Toward a framework for systemic multi-hazard and multi-risk assessment and management

Opportunities and challenges for people-centered multi-hazard early warning systems: Perspectives from the Global South

This perspective critically examines the challenges and opportunities of implementing people-centered multi-hazard early warning systems (MHEWS) in the Global South. Despite global initiatives, such as the Early Warnings for All initiative, operational realities lag behind. By exploring the needs of the most vulnerable and how core concepts of multi-hazard thinking (e.g., hazard interrelationships and vulnerability dynamics) integrate into different pillars and cross-cutting components of an MHEWS, the perspective highlights a mismatch between current ambitions and realities on the ground. Drawing on extensive experience from Practical Action, we identify opportunities to move toward MHEWS through outlining potential entry points in research, policy, and practice. We emphasize a need for localized, inclusive strategies that genuinely address the needs of the most vulnerable populations and fully encompass the meaning of multi-hazards, including hazard interrelationships, the dynamics of risk components, and the complexity of multi-hazard impacts.

Advanced methods for assessment of risks of false decisions in analytical chemistry (testing) laboratories - A review

There are two groups of decision-making risks in an analytical chemistry (testing) laboratory directly influencing quality of measurement/test results. One group consists of the risks of false decisions caused by human errors in performing a test. The second group of risks is from the erroneous interpretation of test results, due to measurement uncertainty, judged against the specification/tolerance limits (conformity assessment). Basic concepts of advanced methods for the assessment of risks of false decisions in an analytical chemistry laboratory that have been developed in the last decade are reviewed in the present paper.

Integrating AI and climate change scenarios for multi-risk assessment in the coastal municipalities of the Veneto region

Global climate is experiencing exceptional warming, leading to a rise in extreme events worldwide. Coastal regions are particularly vulnerable to climate change (CC), due to dense populations, interconnected economies, and fragile ecosystems. These areas face escalating risks as CC intensifies the severity and frequency of extreme weather phenomena, like heavy precipitation, sea-level rise (SLR), storm surges. Integrated approaches are crucial to assess the combined impacts of atmospheric and marine hazards at the land-sea interface. Machine Learning (ML) offer innovative solutions to analyse multi-risk events, leveraging large and heterogeneous datasets and modelling complex, non-linear interactions. This study introduces a two-tier ML approach to estimate risks associated with extreme weather events for the Veneto coastal municipalities under current and future scenarios. The model, tested and validated with present-day data, showed satisfactory performance (error margin ∼20 %). The model was applied to mid-term (until 2045) and long-term (until 2100) periods under different CC scenarios, represented by various Representative Concentration Pathways (RCP). Mid-term analysis reveals an increasing risk trend, driven by SLR under RCP8.5, underscoring the significance of considering non-linear interactions between multiple marine and atmospheric hazards. Long-term analysis highlights how future risks depend mainly on precipitation and SLR across the analysed CC scenarios (RCP2.6/4.5/8.5). Results indicate a gradual increase in the expected annual risk trend, with RCP8.5 scenario showing the most severe outcomes. By 2100, the risks under RCP8.5 are projected to be ten times higher than those observed during the historical period, highlighting the importance of developing effective strategies to address these challenges.

Spatial heterogeneity of the integrated risks of urban heat stress and flooding strike

Clarifying the spatial heterogeneity of the multiple climate-related risks has increasingly become a prerequisite for urban risk management and sustainability. As the datasets become more detailed in social attribute's representation at the fine scale within-city level, in contrast to those at a coarse region level, there is a continuous need to examine the spatial heterogeneity of integrated risk assessment. In this study, we applied the hazard-exposure-vulnerability framework to investigate the spatial variations of the integrations of urban heat stress and flooding strikes at the street block scale within Shenzhen, China. The findings showed approximately 16.85 % of the built-up areas experienced a strong dual pressure of heat and flooding, mostly concentrated in the street blocks constructed before 1990. Another 19.84 % of built-up areas exhibited a high level of heat risk, concentrated in the northern urban areas that developed in the recent period. While 26.28 % demonstrated a high level of flooding risk, located in the old urbanized areas. Such spatial variations of integrated risks resulted from the spatial mismatched hotspots among hazard, exposure, and vulnerability. The spatial heterogeneity of the integrated risk assessment suggests differentiated strategies to reduce the maladaptation of urban heat stress and flooding strike. The findings present opportunities to prioritize the street blocks and develop the most sustainable solutions.

Dynamics of natural hazard risk awareness: Panel analysis insights from Switzerland

The analysis of risk awareness should be the initial stage in integrated natural hazard risk management to promote appropriate and effective measures for mitigating risks and strengthening social resilience inside the multi-risk framework. Nevertheless, earlier studies focused on cross-sectional data and overlooked the changes in risk awareness levels and associated independent variables with time. This study analyzes for the first time a balanced nationwide panel dataset of 1612 respondent-year observations from Switzerland (period 2015-2021, including the epidemic of COVID-19) to examine and compare the effects of potential independent variables on the four dimensions of natural hazard risk awareness (NHRA), ranging from the broadest dimension of Relevance to higher dimensions of Perceived Probability of an event, Perceived Threat to life and valuables, and Perceived Situational Threat. The analysis in this study incorporates multiple methods of Random-Effect Model (RE), Generalized Linear Model (GLM), and mediation analysis. Results show that NHRA increased in Switzerland to different extents (up to 23.24%) depending on the dimension. Event memory, perceived information impact and reported individual informed level appeared to be the most consistent independent variables positively influencing panel NHRA. Among these, perceived information impact as an important indicator of risk communication, was also found to serve as a mediator from risk preparedness to risk awareness. By encouraging residents to engage in "Begin Doing Before Thinking" (BDBT) programs to leverage subliminal effects and self-reflection, this study proposes that behavior-cognition feedback loops may facilitate a virtuous cycle. Our promising observations provide recommendations for an effective awareness-rising strategy design and suggest extensive insights from potential short-interval panel analysis in the future.

A single-building damage detection model based on multi-feature fusion: A case study in Yangbi

Accurate and effective identification, determination of the location, and classification of damaged buildings are essential after destructive earthquakes. However, the accuracy of image change detection is limited because of the many texture features and changes in non-building information. In this context, a model for single-building damage detection based on multi-feature fusion is proposed. First, the normalized Digital Surface Model (nDSM) was extracted from the DSM through iterative filtering and point cloud thinning, followed by the extraction of building contour information. Next, single-building images were generated from different data sources through the region of interest (ROI), and the optimal texture feature parameters were extracted for fusion. Afterward, principal-component analysis (PCA) was conducted to suppress multi-feature correlation-induced information redundancy. Finally, the damage to buildings was quantitatively evaluated, and the model was compared with 13 models. The results confirmed the practicability of the model for the Yangbi MS6.4 and Honghe MS5.0 earthquakes.