Estimating changes in flood risks and benefits of non-structural adaptation strategies - a case study from Tyrol,Austria

Flood damage has increased significantly and is expected to rise further in many parts of the world. For assessing potential changes in flood risk, this paper presents an integrated model chain quantifying flood hazards and losses while considering climate and land use changes. In the case study region, risk estimates for the present and the near future illustrate that changes in flood risk by 2030 are relatively low compared to historic periods. While the impact of climate change on the flood hazard and risk by 2030 is slight or negligible, strong urbanisation associated with economic growth contributes to a remarkable increase in flood risk. Therefore, it is recommended to frequently consider land use scenarios and economic developments when assessing future flood risks. Further, an adapted and sustainable risk management is necessary to encounter rising flood losses, in which non-structural measures are becoming more and more important. The case study demonstrates that adaptation by non-structural measures such as stricter land use regulations or enhancement of private precaution is capable of reducing flood risk by around 30 %. Ignoring flood risks, in contrast, always leads to further increasing losses—with our assumptions by 17 %. These findings underline that private precaution and land use regulation could be taken into account as low cost adaptation strategies to global climate change in many flood prone areas. Since such measures reduce flood risk regardless of climate or land use changes, they can also be recommended as no-regret measures.     

Using home range estimates to construct social networks for species with indirect behavioral interactions

Social network analysis has become a vital tool for studying patterns of individual interactions that influence a variety of processes in behavior, ecology, and evolution. Taxa in which interactions are indirect or whose social behaviors are difficult to observe directly are being excluded from this rapidly expanding field. Here, we introduce a method that uses a probabilistic and spatially implicit technique for delineating social interactions. Kernel density estimators (KDE) are nonparametric techniques that are often used in home range analyses and allow researchers studying social networks to generate interaction matrices based on shared space use. We explored the use of KDE analysis and the effects of altering KDE input parameters on social network metrics using data from a natural population of the spatially persistent forked fungus beetle, Bolitotherus cornutus.     

A framework for definition of logical scenarios for safety assurance of automated driving

Abstract

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments.

Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains.

Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project.

Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.     

The role of scale in integrating climate change adaptation and mitigation in cities

By using a scale framework, we examine how cross-scale interactions influence the implementation of climate adaptation and mitigation actions in different urban sectors. Based on stakeholder interviews and content analysis of strategies and projects relevant to climate adaptation and mitigation in the cities of Copenhagen and Helsinki, we present empirical examples of synergies, conflicts and trade-offs between adaptation and mitigation that are driven by the cross-scale interactions. These examples show that jurisdictional and institutional scales shape the implementation of adaptation and mitigation strategies, projects and tasks at the management scale, creating benefits of integrated solutions, but also challenges. Investigating the linkages between adaptation and mitigation through a scale framework provides new knowledge for urban climate change planning and decision-making. The results increase the understanding of why adaptation and mitigation are sometimes handled as two separate policy areas and also why attempts to integrate the two policies may fail.     

Global population collapse in a superabundant migratory bird and illegal trapping in China

Persecution and overexploitation by humans are major causes of species extinctions. Rare species, often confined to small geographic ranges, are usually at highest risk, whereas extinctions of superabundant species with very large ranges are rare. The Yellow‐breasted Bunting (Emberiza aureola) used to be one of the most abundant songbirds of the Palearctic, with a very large breeding range stretching from Scandinavia to the Russian Far East. Anecdotal information about rapid population declines across the range caused concern about unsustainable trapping along the species’ migration routes. We conducted a literature review and used long‐term monitoring data from across the species’ range to model population trend and geographical patterns of extinction. The population declined by 84.3–94.7% between 1980 and 2013, and the species’ range contracted by 5000 km. Quantitative evidence from police raids suggested rampant illegal trapping of the species along its East Asian flyway in China. A population model simulating an initial harvest level of 2% of the population, and an annual increase of 0.2% during the monitoring period produced a population trajectory that matched the observed decline. We suggest that trapping strongly contributed to the decline because the consumption of Yellow‐breasted Bunting and other songbirds has increased as a result of economic growth and prosperity in East Asia. The magnitude and speed of the decline is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon (Ectopistes migratorius), which went extinct in 1914 due to industrial‐scale hunting. Our results demonstrate the urgent need for an improved monitoring of common and widespread species’ populations, and consumption levels throughout East Asia.     

Risk assessment of metals and organic pollutants for herbivorous and carnivorous small mammal food chains in a polluted floodplain (Biesbosch, The Netherlands)

A risk assessment was made for a carnivorous and a herbivorous food chain in a heavily polluted natural estuary (Biesbosch), by determining the most critical pollutants and the food chain most at risk. Exposure of food chains to metals, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) was assessed by analyzing dietary concentrations, internal concentrations, and biomarkers of exposure. Common shrew (Sorex araneus) and bank vole (Clethrionomys glareolus) were selected as representative small mammal species for the carnivorous and herbivorous food chain, respectively, and earthworms (Lumbricus rubellus) and snails (Cepaea nemoralis) as representative prey species for the carnivorous food chain. Metals contributed most to the total risk for small mammals and earthworms. PCBs, but not PAHs, contributed to the overall risk for S. araneus at regularly flooded locations. The carnivorous food chain appeared most at risk given the higher exposure levels and bioaccumulating potency found for contaminants in S. araneus.