Human impact on wildfires varies between regions and with vegetation productivity

JSBACH (Raddatz et al 2007, Brovkin et al 2009) is the land surface model of the MPI Earth system model (MPI-ESM) (Giorgetta et al 2013). We use the JSBACH version with the soil carbon model YASSO (Goll et al 2015). The process-based fire model SPITFIRE (Thonicke et al 2010) has been evaluated for present day burned area and carbon emissions (Lasslop et al 2014), and is described in detail and in comparison to other global fire models in Rabin et al (2017). JSBACH simulates the terrestrial carbon and water cycle in a process-based way. It provides fuel amounts, vegetation composition and soil moisture for the fire model. The fire model reduces the carbon pools according to the simulated combustion of biomass, and computes tree mortality due to fire. Fire can start if ignition (lightning or human) occurs and a sufficient amount of fuel leads to sufficiently high fire line intensity. Based on the fuel size and moisture, the combustion completeness and rate of fire spread is computed. The combination of the rate of spread with the fire duration (see below) yields the burned area. Tree mortality is a function of the fire line intensity and the residence time of the fire. SPITFIRE accounts for the human influence based on population density and the cropland fraction. Population density (P_D [inhabitants km⁻²]) is used to compute the human ignitions (n_h,ig)):

$$\begin{equation} n_{\rm h,ig} = P_{\rm D}0.4{\rm e}^{-0.5 \sqrt {P_{\rm D}}} a(N_{\rm d})/10. \end{equation} \tag{ 1 }$$

The factor 0.4 was adjusted to tune the model to reproduce the global burned area of the GFED3 dataset (Giglio et al 2010). The regionally varying factor (a (N_d)) was introduced to reflect regional and cultural differences in the human use of fire as a tool. As the burned area fraction varies globally by several orders of magnitude, the factor (which varies between 0.11 and 0.33) has rather low influence. This was found in a study using ORCHIDEE–SPITFIRE (Yue et al 2014) and for JSBACH–SPITFIRE simulations without vegetation dynamics, where the spatial variability of ignitions has low influence on the spatial patterns of burned fraction (Lasslop et al 2014). The JSBACH–SPITFIRE model reduces the fire duration (D_F in minutes) with increasing population density, to improve the relationship between population density and fire size (Hantson et al 2015). The maximum fire duration was increased from 4 hours in the original model version (Thonicke et al 2010) to 12 hours (Hantson et al 2015), assuming that most fires cease during night. While fires in reality often continue during night, due to the low variation of ignitions in the model, a new fire will start the next day unless the conditions for a fire change (changes in moisture conditions or fuel load), and therefore reduce the influence of the 12 hours threshold on longer term fire occurrence. D_F is computed in three different ways: (1) as a function of the fire danger index (FDI):

$$\begin{equation} D_{\rm F}=\frac {723}{1+(240 \cdot {\rm e}^{-11.06 \cdot FDI})}. \end{equation} \tag{ 2 }$$

(2) using P_D and the FDI:

$$\begin{equation} D_{\rm F} = \left\{ {\begin{array}{l@{}l@{}l@{}} {\frac{{723}}{{1 + (240 \cdot {\rm e}^{ - 11.06 \cdot FDI} )}}} \hfill & {{\rm if}\;P_{\rm D} 0.01} \hfill & \\ {\frac{{241 \cdot (4 - \log 10(P_{\rm D} )) \cdot 0.5}}{{1 + (240 \cdot {\rm e}^{ - 11.06 \cdot FDI} )}}} \hfill & {{\rm if}\;0.01 < P_{\rm D} < 100} \hfill & \\ {\frac{{241}}{{1 + (240 \cdot {\rm e}^{ - 11.06 \cdot FDI} )}}} \hfill & {{\rm if}\;P_{\rm D} 100.} \hfill & \\ \end{array}} \right. \end{equation} \tag{ 3 }$$

and (3) additionally reducing D_F with increasing cropland fraction (f_crop) by multiplying equation (3) with (1–f_crop). The first approach is used when no human influence is included, the second includes human influences, and the third is an extension to improve the model's sensitivity to the cropland fraction (see Appendix).

Land use is included in JSBACH following the protocol of Hurtt et al (2011), and described in detail in Reick et al (2013). In SPITFIRE, fires are excluded from cropland area, and pastures are treated the same as natural grasslands. Moreover, land use influences fire by changing the carbon stocks of the available fuels (Wilkenskjeld et al 2014).

In this study we use JSBACH in the offline mode, forced with meteorological forcing which was extracted from simulations with the MPI-ESM version 1.1 for the historical period 1850–2005. The spatial resolution is 1.875° × 1.875°, and the temporal resolution is 30 min. The fire routine is called once a day. For the spinup the first 28 years of forcing (1850–1877) were recycled, and CO₂ concentration fixed at the value of 1850 (284.725 ppm). The historical simulation from 1850 to 2005 uses transient climate, CO₂ concentration and land use. The population dataset is based on (Klein Goldewijk 2001). The decadal temporal resolution is interpolated to annual values, and updated at the beginning of the year.

To assess the impact of humans on the fire regime, we compare burned area from simulations with (simulation name includes tag 'human') and without human influence (tagged 'natural'). We include two different ways of accounting for the human impact on fire duration. The first decreases the fire duration with population density; the second additionally reduces fire duration with increases in the cropland fraction (tag 'Crops'). As the representation of human ignitions in the model already includes a suppression of ignitions for high population density, the simulations cannot factor out fire suppression by humans—only the combined effect of human enhancement and suppression of fires can be separated. A decrease in burned area when including human effects, however, indicates that human suppression dominates, while an increase in burned area would indicate that the additional ignitions are more important. We assess the importance of the feedback between fire and vegetation on the influence of humans on the fire regime, by comparing simulations with (DynVeg) and without (FixVeg) dynamic vegetation. In that case, a consistent initial vegetation cover was necessary, and land use needed to be included (NaturalLU simulations). Therefore, only the amplification of vegetation dynamics on the direct human influence on ignitions and fire suppression could be assessed. The effect of fire–vegetation feedback on the human impact was quantified by comparing the difference between HumanDynVeg and NaturalLUDynVeg to the difference between HumanFixVeg and NaturalLUFixVeg simulations. Overall six simulations with different settings in the computation of ignitions, fire duration, land use and vegetation dynamics were performed (table 1). We performed a spinup simulation of 1000 years for the simulations with dynamic natural vegetation. The spinup period with fixed vegetation cover was only 300 years, as only the rather fast litter carbon pools need to be in equilibrium to stabilize the fire regime.

Data analysis and plotting was done using R 3.3.3 (R Core Team 2016). In the regional analysis we define the boreal region as all grid cells with latitudes >60°, the temperate region with latitudes between −30° and −60° or 30° and 60°, and the tropical with latitudes between −30° and 30°.

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3.1.1. Global impact and spatial patterns

3.1.2. Human impact on burned area along gradients of plant productivity

Fire leads to a reduction in woody vegetation and an increase in highly flammable herbacious vegetation, forming a positive feedback. Due to this positive feedback alternative stable states under the same climatic conditions occur in JSBACH–SPITFIRE (Lasslop et al 2016). This means that increases in burned area can lead to the crossing of a tipping point driving the system in a stable low-tree-cover state with higher burned area. The effect of humans on fire is therefore expected to be higher in simulations with dynamic vegetation cover. The effect is quantified by comparing the difference between the HumanDynVeg and NaturalLUDynVeg simulations to that between HumanFix and NaturalLUFix. Both increases and decreases due to the human impact are stronger if vegetation cover is dynamic (figure 3). When vegetation cover is fixed, the human impact increases the burned area for preindustrial from 287.16 (NaturalLUFix) to 454.83 (HumanFix) Mha (58%), while when including the feedback between fire and vegetation, the human impact increases the burned area from 235.46 (NaturalLUDynVeg) to 456.61 (HumanDynVeg) Mha (93%). If the vegetation–fire feedback is excluded the differences in global burned area caused by the human impact are reduced by 24% in the pre-industrial period and 10% for present day (table 2, (HumanFix-NaturalLUFix) divided by (HumanDynVeg-NaturalLUDynVeg). The lower effect for present day might be due to the larger areas controlled by human land use.

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Over the last century, technological advances in fire fighting and increased resources spent on fire management have certainly impacted the human capacity to suppress fires. Humans change the local vegetation in many ways that may influence the fire regime—for instance, the introduction of new species with higher flammability can increase the area burned, or land management leading to a fuel reduction through harvest or grazing can lead to a reduction of burned area (Bowman et al 2011). These effects, especially their variation over time, are not represented in the model. The limited generalizable understanding and lack of global information limit the possibility of reflecting this knowledge in global models. The representation of humans in global models is largely based on data analyses of spatial datasets, which show that human parameters (e.g. population density and land use) do explain part of the variation observed for burned area, and how they influence burned area.

Modeling human ignitions and suppression as a function of population density is common in global fire models (Hantson et al 2016). It is often assumed that for low population density ignitions increase, and that for high population densities fires are increasingly suppressed. Some models include the suppression for high population density in the function of the ignitions, others include this influence only or additionally on the rate of spread or fire duration, and some models include the human suppression in a scaling function that reduces burned area directly (Hantson et al 2016, Rabin et al 2017). The suppression of fire for high population density is, however, now common in global fire models (Hantson et al 2016, Rabin et al 2017). This spread in the representation of human impact in models reflects the versatile results of data analysis. On the global scale, based on multivariate analysis of global spatial datasets, an increase in burned area with increases in population density even for very low population densities was not detected (Bistinas et al 2013), or detected only with high uncertainty (Knorr et al 2014, Lasslop et al 2015). In a study for Africa, an increase for low population density was found for the number of fires, but not for the burned area (Archibald et al 2009). When applying statistical models to smaller regions in the United States, response functions with a distinct maximum—similar to the modeling approach used here—were detected for many regions, but the exact shape varied widely (Parisien et al 2016).

In the evaluation of the modeled burned fraction along increasing population density, we found an underestimation of burned fraction for high and low population densities (figure A1). However, these regions with high underestimation only contribute 2.5% to the global burned area. Occurrence of ignitions in the model is based on the lightning and population datasets, as humans might also set fires in uninhabited places, or lightning strikes might not be tracked. A small background ignition rate might help to overcome this model error. Due to the small contribution of these areas to the total burned area, we expect that this can only have a small influence on the spatial patterns of human impact. The evaluation (figure A1) does not give a clear indication that our approach of increasing ignitions for low population densities and then reducing the same after a certain threshold should be replaced by a function that represents only suppression of humans, as indicated by some global data analysis (Bistinas et al 2013, Knorr et al 2014).

The second human dimension in global fire models is the fraction of croplands. Global data analysis confirms that the effect of croplands is to reduce burned area (Bistinas et al 2014, Andela and van der Werf 2014). Croplands are often simply excluded from burning in fire models (Rabin et al 2017). LPJ–LMfire is an exception, and includes a passive fire suppression assuming that the cropland fraction is a good proxy for landscape fragmentation (Pfeiffer et al 2013). Our extended model version, which reduces fire duration with increasing cropland fraction, improves the model performance (figure A1) but does not have a strong influence on the patterns of human influence on burned area.

Overall, our model-based study thus shows a plausible picture of limited influence of humans on burned area in regions with low vegetation productivity or meteorological conditions limiting burned area. The strong suppression of humans due to dense population and croplands in temperate regions is well supported by literature, and expected for other global fire models due to a similar representation of the human impact. The effects of humans in sparsely populated areas are most uncertain based on available literature, and comparing global model structures (e.g. whether they allow for anthropogenic enhancement of burned area). We find enhancement of burned area due to the human influence mostly in the tropics, due to the lower cropland fraction (on average cropland fraction is 50% higher in the temperate regions). A lower suppression for the tropical regions compared to the temperate regions can therefore also be expected for models not allowing for an enhancement of burned area due to human ignitions.

Besides the satellite data analyses discussed in the previous section, paleo-data contain information on the variability of fire. These datasets are especially valuable as they cover long time periods. On the other hand, the uncertainty of the datasets is larger, and it is less clear which aspect of the fire regime is captured (Brücher et al 2014). Variability in the charcoal record could be the result of variations in the area burned, or variations in the biomass consumed—for instance, due to changes in vegetation composition. Charcoal (Marlon et al 2008) and ice core (Wang et al 2010) data indicate a strong decrease in biomass burning between the pre-industrial era and present day. This decrease has been attributed to human action, as the decrease in fire occurrence is associated with a strong increase in population density, while the climate records cannot explain a decrease in fire occurrence. Our results confirm these previous findings, showing a strong decrease in burned area only, when human effects are included.

Previous studies have posed hypotheses about the effect of humans on fire regimes based on charcoal data analysis and theoretical considerations (McWethy et al 2013, McWethy et al 2010). They suggest that in temperate regions the influence of humans amplifies fire in regions with high net primary productivity (NPP), while for intermediate NPP suppression is expected (McWethy et al 2013). Our model results show the suppression for intermediate NPP, which disappears for higher NPP, but do not show an increase of burned area due to human influence for high NPP. Paleo records indicate that the arrival of humans is often followed by a strong increase in fire occurrence (McWethy et al 2010). Although the model approach does not distinguish between initial and later phases of human settlement, the cropland fraction might be an indicator for this development. In present day temperate regions, humans have already passed this initial phase, and the region is now mainly characterized by fire suppression.

In tropical regions, a study investigating the effect of different burning patterns in tropical savannas did not find effects on the total area burned (Van Wilgen et al 2004). However, increases are expected, especially if the disturbance of primary forests leads to a higher flammability of the landscape (Cochrane 1999). Based on the uncertainties in how to model the effect of humans for low population densities, and the variety of results from data analysis (see above), we consider the very strong enhancement in the tropics as most uncertain.