An integrative model of human-influenced fire regimes and landscape dynamics
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eLasse Loepfe , Jordi Martinez-Vilalta, Josep Piolty o
F, Ediola de
raphy, and are strongly inuenced by the presence of humans(Johnson, 1992). The relative importance of fuel accumulation andweather conditions varies among ecosystems (Meyn et al., 2007;Falk et al., 2007), as does the impact of human activities. Apartfrom extremely wet rainforests, where re regime is based solely
equations have beenwidely used in models predicting re size andshape, such as FARSITE (Finney, 1998). Comprehensive reviews ofre spread models can be found in Weber (1991), Perry (1998),Pastor et al. (2003) and Sullivan (2007). The FIRESCAPE model(Cary, 1997) extends this approach to the landscape level, allowingfor the investigation of, for instance, the impact of climate changeon re regimes (Cary and Banks, 2000). These models require fuelstructure at the time of burning as a given input. Fuel build-upprocesses are not simulated, as the goal of this type of model is to
* Corresponding author. Tel.: 34 93 581 3355; fax: 34 93 581 4151.E-mail addresses: firstname.lastname@example.org (L. Loepfe), jordi.martinez.vilalta@
Contents lists availab
Environmental Modelling & Software 26 (2011) 1028e1040uab.es (J. Martinez-Vilalta), email@example.com (J. Piol).firstname.lastname@example.orgProgram size: 481 kBAvailability: Freely available on request for non-commercial uses.
Fire regimes are determined by climate, vegetation and topog-
and (often forgotten) land use, a comprehensive re regime modelshould incorporate those processes, together with re behaviour, ina balanced way.
There are many models that calculate ignition probability andthe propagation dynamics of individual res fairly well, but mostlywithout considering spatially distributed ignition probability. Themost popular model for calculating re behaviour in a singledimension was developed by Rothermel (1972). RothermelsVegetation growthFire weather
Name of software: FIRE LADYHardware requirements: PC, Pentium
RAM recommendedSoftware requirements: JAVA-JRE 1.5
independentProgram language: JAVAContact address: Lasse Loepfe, CREA
terra (UAB), 08193 Cerdany1364-8152/$ e see front matter 2011 Elsevier Ltd.doi:10.1016/j.envsoft.2011.02.015informing local re regime management strategies. 2011 Elsevier Ltd. All rights reserved.
equivalent) and 512 MB
ci C, Campus de Bella-l Valls, Spain. Email:
on fuel moisture, and deserts, where the virtual absence of fuelprevents the spread of re, the re regime of a region is determinedby a combination of climate, vegetation and human inuences.
The modelling of re regimes can provide information on theirunderlying mechanisms and predict the consequences of forestmanagement strategies and climate change on burn area and resize distribution. These results can then be employed to assessimpacts on plant composition (Pausas, 1999) or pyrogenic emis-sions (Keane et al., 1997). Because the re regime of a region is theresult of climatic conditions, vegetation growth, re managementFire suppressionLand use changein land cover distribution and tree biomass with promising accuracy. The explicit modelling of humaninuences makes the model a useful and unique tool for assessing the impacts of climate change andLandscape dynamicsModel modelled. The model was calibrated for three regions in NE Spain and reproduces re regimes, changesCentre for Ecological Research and Forestry Applications (CREAF), Autonomous Universi
a r t i c l e i n f o
Article history:Received 18 September 2009Received in revised form21 February 2011Accepted 23 February 2011Available online 21 March 2011
a b s t r a c t
Fire regimes depend on cwater content in fuel and, iincreased ignition frequenfragmentation. Here, we pre regime model that tabehaviour, re suppressionavailability, and stem diammodelled using the Rotherinuences on re regime, sAn integrative model of human-inuenc*
journal homepage: www.All rights reserved.d re regimes and landscape dynamics
f Barcelona, E-08193 Bellaterra, Spain
te, vegetation structure and human inuences. Climate determines thee longer term, the amount of biomass. Humans alter re regimes throughand by hindering the spread of re through re suppression and fuelnt FIRE LADY (FIre REgime and LAndscape DYnamics), a spatially explicitinto account daily weather data, topography, vegetation growth, red land use changes. In this model, vegetation growth depends on waterr and stand density are the fundamental parameters. Fire behaviour isl equations and taking into account both crown re and spotting. Humanas ignition frequency, re suppression and land use changes, are explicitly
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elling & Software
Mediterranean region, an increase of CO2 concentrations is expec-ted to translate into warmer and drier summers with increased riskof re weather (Alcamo et al., 2007; Liu et al., 2010). Many authorsexpect that climate change will translate into increased forest reactivity (Brown et al., 2004; Carvalho et al., 2010; Flannigan et al.,2000; Mouillot et al., 2002; Williams et al., 2001). Other impor-tant factors for re regimes, such as vegetation type and abundance,are also inuenced by climate (Bachelet et al., 2001; Lenihan, 2003).In Mediterranean climate regions, water availability is the mostlimiting factor for plant growth (Boyer, 1982; Hetherington andWoodward, 2003), and climate change could lead to a decreasedbiomass accumulation rate (Ciais et al., 2005), resulting in a lowerre activity than predicted by re weather extrapolations.
Here, we present FIRE LADY (FIre REgime and LAndscapeDYnamics), a re regime model that includes the inuence ofhuman activities on re regimes, a factor that is frequently notconsidered in other models. The goal of FIRE LADY is not to predictthe exact extent of individual res, but to promote an under-standing of the human inuence on annual burn area and re size
elling & Software 26 (2011) 1028e1040 1029predict the exact shape of a single re rather than landscapedynamics over the long term. In contrast, landscape re successionmodels focus on vegetation dynamics. These models generally usea detailed description of plant growth, whereas re is typicallysampled only from the perspective of its historical size distributionand then placed on the terrain randomly or following a probabilitydistribution based on landscape characteristics. Such models nor-mally do not present an explicit re spread module, as the funda-mental interest in re in these models is its average effect onvegetation at the landscape scale rather than the precise location ofre occurrence. For a comprehensive review and classication oflandscape succession models, see Keane et al. (2004) and Schellerand Mladenoff (2007).
A combination of explicit re-growth modelling with a plantgrowth module is used in the extended version of LANDIS-II(Sturtevant et al., 2009), the SEM-LANDmodel (Li, 2000), LANDSUM(Keane et al., 2006) and the BFOLDS model (Perera, 2008). Thisallows for the analysis of the interaction between re regime andvegetation structure and the resulting landscape patterns (Pereraet al., 2003). A comparison of these models can be found in Caryet al. (2007).
In most regions of the world, re regime is highly inuenced byhumans, as a consequence of re management and land coverchanges (Thonicke et al., 2001). Humans have a great impact on reregimes because they alter ignition frequency and fuel fragmenta-tion and suppress res (Guyette et al., 2002). However, most of theexisting re regime models do not include anthropogenicallydriven changes in re regimes (Mouillot and Field, 2005). Currently,a substantial proportion of re management budgets goes towardsre suppression, but the effects of these strategies are controversial.While some authors claim that a reduced number of small andmid-sized res result in a accumulation of fuel that may lead to cata-strophic res under extreme weather conditions (Minnich, 1983,2001; Piol et al., 2005, 2007; Shang et al., 2007), others holdthat, in some ecosystems at least, large res are not dependent onthe age classes of fuels (Moritz, 2003; Moritz et al., 2004) and thatre suppression plays a critical role in offsetting the potentialimpacts of increased ignitions (Keeley et al., 1999). A high refrequency can also induce changes in species composition (Mouillotet al., 2002; Pausas, 1999; Pausas et al., 2006; Syphard et al., 2006,2007c) and hinder vegetation recovery (Daz-Delgado et al., 2002;Pausas et al., 2008).
An often overlooked re management opportunity is land usemanagement, i.e., inuencing the spatial distribution of crop eldsthrough subsidies to farmers. Ruralmigration is oftenpointed out asa cause of increased re occurrence in the Mediterranean region(Bajocco and Ricotta, 2008; Debussche et al., 1999; Terradas et al.,1998; Vega-Garca and Chuvieco, 2006). The spatial distribution ofstand ages and species composition has an important impact on reregimes (Turner and Romme, 1994; Miller and Urban, 2000). Theintermixing of woodland and agricultural land is also very likely toinuence re regime, as agricultural elds can sometimes act asrebreaks because of their lower ammability (Lloret et al., 2002;Loepfe et al., 2010), and land use affects the spatial distribution ofhuman-caused ignitions (Syphard et al., 2007b). Since the beginningof the 20th century, economic development has provoked amassiveabandonment of agricultural land in the Mediterranean region(Debussche et al., 1999), leading to an increased homogeneity ofsemi-natural land uses, such as shrublands and forests (Bielsa et al.,2005). Nevertheless,we foundonly twomodels in the literature thatexplicitlydealwithhuman-inuenced fuel fragmentation, includinglarge non-ammable areas such as residential areas or croplands(Davis and Burrows, 1994; Syphard et al., 2007a).
Weather conditions, such as temperature, wind speed and fuel
L. Loepfe et al. / Environmental Modmoisture content, affect the probability of re propagation. In thedistribution. It can therefore be a useful tool for re managementpolicy developers, as it can be used to assess the effects of differentmanagement strategies e such as re suppression, land usemanagement and ignition control e on a mid-term time horizon(ca. 50 years). Below, we give a detailed description of the model,calibrate it for three study areas in NE Spain, discuss its strengthsand weaknesses and show some possible applications.
2. Materials and methods
FIRE LADY is a spatially explicit landscape re regime model for forest andshrubland ecosystems. In its present form, it is not a vegetation succession modelbecause species composition is xed, and no demographic parameters are consid-ered. It is grid based and has a exible cell size. It uses yearly time-steps for vege-tation growth and land use changes and daily time-steps for re weather andpropagation. It uses land use maps, daily meteorological data and topography asinput data. Vegetation growth is simulated by taking into account the actualevapotranspiration on each cell. Biomass is divided into a tree and an understorylayer. The re module includes ignition and propagation. Ignition probabilitydepends on the location of a cell and its ne fuel moisture content. Propagation isa neighbourhood process: re can spread from a burning cell to its immediateneighbours by surface or crown re with propagation probabilities based on theRothermel (1972) equations. Fire propagation by wind-transported burningbranches (spotting) is also taken into account. Fire brigades and land use changes areexplicitly modelled. An overview of the model structure can be seen in Fig. 1, anda detailed description of the modules is given in the following paragraphs.
Fig. 1. Overview of the model structure. Rectangles represent input data; circles
represent model internally calculated variables; and rhomboids represent manage-ment actions.
ellin2.2. Climate and weather
FIRE LADY uses daily data on rainfall, maximum temperature, wind speed anddirection, relative air humidity and potential evapotranspiration as input data. Thesedata might come either from ameteorological station or from aweather generator. Ifthe study area is too large to reasonably assume equal meteorological conditionsthroughout it, grid maps of yearly average values can be used as inputs to theprogram. FIRE LADY then calculates the weather for each cell, using the daily data toestimate temporal uctuation and the map of yearly averages to describe the spatialvariation across cells. The maximum temperature of a given cell on a given day (Ti,d)is then calculated as
Ti;d TS;d T i;a TS;a
where TS,d is the maximum temperature of the meteorological station for that day,T i;a is the yearly average maximum temperature of cell i according to the providedmap, and TS;a is the yearly average maximum temperature of the meteorologicalstation. The calculations are analogous for the other weather variables. In the case ofrainfall, multiplication by the map-to-station ratio is used, rather than difference, toavoid negative rainfall amounts.
2.3. Fire weather
To translate daily meteorological data into an index of re risk, we used theCanadian Forest Fire Weather Index System (FWI) (Van Wagner and Pickett, 1985),which has proven to be a good index of the moisture content of ne live fuels inMediterranean climate regions (Viegas et al., 2001). The FWI follows the dailymoisture content of fuels of different sizes with different drying rates. Each day,a new index value is calculated from the corresponding previous days index value,maximum air temperature, minimum air humidity, wind speed and precipitation.The FWI consists of a hierarchical system of indexes, from which we used the FineFuel Moisture Code (FFMC) to establish the probability of ignition and the DroughtCode (DC) to determine the probability of propagation. The FFMC is a numeric ratingof the moisture content of litter and other cured ne fuels, whereas DC indicates thegrade of dryness of medium- and large-sized fuel.
The FWI variables indicate daily maximum risk of re ignition and propagation.To simulate the drop in these indices at night as a result of lower temperature andhigher air humidity, we introduced an attenuation factor (fN) that is calculated dailyfrom a normal distribution with mean mfn and standard deviation sfn. The minimumvalue of the FWI variables is then calculated by multiplying the maximum value byfN. The maximumwas set to be reached at 4 pm and the minimum at 4 am. At othertimes of the day, the values of FFMC and DC vary linearly between those extremes.
2.4. Vegetation growth
The vegetation in our model consists of two layers: trees and understory. Thisreects the structure of most Mediterranean forests and is necessary for calculatingthe probability of crown re occurrence. Each cell has one dominant tree speciesassigned,which determines the allometric relation...