Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.
Wildland fires are important disturbances in the Earth system
which affect ecosystems, global vegetation distribution, infrastructures, and
human assets, and contribute to atmospheric composition through the release
of aerosols, reactive trace gases, and greenhouse gases (Bowman et al.,
2011). The ignition and spread of fires in ecosystems depend on the
availability and properties of fuel (i.e. biomass and litter loads,
composition, and moisture content), weather conditions, and human activities
(Krawchuk and Moritz, 2011; Moritz et al., 2012). Human activities have a
predominant role in fire ignition, and affect fire behaviour either directly
through fire restriction or indirectly through land management and landscape
structure (Bowman et al., 2011). Burned area is a key variable to describe
fire impacts on ecosystems and vegetation distribution (Bond, 2005), and to
estimate fire emissions (Seiler and Crutzen, 1980). Recent estimates of
average yearly global burned area range from 3.3 to 3.8 million km
Satellite observations of burned area or of active fires can be used to develop, evaluate, or improve process-oriented global vegetation-fire models (Poulter et al., 2015b). The first fire modules within DGVMs like GlobFIRM (global fire model, Thonicke et al., 2001) were developed in the late 1990s and early 2000s in absence of global burned area datasets as reference. Later, regional satellite-derived burned area datasets were used to evaluate new developed global fire models such as SPITFIRE (SPread and InTensity of FIRE, Thonicke et al., 2010). The first global burned area datasets were derived in the mid-2000s from several optical satellite sensors such ATSR (Simon et al., 2004), MODIS (Roy et al., 2005), and SPOT (Grégoire et al., 2003; Tansey et al., 2008). The increasing temporal coverage of satellite observations enables to derive multi-year harmonized burned area datasets like the products from the Global Fire Emissions Database (GFED) (Giglio et al., 2010, 2013) or from the European Space Agency (ESA) Climate Change Initiative (CCI) on fire (Fire CCI) (Chuvieco et al., 2016). Consequently, global burned area datasets are nowadays commonly used within model benchmarking systems (Kelley et al., 2013) or to evaluate further developments in process-oriented vegetation-fire models (Kloster et al., 2010; Lasslop et al., 2014; Yue et al., 2014). Despite such recent model developments, it is not clear which functional relationships, complexity, and model parametrizations are most adequate to represent fire activity (Hantson et al., 2016).
Satellite observations of fire activity can be further integrated with fire models to estimate model parameters or to assess the adequacy of functional relationships (Knorr et al., 2014; Lasslop et al., 2015; Le Page et al., 2015). For example, parameters of empirical relations were optimized in SIMFIRE (simple fire model) to predict annual fire frequency from vegetation conditions, fire weather conditions, and population density (Knorr et al., 2014). Such parameter optimization approaches are one aspect of model–data integration or model–data fusion that encompasses a continuous cycle from the definition of model structures (i.e. predictor variables and functional relationships), estimation of model parameters, generalization or upscaling of the model, evaluation of model results, to model application and potentially back to a reformulation of the model structure (Keenan et al., 2011; Williams et al., 2009). However, a full model–data integration cycle has been rarely applied in the development of global fire models.
In comparison to process-oriented global vegetation-fire models, data-driven approaches provide an alternative framework to understand and model climate, vegetation, and socioeconomic controls on fire activity. While the development of mathematical and computational process-oriented vegetation-fire models usually starts from a conceptual model (Gupta et al., 2012), data-driven approaches aim to derive mathematical and computational models directly from the data (Solomatine and Ostfeld, 2008). In data-driven approaches, algorithms from artificial intelligence (e.g. neural networks), machine learning (e.g. random forest), or evolutionary algorithms (e.g. genetic optimization) are applied to predict a response variable (here burned area, or fire counts) from a set of potential predictor variables (Solomatine and Ostfeld, 2008). If an adequate data-driven model has been derived, the importance of individual variables and the sensitivities of the response variable to the predictor variables allow the development of a conceptual model of the studied system (Solomatine and Ostfeld, 2008). In global fire modelling, data-driven fire models have been developed using machine learning algorithms such as generalized linear models (Bistinas et al., 2014), maximum entropy (Parisien et al., 2016), or random forest (Aldersley et al., 2011; Archibald et al., 2009), mainly to identify controls on fire activity. However, such machine learning models often have complex structures and are seen as “black boxes”, and thus cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. Alternatively, empirical fire models like SIMFIRE (Knorr et al., 2014) could be generalized to integrate several different candidate predictor variables and to then assess the importance and functional relationships. Consequently, such a flexible data-driven but functional fire modelling approach would allow exploration of different predictor variables, similar to in machine learning algorithms, while potentially revealing model structures that can be more easily adapted for process-oriented vegetation-fire models.
Satellite observations provide several datasets on vegetation and moisture
conditions that can be used as predictor variables in data-driven fire
models. Time-variant biomass datasets would be the first choice to represent
fuel loads in empirical fire models because the availability of fuel is a
prerequisite for fire activity (Krawchuk and Moritz, 2011). However, current
global biomass maps are static (Avitabile et al., 2016; Saatchi et al., 2011;
Thurner et al., 2014) and thus provide only limited information for fire
modelling. Consequently, other proxies of vegetation biomass such as
model-based net primary production (NPP) (Bistinas et al., 2014; Moritz et
al., 2012), satellite-derived vegetation cover (Bistinas et al., 2014;
Lehsten et al., 2010), or the fraction of absorbed photosynthetic active
radiation (FAPAR) (Knorr et al., 2014) have been used as proxies for fuel
loads in global empirical fire models. As an alternative, satellite
retrievals of vegetation optical depth (VOD) might be used as a proxy for
fuel loads. VOD is a vegetation variable that is derived from active or
passive microwave satellite observations and is related to vegetation density
and water content (Liu et al., 2011b; Y. Y. Liu et al., 2013, Vreugdenhil et
al., 2016a, b). VOD has a higher sensitivity to forest biomass than FAPAR
(Andela et al., 2013) and was used to estimate temporal changes in biomass
(Liu et al., 2015). Thus VOD might be a valuable predictor variable for the
biomass-driven variability in fire activity. Satellite datasets of surface
soil moisture might be valuable proxies for the moisture of surface fuels in
empirical fire models (Krueger et al., 2015, 2016) because they represent the
top
Here we aim to describe and apply a flexible data-driven fire modelling approach, called SOFIA (Satellite Observations for FIre Activity). The SOFIA approach provides a framework to identify the importance of and the functional relationships between observational datasets and the spatial and temporal variability of burned area while revealing model formulations that could easily be adapted for more complex vegetation-fire models. We test the approach using observational datasets of land cover, climate conditions, soil moisture, vegetation state, and socioeconomics. Based on the philosophy of model–data integration, we generated several different candidate model structures, and optimized and evaluated each model against observed burned area time series. Additionally, we simulated global burned area with the random forest machine learning approach and with a process-oriented vegetation-fire model (JSBACH-SPITFIRE) to compare the performance of the derived SOFIA models with two independent state-of-the-art data-driven and process-oriented modelling approaches, respectively. We used random forest to test whether a more flexible modelling approach than SOFIA results in better performances. In comparison to random forest, SOFIA has the advantage that it could easily be transferred to or implemented in global process-oriented vegetation-fire models. The SPITFIRE fire module within the JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg) land surface model (Lasslop et al., 2014; Rabin et al., 2017) was used to compare SOFIA results with a global process-oriented vegetation-fire model.
We first describe the observational datasets and the derived variables that we used to develop SOFIA models (Sect. 2). Secondly, we describe the SOFIA approach and the JSBACH-SPITFIRE and random forest modelling approaches (Sect. 3). In Sect. 4, we first present the global performance and complexity of SOFIA models (Sect. 4.1) and how several predictor variables contribute to model performance (Sect. 4.2). Then we compare the best-performing SOFIA models globally against random forest and JSBACH-SPITFIRE (Sect. 4.3) and apply the best SOFIA model to explore spatial patterns of the sensitivity between predictor variables and burned area (Sect. 4.4). Finally, we discuss the performance and equifinality of our results (Sect. 5.1) and the importance of certain predictor variables for global fire modelling (Sect. 5.2), and suggest the use of multiple datasets, data-driven modelling, and model–data integration approaches to improve global process-oriented vegetation-fire models (Sect. 5.3).
We used datasets of global monthly burned area as response variables and several datasets on land cover, climate, soil moisture, vegetation state, and socioeconomic factors as predictor variables in model development. To make a pre-selection of relevant predictor variables, we first tested the predictive performance of various candidate variables such as absolute values, anomalies, or long-term precedent mean values of precipitation, wet days, soil moisture, or vegetation state using a random forest (Fig. A1 in the Appendix). We generally found a higher importance of the absolute variables than of the anomalies. For the development of SOFIA models, we finally selected a set of candidate predictor variables based on their importance, their interpretability, and how closely they are related to fire activity (by avoiding variables that account for indirect effects) (Table 1).
We based the analysis mostly on long-term harmonized or multi-satellite
merged datasets in order to derive appropriate SOFIA models for long-term
(i.e. decadal) variability in burned area that is covered for the period
1995–2015 of the GFED burned area dataset (Giglio et al., 2013). Although
state-of-the-art single satellite sensors may provide information in higher
quality, the use of such datasets would restrict the temporal coverage of the
analysis. Given the common coverage of the used predictor datasets, the
analysis was consequently performed for the period 1997–2011, on monthly
time steps, and at a 0.25
Description of used datasets and derived predictor variables.
Global monthly burned area data were taken from the Global Fire Emissions
Database (GFED) (Giglio et al., 2013) and the ESA Fire CCI datasets (Chuvieco
et al., 2016). GFED version 4 provides monthly burned area time series at a
0.25
Land cover data were taken from the ESA land cover CCI product which provides
three global land cover maps at 300 m spatial resolution covering the epochs
1998–2002, 2003–2007, and 2008–2012. We did not use the original land
cover classification of the maps, but translated land cover classes into
plant functional types (PFTs) to be comparable with the classification used
in global vegetation models (Poulter et al., 2011). The translation followed
largely the rules by Poulter et al. (2015a) with some modifications to avoid
coverage of broad-leaved evergreen trees and shrubs in boreal and Arctic
regions (Table A1). The following nine PFTs were derived: broadleaved
evergreen tree and shrub (Tree.BE, Shrub.BE), broadleaved deciduous tree and
shrub (Tree.BD, Shrub.BD), needle-leaved evergreen tree and shrub (Tree.NE,
Shrub.NE), needle-leaved deciduous tree (Tree.ND), natural grass or
herbaceous vegetation (Herb), and managed grasslands or crops (Crop). The
land cover maps were spatially aggregated and expressed as the fractional
coverage of PFTs within a 0.25
We further aggregated the coverage of PFTs within each 0.25
As land cover distribution is affected by fires, the land cover maps may
regionally contain effects of past fires. Consequently, it can happen that
fire activity is explained by the impact of the actual fire activity already
present in a land cover map. We tried to reduce this effect by shifting the
land cover maps by 2 years. This means that the map for the epoch 1998–2002
is used for the years
We used monthly data of mean air temperature, diurnal temperature range
(DTR), and monthly number of wet days from the Climate Research Unit (CRU)
TS3.2 dataset (Harris et al., 2014). DTR has been long used as predictor for
fire weather conditions because it is sensitive to stable weather conditions
that are usually associated to low humidity and are supportive for fire
activity (Bistinas et al., 2014; Venevsky et al., 2002). These datasets
provide monthly climate time series at 0.5
We used the monthly values and long-term conditions of climate datasets as predictor variables (Table 1). As long-term conditions, we computed the mean temperature, mean diurnal temperature range, mean number of wet days, and total precipitation of the actual month and the 12 preceding months.
Surface soil moisture was taken from the ESA CCI soil moisture dataset
(version 02.3 COMBINED) which is based on a merging of soil moisture products
from various active and passive satellite sensors (Dorigo et al., 2015; Liu
et al., 2011a, 2012). The dataset represents the upper soil layer
(
As soil moisture cannot be accurately retrieved underneath dense (tropical) forests, estimates are not available in all regions, and thus the dataset has spatial gaps. We excluded such grid cells in the full analysis. Soil moisture time series were aggregated to monthly mean values. Temporal gaps in soil moisture time series were filled using a season-trend regression model as described in Forkel et al. (2013) and based on Verbesselt et al. (2010a, b), but without accounting for breakpoints. However, some years in some grid cells were excluded from the entire analysis if soil moisture estimates were only available for less than 3 months within this year.
We used the monthly soil moisture values and long-term soil moisture conditions as predictor variables (Table 1). Long-term soil moisture conditions were computed as the mean soil moisture of the actual month and the 12 preceding months.
To account for effects of vegetation phenology, biomass, or vegetation water
content on fire activity, we used the GIMMS3g FAPAR (Zhu et al., 2013)
and a VOD dataset (Liu et al., 2011b). GIMMS3g FAPAR is a long-term
multi-sensor merged dataset of FAPAR and is based on the GIMMS3g NDVI
(Normalized Difference Vegetation Index) dataset with a spatial resolution of
Permanent gaps in FAPAR or VOD time series (mostly gaps occurring in winter at northern latitudes) were filled with the minimum value of each time series (Forkel et al., 2015) and remaining gaps were filled using the season-trend regression model (Forkel et al., 2013).
We used the monthly FAPAR or VOD values of the precedent month as predictor variables because the vegetation of the actual month is likely affected by the fire event which we aim to explain. Additionally, we computed the mean FAPAR and VOD of the 12 precedent months as long-term vegetation state predictor variables.
We used satellite-based datasets on population density and socioeconomic development as predictor variables for burned area.
Population density (PD) was taken from the Global Rural-Urban Mapping Project
(GRUMP) V1 dataset (Balk et al., 2006). This dataset is based on
(sub-)national population statistics, satellite observations of night-time
lights, and the spatial distribution of cities to provide estimates of
population density on a 1 km grid for the years 1990, 1995, and 2000. The
dataset was aggregated to 0.25
As an indicator of socioeconomic development, we used the Night Light
Development Index (NLDI) (Elvidge et al., 2012). The NLDI is derived from
satellite observations of light emissions during night and an independent
estimate of population density. The NLDI ranges between 0 (light emissions
equally distributed among people, highest development) and 1 (light emissions
concentrated on one person, lowest development). The NLDI is highly
correlated with electrification rates and the human development index
(Elvidge et al., 2012). The dataset is available at a 0.25
SOFIA is a data-driven fire model approach that allows us to test several
alternative functional relationships and associated variables to predict
fractional burned area. The basic structure of SOFIA fire models is inspired
by SIMFIRE (simple fire model) which uses empirical relationships to estimate
fire frequency from vegetation (i.e. FAPAR), fire weather conditions, and
socioeconomic variables (Knorr et al., 2014). In SOFIA we generalize the
SIMFIRE approach by using and testing several alternative predictor variables
as controls for fire activity. Each SOFIA model structure is based on the
assumption that potentially the entire vegetated area can burn, but burning
is actually restricted by several functional relationships to controlling
factors:
SOFIA models allow us to reproduce the typical right-tailed distribution of burned area (i.e. many grid cells and months with no burned area in comparison to relatively few grid cells and months with fire activity). The underlying functional relationships can take step-wise, linear, sigmoidal, or exponential shapes depending on the parameters of the logistic functions (Fig. 1). Similar model structures like SOFIA where a response variable is controlled by a product of several functions have been previously applied in environmental modelling, for example, in light-use efficiency models to simulate NPP (Cai et al., 2014; Nemani et al., 2003) or in phenology models to simulate leaf development (Forkel et al., 2014; Jolly et al., 2005; Stöckli et al., 2011). The response value of the functional relationship can also be used to map sensitivities of burned area to environmental or socioeconomic variables. Such a mapping of controls was previously done for plant productivity (Nemani et al., 2003) and phenology (Forkel et al., 2014; Jolly et al., 2005) based on red–green–blue (RGB) composite maps. Here we will demonstrate how this approach can be used to investigate spatial patterns of sensitivities between burned area and climatic, environmental, and socioeconomic controls on fire activity.
Example of a SOFIA model structure with three land cover groups
(i.e. herbaceous vegetation and crops, shrubs, trees) and five controlling
factors on fire activity. The example is taken from SOFIA model SF.124421
(Table 2).
To test appropriate controlling factors and related predictor variables in SOFIA models, we defined several alternative model structures. Each SOFIA model uses a specific land cover grouping scheme and several functional relationships for fire activity.
Performance of the best SOFIA and of random forest models in predicting global distributed monthly burned area time series in the optimization and evaluation data subsets, respectively. Results for all SOFIA models are provided in Table A2. Please note that results for JSBACH-SPITFIRE are not included in this table because of its coarser spatial resolution.
We tested different land cover grouping schemes to assess the required
complexity of SOFIA models to regionalize model parameters. As grouping
schemes we either used growth forms (“GrowthForm” including the variables
We defined five controlling factors on fire activity and assigned several corresponding predictor variables to each controlling factor to evaluate the following required components of SOFIA models.
We also allowed that a certain controlling factor is not included in a model
to test whether this controlling factor is generally needed in the SOFIA
model. This set-up of controlling factors and associated predictor variables
allows the definition of several candidate model structures (Table A2). For
example, SOFIA model SF.124421 (the coding is described in Table A2) used
growth forms as a land cover grouping scheme, the NLDI for human influences,
diurnal temperature range as a temperature effect, the number of wet days as
a direct wetness effect, the previous month's FAPAR as a direct vegetation
effect, and long-term precedent VOD as a long-term vegetation effect
(Fig. 1). The model structure determines the complexity which we assess here
based on the number of controlling factors within a SOFIA model and on the
number of parameters
After the definition of candidate SOFIA models, parameters for each
controlling function need to be estimated for each model to achieve an
optimal performance. The parameters
The minimization of SSE was performed by applying a genetic optimization algorithm. The used algorithm (GENOUD, genetic optimization using derivatives) combines a global search algorithm (i.e. genetic optimization) with a local search algorithm (i.e. BFGS) (Mebane and Sekhon, 2011). GENOUD was already previously used to estimate parameters in a dynamic global vegetation model (Forkel et al., 2014). Here we applied GENOUD by using 500 individuals (i.e. parameter sets) per generation, and allowed the algorithm to run for a maximum of 30 generations. The parameter sets of the first generation were generated randomly. The second generation is generated by using several operators to clone, mutate, and crossover the best parameter sets of the first generation (Mebane and Sekhon, 2011). The BFGS local search algorithm was first used starting from the best parameter set that evolved in the 28th generation in order to avoid overly fast convergence of the algorithm towards a local optimum.
We selected the best-performing SOFIA models from all optimized candidate
models based on the Akaike information criterion (AIC) (Burnham and Anderson,
2002). The AIC is a metric to empirically infer appropriate model structures
from several candidate models based on performance (in terms of SSE) and by
penalizing for model complexity (in terms of the number of model parameters
To evaluate the simulated spatial–temporal patterns and temporal dynamics of
fractional burned area, we used the index of agreement (IoA) and the
fractional variance (FV) (Janssen and Heuberger, 1995):
We sampled several grid cells from the global datasets (0.25
The sampled grid cells were further divided into a subset for optimization
(60 % of the sampled grid cells) and for evaluation (40 % of the
sampled grid cells). The time periods in both subsets were further divided
according to years for which the monthly data were used for optimization
(even years in 1998 to 2010) and for which the monthly data were used for
evaluation (uneven years in 1997 to 2011). We used every second year for
optimization or evaluation to avoid potential temporal changes in the quality
of multi-sensor satellite datasets (e.g. burned area, soil moisture, FAPAR,
and VOD) affecting the evaluation of model results. Based on this sampling
scheme, 1817 grid cells (
We applied the best-performing SOFIA models to all global 0.25
We used the random forest machine learning approach to evaluate if the basic structure of SOFIA models is flexible enough to predict burned area or if a more flexible modelling approach can reach higher performances. Random forest is a regression approach that can consider non-linear, non-monotonic and abrupt, and non-additive relations between multiple predictor variables and a response variable (Breiman, 2001). Random forest is an ensemble of multiple regression trees that are trained based on the response variable. Each tree uses a randomly selected set of predictor variables and data points (Breiman, 2001). Random forest was already previously applied to identify controls on vegetation dynamics and on fire activity (Aldersley et al., 2011; Archibald et al., 2009). We used 500 trees per random forest. For the training of the random forest, we used the same data subset that was also used to optimize SOFIA models (Sect. 3.3.3). The analysis was performed using the randomForest package in R (Liaw and Wiener, 2002).
We performed three different random forest model experiments. Model experiment RF1 used all predictor variables from Table 1 to explore the potential performance of the used datasets to predict burned area. Model experiment RF2 used all predictor variables except for the variables from the soil moisture dataset in order to apply random forest globally and to compare the results with SOFIA independently of the spatial gaps of the soil moisture dataset. Model experiment RF.124421 uses the same predictor variables as SOFIA model SF.124421 (i.e. CCI.LC.Tree/Shrub/HrbCrp, NLDI, CRU.WET.orig, Liu.VOD.annual, GIMMS.FAPAR.pre, CRU.DTR.orig) in order to compare the performance of the two model approaches based on the same predictor variables.
We simulated burned area with the SPITFIRE (spread and intensity of fire) fire module within the JSBACH (Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg) land surface model in order to compare the performance of SOFIA models to a state-of-the art global vegetation-fire model. This comparison potentially allows us to provide suggestions for the further development of global vegetation-fire models.
JSBACH is the land component of the MPI (Max Planck Institute for Meteorology) Earth system model (Raddatz et al., 2007). SPITFIRE is a physically based fire module that simulates fire ignitions (based on lightning and population density), fire spread, and fire effects depending on weather conditions, vegetation type and structure, fuel moisture, and fuel size (Thonicke et al., 2010). SPITFIRE was originally developed for the LPJ (Lund-Potsdam-Jena) dynamic global vegetation model (Thonicke et al., 2010). For the implementation of SPITFIRE in JSBACH, two parameters in SPITFIRE were adjusted, one related to human ignitions and the other related to the drying of fuels (Lasslop et al., 2014). Additionally, the relation between wind speed and the rate of fire spread was modified (Lasslop et al., 2015) and a decrease in fire duration with increasing population density was implemented (Hantson et al., 2015a).
JSBACH was applied at a spatial resolution of
1.875
Effect of the complexity of SOFIA models on the performance. Model
performance is expressed as the index of agreement between simulated and
observed (GFED) monthly burned area time series in the optimization data
subset.
The optimized candidate SOFIA models covered wide ranges of complexities and
performances (Fig. 2, Table 2). The best-performing SOFIA models reasonably
explained the monthly spatial–temporal patterns of fractional burned area
(i.e. up to IoA
We also tested if alternative cost functions in the optimization of SOFIA
models would reduce the underestimation of the observed variance of burned
area. The tested alternative cost functions explicitly accounted for
variance, burned area anomalies, or were based on transformed burned area
values (Table A3). Although a cost function based
on IoA and FV reached better performances in terms of IoA (best IoA
The performance of SOFIA models varied with model complexity. SOFIA models
that used a higher number of controlling factors (
Random forest models reached slightly better performances than the
best-performing SOFIA models. The random
forest model based on all variables reached very good performance in training
(IoA
Effect of controlling factors and associated predictor variables
in SOFIA models on the performance in simulating global monthly burned area
dynamics. Performance is expressed as the index of agreement between
simulated and observed (GFED) monthly burned area for the training data
subset. Boxplots show the distribution of IoA based on all SOFIA model
experiments that include the respective variable. Star symbols indicate a
significantly higher IoA of a variable in comparison to the “no” group of
each controlling factor (Wilcoxon rank sum test,
The performance of SOFIA models depended on the controlling factor and associated predictor variables that were used in model structures (Fig. 3). The choice of a certain land cover grouping scheme in SOFIA models to regionalize model parameters had only weak effects on model performance (Fig. 3a). Although models based on the GrowthForm scheme had on average weaker performances than models based on land cover grouping schemes with croplands, the best SOFIA models were not related to a certain land cover grouping scheme.
Including human influences as controlling factors in SOFIA models did not improve model performance (Fig. 3b). The best models either did not consider human influences or considered human influences through NLDI as global controlling function. However, NLDI did in average not contribute to higher performances. SOFIA models that used population density had on average weaker performance than SOFIA models that used NLDI or that did not consider human influences. The weaker performance of population density as component in SOFIA models could be caused by the general model structure in which potential burned area equals the total vegetated area: As highly populated areas are usually associated with low vegetation cover, potential burned area is low as well, and thus population density does not provide further information. However, the SOFIA models (SF.314511) revealed a global decline of burned area with increasing population density (Fig. A4), a finding which is in agreement with previous studies (Andela et al., 2017; Bistinas et al., 2014; Knorr et al., 2014). Although two of the best SOFIA models did not contain any variable for human influences (SF.204422, SF.203512), they however considered the fractional coverage of croplands in the used land cover grouping scheme. Consequently, these two models considered human influence on fire indirectly through the coverage of croplands. These results suggest that human influences on fire activity can be relatively interchangeably described in SOFIA models by the coverage of croplands, NLDI, or population density.
Considering temperature variables in SOFIA models caused on average better model performances than model structures without temperature variables (Fig. 3c). However, we also found one model without a temperature control that reached good performance (SF.233210, Table A2). All of the best-performing models included a diurnal temperature range or pre-fire annual mean temperature as controlling factors. These results show that temperature-related variables are important predictors in SOFIA.
The consideration of direct wetness effects in SOFIA models had the largest
positive impact on model performance (Fig. 3d).
Models that did not consider direct wetness effects had lower performances
than models that used soil moisture, precipitation, or the number of wet
days. Especially models based on the number of wet days reached significant
higher IoA than models without direct wetness effects (Wilcoxon rank sum
test,
Including or not including direct vegetation controls did not lead to a significant change in the performance of the SOFIA models (Fig. 3e). The best models either did not consider direct vegetation effects (SF.324202) or used pre-fire FAPAR (SF.204422, SF.124421) or pre-fire VOD (SF.203512). This suggests that precedent FAPAR and VOD conditions did not provide additional information to predict burned area in SOFIA models.
On the contrary, considering long-term wetness or vegetation effects in SOFIA models caused significantly higher model performances than not considering these effects (Fig. 3f). Especially SOFIA models that used pre-fire annual precipitation or VOD reached significantly higher IoA. Models with long-term effects based on soil moisture, the number of wet days, or FAPAR had on average similar performances to models without long-term effects. However, we also found some good models that used long-term conditions of FAPAR (e.g. SF.203512). These results demonstrate that long-term conditions in vegetation productivity (reflected by annual precipitation) or vegetation structure (reflected by VOD or FAPAR) were required components of SOFIA models to predict burned area.
Based on the performances of the different controlling factors and associated predictor variables, the ideal SOFIA model should include the NLDI as a human influence, one variable to account for temperature effects, the number of wet days as a direct wetness effect, and pre-fire annual conditions of precipitation or VOD as long-term wetness/vegetation effects. This ideal model structure is realized in two of the best-performing SOFIA models (SF.124421 and SF.324202, Fig. 3). The choices of a certain land cover grouping scheme or of a direct vegetation effect are secondary components of SOFIA model structures. The distribution of model parameters in SF.124421 after optimization reflects the fact that parameters for the functional relationships with the NLDI, the number of wet days, and VOD were well constrained and thus were the most sensitive parameters within this model to estimate global monthly burned area dynamics. These parameter estimates and distributions could potentially be used as prior parameter estimates to further constrain SOFIA models.
The best SOFIA models were applied globally to assess their performance in
simulating global and regional spatial–temporal patterns of annual total
burned area with respect to random forest models and JSBACH-SPITFIRE. All
three model approaches reproduced well the global spatial pattern of mean
annual burned area with large burned area in Africa, Australia, and tropical
South America, and smaller amounts of burned area in the rest of the world
(0.663
Mean annual fractional burned area in 2005–2011 from observational
datasets and global fire models. Numbers in brackets are the global mean
annual burned area. In the case of the
Regionally, we found varying performances of SOFIA models, random forest, and
JSBACH-SPITFIRE in simulating spatial–temporal and statistical distributions
of annual total burned area (Fig. 5). In northern regions (boreal forests and
tundra), differences between all datasets and models were large: whereas
three SOFIA models produced almost no fire activity and thus had very poor
performances, model SF.124421 reached medium performances (IoA
In the tundra, all models had very low performances, but SF.124421 reproduced
at least the mean annual burned area from the GFED dataset. However, the GFED
and CCI datasets also strongly disagree in the tundra (IoA
In boreal needle-leaved deciduous forests, the random forest models reached
the highest performance (IoA
Regional distributions of annual total burned area per
1.875
In temperate regions, SOFIA models generally outperformed random forest models and JSBACH-SPITFIRE in reproducing the observed spatial–temporal and statistical distributions of annual total burned area (Fig. 5d–f). The random forest models and JSBACH-SPITFIRE overestimated mean annual burned area in all temperate regions.
In temperate forests and croplands, SF.124421 reached the best performance
of all models (IoA
In the Mediterranean, all SOFIA models had medium to good performances
(0.28
In the steppes, all SOFIA models reproduced the observed mean annual burned
area, and some reached medium performances (IoA
In tropical regions, SOFIA models had good performances in reproducing the
observed spatial–temporal and statistical distributions of annual total
burned area and had comparable or better performances than the random forest
models and JSBACH-SPITFIRE (Fig. 5g–h). In savannahs and tropical croplands,
all SOFIA and random forest models and JSBACH-SPITFIRE had good performances
in reproducing the spatial–temporal distribution of annual total burned area
(0.63
In tropical forests, all SOFIA models had medium to good performances in
reproducing the spatial–temporal distribution of annual total burned area
(0.61
In summary, we found that all modelling approaches (SOFIA, random forest, JSBACH-SPITFIRE) had relatively good performances in savannahs and tropical croplands. All SOFIA models had relatively good performances in tropical forests and the Mediterranean. Only some SOFIA models reached good performances in temperate forests and croplands (SF.124421) and in steppes (SF.324202). Random forest models and JSBACH-SPITFIRE had generally weaker performances than SOFIA models. Model SF.124421 (Fig. 1) had the best performance from all SOFIA models in the tundra, boreal forests, temperate forests and croplands; it had very good performance in savannahs and tropical forests; and it outperformed random forest and JSBACH-SPITFIRE in steppes and the Mediterranean. Consequently, we finally identified SF.124421 as the globally best-performing SOFIA model from the tested set of model structures.
The underlying functional relationships in SOFIA models allow us to map the sensitivities of burned area to human, vegetation, and climate variables. To demonstrate such a potential application of a SOFIA model, we mapped mean responses from each functional relationship for the period 1997–2011 from SOFIA model SF.124421 (Fig. 6). Based on this model, human influences (i.e. the NLDI) restricted burned area in most parts of Europe and southern Russia, eastern and south-eastern Asia, India, central and eastern North America, south-eastern South America, southern Australia, and New Zealand (Fig. 6a). These regions correspond to the most populated and developed regions of the world. This pattern was caused by the underlying functional relationship of SF.124421 where NLDI < 1 (i.e. developed regions) restricted and NLDI > 1 (i.e. unpopulated regions or natural ecosystems) allowed fire activity (Fig. 1b). These results indicate a predominant restricting effect of humans on fire activity.
Temperature effects in SF.124421, expressed as diurnal temperature range, allowed fire activity mostly in the semi-deserts of western North America, in the Sahel, and in Australia, and had a moderate restriction effect in tropical forests and the tundra (Fig. 6b). These spatial patterns were caused by the controlling function that had a strong sigmoidal increase in fire activity with a diurnal temperature range in shrublands and allowed moderate fire activity in herbaceous vegetation and croplands (Fig. 1c).
Direct wetness effects, expressed as the number of wet days, generally allowed fire activity in all forest regions and moderately restricted fire activity in the rest of the world (Fig. 6c). The underlying controlling function in SF.124421 showed no sensitivity for forests, a weak positive relation in herbaceous vegetation and croplands, and a strong exponential decrease in fire activity with an increasing number of wet days in shrublands (Fig. 1d).
As a direct vegetation effect, pre-fire FAPAR restricted fire activity in herbaceous vegetation and croplands of central North America, central Asia, the northern Sahel, the Kalahari, central Australia, and parts of South America (Fig. 6d). On the other hand, pre-fire FAPAR supported fire activity mostly in the southern Sahel and northern and eastern Australia. These patterns were caused by a general strong restriction of fire activity with pre-fire FAPAR in herbaceous vegetation and croplands and an exponential increase in fire activity with increasing pre-fire FAPAR in shrublands in SF.124421 (Fig. 1e).
As a long-term vegetation effect, 12-month precedent mean vegetation optical depth strongly supported fire activity in central North America, central Asia, the Tibetan Plateau, the Sahel, parts of India, the Kalahari, Australia (except the interior), and northern Patagonia (Fig. 6e). In all other regions, annual VOD had a moderate effect on fire activity in SF.124421. The underlying controlling function in SF.124421 showed an exponential increase in fire activity with annual VOD in shrublands, an exponential decrease with annual VOD in herbaceous vegetation and croplands, and a strong restriction across all VOD ranges for trees (Fig. 1f). The diverging responses with annual VOD in shrublands and herbaceous vegetation indicate that fire activity increases with higher vegetation density or biomass in shrublands but decreases with increasing vegetation water content in herbaceous vegetation, respectively. Additionally, the general restriction of fire activity with VOD for trees indicates that fire activity is restricted by vegetation density or high vegetation water content in forests.
We further combined the controlling functions of SF.124421 to investigate combined controls on fire activity. Therefore we created a red–green–blue composite map in which the red channel contains the NLDI functional relationship, the green channel contains the mean of the direct (precedent month FAPAR) and long-term vegetation (12-month precedent VOD) effect, and the blue channel contains the climate effects (mean response of functional relationships to the number of wet days and diurnal temperature range) from SF.124421 (Fig. 6f). Generally, bright colours on this map indicate a strong restriction of fire activity (small burned area) and dark colours indicate that fire activity is allowed (large burned area). Regionally, different combinations of socioeconomic, vegetation, and climate factors controlled fire activity. Socioeconomic development dominantly restricted fire activity in western North America and in populated regions of boreal forests (red colours). Vegetation predominantly suppressed fire activity in southern boreal and tropical forests (green colours). Primarily climate conditions and secondly socioeconomic development restricted fire activity in semi-deserts of the northern Sahel, central Asia, the Kalahari, and south-western Australia (purple colours). Socioeconomic development and climate equally suppressed fire activity in the Mediterranean, India, eastern Asia, and eastern South America (pink colour). Both socioeconomic development and vegetation conditions suppressed fire activity in most parts of Europe, central and eastern North America, and eastern China (yellow/orange colours). Both climate and vegetation conditions suppressed fire activity in the tundra and in central Australia (cyan colours). All factors moderately supported fire activity in boreal forests and strongly support fire activity in large parts of the Sahel, southern Africa, northern Australia, and western North America (dark colours). We want to point out that these sensitivities might look different if SOFIA models with alternative but adequate model structures are applied for such an analysis. However, the results highlight that fire activity is controlled by regionally diverse and complex interactions of human, vegetation, and climate factors.
Example of combined climate, vegetation, and human controls on
fire activity based on the SOFIA model SF.124421. The maps in
We developed the SOFIA modelling approach as a framework to explore the importance of and the functional relationships between different predictor variables and burned area while relying on relatively simple model structures. The best SOFIA models reached globally average performances but outperformed the JSBACH-SPITFIRE state-of-the-art process-oriented vegetation-fire model. We interpret the globally medium and regionally varying performances as current upper limits that can be reached with the used predictor datasets and variables because the more flexible and highly adaptive machine learning algorithm random forest did not achieve much higher performance in the evaluation data subset. These upper limits in model performance might be for several reasons.
Uncertainties in the observations for the predictor and response variables inhibit the development of models with high performance. For example, we found regionally partly large differences between the two burned area datasets, especially in northern regions. These uncertainties originate from differences in sensor characteristics and in the ability of the used algorithms to detect small fires.
Other processes and variables are important for the spread of fires but
cannot be resolved at the used spatial and temporal resolution. For example,
on local to regional scales the spread of fire is controlled by landscape
structure and topography whereas climatic controls are usually more important
on larger scales (Archibald et al., 2009; Z. Liu et al., 2013; Parisien et
al., 2010). Most of the regional controls can likely not be resolved at the
used spatial resolution (0.25
There is a lack of global observations that directly represent fuel loads, fuel moisture, or modes of human fire usage. For example, all of the used predictor variables are only proxies for fuel loads (FAPAR or VOD) or fuel moisture (surface soil moisture), but do not directly represent such fuel conditions. Similarly, data on population density or socioeconomic development are used as proxies for human effects on fire, but cannot represent the complex social, economic, and cultural practices and policies of human fire use and management.
The four best SOFIA models reached similar performances in savannas and tropical croplands, and in tropical forests, which demonstrates the equifinality in fire modelling. Equifinality, i.e. the presence of multiple adequate models and parameter sets that result in very similar responses, is a general problem in environmental modelling (Beven, 2006). General approaches to avoid equifinal models are the use of multiple datasets of the same variable to account for errors or uncertainties in model forcing or reference data, the testing of different cost functions to constrain certain parameters, the inclusion of prior parameter uncertainties in the cost function, or the application of models to new observational data or under different conditions (Beven, 2006; Beven and Binley, 2014; Williams et al., 2009). In our analysis, we were able to rule out three of four initially equifinal SOFIA models based on the application of these models to the global data and by regional comparisons against two burned area datasets. The results from the optimized SOFIA models allow extraction of parameter values and ranges for each functional relationship. To give an example, parameters that control the functional relationship with (1) socioeconomic development (NLDI), with (2) diurnal temperature range and the number of wet days in shrublands, and with (3) VOD were well constrained in SOFIA model SF.124421 (Fig. A3). These parameters could potentially be used as prior parameter values in a more constrained analysis in the future. The presence of equifinality in SOFIA model structures suggests the inclusion of such prior parameter uncertainties for each functional relationship to better constrain individual SOFIA models. This technique can be applied in future generations of individual SOFIA models by using the current versions as prior parameter estimates and uncertainties.
The derived SOFIA models and the spatial patterns of sensitivities show a sharp decline in burned area with increasing socioeconomic development or population density and thus agree with previous studies that show a primarily negative effect of human activities, population density, or croplands on burned area (Andela et al., 2017; Archibald et al., 2013; Bistinas et al., 2014; Chuvieco and Justice, 2010; Knorr et al., 2014). Strikingly, our results suggest that human effects on global burned area can be expressed by either cropland area, NLDI, or population density, but the combination of these factors did not improve the performances of SOFIA models. These variables all serve as proxies for the negative relationship between humans and burned area, but do not directly describe human activities of fire use or suppression. For example, regional studies have shown that various information on infrastructure, land use, and other relevant socioeconomic indicators are important to predict fire activity (Archibald et al., 2009; Arndt et al., 2013; Parisien et al., 2016). However, such spatially and temporally resolved datasets and assessments are missing for the global scale. Certainly, our results do not imply that croplands are unimportant for the global variability of burned area. Agricultural fires account for around 10 % of all global fires (Korontzi et al., 2006) and for around 5 % of global burned area (Giglio et al., 2013) and are used to remove harvest residues or to fertilize soils. However, croplands show more small fires than large fires (Hantson et al., 2015b). As we here used the GFED burned area datasets that were not corrected for small fires (Giglio et al., 2013), small agricultural fires are likely misrepresented in this dataset and thus cannot be accurately analysed within the SOFIA approach. The representation of agricultural fires in a global fire model needs to account for various land use patterns and practices that go far beyond natural climate–vegetation relationships (Le Page et al., 2015; Magi et al., 2012; Rabin et al., 2015). By taking into account this complexity, agricultural fires are often not represented in global vegetation-fire models because they do not directly affect natural vegetation and carbon cycle dynamics (Hantson et al., 2016), unless agricultural fires escape to nearby forests (Cano-Crespo et al., 2015). In summary, an improved representation of human effects on fire in global vegetation-fire models is currently lacking since globally consistent, temporally and spatial resolved, relevant information on infrastructure and socioeconomics is not available.
Direct wetness effects, especially based on the number of wet days, were the component of SOFIA models that contributed most to model performance (Fig. 3). These results are in agreement with previous results that identified the number of dry days (the inverse of the number of wet days) as an important variable to predict fire activity (Bistinas et al., 2014). Especially for shrublands, we identified strong exponential relationships with the number of wet days and the diurnal temperature range. Currently, shrubs are not considered in all ecosystem models (e.g. not in models of the LPJ family, Sitch et al., 2003), which suggests the need to implement and parameterize shrub PFTs to improve simulations of fire activity. The number of wet days and the diurnal temperature range are also used in process-oriented fire models like SPITFIRE to compute the Nesterov index (a fire weather index) and fuel moisture content (Thonicke et al., 2010). Here we confirm that the use of the diurnal temperature range and the number of wet days are appropriate predictor variables to simulate fuel moisture conditions and thus fire activity. However, while the Nesterov index is used as a fire weather index in many fire modules of global vegetation models (Lasslop et al., 2014; Prentice et al., 2011; Thonicke et al., 2010; Venevsky et al., 2002; Yue et al., 2014), studies on forest fire management rely more often on alternative fire weather indices such as from the Canadian Forest Fire Weather Index (FWI) (Bedia et al., 2012; Stocks et al., 1989). We also show that direct wetness effects can be represented by satellite-derived surface soil moisture. Additionally, several other indices have been derived from satellite data to estimate fuel moisture conditions (Yebra et al., 2013). Consequently, it is necessary to systematically compare the predictive power of fire weather indices, satellite-derived and reanalysis-based surface soil moisture data, and soil moisture schemes of ecosystem models to potentially improve the direct effect of wet conditions on fire activity in global vegetation-fire models.
Long-term vegetation effects contributed strongly to the performance of SOFIA models and thus indicate an important role of vegetation dynamics in the spatial–temporal variability of fire activity. Consequently, global vegetation models require a good representation of vegetation distribution and dynamics to realistically simulate fire activity. Vegetation distribution can be improved either through the prescription of high-quality land cover maps in land surface models or by improving model structures and by constraining model parameters that affect vegetation dynamics in DGVMs. For both approaches, time-variant, e.g. annually resolved, land cover maps would be very valuable for realistically reflecting vegetation dynamics. However, it is currently unclear how realistic land cover dynamics are represented for example by the three epochs of the ESA CCI land cover maps or by annual or seasonal maps of the MODIS land cover product (Broxton et al., 2014). Hence intensified efforts are required to check the plausibility of land cover changes in current and upcoming time-variant land cover maps.
SOFIA models with a long-term effect of VOD had better performances than models without this effect. The good performance of SOFIA models with VOD as predictor variable likely reflects variability in fuel loads because VOD is sensitive to vegetation density and biomass (Andela et al., 2013; Liu et al., 2015). The importance of VOD suggests that processes such as carbon allocation, turnover and vegetation mortality which all control biomass dynamics need to be carefully assessed in global vegetation models in order to accurately simulate fuel loads and hence fire activity. The finding of a strong restriction of fire activity with VOD in forests corresponds to previous findings that show that woody vegetation tends to restrict burned area either because moist wood is more difficult to ignite than dry grass or litter, or because forests provide generally more moist conditions (Kelley and Harrison, 2014). Fire activity increases with biomass at low vegetation densities and strongly decreases with increasing biomass and very high vegetation densities but the actual fire activity is enhanced or restricted by moisture conditions (Krawchuk and Moritz, 2011; Murphy et al., 2011). Consequently, the SOFIA approach and the identified sensitivities of fire activity with direct wetness effects and with VOD confirm and implement previous conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions (Krawchuk and Moritz, 2011; Murphy et al., 2011).
The better performance of SOFIA models compared to JSBACH-SPITFIRE and the generally good performance especially in temperate and tropical regions demonstrate the potential of the SOFIA approach to improve global vegetation-fire models. The SOFIA approach can be potentially adapted to more complex global vegetation-fire models such as SPITFIRE. Thereby the functional relationships in SOFIA models should rely on forcing datasets (e.g. temperature, precipitation) and simulated state variables (e.g. litter and soil moisture, biomass compartments, litter stocks, vegetation structure) of the vegetation models. This also allows the representation of feedbacks of changing vegetation conditions on fire activity. By applying the SOFIA approach to forcing and state variables of a process-oriented vegetation model, more adequate predictor variables could be potentially identified and finally model performance could be improved.
In order to represent realistic vegetation-fire interactions, vegetation models need to satisfactorily reproduce observed patterns and dynamics of fuel moisture and vegetation state variables. Consequently, it is necessary to test and improve global vegetation-fire models against multiple observational datasets that cover various aspects of vegetation-fire interactions: for example, satellite datasets on land cover, FAPAR, VOD, biomass (Avitabile et al., 2016; Saatchi et al., 2011; Thurner et al., 2014), and estimates of litter fuels (Pettinari and Chuvieco, 2016) may be useful to constrain vegetation dynamics, biomass allocation, and fuel loads; datasets on surface soil moisture, VOD, and evapotranspiration (Tramontana et al., 2016) may be useful to test hydrological schemes and to constrain fuel moisture; and datasets on burned area, fire size (Hantson et al., 2015b), fire radiative power, fuel consumption (Andela et al., 2016; van Leeuwen et al., 2014), or separations between natural and agricultural fires (Korontzi et al., 2006; Le Page et al., 2010; Magi et al., 2012) may be useful for constraining fire behaviour. Such datasets are currently under-exploited in the development of global vegetation-fire models because (1) they were still missing at the time of model development (Thonicke et al., 2001), (2) there is only little experience in applying formal model–data integration approaches within global fire modelling, or (3) no appropriate model components or observation operators exist that link for example modelled fuel moisture with satellite-derived surface soil moisture or modelled biomass compartments with VOD. For example, it is currently unclear which physiological processes, morphological plant components, and ecosystem structures contribute to a certain VOD signal (Vreugdenhil et al., 2016a). Consequently, it is necessary to better understand the plant and ecosystem controls on VOD to improve global vegetation-fire models.
Previously developed global fire models commonly used observed data for model evaluation, but did not undertake a formal model–data integration cycle from the definition of model structures, model parameter estimation, to model evaluation, and potentially back to a re-formulation of model structures by using observational data. In our study we firstly applied the full model–data integration cycle to derive an optimal structure for an empirical global fire model to predict global burned area. However, in order to apply model–data integration for global process-oriented vegetation-fire models, multiple datasets on vegetation, hydrological, and fire-related variables should be used to realistically constrain vegetation-fire interactions. Hence there is a need to develop appropriate observation operators and to extend currently existing model–data integration frameworks of global vegetation models (Forkel et al., 2014; Kaminski et al., 2013; MacBean et al., 2016; Schürmann et al., 2016) to the corresponding fire modules in order to formally assess model structures and to constrain model parameters. In summary, model–data integration frameworks need to be developed that make use of multiple satellite datasets on vegetation and moisture proxies in order to improve the representation of fire in global vegetation models and thus to better understand interactions of fire with ecosystems and the atmosphere within the Earth system.
The
code for this study is organized into several R packages and is available
from
The used original data are available under the URLs or
DOIs, or can be obtained from PIs as indicated in Table 1. The pre-processed
(spatially and temporally interpolated) data for the optimization and
evaluation data subsets are included as the example dataset
“
Importance of several predictor variables for predicting monthly burned area using a random forest. Importance is expressed as the percentage increment in mean squared error if a certain variable is not included in a random forest. Thus, the most important variables cause the largest increment in MSE. Variables that include “orig” or “anom” indicate original absolute values and anomalies (relative to the mean seasonal cycle), respectively. “filterX” indicates mean values over the X precedent months before the actual month for which burned area should be predicted. In total 132 variables were included in this analysis, but variables below rank 53 are not shown in this figure).
Representativeness of sampled 0.25
Uncertainty in parameters of SOFIA model SF.124421 after genetic
optimization. Shown are distributions (outlines), mean values (
The response function from the best SOFIA model including population density (SF.314511) globally shows a decline in fire activity with increasing population density, a finding which is in agreement with independent studies (Andela et al., 2017; Bistinas et al., 2014; Knorr et al., 2014).
Land cover to plant functional type conversion table. The units are % coverage of each PFT per land cover class. The conversion factors are based on Poulter et al. (2015a) with some modifications that affect boreal and Arctic regions, i.e. to avoid coverage of broadleaved evergreen PFTs in these regions and to reach a total tree cover that is comparable to the MODIS tree cover product (Hansen et al., 2003).
Structure and performance of all tested candidate SOFIA models.
Continued.
Performance of SOFIA model SF.124421 depending on the type of cost function that is used in optimization.
MF and WD designed the study and experimental setup. MF developed code, carried out the analysis, and mainly wrote the manuscript. IT contributed with data pre-processing. GL performed JSBACH-SPITFIRE model runs. KT and EC contributed with conceptual ideas and references. All co-authors discussed results and contributed to the manuscript.
The authors declare that they have no conflict of interest.
This work was supported by the European Space Agency through a Living Planet Fellowship for Matthias Forkel (CCI4SOFIE, CCI data for assessing soil moisture controls on fire emissions) and by the TU Wien Wissenschaftspreis 2015, a personal science award assigned to Wouter Dorigo from the Vienna University of Technology. We further thank the following organizations, projects, portals, and researchers for providing datasets: ESA CCI, GFED, CRU, GPCC, GIMMS, NASA SEDAC, NOAA EOG, and Yi Liu. Edited by: Gerd A. Folberth Reviewed by: Gerd A. Folberth and two anonymous referees