Mario Marin Herrera

Bio: Mario Marin Herrera is an academic researcher. The author has contributed to research in topics: Global warming & Climate change. The author has an hindex of 6, co-authored 7 publications receiving 254 citations.

Escalating impacts of climate extremes on critical infrastructures in Europe

Abstract: Extreme climatic events are likely to become more frequent owing to global warming. This may put additional stress on critical infrastructures with typically long life spans. However, little is known about the risks of multiple climate extremes on critical infrastructures at regional to continental scales. Here we show how single- and multi-hazard damage to energy, transport, industrial, and social critical infrastructures in Europe are likely to develop until the year 2100 under the influence of climate change. We combine a set of high-resolution climate hazard projections, a detailed representation of physical assets in various sectors and their sensitivity to the hazards, and more than 1100 records of losses from climate extremes in a prognostic modelling framework. We find that damages could triple by the 2020s, multiply six-fold by mid-century, and amount to more than 10 times present damage of €3.4 billion per year by the end of the century due only to climate change. Damage from heatwaves, droughts in southern Europe, and coastal floods shows the most dramatic rise, but the risks of inland flooding, windstorms, and forest fires will also increase in Europe, with varying degrees of change across regions. Economic losses are highest for the industry, transport, and energy sectors. Future losses will not be incurred equally across Europe. Southern and south-eastern European countries will be most affected and, as a result, will probably require higher costs of adaptation. The findings of this study could aid in prioritizing regional investments to address the unequal burden of impacts and differences in adaptation capacities across Europe.

Escalating impacts of climate extremes on critical infrastructures in Europe

Abstract: Extreme climatic events are likely to become more frequent owing to global warming. This may put additional stress on critical infrastructures with typically long life spans. However, little is known about the risks of multiple climate extremes on critical infrastructures at regional to continental scales. Here we show how single- and multi-hazard damage to energy, transport, industrial, and social critical infrastructures in Europe are likely to develop until the year 2100 under the influence of climate change. We combine a set of high-resolution climate hazard projections, a detailed representation of physical assets in various sectors and their sensitivity to the hazards, and more than 1100 records of losses from climate extremes in a prognostic modelling framework. We find that damages could triple by the 2020s, multiply six-fold by mid-century, and amount to more than 10 times present damage of €3.4 billion per year by the end of the century due only to climate change. Damage from heatwaves, droughts in southern Europe, and coastal floods shows the most dramatic rise, but the risks of inland flooding, windstorms, and forest fires will also increase in Europe, with varying degrees of change across regions. Economic losses are highest for the industry, transport, and energy sectors. Future losses will not be incurred equally across Europe. Southern and south-eastern European countries will be most affected and, as a result, will probably require higher costs of adaptation. The findings of this study could aid in prioritizing regional investments to address the unequal burden of impacts and differences in adaptation capacities across Europe.

Increasing the detail of European land use/cover data by combining heterogeneous data sets

Abstract: Data on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their in...

Integrating the MOLAND and the Urban Atlas Geo-databases to Analyze Urban Growth in European Cities

Abstract: The MOLAND (MOnitoring LANd use/cover Dynamics) and the Urban Atlas (UA) are two well-known, detailed data sets of land use/cover information focused on European cities. The MOLAND data set contains a unique time series of land use/cover changes for more than thirty urban areas covering a wide temporal window (1950 to late 1990s). The UA is a more recent project that mapped land use/cover for more than 300 cities for the year 2006. In this paper we discuss the integration of both data sets in order to produce a single geo-database covering an extended time series spanning from 1950 to 2006. The different cartographic specifications of the two input data sets, particularly in terms of spatial and thematic resolution, impeded a straightforward integration. A methodology was therefore set up to harmonize the two data sets and merge them into a consistent and comparable geo-database that can be easily queried and used for both visual and analytical purposes. The usefulness of the newly integrated geo-database...

The case for systems thinking about climate change and mental health

Abstract: It is increasingly necessary to quantify the impacts of climate change on populations, and to quantify the effectiveness of mitigation and adaptation strategies. Despite growing interest in the health effects of climate change, the relationship between mental health and climate change has received little attention in research or policy. Here, we outline current thinking about climate change and mental health, and discuss crucial limitations in modern epidemiology for examining this issue. A systems approach, complemented by a new style of research thinking and leadership, can help align the needs of this emerging field with existing and research policy agendas.

The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

Abstract: . Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

Changes in air passenger demand as a result of the COVID-19 crisis: using Big Data to inform tourism policy

Abstract: This paper develops a methodology for the early detection of reactivation of tourist markets to help mitigate the effects of the COVID-19 crisis, using Skyscanner data on air passenger searches (>5,000 million) and picks (>600 million), for flights between November 2018 and December 2020, through ForwardKeys. For future travel during the May to September 2020 period, the desire to travel (based on the number of flight searches) has dropped by about 30% in Europe and the Americas, and by about 50% in Asia, while intention to travel (the number of flight picks, the final selections amongst flight searches) has dropped a further 10-20%. Most source markets remain optimistic about air travel during the last quarter of 2020, suggesting a U shape recovery. However, optimism has dwindled as time passes, suggesting a flatline L shape. A traffic light dashboard for domestic and inbound air travel demand to Spain shows how destination managers might use Big Data relating to the early recovery of key source markets to develop targeted marketing strategies. We show how Big Data provides timely granular data essential in highly volatile situations, and we argue that destination management organisations must improve their Big Data analytical and evidence-based, decision-making skills.