the spatial patterns and causes (including both socioeconomic and biophysical) of wildfire ignition probabilities in central Spain. The poster abstract is presented below and we’re working on writing a couple of papers related to this right now.
Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling
R. Romero-Calcerrada, J.D.A. Millington
In countries where more than 95% of wildfires are caused by direct or indirect human activity, such as those in the Iberian Peninsula, ignition risk estimation must consider anthropic influences. However, the importance of human factors has been given scant regard when compared to biophysical factors (topography, vegetation and meteorology) in quantitative analyses of risk. This disregard for the primary cause of wildfires in the Iberian Peninsula is owed to the difficulties in evaluating, modelling and representing spatially the human component of both fire ignition and spread. We use logistic regression and weights-of-evidence based GIS modelling to examine the relative influence of biophysical and socio-economic variables on the spatial distribution of wildfire ignition risk for a six year time series of 508 fires in the south west of the Autonomous Community of Madrid, Spain. We find that socioeconomic variables are more important than biophysical to understand spatial wildfire ignition risk, and that models using socioeconomic data have a greater accuracy than those using biophysical data alone. Our findings suggest the importance of socioeconomic variables for the explanation and prediction of the spatial distribution of wildfire ignition risk in the study area. Socioeconomic variables need to be included in models of wildfire ignition risk in the Mediterranean and will likely be very important in wildfire prevention and planning in this region.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Economic, Environmental, Geographic, Modelling, Publications, Social, Wildfire | 1 Comment »
Tuesday, March 13th, 2007
So, finally, it is done. As I write, three copies of my PhD Thesis are being bound ready for submission tomorrow! I’ve posted a short abstract below. If you want a more complete picture of what I’ve done you can look at the Table of Contents and read the online versions of the Introduction and Discussion and Conclusions. Email me if you want a copy of the whole thesis (all 81,000 words, 277 pages of it).
So just the small matter of defending the thesis at my viva voce in May. But before that I think it’s time for a celebratory beer on the South Bank of the Thames in the evening sunshine…
Modelling Land-Use/Cover Change and Wildfire Regimes in a Mediterranean Landscape
James D.A. Millington
March 2007
Department of Geography
King’s College, London
Abstract
This interdisciplinary thesis examines the potential impacts of human land-use/cover change upon wildfire regimes in a Mediterranean landscape using empirical and simulation models that consider both social and ecological processes and phenomena. Such an examination is pertinent given contemporary agricultural land-use decline in some areas of the northern Mediterranean Basin due to social and economic trends, and the ecological uncertainties in the consequent feedbacks between landscape-level patterns and processes of vegetation- and wildfire-dynamics.
The shortcomings of empirical modelling of these processes are highlighted, leading to the development of an integrated socio-ecological simulation model (SESM). A grid-based landscape fire succession model is integrated with an agent-based model of agricultural land-use decision-making. The agent-based component considers non-economic alongside economic influences on actors’ land-use decision-making. The explicit representation of human influence on wildfire frequency and ignition in the model is a novel approach and highlights biases in the areas of land-covers burned according to ignition cause. Model results suggest if agricultural change (i.e. abandonment) continues as it has recently, the risk of large wildfires will increase and greater total area will be burned.
The epistemological problems of representation encountered when attempting to simulate ‘open’, middle numbered systems – as is the case for many ‘real world’ geographical and ecological systems – are discussed. Consequently, and in light of recent calls for increased engagement between science and the public, a shift in emphasis is suggested for SESMs away from establishing the truth of a model’s structure via the mimetic accuracy of its results and toward ensuring trust in a model’s results via practical adequacy. A ‘stakeholder model evaluation’ exercise is undertaken to examine this contention and to evaluate, with the intent of improving, the SESM developed in this thesis. A narrative approach is then adopted to reflect on what has been learnt.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
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Friday, February 9th, 2007
This is my fifth contribution to JustScience week.
The last couple of days I’ve discussed some techniques and case studies of statistical model of landscape processes. Monday and Tuesday I looked at the power-law frequency-area characteristics of wildfire regimes in the US, Wednesday and Thursday I looked at regression modelling for predicting and explaining land use/cover change (LUCC). The main alternative to these empirical modelling methods are simulation modelling techniques.
When a problem is not analytically tractable (i.e. equations cannot be written down to represent the processes) simulation models may be used to represent a system by making certain approximations and idealisations. When attempting to mimic a real world system (for example a forest ecosystem), simulation modelling has become the method of choice for many researchers. This may have become the case since simulation modelling can be used when data is sparse. Also, simulation modelling overcomes many of the problems associated with the large time and space scales involved in landscapes studies. Frequently, study areas are so large (upwards of 10 square kilometres – see photo below of my PhD study area) that empirical experimentation in the field is virtually impossible because of logistic, political and financial constraints. Experimenting with simulation models allows experiments and scenarios to be run and tested that would not be possible in real environments and landscapes.
Spatially-explicit simulation models of LUCC have been used since the 1970s and have dramatically increased in use recently with the growth in computing power available. These advances mean that simulation modelling is now one of the most powerful tools for environmental scientists investigating the interaction(s) between the environment, ecosystems and human activity. A spatially explicit model is one in which the behaviour of a single model unit of spatial representation (often a pixel or grid cell) cannot be predicted without reference to its relative location in the landscape and to neighbouring units. Current spatially-explicit simulation modelling techniques allow the spatial and temporal examination of the interaction of numerous variables, sensitivity analyses of specific variables, and projection of multiple different potential future landscapes. In turn, this allows managers and researchers to evaluate proposed alternative monitoring and management schemes, identify key drivers of change, and potentially improve understanding of the interaction(s) between variables and processes (both spatially and temporally).
Early spatially-explicit simulation models of LUCC typically considered only ecological factors. Because of the recognition that landscapes are the historical outcome of multiple complex interactions between social and natural processes, more recent spatially-explicit LUCC modelling exercises have begun to integrate both ecological and socio-economic process to examine these interactions.
A prime example of a landscape simulation model is LANDIS. LANDIS is a spatially explicit model of forest landscape dynamics and processes, representing vegetation at the species-cohort level. The model requires life-history attributes for each vegetation species modelled (e.g. age of sexual maturity, shade tolerance and effective seed-dispersal distance), along with various other environmental data (e.g. climatic, topographical and lithographic data) to classify ‘land types’ within the landscape. Previous uses of LANDIS examined the interactions between vegetation-dynamics and disturbance regimes , the effects of climate change on landscape disturbance regimes , and simulated the impacts of forest management practices such as timber harvesting.
Recently, LANDIS-II was released with a new website and a paper published in Ecological Modelling;
LANDIS-II advances forest landscape simulation modeling in many respects. Most significantly, LANDIS-II, 1) preserves the functionality of all previous LANDIS versions, 2) has flexible time steps for every process, 3) uses an advanced architecture that significantly increases collaborative potential, and 4) optionally allows for the incorporation of ecosystem processes and states (eg live biomass accumulation) at broad spatial scales.
During my PhD I’ve been developing a spatially-explicit, socio-ecological landscape simulation model. Taking a combined agent-based/cellular automata approach, it directly considers:
- human land management decision-making in a low-intensity Mediterranean agricultural landscape [agent-based model]
- landscape vegetation dynamics, including seed dispersal and disturbance (human or wildfire) [cellular automata model]
- the interaction between 1 and 2
Read more about it here. I’m nearly finished now, so I’ll be posting results from the model in the near future. Finally, some other useful spatial simulation modelling links:
Wisconsin Ecosystem Lab – at the University of Wisconsin
Center for Systems Integration and Sustainability – at Michigan State University
Landscape Ecology and Modelling Laboratory – at Arizona State University
Great Basin Landscape Ecology Lab – at the University of Nevada, Reno
Baltimore Ecosystem Study – at the Institute of Ecosystems Studies
The Macaulay Institute – Scottish land research centre
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Computing, Ecological, Environmental, Modelling, MyPhD, Wildfire | Comments Off
Tuesday, February 6th, 2007
This post is my second contribution to JustScience week, and follows on from the first post yesterday.
During my Master’s Thesis I worked with Dr. Bruce Malamud to examine wildfire frequency-area statistics and their ecological and anthropogenic drivers. Work resulting from this thesis led to the publication of Malamud et al. 2005
We examined wildfires statistics for the conterminous United States (U.S.) in a spatially and temporally explicit manner. Using a high-resolution data set of 88,916 U.S. Department of Agriculture Forest Service wildfires over the time period 1970-2000 to consider wildfire occurrence as a function of biophysical landscape characteristics. We used Bailey’s ecoregions as shown by Figure 1A below.
Figure 1.
In Bailey’s classification, the conterminous U.S. is divided into ecoregion divisions according to common characteristics of climate, vegetation, and soils. Mountainous areas within specific divisions are also classified. In the paper, we used ecoregion divisions to geographically subdivide the wildfire database for statistical analyses as a function of ecoregion division. Figure 1B above shows the location of USFS lands in the conterminous U.S.
We found that wildfires exhibit robust frequency-area power-law behaviour in the 18 different ecoregions and used power-law exponents (normalized by ecoregion area and the temporal extent of the wildfire database) to compare the scaling of wildfire-burned areas between ecoregions. Normalizing the relationships allowed us to map the frequency-area relationships, as shown in Figure 2A below.
Figure 2.
This mapping exercise shows a systematic change east-to-west gradient in power-law exponent beta values. This gradient suggests that the ratio of the number of large to small wildfires decreases from east to west across the conterminous U.S. Controls on the wildfire regime (for example, climate and fuels) vary temporally, spatially, and at different scales, so it is difficult to attribute specific causes to this east-to-west gradient. We suggested that the reduced contribution of large wildfires to total burned area in eastern ecoregion divisions might be due to greater human population densities that have increased forest fragmentation compared with western ecoregions. Alternatively, the gradient may have natural drivers, with climate and vegetation producing conditions more conducive to large wildfires in some ecoregions compared with others.
Finally, this method allowed us to calculate recurrence intervals for wildfires of a given burned area or larger for each ecoregion (Figure 2B above). In turn this allowed for the classification of wildfire regimes for probabilistic hazard estimation in the same vein as is now used for earthquakes.
Read the full paper here.
Technorati Tags: wildfire, statistics, mapping, risk, hazard
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
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Monday, February 5th, 2007
This post is the first of my contribution to JustScience week.
Wildfire is considered an integral component of ecosystem functioning, but often comes into conflict with human interests. Thus, understanding and managing relationship between wildfire, ecology and human activity is of particular interest to both ecologists and wildfire managers. Quantifying the wildfire regime is useful in this regard. The wildfire regime is the name given to the combination of the timing, frequency and magnitude of all fires in a region. The relationship between the frequency and magnitude of fires, the frequency-area distribution, is one particular aspect of the wildfire regime that has become of interest recently.
Malamud et al. 1998 examined ‘Forest Fire Cellular Automata‘ finding a power-law relationship between the frequency and size of events. The power-law relationship takes the form:
where  is the frequency of fires with size  , and  is a constant. is a measure of the ratio of small to medium to large size fires and how frequently they occur. The smaller the value of , the greater the contribution of large fires (compared to smaller fires) to the total burned area of a region. The greater the value, the smaller the contribution. Such a power-law relation is represented on a log-log plot as straight line, as the example from Malamud et al. 2005 shows:
Shown circles are number of wildfires per “unit bin” of 1 km^2 (in this case normalized by database length in years and area in km^2) plotted as a function of wildfire area. Also shown is a solid line (best least-squares fit) with coefficient of determination r^2. Dashed lines represent lower/upper 95% confidence intervals, calculated from the standard error. Horizontal error bars on burned area are due to measurement and size binning of individual wildfires. Vertical error bars represent two standard deviations of the normalized frequency densities and are approximately the same as the lower and upper 95% confidence interval.
As a result of their work on the forest fire cellular automata Malamud et al. 1998 wondered whether the same relation would hold for empirical wildfire data. They found the power-law relationship did indeed hold for observed wildfire data for parts of the US and Australia. As Millington et al. 2006 discuss, since this seminal publication several other studies have suggested a power-law relationship is the best descriptor of the frequency-size distribution of wildfires around the world.
During my Master’s Thesis I worked with Dr. Bruce Malamud to examine wildfire frequency-area statistics and their ecological and anthropogenic drivers. Work resulting from this thesis led to the publication of Malamud et al. 2005 which I’ll discuss in more detail tomorrow.
Technorati Tags: wildfire, statistics, JustScience,
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Sunday, January 21st, 2007
The preliminary program and schedule of sessions for the 2007 AAG (Association of American Geographers) National Meeting in San Francisco, April 17-21, is now available online.
It looks like I should have some time during April, and several colleagues from King’s Geography Dept. are going to San Francisco, so it might be good to go. Unfortunately, I wasn’t banking on having the opportunity so I haven’t submitted anything to present.
The alternative would be to go to the EGU (European Geophysics Union) General Assembly 2007 in Vienna, Austria, 15 – 20 April. I’m second author on a poster due to be displayed there:
Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling
Romero-Calcerrada, R. and Millington, J.D.A
Session NH8.04/BG1.04: Spatial and temporal patterns of wildfires: models, theory, and reality (co-organized by BG & NH)
I’ll have a think about it…
Technorati Tags: AAG, EGU, academic, conference, wildfire, modelling, statistics
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Tuesday, January 9th, 2007
It’a been out for a while (so there are several reviews available ) but I only just got and started reading Jared Diamond’s Collapse (How societies choose to fail or survive). I’ve only read the first part (Modern Montana) so far, but already I’ve come across several parallels between the socio-economic changes, and their potential ecological impacts, occuring in the landscapes under Montana’s Big Sky and Madrid’s Sun-Blessed Skies.
The broad similarity between the change Diamond describes in Montana and that occurring in my PhD study area (SPA 56, an EU protection area for endangered bird species to the west of Madrid, Spain) is the shift from an economy and landscape driven by agricultural activity to one driven by recreational activities. Such a shift reflects both the differing visions of multiple stakeholders within these landscapes, but also generational changes in attitude between older inhabitants and their children and grandchildren. In Montana’s Bitteroot Valley larger macroeconomic changes nationally and internationally have made previously profitable extractive industries (forestry, mining and agriculture) largely unsustainable economically. This has come about as land is now valued not according to resource and agricultural production but according to real-estate potential for incoming retirees, second-homers and tourists. Incoming (usually older) ‘out-of-staters’ arrive to enjoy the outdoor recreation (fishing, hiking, etc.), beauty and lifestyle opportunities, replacing the younger generation of Montanans going the other way to seek modern urban lifestyle opportunities and lifestyles;
“It’s a wonderful lifestyle to get up before dawn and see the sunrise, to watch fly hawks overhead, and to see deer jump through your hay field to avoid your haying equipment. … Occasionally I get up at 3 AM and work until 10 AM. This isn’t a 9 to 5 job. But none of our children will sign up for being a farmer if it is 3 AM to 10 PM every day.”
Dairy Farmer, Montana
Locals in SPA 56 have expressed similar feelings and ideas when I have visited over the last few years. Younger generations that would have previously continued the family farm that has passed through generation upon generation of farmers, are now seeking out employment in construction and service sectors to secure what is understood as a more ‘modern’ lifestyle. A lifestyle that affords leisure time at specified times of the week and at regular intervals (i.e. the weekends and paid holidays);
“Most farmers are part-time, maintaining the tradition agriculture. The children or grandchildren of those [farmers] do not have interest [in agriculture] because is it not profitable and requires a lot of dedication. The youths go or they seek other work.”
Local Development Official, Madrid (2006)
In Montana, Diamond describes the conflicts that have arisen between existing inhabitants and the new-comers, each with differing world-views, priorities and values. For example, contrast the attitudes of the third generation dairy farmer fighting to ensure the survival of his farm in the global economy vs. the lady who complained to him when she got manure on her white running shoes. Of course, these multiple perspectives within the landscape are inevitable in a changing world and tools and strategies must be found and employed to ensure appropriate decisions and compromises are made. In my simulation model of agricultural decision-making I have attempted to represent the influence of two differing world-views on landscape change (as have other modellers). I have termed the representative agents ‘commercial’ and ‘traditional’; the former behaving as a perfectly rational actor (in economic terms), the latter designed to reflect the importance of traditional cultural values in land-use decision-making;
“Whoever has a vineyard nowadays is like a gardener… they like to keep it, even if they lose money. They maintain vineyards because they have done it all their life and they like it, even having to pay for it. If owners were looking for profitability there would be not a singe vineyard… People here grow wine because of a matter of feeling, love for the land…”
Vinter, Madrid (2005)
As the primary thesis of his book Diamond highlights, for both contemporary and historical societies, the impacts of social, economic and technological change on the physical environment, and the sustainability of those changes. Of the several issues of concern in Montana, those related to forestry and water availability are likely to be of most concern in SPA 56. One particular interest of my PhD thesis is the importance of changes in the landscape for wildfire regimes, which Diamond discusses with reference to previous management strategies of the Unites States Forest Service (USFS). Commercial forestry has not been a widespread activity in SPA 56, the nature and human history of Mediterranean ecosystems restricting contemporary timber productivity. However, the problems of increased fuel loads due to the fire suppression policies of the USFS during the 20th century may be beginning to present themselves in SPA 56. If the agricultural sector continues to decline due to the social and economic trends just outlined, farmland will (continue to) be abanoned or converted to recreational uses (for example, hunting reservations). In turn this will leading to increased biomass and fuel loads in the landscape. As yet the consequences of such change on the frequency and magnitude of fires in the region is unclear due to spatial relationships and feedbacks between vegetation growth and burning. In the very near future the results of my simulation model will be able shed some light on this aspect of the region’s changing landscape and ecology.
Buy on Amazon
Diamond Reviews
GristMill
Ecological Economics
Futures
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Posted in Book_Review, Ecological, Economic, Environmental, Landscapes, MyPhD, Social, Wildfire | Comments Off
Friday, December 15th, 2006
Bruce Edmonds has reviewed Phillip Ball’s 2005 book Critical Mass: How One Thing Leads to Another for the Journal of Artificial Societies and Social Simulation (JASSS). Providing a popular science account of the history the development of sociophysics and abstract social simulation the book (apparently – I haven’t read it) makes the common mistake of conflating models and their results for the systems they have been built to represent. In Edmonds’ words:
In all of this the book is quite careful as to matters of fact – in detail all its statements are cautiously worded and filled with subtle caveats. However its broad message is very different, implying that abstract physics-style models have been successful at identifying some general laws and tendencies in social phenomena. It does this in two ways: firstly, by slipping between statements about the behaviour of the models and statements about the target social phenomena, so that it is able to make definite pronouncements and establish the success and relevance of its approach; and secondly, by implying that it is as well-validated as any established physics model but, in fact, only establishing that the models can be used as sophisticated analogies – ways of thinking about social phenomena. The book particularly makes play of analogies with the phase transitions observed in fluids since this was the author’s area of expertise.
This book is by no means unique in making these kinds of conflation – they are rife within the world of social simulation.
(from Edmonds 2006, JASSS)
And not only within social simulation. In a previous paper, I highlighted with some colleagues that the name given to the ‘Forest Fire Cellular Automata’ made famous by Per Bak and colleagues, is better treated as a metaphor than an accurate representation of the dynamics of a real world forest fire (Millington et al 2006). This may be a seemingly an obvious point to make, but simulation models can provide an unjustified sense of verisimilitude and the appearance of the reproduction of complex empirical systems’ behaviour by simple models can lead to the false conclusion that those simple mechanisms are the cause of the observed complexity.
In a forthcoming paper with Dr. George Perry in a special issue of Perspectives in Plant Ecology, Evolution and Systematics, we discuss the lure of these ‘metaphor models’ and other issues regarding the approaches to spatial modelling of succession-disturbance dynamics in terrestrial ecological systems. I’ll keep you posted on the paper’s progress…
Categories: forestfire, wildfire, model, social, simulation, modelling, metaphors, article, cellular, automata
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Monday, November 13th, 2006
Some guys at the University of Granada, Spain, have developed software for managing wildfire-fighting efforts. SIADEX is designed to speed decision-making for resource allocation, as an article in New Scientist describes:
“Computerised maps are already used by people in charge of managing the fire-fighting effort. These maps are used to plan which areas to focus on and which resources to deploy, such as fire engines, planes and helicopters.
But working out the details of such a plan involves coordinating thousands of people, hundreds of vehicles and many other resources. SIADEX is able to help by rapidly weighing up different variables.
Shift patterns
For example, it calculates which fire engines could reach an area first, where aircraft could be used, and even how to organise the shift patterns of individual fire fighters. It then very quickly produces several different detailed plans. … One plan might be the cheapest, another the fastest, and a third the least complicated.”
I wonder how Normal Maclean would have felt about this approach to fire-fighting. I imagine like me he’d be interested in how this new tool can be used to aid and protect wildland fire-fighters, but the given the unpredictability of fire behaviour (in the light of current understanding) would still maintain that human experience, gained over many years dealing with unique situations, will be invaluable in managing fire-fighters and their resources. As with much computer software, this should remain as a tool to aid human decision-making, not replace it.
Categories: wildfire, fire, fire-fighting, software, decision-making
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Saturday, October 28th, 2006
Posted in Ecological, Environmental, Wildfire | Comments Off
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