Widespread but heterogeneous responses of Andean forests to climate change.

Global warming is forcing many species to shift their distributions upward, causing consequent changes in the compositions of species that occur at specific locations. This prediction remains largely untested for tropical trees. Here we show, using a database of nearly 200 Andean forest plot inventories spread across more than 33.5° latitude (from 26.8° S to 7.1° N) and 3,000-m elevation (from 360 to 3,360 m above sea level), that tropical and subtropical tree communities are experiencing directional shifts in composition towards having greater relative abundances of species from lower, warmer elevations. Although this phenomenon of 'thermophilization' is widespread throughout the Andes, the rates of compositional change are not uniform across elevations. The observed heterogeneity in thermophilization rates is probably because of different warming rates and/or the presence of specialized tree communities at ecotones (that is, at the transitions between distinct habitats, such as at the timberline or at the base of the cloud forest). Understanding the factors that determine the directions and rates of compositional changes will enable us to better predict, and potentially mitigate, the effects of climate change on tropical forests.

Classifying disaster risk reduction strategies: conceptualizing and testing a novel integrated approach.

BACKGROUND: Although disaster risk reduction (DRR) addresses underlying causes and has been shown to be more cost-effective than other emergency management efforts, there is lack of systematized DRR categorization, leading to insufficient coherence in the terminology, planning, and implementation of DRR. The aim of this study was to conceptualize and test a novel integrated DRR framework that highlights the intersection between two existing classification systems. METHODS: Grounded theory was used to conceptualize a novel DRR framework. Next, deductive conceptual content analysis was used to categorize interventions from the 2019 Cities100 Report into the proposed DRR framework. The term "connection" indicates that an intervention can be categorized into a particular section of the novel integrated approach. A "connection" was determined to be present when the intervention description stated an explicit connection to health and to the concept within one of the categories from the novel approach. Further descriptive statistics were used to give insight into the distribution of DRR interventions across categories and into the application of the proposed framework. RESULTS: The resulting framework contains nine intersecting categories: "hazard, prospective", "hazard, corrective", "hazard, compensatory", "exposure, prospective", "exposure, corrective", "exposure, compensatory", "vulnerability, prospective", "vulnerability, corrective", and "vulnerability, compensatory". The thematic analysis elucidated trends and gaps in the types of interventions used within the 2019 Cities100 Report. For instance, exposure-prospective, exposure-compensatory, and vulnerability-compensatory were the most under-utilized strategies, accounting for only 3% of the total interventions. Further descriptive statistics showed that upper middle-income countries favored "hazard, corrective" strategies over other DRR categories while lower middle-income countries favored "exposure, corrective" over other DRR strategies. Finally, European cities had the highest percentage of DRR connections (51.39%) compared to the maximum possible DRR connections, while African cities had the lowest percentage of DRR connections (22.22%). CONCLUSIONS: The study suggests that the proposed DRR framework could potentially be used to systematically evaluate DRR interventions for missing elements, aiding in the design of more equitable and comprehensive DRR strategies.

Flood economic vulnerability and risk assessment at the urban mesoscale based on land use: A case study in Changsha, China.

With climate change and urbanization, flood disasters have significantly affected urban development worldwide. In this study, we developed a paradigm to assess flood economic vulnerability and risk at the urban mesoscale, focusing on urban land use. A hydrological simulation was used to evaluate flood hazards through inundation analyses, and a hazard-vulnerability matrix was applied to assess flood risk, enhancing the economic vulnerability assessment by quantifying the differing economic value and flood losses associated with different land types. The case study of Wangchengpo, Changsha, China, found average total economic losses of 126.94 USD/m2, with the highest risk in the settlement core. Residential areas had the highest flood hazard, vulnerability, and losses (61.10% of the total loss); transportation areas accounted for 27.87% of the total economic losses due to their high flooding depth. Despite low inundation, industrial land showed greater economic vulnerability due to higher overall economic value (10.52% of the total). Our findings highlight the influence of land types and industry differences on flood vulnerability and the effectiveness of land-use inclusion in urban-mesoscale analyses of spatial flood characteristics. We identify critical areas with hazard and economic vulnerability for urban land and disaster prevention management and planning, helping to offer targeted flood control strategies to enhance urban resilience.

A novel integrated Urban flood risk assessment approach coupling GeoDetector-Dematel and clustering method.

Urban flood risk assessment plays a crucial role in disaster prevention and mitigation. A scientifically accurate assessment and risk stratification method are of paramount importance for effective flood risk management. This study aims to propose a comprehensive urban flood risk assessment approach by coupling GeoDetector-Dematel and Clustering Method to enhance the accuracy of urban flood risk evaluation. Based on simulation results from hydraulic models and existing literature, the research established a set of urban flood risk assessment indicators comprising 10 metrics across two dimensions: hazard factors and vulnerability factors, among which vulnerability factors include exposure factors, sensitivity factors, and adaptability factors. Subsequently, the research introduced the GeoDetector-Dematel method to determine indicator weights, significantly enhancing the scientific rigor and precision of weight calculation. Finally, the research employed the K-means clustering method to risk zonation, providing a more scientifically rational depiction of the spatial distribution of urban flood risks. This novel comprehensive urban flood risk assessment method was applied in the Fangzhuang area of Beijing. The results demonstrated that this integrated approach effectively enhances the accuracy of urban flood risk assessment. In conclusion, this research offers a new methodology for urban flood risk assessment and contributes to decision-making in disaster prevention and control measures.

Quantitative risk assessment of typhoon storm surge for multi-risk sources.

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the "Mangkhut" as a case study, the method's feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the "Mangkhut" case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0-3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents' life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

Challenges and perspectives in applying the environmental sensitivity index to pipelines: a review on the prevention and management of oil spills disasters.

The oil spill environmental sensitivity index is a key tool for preventing and dealing with environmental disasters caused by oil spills. This study aims to review the available literature on the subject and highlight the importance of methodological advances to improve how the index is applied in continental areas, especially in regions crossed by pipelines. Most current mapping techniques focus on coastal areas and fail to consider the stretches of land that are vulnerable to geodynamic natural disasters. In this context, the need to implement environmental sensitivity indices specific for pipelines has become urgent. This study also presents an overview of the main accidents around the world and a detailed analysis of the history of Brazilian disasters related to oil spills along continental stretches, with a focus on pipelines and natural disasters. In addition, this work highlights the importance of carrying out new research in mountainous areas of Brazil and is aimed at preventing Natechs (natural hazard triggering technological disasters) and improving contingency plans. As a result, several pathways have been identified, which involves the necessity of resolving gaps in terrestrial environmental sensitivity mapping methodologies, particularly as applied to pipelines. Furthermore, solutions must be capable of integrating terrestrial, fluvial, coastal, and maritime environmental sensitivity mapping techniques. Moreover, the need to implement dynamic risk monitoring systems in real time is critical to help manage such a complex problem.

Introducing the 21st Century's New Four Horsemen of the Coronapocalypse.

As the world struggles through the COVID-19 pandemic, we should also be asking what systems-level measures will be needed to prevent this or even worse disasters from happening in the future. We argue that the pandemic is merely one of potentially myriad and pleiomorphic future global disasters generated by the same underlying dynamical system. We explain that there are four broad but easily identifiable systemic, pathologically networked conditions that are hurtling civilization toward potential self-destruction. As long as these conditions are not resolved, we should consider catastrophe as an inevitable emergent endpoint from the dynamics. All four conditions can be reversed with collective action to begin creating an enduring and thriving post- COVID-19 world. This will require maximal application of the precautionary principle.

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention.

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios.

A Decision Support System (DSS) is a highly efficient concept for managing complex objects in nature or human-made phenomena. The main purpose of the present study is related to designing and implementation of real-time monitoring, prediction, and control system for flood disaster management as a DSS. Likewise, the problem of statement in the research is correlated to implementation of a system for different climates of Iran as a unique flood control system. For the first time, this study coupled hydrological data mining, Machine Learning (ML), and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. Likewise, it created the platform for conditional management of floods in Iran's different clusters of climates. According to the KMeans clustering system, which determines homogeneity of the hydrology of a specific region, Iran's rainfall is heterogeneous with 0.61 score, which is approved high efficiency of clustering in a vast country such as Iran with four seasons and different climates. In contrast, the relation of rainfall and flood disaster is evaluated by Nearest Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian Process Classifier (GPC), and Neural Network (NN) algorithms which have an acceptable correlation coefficient with a mean of 0.7. The machine learning outputs demonstrated that based on valid data existence problems in developing countries, just with verified precipitation records, the flood disaster can be estimated with high efficiency. In the following, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) technique ranked the preventive flood damages strategies through three social (Se 1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of flood disaster management are collected from literature review, and the opinion approves them of 9 senior experts who are retired from a high level of water resource management positions of Iran. The outcomes of the TOPSIS method proved that National announcement for public-institutional participation for rapid response and funding (G1-2), Establishment of delay structures to increase flood focus time to give the animals in the ecosystem the opportunity to escape to the upstream points and to preserve the habitat (G 2-8), and Granting free national financial resources by government agencies in order to rebuild sensitive infrastructure such as railways, hospitals, schools, etc. to the provincial treasury (G3-10) are selected as the best solution of flood management in Social, Environmental, and Economic crises, respectively. Finally, the collected data are categorized in Social, Environmental, and Economic aspects as three dimensions of Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 experts in the five provinces of present case studies.

A National Framework to Improve Mortality, Morbidity, and Disparities Data for COVID-19 and Other Large-Scale Disasters.

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report's recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.