Integration of ecological and socio‐economic factors to assess global vulnerability to wildfire

Abstract

Aim This paper presents a map of global fire vulnerability, estimating the potential damage of wildland fires to global ecosystems. Location Global scale at 0.5° grid resolution. Methods Three vulnerability factors were considered: ecological richness and fragility, provision of ecosystem services and value of houses in the wildland–urban interface. Each of these factors was estimated from existing global databases. Ecological values were estimated from biodiversity relevance, conservation status and fragmentation based on Olson's ecoregions. The ecological regeneration delay was estimated from adaptation to fires and soil erosion potential. The former was assessed by comparing actual land cover with fire‐off simulations based on a dynamic global vegetation model (ORCHIDEE). The annual loss of ecosystem services was estimated with values transferred from other studies and loss coefficients. This was integrated throughout time by considering the regeneration delay. Value of houses was estimated at country level according to the market prices of real‐estate and land, the level of economic development and the population density. Economic and ecological evaluations were merged through cross‐tabulation logic to obtain qualitative ranks of fire vulnerability. Results The most vulnerable areas were found to be the rain forest of the Amazon Basin, Central Africa and Southeast Asia, the temperate forest of Europe, South America and north‐east America, and the ecological corridors of Central America and Southeast Asia. The lowest vulnerability was observed in boreal regions, particularly those already affected by fires or having low biodiversity, agricultural regions of Australia, India, Latin America and Central Asia. Main conclusions This is the first attempt to produce a map of global fire vulnerability, based on a wide variety of factors related to the impacts of fire on ecological and socio‐economic values. This product will help current efforts to model future scenarios of the impacts of biomass burning for different climate and land‐use scenarios.

Climate change impacts and adaptation in forest management: a review

“…In a recent study, Chuvieco et al (2014) assessed ecosystem vulnerability to fire using an index based on ecological richness and fragility, provision of ecosystem services and value of houses in the wildland-urban interface. The most vulnerable areas were found to be the rainforests of the Amazon Basin, Central Africa and Southeast Asia; the temperate forest of Europe, South America and north-east America; and the ecological corridors of Central America and Southeast Asia.…”

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

“…First, prefecturallevel asset value is downscaled to the county level within the prefecture by the variation of accumulated total investment of fixed assets (TIFA) to its located prefecture in the last 15 years. Invest R county denotes the proportion of the county-level TIFA to its located prefecture in the last 15 years, 7 which indicates how much the county asset value differs from the prefecture's, i.e. :…”

“…Obviously, applying this to all 344 prefectures in China is questionable but simply a suboptimal choice to deal with the lack of available data in other provinces. 7 According to Wu et al, (27) fixed asset value is significantly related to the latest fixed assets investment as economic depreciation of the old assets went on, and county-level TIFA data are usually available since 2000. Accuracy assessment is included in the following section.…”

“…(1)(2)(3) The importance of wealth exposure evaluation in disaster risk assessment is widely recognized, (4)(5)(6)(7)(8) and spatial variability (or heterogeneity) is increasingly recognized as a key component in environmental exposure and risk assessment, (9)(10)(11) but high spatial resolution data on actually exposed assets are lacking or not sufficiently detailed. (12) Bouwer et al (13) projected both future land use change and future increase of asset value combined with flood scenarios to estimate the flood risk in the Netherlands, and indicated that asset value increase plays a large role in flood losses increase.…”

A GIS-Based Assessment of Vulnerability to Aeolian Desertification in the Source Areas of the Yangtze and Yellow Rivers

“…In principle, the indicators should reflect (i.e., be symptoms of) the underlying driving forces that are responsible for aeolian desertification, and should be reliable, regularly updatable, available at low cost, and readable to cover large areas at high spatial resolution [20]. Many studies have focused on measuring the vulnerability by integrating information from the natural and social sciences [21,22]. Based on the abovementioned principles, attributes of aeolian desertification, and data availability for our study area, we developed an indicator system to assess the vulnerability of land in the source areas of the two rivers to aeolian desertification.…”