Fuel Shapes the Fire-Climate Relationship: Evidence from Mediterranean Ecosystems

Pausas et al. (2012) wish to understand how vegetation affects fire-climate dynamics. They predict that fuel and vegetation structure dictate ecosystem sensitivity to fire and will switch climatic conditions to high flammability. They observe 13 regions distributed along an aridity gradient on the Iberian Peninsula. They assessed the changes in the temporal fire-climate relationship across the regions by estimating various variables. The variables were then related to fuel structure indicators and regional aridity. Pausas et al. find that the aridity level switch to flammable conditions increased along the aridity gradient and that the differences in fire activity between regions was explained by the sensitivity of fire to Mediterranean conditions. They conclude that fuel structure is a more significant driver of fire activity and their results highlight the role of vegetation structure in shaping future and current fire-climate relationships at a regional scale. –Loren Stutts
Pausas, J.G., and Paula S. 2012. Fuel shapes the fire-climate relationship: evidence from Mediterranean ecosystems. Global Ecology & Biogeography, in press.

            Fire profoundly shapes ecosystems and biogeochemical cycles. Current changes in fire regimes are significantly impacting biodiversity and ecosystem functioning thus creating a growing interest for a deeper scientific understanding of the drivers of fire regimes. Climate influences fire regimes by affecting fuel structure and fuel moisture. Fuel moisture controls plant flammability while fuel structure determines the amount and connectivity of burnable resources. The roles of both fuel structure and fuel flammability in determining fire activity vary along the global productivity gradient. Specifically, in productive and moist regions, fire activity is controlled by the frequency with which flammable conditions are attained; however in unproductive and arid regions, fuel limitation restricts fire activity. In productive ecosystems, denser vegetation allows low intensity fire to spread more easily while sparse vegetation in arid ecosystem dry weather conditions propagates fire. This suggests that fuel or vegetation structure controls fire-climate relationships because it determines the climatic conditions needed to promote fires.
            Pausas et al. hypothesize that vegetation and landscape structure shape the fire climate relationship at a regional scale. The climatic conditions that increase flammability depend on fuel structure and thus change along the aridity/productivity gradient. The authors analyze whether the monthly aridity that dictates fire activity depends on regional climate and thus on fuel structure, along a climatic gradient on the Iberian Peninsula. Pausas et al. select the Iberian Peninsula because its high environmental variability provides a wide range of productivity conditions in a singular biogeographic unit.
            Pausas et al. use fire data (19682007) obtained from the Spanish Forest Service that include size, date, and location of each wildfire for all of Spain except the Basque Country and Navarra. They used a CORINE land cover map of Spain to differentiate wildland from non-forested areas and to analyze fuel cover statistics. They used the Forest Potential Productivity (FPP) map as an indicator of productivity. Monthly potential and actual evapotranspiration (AET, PET) for 19682007 was collected from layers produced by the Spanish government’s environmental bureau. AET layers were obtained by running the SIMPA hydrological model with PET and precipitation data. PET layers were produced from mean temperature data using the Thornthwaite method. To evaluate climatic variability within and between regions, Pausas et al. used temperature and precipitation records from the 19682007 period, and mean monthly wind velocity generated by the Spanish Meteorological Agency (AEMET). To define environmentally homogeneous region on the Iberian Peninsula, Pausas et al. combined available information and finally obtained 13 regions covering 82% of the Iberian Peninsula.
            In terms of data analyses, Pausas et al. considered the parameters of forest potential productivity (FPP), proportion of woodland area, distance between wildland patches, and proportion of wildland area to obtain of general characterization of the fuel structure in each region. They computed total woodland and wildland areas by adding up the corresponding patch areas obtained from the CORINE land cover map. The distance between forest patches was computed using FRAGSTATS. They used this measure since it directly related to fuel continuity across landscapes and thus to fire activity and spread. For climate analyses, Pausas et al.defined the Aridity Index as the difference between PET and AET. The difference integrates energy and water supplies, which are the climatic determinants of vegetation distribution and plant growth. This Aridity Index was computed monthly for each region for the whole study period (1968-2007) and for the average condition of each region. The mean annual Aridity Index was correlated with productivity indicators (AET and FPP) and with variables related to vegetation structure and landscape density. For fire season analyses, fire climate relationships were analyzed for the months of June to September. For thresholds, Pausas et al. sorted the monthly area burnt by the monthly Aridity Index and estimated the breakpoint with a test in their specific statistical analysis software. This breakpoint was considered to be the Aridity Index Threshold beyond which a switch to flammable conditions occurs. To determine patterns along the aridity gradient, Pausas et al. used the following variables: a) the Aridity Threshold, b) frequency of flammable conditions, and c) the anomaly in the area burnt under such conditions. They then analyzed the changes in these variables along the aridity gradient by testing their relation to the mean annual Aridity Index of each region. They used a linear regression analysis to test whether the aridity gradient explained the variability in the Aridity Threshold. They used a generalized mixed model (GLMM) to analyze changes in the frequency of flammable conditions along the aridity gradient. And finally to assess the variability of the standardized anomaly in the area burnt along the aridity gradient, Pausas et al. used a linear mixed model with the mean annual Aridity Index. For mixed models, model fit and estimation of dispersion was conducted using an analysis of deviance. They assessed the spatial autocorrelation in all studied parameters by using the Moran’s I Autocorrelation Index. They then estimated the Moran’s I of the residuals of each regression considered.
            Pausas et al. find that the relationship between monthly burned area and monthly Aridity Index exemplifies a threshold pattern in the 13 regions. Specifically they found that the drier the region, the higher the Aridity Threshold and similarly the Aridity Threshold was higher for less productive regions with lower fuel loads and connectivity. They also found that the required change in the Aridity Index to attain flammable conditions was negatively related to the mean annual Aridity Index meaning that productive (wet) regions need a greater reduction in moisture to become flammable. Yet fire activity was negatively related to the aridity of the region suggesting that productive regions burned more than arid regions. The Pausas et al. findings on the global aridity gradient imply that fuel structure is more relevant than the frequency of drought.
            Their findings provide evidence that flammability and fuel structure act simultaneously in driving fire regimes though not necessarily over the same temporal/spatial scale, and that the sensitivity of fire activity to dry conditions increases with productivity meaning the switch to flammable conditions has a greater effect on fire activity in productive system than in dry ones. In mesic or wet regions, fuel is less relevant and fire depends on the climatic conditions conducive to fire propagation and ignitability. In drier regions, area burned is low as a result of low fuel load and connectivity. In their study area, the more dry the region, the higher the dryness level needed for switching to flammable conditions thereby indicating that the Aridity Threshold is influenced by fuel.
            The essential role the Aridity Threshold plays in the ecosystems of the Iberian Peninsula reveals the importance of landscape structure in fire-climate relationships along the spatial scale. Specifically fuel structure climatically controls fire activity since fuels determine the climatic conditions that drive the switch to high flammability. Increased fire activity is predicted in highly productive regions, and Pausas et al.’s findings support this claim since they found that the fire-climate relationship changes along the productivity gradient and that wetter systems become flammable under wetter conditions in comparison to drier regions. Fuel structure plays a key role in shaping current fire regimes and will also dictate the direction of future fire regimes. Pausas et al. highlight that fuel structure does depend exclusively on environmental conditions. The relationship between fire and climate changes spatially with fuel along the aridity gradient but also temporally in response to different land use and management practices.

Community-based Model for Bioenergy Production Coupled to Forest Land Management for Wildfire Control using Combined Heat and Power

With wildfires becoming more frequent and severe in North America and around the world, forest management plans have come under review in an effort to mitigate higher fire suppression costs as well as human and climate induced fire regime changes. When implementing forest management plans, small communities located deep within the wildland urban interface (WUI) are often left out of the equation for reasons largely to do with economies of scale. Yablecki et al. (2011) developed a comprehensive approach to treating fuels to minimize the threat of wildfires in remote areas while using the biomass generated from the forest treatment process for electrical generation, making the communities more sustainable and self-sufficient. Additionally this community-based model afforded long term lowered utility costs and greenhouse gas (GHG) emission reductions. The authors conclude that their proposition combines wildfire mitigation through forest treatment, power generation through use of biomass, and all other associated benefits, in a model that is entirely managed by the community. –Lindon Pronto

Yablecki, Jessica, Bibeau, Eric L., Smith, Doug W., 2011. Community-based model for bioenergy production coupled to forest land management for wildfire control using combined heat and power. Biomass and Bioenergy 35, 2561–2569.

          Using previously published work and available information, Yablecki et al. established and presented a general understanding of the wildfire threats and range of energy (acquisition) needs, and coupled them with common fuels treatment processes and costs per hectare under forest management plans in the USA and Canada. An estimated 20, 000 communities have been identified in the US as vulnerable to wildfires, many of the most severely threatened and previously impacted, lying within the Wildland Urban Interface (WUI)—the area where communities integrate into forested land. In these areas there is less access (escape routes), more dangerous fuel loading in close proximity to homes, and in more remote areas, very limited fire suppression resources. This study postulates that reactive fire management plans are no longer effective, and that in addition to other factors, proactive fuel treatment is preferred to heighten public safety, reduce the high cost of fire suppression activities, and to limit the devastating effects of home and business loss. In more remote communities, the authors propose an all encompassing model to accomplish the aforementioned goals, through community involvement and innovation in sustainable design, while addressing other community needs such as energy generation. In order to partially offset the cost of the forest treatment processes which are to occur every 15 years (in any given area), the use of onsite bioenergy generation is proposed under three models; operating scenarios are illustrated for two of them.
          The first aspect of this model was an evaluation of fuel treatment costs in threatened communities. Costs were determined to vary from a low of $130 per hectare for prescribed fire alone, to nearly $3,000 per hectare with a combination of prescribed fire and mechanical treatment. Although the cost of mechanical treatment was significantly higher, so are the secondary use options, and hence the potential for additional revenue. One commonly associated issue with mechanical treatment is the cost of transporting removed biomass to be processed offsite—something unfeasible for very remote areas. Because the proposed model makes use of biomass onsite, these costs are eliminated. Biomass that was required to meet energy needs under three energy generating system types, were based on estimates of total annual energy use within a given community. The fuels treatment plan was adjusted accordingly to produce a sufficient amount of biomass for the bioenergy systems; the preferred 15–20 year cycles (estimated time before fuel loading becomes hazardous again) was taken into account and the threat of wildfires was greatly reduced under the new management plan.
          The three proposed energy generating systems all fall under the category of combined heat and power (CHP) systems, and are best suited for small scale operations; they are therefore of the more appropriate technologies for these remote communities (most often removed from the power grid to begin with). They are the small-scale CHP steam Rankine system, the organic Rankine cycle (ORC), and the entropic cycle. The small-scale steam Rankine system produces high pressure steam for electricity generation through a direct-fired biomass conversion system that uses a boiler. This system however has the highest capital cost and requires specialized labor. The ORC system, of which there is a proven model commercially available in Europe, has a lower environmental impact and a higher operating efficiency with a 10% (electrical) energy conversion rate. However, it uses a variety of working fluids as alternatives to water, many of which are very volatile. The final approach evaluated, and found to be most suitable, was the entropic cycle. This system uses a process combination of the ORC system and small scale Rankine system to have an overall conversion efficiency of 68% with 12% representing the electrical conversion portion. The entropic cycle is the safest option, does not require specialized labor, and is a closed loop system so it does not require external cooling components and is therefore smaller in size.
Yablecki et al. chose a base case community of 100 residents expending an estimated 240kW (from three small diesel generators) for the modeling exercise; they used data from small communities in British Columbia as reference. They ran two scenarios with the selected three models. The first scenario utilized the CHP systems at 75–100% operating capacity year-round, while using some energy derived from diesel generators to offset a small portion of unmet energy needs in peak times (i.e. winter). The second scenario utilized only biomass; therefore the biomass required as well as the radius of fuel treatment needed, was greater. Between all three CHP energy systems, the entropic system proved to have the lowest capital investment, the highest return, and the lowest biomass input requirements. It therefore had the lowest need for labor intensive treatment processes and the associated costs as well.
To evaluate the GHG emission reductions as a consequence of this community based CHP bioenergy production and forest management model, the authors replaced gasoline fueled vehicles with electrical plug-in hybrid vehicles.  This new fleet of vehicles could derive all their power from the CHP system(s) while only minimally expanding the community bioenergy production model, simultaneously reducing the communities GHG emissions and their dependency on imported fuels. Finally, Yablecki et al. formulated a loose revenue model largely based on overall long term savings while highlighting the revenue streams under the two scenarios. The payback periods under the Entropic and ORC systems were 18 and 24 years, respectively. Considered for example, were the fuels treatment costs per hectare (an average of $1389), and a fuel consumption of 4.8 L per 100km for the hybrid vehicles (PHEV60).

Though the authors cautioned against the variability possible when applying this model to different areas on different scales, they contend that it is a valuable comprehensive community-based solution that goes beyond just mitigating the often devastating effects of wildfires within the WUI in the US and Canada. Yablecki et al. suggest that this model revitalizes communities and addresses a host of issues from public safety, preventative forest fire mitigation practices in remote areas, and maintaining forest health, while reducing GHG emissions and dependence on imported fuels. Overall, this model, suited for small communities, is a sustainability and bioenergy model that uses mechanical forest treatment as its primary support and supply mechanism to provide a wide range of community benefits.