Planning Mitigation Efforts Research Articles

Railroad-Associated Mortality Hot Spots for A Population of Romanian Hermann's Tortoise (Testudo Hermanni Boettgeri): A Gravity Model for Railroad-Segment Analysis

Abstract Road-kill can lead to a sharp local decline of herpetofauna species. For this reason, transportation agencies are more and more interested to implement mitigation measures in order to eliminate this threat. The present study proposes to identify the railroad network induced threats at a railroad segment spatial scale on Getic Tableland, south-western Romania, by highlighting associated mortality hot spots for Testudo hermanni boettgeri. The railroad segment was chosen due to the reported road-kills and high traffic volume. In order to identify road associated mortality hot spots, we adapted a gravity model by including a weighting coefficient for overtaking obstacles. The model was adapted after observing that the cuts, fills, ditches and guardrails can change the tortoises behavior, making them avoid dangerous crossings, thus influencing the distribution of hot spots. As a main result, our study managed to adapt a gravity model for a more accurate assessment of railroad associated mortality. The average value of inter-habitat interaction is reduced by 23.37% after introducing the coefficient of overtaking the obstacles. However, despite the numerous obstacles, at a home range spatial scale, the maximum inter-habitat interaction value is not decreased, the range being stable (range = 0 - 99.66). Instead, the spatial extent of the hot spots is modified because of the increased territorial dependence and home range multi-annual stability, both severely threatening the tortoise that have a home range bisected by a major railroad. Our study accurately identifies the hot spots, which is particularly important in planning mitigation efforts, for building effective underpasses and fences systems.

Social vulnerability assessment for Norway: A quantitative approach

The article presents a method for quantifying social vulnerability to natural hazards in Norwegian municipalities. In the analysis, a large number of variables that each measures a facet of a municipality's susceptibility to a potential hazard are used. Using factor analysis, the information in the variables is reduced to a smaller number of factors and socioeconomic and built environment vulnerability scores for each Norwegian municipality are calculated. The resulting scores in the Socioeconomic Vulnerability Index and Built Environment Index are mapped for each municipality. The results show that there are pronounced regional differences: municipalities with high socioeconomic vulnerability cluster in the northern half of Norway and parts of the south-east. The least vulnerable region is south-western Norway. Built environment vulnerability is highest in densely populated areas. By indicating municipalities with a high level of vulnerability, the method presented in this article is a useful tool in identifying regions which are likely to face significant challenges in coping with a large-scale event. The results can be used in, for example, planning mitigation efforts against extreme weather events, which are likely to be more frequent and severe in the future due to climate change.

Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA

A key challenge in modern wildfire mitigation and forest management is accurate mapping of forest fuels in order to determine spatial fire hazard, plan mitigation efforts, and manage active fires. This study quantified forest fuels of the montane zone of Boulder County, CO, USA in an effort to aid wildfire mitigation planning and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 randomly stratified field plots, pre-existing vegetation maps, and derived variables, predictive classification and regression tree models were created for four fuel parameters necessary for spatial fire simulation with FARSITE (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56–62% of the variability in forest fuels and produced fuel maps that predicted 91.4% and 88.2% of the burned area of two historic fires simulated in the FARSITE model. Simulations of areas burned based on LANDFIRE national fuel maps were less accurate, burning 77.7% and 40.3% of the historic fire areas. Our results indicate that fuel mapping efforts that utilize local area information and biotic as well as abiotic predictors will more accurately simulate fire spread rates and reflect the inherent variability of forested environments than do current LANDFIRE data products.