• Energy Research

AbstractRecent wildfire events (e.g. Mediterranean region, USA, and Australia) showed that this hazard poses a serious threat for wildland–urban interface (WUI) areas around the globe. Furthermore, recent events in regions where wildfire does not constitute a frequent hazard (e.g. Siberia, Scandinavia) indicated that the spatial pattern of wildfire risk might have significantly changed. To prepare for upcoming extreme events, it is critical for decision-makers to have a thorough understanding of the vulnerability of the built environment to wildfire. Building quality and design standards are important not only because building loss is costly but also because robust buildings may offer shelter when evacuation is not possible. However, studies aiming at the analysis of wildfire vulnerability for the built environment are limited. This paper presents an innovative solution for the vulnerability assessment to wildfires, making use of an all-relevant feature selection algorithm established on statistical relationships to develop a physical vulnerability index for buildings subject to wildfire. Data from a recent and systematically documented wildfire event in Greece (Mati, 2018) are used to select and weight the relevant indicators using a permutation-based automated feature selection based on random forests. Building characteristics including the structural type, the roof type, material and shape, the inclination of the ground, the surrounding vegetation, the material of the shutters and the ground covering were selected and formed into the index. The index may be used in other places in Europe and beyond, especially where no empirical data are available supporting decision-making and risk reduction of an emerging hazard amplified by climate change.

In the context of the United Nations’ “Agenda 2030 for Sustainable Development” and the presented Sustainable Development Goals (SDGs), the process of developing and agreeing on indicators to monitor the SDGs implementation becomes fundamental. In this paper, we identify indicators for the sustainable development of cities that have the greatest potential for their underlying data to be measured by means of remote sensing. We first identified existing indicators, which are derived from the International Standard ISO 37120, “Indicators for city services and quality of life”, as being partly or fully measured by the use of remote sensing, and then presented these indicators to remote sensing experts in an assessment procedure. We then investigated Multi-Criteria Decision-Making (MCDM) weighting methods to identify the most relevant quality of life indicators that can be captured by means of remote sensing techniques. We assess the remote sensing experts’ knowledge in the context of Decision Support Systems (DSS), and by means of both a questionnaire-based approach and a pairwise comparison approach. The approaches are compared with each other regarding their complexity, their potentials and limitations, and the respectively identified remote sensing based indicators. We identified three indicators related to surface characteristics as having the highest remote sensing potential. When contrasted to the results of the pairwise comparison, the questionnaire-based approach revealed high usability and confirmability. In the end, this approach enables cities’ administrations to decide which indicators they want to cover by means of remote sensing, depending on the capacities of their departments.

Data description Climate data The climate data comprises observational and climate scenario data for rainfall events that potentially trigger torrent processes. Empirical intensity-duration relationships were established by integrating event data from the Austrian torrent event catalog with high-resolution, hourly re-analysis precipitation data. Critical rainfall conditions were identified for two primary process types: debris flow and fluvial. The data represents the number of precipitation events per year that exceed critical rainfall thresholds over a period of up to five consecutive days. An event is counted only the fist time it exceeds a critical threshold to prevent double-counting. The dataset includes one file for each process type for the observational data SPARTACUS, and one file for each process type and model for the Austrian climate scenarios ÖKS15. Resolution: Time: Annual Space: 1 x 1 km grid Domain: Austria Torrent data Torrent event data include the event date, process type, and triggering rainfall metrics (total event precipitation, rainfall duration, rainfall intensity, maximum intensity, antecedent precipitation for 1, 3, 7 and 28 days), mapped to the catchment centroids. The data source denotes the WLK database (source = 1), supplemented with events from ERDOK (source = 2), respectively. Data sources INCA: https://doi.org/10.60669/6akt-5p05 SPARTACUS: https://doi.org/10.60669/m6w8-s545 ÖKS15: https://doi.org/10.60669/b37q-jd39

Data description This dataset contains observational and climate scenario data for rainfall events that trigger certain landslide processes. Empirical intensity-duration relationships were derived from the Austrian torrent event catalog together with high-resolution, hourly re-analysis data for precipitation. Critical rainfall conditions were determined for two main process types: debris flow and fluvial. The data represents the number of precipitation events per year that exceed critical rainfall thresholds derived for up to five consecutive days. An event is counted only the fist time it exceeds a critical threshold to avoid double-counting. The dataset contains one file for each process type for observational data SPARTACUS, and one file for each process type and model for the Austrian climate scenarios ÖKS15. Data resolution Time: Annual Space: 1 x 1 km grid Domain: Austria Data sources INCA: https://doi.org/10.60669/6akt-5p05 SPARTACUS: https://doi.org/10.60669/m6w8-s545 ÖKS15: https://doi.org/10.60669/b37q-jd39

In recent years, losses due to torrential flooding have been increasing in the Eastern European Alps, with effects of climate change and settlement growth being postulated root causes. In the context of flood mitigation, however, it remains unclear to which degree loss dynamics can be attributed to these causes. We addressed this question based on a record of approximately 12,000 events covering the period between 1962 and 2017, a database containing roughly 120,000 mitigation structures, an inventory of the building stock and 15 climate indices related to hazard triggering conditions. While the indices of triggering precipitation and the number of exposed buildings increased steadily, frequency, magnitude and seasonality of damage-inducing torrential flooding did not show clear trends. This contradiction was attributed to a compensatory effect of the increasing number of technical mitigation structures. Maintaining these structures is of paramount importance to counteract future effects of climate change on the magnitude and frequency of events and the increasing demand for land development in hazard-prone areas.

Information on frequency, magnitude and seasonality of damage-inducing torrential flooding events from steep, alpine headwater catchments (torrents) in Austria, for the period from 1962 to 2017. The datasets are based on information from the Austrian torrential event catalogue. The frequency data set is complemented with information on the number of functional torrential structures (technical mitigation measures), the number of exposed buildings as well as a multitude of climate indices related to precipitation, snow melt and the sum of precipitation plus snowmelt. Annual aggregates are derived by using area-weighted means across all catchments.