The Tuscan Region (Central Italy) spends about 12 million euros every year in the prevention and suppression of forest fires. In this context, this study aims to analyse the economic and environmental benefits derived from fire suppression activities. Starting from a case study of a real fire event in Tuscany, we simulated three hypothetical scenarios (with different fire durations) without fire extinction activities planned by using the open source software FARSITE. Benefits derived from fire extinction activities can be quantified as the avoided damage, which has been calculated through the estimation of the total economic value of forests not destroyed by fire thanks to the extinction action. The avoided damage is represented by the difference between values of forest areas burned by the real fire event and those burned by simulated fire. By providing an economic estimation of avoided damages, our results confirm that forest fire services and forest management have a high impact on both the economy and the environment.
In the last few decades, changes in land use and society have led to a significant increase in the number of wildfires. This phenomenon has become an important socio-economic and environmental problem that requires great attention, especially in terms of prevention (
The development of urbanized areas and viability in wild and mountainous areas are the main factors of this phenomenon. Indeed, the trigger point of numerous fires is close to the edge of roads and highways (
Despite the increase in the number of fires, the surfaces covered by fire are progressively decreasing in extent. In Italy, in the decade between 1995 and 2005, 1,185,000 hectares (ha) of surface burned, while 765,000 ha were destroyed by fire in the previous decade (2006-2015), corresponding to a reduction of 35% (
The regional administration of Tuscany invests almost 12 million euro per year in forest fire prevention and repression activities. Despite such significant financial commitment, both the extent of the damage caused to goods and that of the damage avoided thanks to fire prevention and repression are still unclear. Knowing the magnitude of such effects would allow better efficiency and effectiveness of investment planning policies.
Several studies in the literature aimed to assess the damage caused to agroforestry areas by fires (
Avoided damages are related to forests and the ecosystem services they provide (
The goal of the paper is to quantify the avoided damage by forecast a burned area by simulating a fire event with FARSITE and calculate the corresponding TEV values.
The wildfire examined occurred in Verniano, Colle di Val d’Elsa, near Siena (Tuscany, Central Italy) during the period between July 11 and August 3, 2012 (
The fire was certainly arson and caused by pruning residues burned in a farm near the affected area. The deployment of intervention forces was difficult, due to adverse weather conditions that required complex action plans and the intervention of three Canadair firefighting aircrafts over four days.
The area affected by the fire included mixed stands of conifers and broad-leaved trees, with a prevalence of Mediterranean pines and cypresses (domestic pine, maritime pine, Aleppo pine).
The estimated damage of the real fire event is based on the perimeter delineated by firefighters. For determining the costs of extinction or specific costs of the fight, reference was made to studies conducted by the Italian Academy of Forest Sciences in 2007 (
Simulations of wildfire were performed using the software FARSITE (Fire Area Simulator). FARSITE, developed by
The new FlamMap is able to model the potential fire behavior (spread rate, flame length, fireline intensity, etc.) under constant environmental conditions (weather and fuel moisture). In particular, thanks to the inclusion of FARSITE, the software can compute wildfire growth and behavior for longer time periods under heterogeneous conditions of terrain, type of fuels, fuel moistures and weather.
The FlamMap algorithm calculates the minimum fire travel time between nodes over a gridded landscape relying upon the spatial variability of fuels and topography to drive fire growth. However, FlamMap algorithm is not a complete fire growth simulation model like FARSITE. Despite this limitation, FlamMap will be evaluated to determine if it might offer computational advantages over FARSITE in an operational forecast setting (
Furthermore, the FARSITE was recently implemented with FlamMap in the Wildland Fire Decision Support System able to support fire officers in tactical and strategic management decisions in the long-term (up to 14 day). In detail, the model produces vector fire perimeters at specified time intervals; the vertices of these polygons contain information which is then interpolated to produce raster maps of fire behaviour.
The vector modelling approach proved to be a practical technique for incorporating separate models for surface fire, crown fire, acceleration, spotting, and fuel moisture. The model integration was relatively straightforward because the one-dimensional calculations for each model apply directly to the vertices on the fire front.
The semi-empirical propagation model of Rothermel’s grazing fire is based on statistical observations of the fire phenomenon under controlled conditions, combined with physical considerations of the combustion event. It derives from the correction of the Frandsen equation of 1971 (
where
In the literature, the FARSITE software has been applied to enhance data assimilation capabilities on both fire perimeters and fuel adjustment factors, thus improving the accuracy of fire spread predictions.
The FARSITE outputs illustrate the strict spatial consequences of fire behaviour by incorporating the models into a two-dimensional simulation. Simplified test conditions show that surface fire growth and intensity conform to idealized patterns. Similarities also exist between simulated crown fires and observed patterns of extreme wind-driven fires. According to
The input of the model included several data layers which can be grouped in three main categories: (i) landscape data; (ii) weather data; and (iii) fuel data.
(i) Landscape data include the digital elevation model, slope, canopy cover, and fuel type. To ensure a good representation of the FARSITE model in the Italian context, we defined the fire growth of individual fuel types by reclassifying land uses as fuel type codes which reflect the susceptibility to burning of each land use. To this purpose, the 1972 Rothermel model was modified in order to match the CLC (Soil Corine Land Cover 2012 level V, ver. 18.5.1) classes and the Rothermel Fuel Model classes (
(ii) Weather data (start-end precipitation, min/max temperature, min/max humidity, etc.) and data of the event (such as ignition point, direction, wind speed, etc.) were obtained from
(iii) Fuel files data are related to characteristics of land use such as crown bulk density, crown base height, foliar moisture content, stand height, etc.
Combining the above-mentioned data in the FARSITE input, three different scenarios were simulated which differed in the duration of the wildfire event without fire extinction activities: (i) Simulation 0 = same duration of the real event in Verniano; (ii) Simulation 1 = 7 days more than the duration of the real event; (iii) Simulation 2 = 14 days more than the duration of the real event.
Forest produces both private goods (timber production, non-wood products) and public utility services (recreational activities, hydrogeological function, biodiversity protection, CO2 storage, etc.). Considering fire damages, the multifunctional role of forest introduces a significant problem related to the compensation of two subjects involved: private owners for damage suffered by their incomes, and public owners for damage to ecosystem services. We computed the avoided damage due to fire extinction activities by quantifying the ecosystem function/services provided by the forest. These benefits represent the Total Economic Value (TEV) of the forest considering both private and public environmental functions, including incomes (
Regarding the degree of damage and the intensity of the event, the proposed approach requires to set some initial hypotheses. First, it is assumed that the damage to the forest is total, not partial; second, we hypothesized that the effects on private and public functions are temporary and therefore all functions can be restored after a recovery time.
Several authors in the literature analysed the damage to forests through the quantification of the TEV.
A wide literature provides a schematic classification of TEV.
The following functions have been considered in this study: (i) tourist-recreational function; (ii) naturalistic function; (iii) hydrological function; (iv) drinking water service; (v) timber production; (vi) carbon sequestration.
The recreational tourist function is given by the sum of recreational tourism activity, hunting activity and mushroom collecting activity. The first activity was estimated using the Travel Cost Method (TCM) with specific logit models for naturalistic areas (
Natural functions were calculated according to literature reviews (
The water flow service was estimated based on the refurbishment costs that would be necessary to pay in order to guarantee the maximum flow rates in the absence of the forest. In particular, considering run-off index and aridity index of each basin (
The drinking water service was assessed by hypothesizing that the best alternative to groundwater is represented by the water reserves stored in artificial basins and the consequent contribution of forest soils to the production of drinking water calculated using the water balance method. In this case, the values of water storage in the watersheds in Tuscany were defined based on the studies carried out by
Timber production was calculated by converting the capital value of the forest, obtained with the classic Faustmann formula, into a yearly value. According to the methodology reported in
Finally, the climate change mitigation service was quantified by assessing the carbon stored in the trees (and therefore not released into the atmosphere) which is related to tree growth. The calculation was based on the biomass expansion factor (BEF) to quantify the annual quantity of carbon fixed in the trees (
All technical parameters, data source and calculations of each function are available in
The economic damage which was avoided thanks to fire suppression activities was quantified using the above-mentioned TEV.
TEV was calculated for each pixel falling within the burned area both during the real event and for each of the three simulated scenarios (see above) as follows (
where TEVik is the TEV of
Previous studies have considered separately the different forest functions/services (
The overall TEV quantified over the Tuscany region is shown in
The avoided damage (AD) is represented by the TEV calculated only on the areas preserved from fire (unburned) thanks to the fire extinction activities. For this purpose, the fire growth simulation model was run for a duration equal to that of the real fire (as recorded in the intervention sheet) in order to verify which areas have been preserved thanks to fire suppression interventions (Simulation 0). Additional simulations were performed based on the scenario of no fire suppression, with a fire duration of one (Simulation 1) and two (Simulation 2) additional weeks. The avoided damage due to fire extinction activities has thus been estimated by the difference between the TEV of the simulated fire (no fire suppression) and the TEV of the real event (where fire suppression took place), using the following formula (
where
The surface destroyed by fire in Simulation 0 (
The TEV of areas destroyed by the real fire event (which had been controlled by fire suppression activities) is equal to 51,660 euro per year (sum of
The avoided annual damage estimated by eqn. 3 were: (i) Simulation 0 = 14,537 euros per year (28.1% of real TEV); (ii) Simulation 1 = 19,011 euros per year (36.8% of real TEV); (iii) Simulation 2 = 19,040 euros per year (36.8% of real TEV).
It is important to note that on the 8th day after fire ignition the fire front reached rocky and clayey areas poor in fuel, thus it seems plausible to hypothesize that the wildfire would likely be estinguished anyway due to fuel depletion.
The recovery time of forest stands and the restoration of ecosystem functions depends on many variables; in particular, it is strongly influenced by the tree species and the turnover of forest management (coppice or high forest). However, during recovery, the annual value of the forest functions is lost. According to
where AADk is the accumulation of avoided damage of
As argued by
The Tuscan Region spends about 12 million euros every year in the prevention and suppression of forest fires. In this context, this study has analysed the economic and environmental benefits derived from the activities of fire suppression.
Regarding the extinction activities, a monetary approach for the quantification of direct damage “avoided” to the environmental components and anthropic activities has been applied. In particular, the difference between the TEV of fire events simulated by FARSITE and the TEV of the real event represents the avoided damages due to fire suppression activities. For this reason, the fire growth simulation model is particularly important to define the surfaces preserved from fire thanks to the fire suppression intervention. The inclusion of different variables (
It is important to highlight that the observed results are strictly related to the specific fire event occurred in the case study area, as each wildfire is obviously different. Nonetheless, the methodology applied in this study could be adopted in other contexts in order to map the fire events both in a specific area (municipality, province, region, etc), in a specific period (days, weeks, months, etc.) and/or under specific weather conditions.
All data used in this study have been georeferenced with a high level of detail (pixel resolution: 10 × 10 m) using a Geographical Information System. The use of high-resolution georeferenced data represents a new frontier in spatial territorial planning, as argued by
The main drawback of this study relies in the implicit assumption that the values estimated for each ecosystem function/service at each pixel can be added up together to quantify the TEV. This can be overcome in different ways in future studies. Nonetheless, the monetary quantifications of ecosystem services allowed to analyse fire damages from an economic and environmental point of view. Indeed, we highlighted the potential loss of the economic value of ecosystem services due to fire. Combining the information on the TEV in different fire scenarios based on simulations, we were able to quantify the damage avoided by fire suppression activities, which was equal to 14,537 euros year-1 for the Simulation 0 scenario,
The sensitivity analysis carried out allowed to forecast the future revenues that could be lost due to fire. This analysis provides different results using different temporal scenarios and different discount rates. The aim is to guide stakeholders towards an optimal planning decision.
In the case study, the fire suppression activities were characterized by a massive use of men and airplanes in the first four days to avoid fire propagation towards an inhabited area, and this justifies the high fire suppression costs. It is important to highlight how public goods have no effect on high suppression costs. The sequence of extinction activities and their intensity is listed in all operating manuals (
Regarding the FARSITE model used in this study, the main weakness is the large amount of meteorological data necessary to apply the model to a specific area. However, the model does not consider the “management” choices of the coordinator of fire suppression operations, which deeply affects the fire extinction time and consequently the fire damage.
This study represents a first step to support the economic sustainability of fire extinction activities, and offers a useful basis to further improve the choice of correct planning strategies based on sustainable management of natural areas, as argued by
The study area in Verniano, Tuscany (Central Italy).
Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 0” (no fire suppression, same duration).
Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 1” (no fire suppression, +7 days duration).
Extension of the area burned during the real fire event (in white) and that simulated under the “Scenario 2” (no fire suppression, +14 days duration).
Total Economic Value (TEV) of each forest function/service calculated over all the Tuscany region (data expressed in euros yr-1).
Function/Service | Value |
---|---|
Recreational/tourist function | 219,860,253 |
Naturalistic function | 210,043,738 |
Water flow control | 28,224,320 |
Drinking water service | 59,382,140 |
Wood production | 25,116,257 |
Climate change mitigation service | 59,017,484 |
TEV | 601,644,192 |
Estimated values of damage avoided due to fire suppression activities under different scenarios (different duration of the fire event).
Event | TEV(€ yr-1 - eqn.1) | Annualavoided damage (AD, € yr-1 - eqn. 2) | Accum. of avoided damage (AAD, € 20yr-1 - eqn. 3) |
---|---|---|---|
Real | 51,660 | - | - |
Scenario 0 | 66,197 | 14,537 | 237,701 |
Scenario 1 | 70,671 | 19,011 | 310,857 |
Scenario 2 | 70,700 | 19,040 | 311,331 |
Sensitivity analysis of TEV for “Scenario 0” (data expressed in euros).
Restoringtime | Discount rate | |||||||
---|---|---|---|---|---|---|---|---|
1% | 2% | 3% | 4% | 5% | 6% | 7% | 8% | |
20 | 262,328.20 | 237,700.79 | 216,273.85 | 197,562.57 | 181,163.15 | 166,738.24 | 154,005.19 | 142,726.41 |
40 | 477,317.83 | 397,666.60 | 336,019.44 | 287,727.55 | 249,441.64 | 218,728.02 | 193,803.05 | 173,348.10 |
60 | 653,511.39 | 505,319.01 | 402,319.67 | 328,877.67 | 275,175.08 | 234,938.67 | 204,087.58 | 179,917.93 |
80 | 797,909.85 | 577,765.99 | 439,028.50 | 347,658.05 | 284,873.75 | 239,993.23 | 206,745.29 | 181,327.48 |
100 | 916,250.81 | 626,520.74 | 459,353.29 | 356,229.17 | 288,529.07 | 241,569.27 | 207,432.10 | 181,629.89 |