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The existing body of research into the environmental and socio-economic benefits of green infrastructure supports the case for it to be positioned as a form of critical infrastructure, particularly in urban settings. It is broadly recognized that extreme weather and climate change pose significant risks to critical infrastructure systems linked to the provision of services, including electricity, water, communications, and transport, and consequently risk assessments and associated adaptation strategies are common practice. However, although green infrastructure is also at risk from extreme weather and climate change, threatening the realization of benefits that it can deliver in urban settings, associated risks to green infrastructure are not widely understood or assessed in practice. This paper discusses the status of existing research on this topic and uses this as a foundation for a Greater Manchester (UK) case study that assesses the risk of low water availability to grassed areas, which represent a key element of the city-region's green infrastructure. In doing so, the paper demonstrates how risks linked to extreme weather and climate change can be assessed spatially to inform green infrastructure planning. In summary, this paper aims to raise awareness of extreme weather and climate change risk to urban green infrastructure, present an empirical case study and associated methodological approach on this topic, and ultimately support efforts to enhance the resilience of urban green infrastructure to extreme weather and climate change. [ABSTRACT FROM AUTHOR]

On 20 July 2021, an extreme rainstorm battered Zhengzhou in China's Henan Province, killing 302 people, including 14 individuals who drowned in a subway tunnel and 6 who drowned in a road tunnel. As the global climate warms, extreme weather events similar to the Zhengzhou flood will become more frequent, with increasingly catastrophic consequences for society. Taking a case study-based approach by focusing on the record-breaking Zhengzhou flood, this paper examines the governance capacity of inland cities in North China for managing extreme precipitation and flooding events from the perspective of the flood risk management process. Based on in-depth case analysis, our paper hypothesizes that inland cities in North China still have low risk perceptions of extreme weather events, which was manifested in insufficient pre-disaster preparation and prevention, poor risk communication, and slow emergency response. Accordingly, it is recommended that inland cities update their risk perceptions of extreme rainfall and flooding events, which are no longer low-probability, high-impact "black swans", but turning into high-probability, high-impact "gray rhinos." In particular, cities must make sufficient preparation for extreme weather events by revising contingency plans and strengthening their implementation, improving risk communication of meteorological warnings, and synchronizing emergency response with meteorological warnings. [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

Geosites are windows into the geological past, which may be recorded in rocks and their properties, the fossil content, and landform produced by processes no longer operating. Since the histories of sedimentation, life, and landscape evolution are to a certain extent controlled by climatic conditions, some geosites may be used as illustrations of various themes linked to the issue of climate change. In this paper, a coherent systematic framework is proposed for how to look at geosites through the lens of climate change. Four major aspects of relevance are recognized: (i) geosites providing evidence of changing climatic conditions in the past; (ii) geosites providing evidence of an environment different than that of today at the place; (iii) geosites providing evidence of extreme weather events; and (iv) dynamic geosites, subject to change as a response to ongoing climate change. The use of geosites to raise awareness and educate the public about climate change faces various interpretation challenges. In particular, linking with ongoing climate change requires caution and balanced presentation as most geosites record changes which occurred without any anthropogenic component. The preferred focus should be on environmental instability in general rather than on any specific reasons for change. [ABSTRACT FROM AUTHOR]

This paper compares the ideas contained in the main papers published on climate change since World War II to arrive at a suggested consensus of our present knowledge regarding climatic changes and their causes. Atmospheric carbon dioxide is only suggested as a cause in one theory, which, despite its wide acceptance by Politicians, the media, and the Public, ignores the findings in other studies, including the ideas found in the Milankovitch Cycles. It also does not explain the well-known NASA map of the changes between the global 1951–1978 and the 2010–2019 mean annual temperatures. The other theories by Oceanographers, Earth scientists, and Geographers fit together to indicate that the variations in climate are the result of differential solar heating of the Earth, resulting in a series of processes redistributing the heat to produce a more uniform range of climates around the surface of the Earth. Key factors are the shape of the Earth and the Milankovitch Cycles, the distribution of land and water bodies, the differences between heating land and water, ocean currents and gateways, air masses, and hurricanes. Low atmospheric carbon dioxide levels during cold events could result in too little of this gas to support photosynthesis in plants, resulting in the extermination of most life on Earth as we know it. The 23 ka Milankovitch cycle has begun to reduce the winter insolation received at the surface of the atmosphere in the mid-latitudes of the Northern Hemisphere starting in 2020. This results in extreme weather as the winter insolation reaching the surface of the atmosphere in the higher latitudes of the Northern Hemisphere decreases while the summer air temperatures increase. It heralds the start of the next glaciation. A brief outline is given of some of the climatic changes and consequences that may be expected in western Canada during the next 11.5 ka. [ABSTRACT FROM AUTHOR]

The urgency of addressing housing challenges in low-income areas is increasing due to widening socio-economic inequalities and the worsening impact of natural disasters. Saint Martin, a small Caribbean island, is struggling to provide affordable housing amidst hurricanes, floods, and heat waves. As a result, there has been a rise in self-organized housing units, which are built incrementally and are susceptible to risks. The main challenge is to balance durability, functionality, and esthetic appeal over time. Inspired by St. Martin's self-organized units, this article explores housing considerations in low-income, hazard-prone contexts by emphasizing their temporalities. Integrating insights from a formative study, including a literature review and ethnographic research, the paper draws on Stewart Brand's "Layers of Change" and the concept of "Flow". The study identifies layers within self-organized units corresponding to durability, functionality, and esthetic appeal. It delves into their connection with building activities over time, unveiling the temporalities of housing considerations. This exploration leads to the proposition of "Designing for a Flow" as a novel design approach. Offering practical insights within a concise framework, the study provides nuanced perspectives on mitigating housing challenges in low-income and hazard-prone contexts. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using two NDVI datasets (GIMMS and MODIS) originated from satellite remote sensing, the variability characteristics of NDVI over the TP on the interannual time scale and associated local climatic factors were investigated. The results show that two primary patterns of NDVI governed TP during the main growing season (June–September, JJAS) for the period 1982–2020. The first one is a uniform pattern, with a consistent spatial variation over the entire TP, and the second is a dipole pattern, with an out-of-phase spatial variation of NDVI between the northern and southern TP. Interannual variations of the different climatic factors regulate the NDVI variability over the different regions of the TP. The interannual variability of the uniform NDVI pattern is mainly affected by the two local climatic factors, the preceding May–August precipitation and simultaneous JJAS sunshine duration. Specifically, NDVIs over the southern and eastern TP have a more significant response to the preceding precipitation and simultaneous sunshine duration, respectively. The variability of the dipole NDVI pattern is primarily modulated by the preceding May–August precipitation and simultaneous surface air temperature, ground surface temperature, and sunshine duration. However, NDVIs over the northern and southern TP have different degrees of response to the four climatic factors, with the most significant response being to preceding precipitation. The combined effect of these factors contributes to the formation of the interannual variability in the uniform and dipole patterns. This paper may shed light on deeply understanding the reasons for the inconsistency in variations of vegetation over the different regions of the TP under climate change. In addition to the effect of local climatic factors that this study focuses on, the influence of external climatic factors on the variability of the TP NDVI deserves further research in the future. [ABSTRACT FROM AUTHOR]

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model's accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA's capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. [ABSTRACT FROM AUTHOR]

With climate change, more intense weather phenomena can be expected, including pressure drops related to the arrival of an atmospheric front. Such drops of pressure are the main reason for gas emissions from closed mines to the surface, and a closed, empty mine shaft is the most likely route of this emission. Among the gases emitted, the most important are carbon dioxide and methane, creating a twofold problem—greenhouse gas emissions and gas hazards. The work presented in this paper simulated the spread of the mentioned gases near such an abandoned shaft for four variants: model validation, the most dangerous situations found during measurements with or without wind, and a forecast variant for a possible future pressure drop. It was found that a momentary CO2 emission of 0.69 m3/s and a momentary CH4 emission of 0.29 m3/s are possible, which for one hour of the appropriate drop would give hypothetically 2484 m3 CO2 and 1044 m3 CH4. In terms of gas hazards, the area that should be monitored and protected may exceed 25 m from a closed shaft in the absence of wind influence. The wind spreads the emitted gases to distances exceeding 50 m but dilutes them significantly. [ABSTRACT FROM AUTHOR]

Climate dictates the critical aspects of human environmental conditions. The frequency and intensity of extreme weather conditions due to human-induced climate change have alarmingly increased. Consequently, climate change directly affects environmental sustainability and human mortality in the short term and creates prolonged and complicated long-term indirect grave risks. This paper examines three-level environmental impact risks associated with climate change on human mortality. It proposes a conceptual framework for developing an empirical event-based human mortality database related to climate change and communication strategies to enhance global environmental adaptation, resilience, and sustainability. [ABSTRACT FROM AUTHOR]

Small fishing harbours substantially contribute to coastal economies as they support not only fishing but also tourism activities. They are located at the land–sea interface and are considered vulnerable infrastructure affected by the increased human activities but also by the impacts of climate change, including rising sea levels and extreme weather events. In this paper, the 16 existing fishing shelters of Cyprus are used as a case study to develop a complex vulnerability index for assessing the shelters' vulnerability. The index incorporates physical, environmental, technical and socioeconomic variables, which are quantified and scored to denote the current state of vulnerability. The results are validated through on-site visits, questionnaires answered by local fishermen and targeted interviews with representatives of the port authorities. Furthermore, climate change projections are taken into account for the physical variables to evaluate the impact of climate change on vulnerability changes. The study highlights the complex interactions between a variety of factors characterising the fishing shelters and driving vulnerability. The proposed index can assist decisionmakers with prioritising interventions, allocating funding and designing adaptation pathways that reduce the shelters' vulnerability while increasing their resilience. [ABSTRACT FROM AUTHOR]

Vineyards are agroecosystems of great importance in the Niagara Region, Ontario (Canada). Due to its microclimate, this region is projected to be impacted by climate change with temperature increases, changes in precipitation patterns in all seasons, and greater frequency of extreme weather events. The aim of this review paper is to summarize which seasonal changes are expected to occur in the Niagara Region and assess how such changes are likely to affect the main components of the vineyard ecosystem (i.e., soil, vines, invertebrates, and pathogens). It is expected that by 2080 the region will experience an increase in temperature in all four seasons; an increase in precipitation during the fall, winter, and spring; and a decrease in precipitation during summer months. Impacts of the projected changes will likely lead to vine water stress, yield loss, increases in incidents of diseases, increases in the spread of new pests, and changes in grape quality ultimately resulting in lower wine quality and/or production. Current management practices will need to be better understood and adaptive strategies introduced to enhance grape growers' ability to minimize these impacts. [ABSTRACT FROM AUTHOR]

The impact of climate change on the physical environment, ecosystems, and human societies is increasingly recognized as the most important global challenge. Climate change may alter, among others, the thermal environment, the occurrence of extreme weather events, and the human exposure to physical, chemical, and biological pollutants, thus affecting human health with several potential outcomes. The impact of climate change on occupational health and safety has been receiving increasing attention in last years. In the European Union, the health and safety of workers is under the rule of Directive 89/391 and its daughters. In a changing climate, compliance with all requirements of the existing EU regulation entails an additional effort to implement preventive and protective measures. A central role in workers' health protection is played by proper workers' information and training, which is partly in charge of the occupational physicians. This paper provides a basic proposal on topics related to climate change to update workers' information and training and to integrate the curricula of occupational physicians. Importantly, suitable information and training may contribute to promoting workers' health and to implement adaptation measures, which are part of the individual, societal, and global responses to climate change. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this research in agriculture is related to the rapid progression of climate change, which has drastic effects on crops. We can build an accurate and robust model that can help identify the optimum planting day to maximise the crop yield, based on the weather forecast and meteorological conditions of the region. This can support farmers in their agricultural activity planning and help minimise the impact of weather anomalies. Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of sunflower crops, we identified datasets that are not very large and have many missing values, generating a low-performance regression model. In this paper, our aim is to study and compare several approaches for missing-value imputation in order to improve our regression model. In our experiments, we compare nine imputation methods, using mean values, similar values, interpolation (linear, spline, pad), and prediction (linear regression, random forest, extreme gradient boosting regressor, and histogram gradient boosting regression). We also employ four unsupervised outlier removal algorithms and their influence on the regression model: isolation forest, minimum covariance determinant, local outlier factor and OneClass-SVM. After preprocessing, the obtained datasets are used to build regression models using the extreme gradient boosting regressor and histogram gradient boosting regression, and their performance is compared. The evaluation of the models shows an increased R 2 from 0.723 when removing instances with missing data, to 0.938 for imputation using Random Forest prediction and OneClass-SVM-based outlier removal. [ABSTRACT FROM AUTHOR]

Countering climate impacts by increasing resilience is a pivotal issue in scientific debate, in which the awareness of the risks of extreme weather phenomena is growing. Cities have been revealed to be increasingly unsuited to the changing climate and vulnerable to it due to their settlement patterns, constructive practices and living habits. Scientifically addressing the issue of climate-proof design requires the development of knowledge models and processes capable of managing the complexity of information needed to guide the transformation of the built environment. In this paper, a model for assessing climate resilience scenarios for the heatwave phenomenon is proposed by implementing a database of technical climate-proof solutions for climate adaptation and mitigation aimed at increasing the indoor comfort and reducing the CO2 emissions of buildings. The model is implemented through a GIS-based framework and was tested on the city of Naples (Italy), measuring the reduction in the heatwave impact/risks determined by the selected climate-proof solutions. The test results show the effectiveness of the climate-proof solutions applied to the built environment through an increase in climate resilience. The framework provides support for planning climatic resilience design strategies at the building scale. It could be applied in future local climate adaptation plans or as a knowledge resource to achieve resilient built environments. [ABSTRACT FROM AUTHOR]

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation. [ABSTRACT FROM AUTHOR]

With global warming, the frequency, intensity, and period of extreme climates in more areas will probably increase in the twenty first century. However, the impact of climate extremes on forest vulnerability and the mechanisms by which forests adapt to climate extremes are not clear. The eastern Tianshan Mountains, set within the arid and dry region of Central Asia, is very sensitive to climate change. In this paper, the response of Picea schrenkiana and Larix sibirica to climate fluctuations and their stability were analyzed by Pearson's correlation based on the observation of interannual change rates of climate indexes in different periods. Additionally, their ecological adaptability to future climate change was explored by regression analysis of climate factors and a selection of master control factors using the Lasso model. We found that the climate has undergone significant changes, especially the temperature, from 1958 to 2012. Around 1985, various extreme climate indexes had obvious abrupt changes. The research results suggested that: (1) the responses of the two tree species to extreme climate changed significantly after the change in temperature; (2) Schrenk spruce was more sensitive than Siberian larch to extreme climate change; and (3) the resistance of Siberian larch was higher than that of Schrenk spruce when faced with climate disturbance events. These results indicate that extreme climate changes will significantly interfere with the trees radial growth. At the same time, scientific management and maintenance measures are taken for different extreme weather events and different tree species. [ABSTRACT FROM AUTHOR]

This paper presents a multidisciplinary approach to understand the impacts of temperature increase on the retention of particulate pollutants, such as heavy metals and microplastics, by the road pavement material. A soil with a particle size distribution similar to road dust was selected. A Wheel Tracking device was used to assess the permanent deformation behavior of prismatic specimens and the retention of the dust, at controlled temperatures of 40 °C and 60 °C, likely to occur on pavement in the future. The soil representing road pollutants was placed at the top of the slabs prior to the Wheel Tracking Test (WTT), based on the European Standard EN 12697-22:2020. After the WTT, two common methods were used, in order to evaluate the soil retention (pollution accumulation) on road pavement. The results confirm that the viscoelastic behavior of bituminous mixtures under increased temperatures can contribute to particle retention at the pavement. Future studies are needed to understand the phenomena, the retention characteristics by different bituminous mixtures, and the efficiency of pollutants capture. The work opens the opportunity to develop innovative road pavement bituminous mixtures that can reduce the discharge of road particulate pollutants, and have increased resilience and sustainability in extreme weather conditions. [ABSTRACT FROM AUTHOR]

"Changes of the Coastal Zones Due to Climate Change" is a research article that explores the relationship between coastal climate change and sea dynamics. The article highlights the vulnerability of coastal areas to environmental shifts such as rising sea levels and extreme weather events. It discusses various research papers that investigate the impacts of climate change on coastal ecosystems, shoreline changes, and storm surge inundation. The article also emphasizes the importance of understanding waves and storm surges to develop effective strategies for coastal flooding mitigation. Additionally, it explores the potential for renewable offshore energy exploitation and ecosystem-based solutions for climate change adaptation in coastal areas. The research findings contribute to a comprehensive understanding of coastal climate dynamics and provide valuable insights for policymakers, researchers, and stakeholders in developing adaptation strategies and fostering resilience in the face of climate change. [Extracted from the article]

Agriculture is widely recognized as a solution to food insecurity and poverty, especially in rural areas. However, 75% of the world's poor live in rural areas, and agriculture is the primary source of their livelihood. One may wonder if the observed correlation between agriculture and poverty also suggests causation. If that is the case, then what such causal relationship might exist? Is agriculture a vehicle for poverty alleviation or a source of poverty trap? The role of climate change is rather undisputed: associated extreme weather phenomena cause severe negative impacts on agriculture, exacerbating rural poverty. However, climate-smart agriculture (CSA) is acclaimed to potentially reverse the situation by eliminating poverty and food insecurity. Against this backdrop, the paper investigates whether smallholder farmers who adopt CSA could achieve food security and better income. This aim was approached through three key research objectives (i) to examine the effects of climate change on smallholder farmers, (ii) to examine the extent to which smallholder farmers adopt CSA and the barriers to adoption, and (iii) to investigate empirically the effects of CSA practices in terms of food security and poverty alleviation. The Upper West and Upper East regions in Ghana were selected purposively for the case study, and the data collected were analyzed using inferential and descriptive techniques. The results revealed no statistically significant positive relationship between the adoption of CSA with food security and income. Poor socioeconomic and market conditions marred the expected positive effects of CSA, hence the need for the provision of agricultural infrastructures and inputs as well as the creation of market for commodities. [ABSTRACT FROM AUTHOR]

Coastal regions in Southwest Europe have experienced major interventions and transformations of the territory with unprecedented urban development, primarily related to growing tourism activity. The coast is the place where marine and terrestrial processes converge, making it highly vulnerable to the effects of climate change. However, the lack of information on the frequency of these extreme weather events and their impacts on the coast hampers an accurate analysis of the consequences of global change. This paper provides a detailed analysis of the extreme weather events (EWE) that have affected the Atlantic and Mediterranean coasts of Southwest Europe during the period from 1 January 2009 to 28 February 2020, as well as a quantification of their impacts: fatalities, injuries and economic damage. Official sources from France, Portugal and Spain were consulted, along with technical reports, scientific articles, etc., to generate a unified database. A total of 95 significant extreme events have caused 168 fatalities, 137 injuries and almost €4000 M in direct economic losses. Cyclone Xynthia (February 2010) on the French Atlantic coast stands out, having caused 47 fatalities, 79 injuries and substantial economic losses valued at €3000 M. The study shows a slight upward trend in the number of events recorded, especially during the last three years of the analysis, as well as in human losses and damages. The results reveal a higher exposure of the Mediterranean coast of Southwest Europe when compared to the Atlantic, especially the Spanish Mediterranean coast, with 61% of the fatalities recorded there during the study period. This is primarily due to a model of exponential tourism growth on the Mediterranean coast, with an enormous urban and infrastructure development during the last decades. Traditionally, the Mediterranean coast is less prepared to reduce the effects of marine storms, extreme events that are becoming more frequent and virulent in the context of climate and global change. This work highlights the need to create a continuous monitoring system–at the European level–of the impacts of extreme weather events on the coast, where 40% of the European population is concentrated. This observatory should serve as a source of information for risk mitigation policies (predictive, preventive and corrective), as well as for emergency management during disasters. [ABSTRACT FROM AUTHOR]

Weather and climate extremes such as heat waves, droughts and floods are projected to become more frequent and intense in several regions. There is compelling evidence indicating that changes in climate and its extremes over time influence the living conditions of society and the surrounding environment across the globe. This study applies max-stable models to capture the spatio–temporal extremes with dependence. The objective was to analyse the risk of drought caused by extremely high temperatures and deficient rainfall. Hopkin's statistic was used to assess the clustering tendency before using the agglomerative method of hierarchical clustering to cluster the study area into n = 3 temperature clusters and n = 3 precipitation clusters. For the precipitation and temperature data, the values of Hopkin's statistic were 0.7317 and 0.8446, respectively, which shows that both are significantly clusterable. Various max-stable process models were then fitted to each cluster of each variable, and the Schlather model with several covariance functions was found to be a good fit on both datasets compared to the Smith model with the Gaussian covariance function. The modelling approach presented in this paper could be useful to hydrologists, meteorologists and climatologists, including decision-makers in the agricultural sector, in enhancing their understanding of the behaviour of drought caused by extremely high temperatures and low rainfall. The modelling of these compound extremes could also assist in assessing the impact of climate change. It can be seen from this study that the size, including the topography of the location (cluster/region), provides important information about the strength of the extremal dependence. [ABSTRACT FROM AUTHOR]

Millions of local communities in southern Africa depend on forest ecosystems and the goods and services they provide for their livelihoods. This paper aims to assess the socio-economic benefits of forest goods and services in a changing climate by focusing on the forest products of Colophospermum mopane (C. mopane) in the Kunene and Omusati regions in northern Namibia. We used C. mopane product data from 2011 to 2021. Our analyses showed that local communities harvested five main products from C. mopane, namely firewood, poles, droppers, rafters, and roots. Firewood and poles were the primary C. mopane products harvested by local communities, mainly for subsistence use. Our results suggest that C. mopane potentially continues to the provision of goods and services for the livelihood of local communities, despite the changing climate in northern Namibia. We propose future studies in predictive analysis focus on extreme weather events, such as forest fires, droughts, floods, and other climate-related hazards that affect goods and services provided by forest ecosystems in the northern regions and the entire country. [ABSTRACT FROM AUTHOR]

This paper presents a research study of expected precipitation extremes across the Gandaki Province, Nepal. The study used five indices to assess extreme precipitation under climate change. Precipitation output of two Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase Six (CMIP6) were used to characterize the future precipitation extremes during the rainfall season from June to September (JJAS) and overall days of the year. To characterize extreme precipitation events, we used daily precipitation under the SSP2–4.5 and SSP5–8.5 scenarios from the Beijing Climate Center and China Meteorological Administration, China; and Meteorological Research Institute (MRI), Japan. Considering large uncertainties with GCM outputs and different downscaling (including bias correction) methods, direct use of GCM outputs were made to find change in precipitation pattern for future climate. For 5-, 10-, 20-, 50-, and 100-year return periods, observed and projected 24 h and 72 h annual maximum time series were used to calculate the return level. The result showed an increase in simple daily intensity index (SDII) in the near future (2021–2040) and far future (2081–2100), with respect to the base-year (1995–2014). Similarly, heavy precipitation days (R50 mm), very heavy precipitation days (R100 mm), annual daily maximum precipitation (RX1day), and annual three-day maximum precipitation (RX3day) indices demonstrated an increase in extreme precipitation toward the end of the 21st century. A comparison of R50 mm and R100 mm values showed an extensive (22.6% and 63.8%) increase in extreme precipitation days in the near future and far future. Excessive precipitation was forecasted over Kaski, Nawalparasi East, Syangja, and the western half of the Tanahun region. The expected increase in extreme precipitation may pose a severe threat to the long-term viability of social infrastructure, as well as environmental health. The findings of these studies will provide an opportunity to better understand the origins of severe events and the ability of CMIP6 model outputs to estimate anticipated changes. More research into the underlying physical factors that modulate the occurrence of extreme incidences expected for relevant policies is suggested. [ABSTRACT FROM AUTHOR]

In an effort to improve their quality of life and battle poverty, many urban residents are turning to agriculture as an alternative source of income, employment, and food security. However, climate-related hazards such as heatwaves, floods, and droughts have had an effect on urban agriculture. The purpose of this study was to determine how climate change-related hazards affected the urban livestock industry in Masvingo City. These researchers administered a structured questionnaire on urban livestock farmers, the results of which were triangulated with in-depth interviews with livestock stakeholders. The results show that the urban livestock industry is significantly impacted by climate-related hazards. Farmers lose livestock to diseases, poor pastures, and extreme weather conditions. Furthermore, the hazards badly affect the storage and distribution of livestock products, the labour supply and productivity, and the profitability of livestock enterprises. This study contributes to the body of knowledge on the urban livestock industry and climate change-related hazards. The results are significant to policy makers and livestock stakeholders to understand climate change effects on the urban livestock sector so as to formulate mitigation, adaptation, and coping strategies against any adverse effects. This paper is a foundation for future studies and these researchers suggest that future studies be on location-specific adaptation strategies. [ABSTRACT FROM AUTHOR]

Climate change is one of the most pressing issues of our time. Rising temperatures, changing precipitation and more weather extremes pose risks to local societies worldwide. Yet, climate change is most often presented and reported on a global or national scale. This paper aims to analyze the key aspects of climate change on the local scale by assessing temporal and spatial changes in temperature and precipitation in the Westfjords in north-western Iceland and evaluate their impacts on the region's livability. Existing temperature and precipitation data were used to model trends in climate change at an unprecedented resolution. The results show that the period of 2001–2020 was warmer than the 1961–1990 reference period in almost every month of every year, and that warming was more pronounced in the winter months. Furthermore, precipitation increased during 1991–2020 period compared to 1961–1990. These detected local patterns confirm some of the major predictions about climate change on the global scale. Considering the impact of climate change at the local level is critical, as it allows the community to envisage their future and provides better possibilities to mitigate, prepare for or adapt to the predicted changes. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is the main staple food of more than 50% of the world's population. However, global production may need to increase by more than 70% before 2050 to meet global food requirements despite increasing challenges due to environmental degradation, a changing climate, and extreme weather events. Rice production in Ecuador, mainly concentrated in lowland tropical plains, declined in recent years. In this paper, we aim to calibrate and validate Kobayashi's 'Very Simple Model' (VSM) and, using downscaled corrected climate data, to quantify the potential impact of climate change on rice yields for Ecuador's two main rice-growing provinces. The negative impact is expected to be highest (up to −67%; 2946 tons) under the Representative Concentration Pathway (RCP) 8.5, with a lower impact under RCP 2.6 (−36%; 1650 tons) yield reduction in the Guayas province. A positive impact on yield is predicted for Los Ríos Province (up to 9%; 161 tons) under RCP 8.5. These different impacts indicate the utility of fine-scale analyses using simple models to make predictions that are relevant to regional production scenarios. Our prediction of possible changes in rice productivity can help policymakers define a variety of requirements to meet the demands of a changing climate. [ABSTRACT FROM AUTHOR]

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. [ABSTRACT FROM AUTHOR]

Climate change is having a significant impact on farmers and agriculture. Rising temperatures and extreme weather events, such as droughts and floods, are causing crop failures and reducing yields. This study evaluated existing publications from 2006 to 2022, using the Web of Science database, Citespace, and the Bibliometrix package in R language for a systematic analysis. A total of 426 publications were identified, written by 1449 authors from 56 countries. The results showed that China has the highest share of publications (16.4%), followed by the United States (14.3%) and Australia (6.8%), with China and Pakistan collaborating most frequently. The keyword timeline analysis from 2006 to 2022 identified 11 clusters of research topics related to farmers' climate change risk (CCRF). Cluster #1, "water conservation measures," had the longest duration, highlighting its significance. Key areas of CCRF research include the vulnerability of land-lost farmers, farmers' use of meteorological information, sources of risk, barriers to implementing conservation agriculture, farmers' attitudes towards heavy metal pollution, and the use of protection motivation theory in agricultural adaptation. In conclusion, climate change poses significant threats to farmers, impacting crop yields, soil fertility, and water availability. Farmers adopt adaptation strategies, but effectiveness varies due to resource limitations and policy gaps. The research on farmer adaptation to climate change is growing, emphasizing the need for supportive policies, resources, and knowledge-sharing to achieve sustainable agriculture and food security. [ABSTRACT FROM AUTHOR]

Extreme weather events have led to widespread yield losses and significant global economic damage in recent decades. African agriculture is particularly vulnerable due to its harsh environments and limited adaptation capacity. This systematic review analyzes 96 articles from Web of Science, Science Direct, and Google Scholar, focusing on biophysical studies related to maize in Africa and worldwide. We investigated the observed and projected extreme weather events in Africa, their impacts on maize production, and the approaches used to assess these effects. Our analysis reveals that drought, heatwaves, and floods are major threats to African maize production, impacting yields, suitable cultivation areas, and farmers' livelihoods. While studies have employed various methods, including field experiments, statistical models, and process-based modeling, African research is often limited by data gaps and technological constraints. We identify three main gaps: (i) lack of reliable long-term experimental and empirical data, (ii) limited access to advanced climate change adaptation technologies, and (iii) insufficient knowledge about specific extreme weather patterns and their interactions with management regimes. This review highlights the urgent need for targeted research in Africa to improve understanding of extreme weather impacts and formulate effective adaptation strategies. We advocate for focused research on data collection, technology transfer, and integration of local knowledge with new technologies to bolster maize resilience and food security in Africa. [ABSTRACT FROM AUTHOR]

Agro-meteorological disasters are a significant cause of crop yield reduction. Northeast China is a major base for commodity grain production and is also highly sensitive to climate change. Early frost is one of the most significant meteorological disasters in Northeast China. The typical weather system serves as the primary meteorological cause of the occurrence of early frost. The Northeast Cold Vortex is a cyclonic system of certain intensity located in Northeast China, which has the potential to induce severe weather conditions such as extreme low temperatures and intense convection. Despite extensive research on the first occurrence of frost in Northeast China, the evolutionary characteristics under the combined influence of climate change and the Northeast Cold Vortex remain unclear. This limitation hinders the development of effective monitoring and early warning systems for early frost, as well as the formulation of disaster prevention and mitigation plans for crop production. Therefore, this study aims to objectively document the occurrence of early frost in maize crops in Northeast China from 1961 to 2021 under the influence of the Northeast Cold Vortex. It seeks to unveil the climatic characteristics and evolutionary patterns of early frost events in maize crops within this region, considering the impact of the Northeast Cold Vortex. Additionally, it endeavors to analyze the factors contributing to varying degrees of early frost caused by the Northeast Cold Vortex. The results showed that the occurrence of both early frost and frost influenced by the Northeast Cold Vortex exhibited a declining trend. Furthermore, there was also a decreasing proportion of initial frost attributed to the Northeast Cold Vortex, with a decline rate of 2% per decade, indicating a diminishing dominance of initial frost caused by this weather system. The onset date for the early frost under the influence of the Northeast Cold Vortex progressively advanced from southeast to northwest, occurring 4 days earlier than during the period from 1961 to 1990 between 1991 and 2021. While early frost displayed an increasing spatial distribution from southeast to northwest, it is noteworthy that the majority concentration of the Northeast Cold Vortex was observed in central regions, highlighting its predominant role in causing early frost in Northeast China. [ABSTRACT FROM AUTHOR]

The climate crisis poses a grave threat to numerous small island developing states (SIDS), intensifying risks from extreme weather events and sea level rise (SLR). This vulnerability heightens the dangers of coastal erosion, chronic water quality degradation, and dwindling coastal resources, demanding global attention. The resultant loss of ecological persistence, functional services, and ecosystem resilience jeopardizes protection against wave action and SLR, endangering coastal habitats' economic value, food security, infrastructure, and livelihoods. Implementing integrated strategies is imperative. A thorough discussion of available strategies and best management practices for coastal ecosystem restoration is presented in the context of SIDS needs, threats, and major constraints. Solutions must encompass enhanced green infrastructure restoration (coral reefs, seagrass meadows, mangroves/wetlands, urban shorelines), sustainable development practices, circular economy principles, and the adoption of ecological restoration policies. This requires securing creative and sustainable funding, promoting green job creation, and fostering local stakeholder engagement. Tailored to each island's reality, solutions must overcome numerous socio-economic, logistical, and political obstacles. Despite challenges, timely opportunities exist for coastal habitat restoration and climate change adaptation policies. Integrated strategies spanning disciplines and stakeholders necessitate significant political will. [ABSTRACT FROM AUTHOR]

Climate change has resulted in a worldwide increase in intensity and frequency of extreme weather events including bushfires. Previous research has shown that communities often do not engage in disaster preparedness, even when sufficient education and resources are provided. With the projected increase in natural disasters, preparedness is paramount, and more research is needed to gain an understanding into what impacts community preparedness in the face of climate change. This study investigated one rural Australian community's preparedness for the 2019–2020 bushfires. Thirteen Australian adults who resided within a small rural community in New South Wales during the 2019–2020 bushfires participated in semi-structured interviews. Data were analysed using inductive thematic analysis. Participants reported being unprepared for the 2019–2020 bushfires and that the community has started to prepare for future bushfires. However, they also described a belief in 'climate cycles' rather than climate change, limiting engagement in preparedness for future hazards. Participants also reported that they did not talk about the 2019–2020 bushfires, although described experiencing residual anxiety. Recommendations included support needed for rural communities to help with future preparedness efforts and mental health symptoms. [ABSTRACT FROM AUTHOR]

The authors reported that the changes in alpine lakes were closely related to the supply coefficient (basin/lake area ratio) but weakly related to the G-L ratio (glacier/lake area ratio), and the spatial pattern of lake change was consistent with that of climate change. The impacts of climate change already pose major challenges for the environment, and the trend is rising. [Extracted from the article]

This review seeks to enhance the understanding of the critical concepts of vulnerability, adaptation, and resilience within the context of global environmental challenges, with a particular focus on climate change. Climate change is characterized by rising global temperatures and an increase in extreme weather events, making the comprehension and addressing of these concepts crucial for effective adaptation strategies. Despite widespread recognition of the interconnectedness of vulnerability, adaptation, and resilience, there remains a gap in a comprehensive understanding of how these concepts interrelate. Through synthesizing existing literature, this review provides a detailed examination of their definitions and the interrelationships among vulnerability, adaptation, resilience, and climate-related disasters. Additionally, it explores the impact of climate change on future disaster risk reduction efforts by analyzing the nexus between climate change adaptation and disaster risk reduction. Key findings highlight the necessity of incorporating social, institutional, economic, and environmental factors into adaptation planning and call for innovative approaches to boost adaptive capacity and resilience. This review not only furthers the discourse in research, policy, and practice in this vital area but also offers strategic insights for developing more resilient and adaptive societies amidst the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Exploring the realm of extreme weather events is indispensable for various engineering disciplines and plays a pivotal role in understanding climate change phenomena. In this study, we examine the ability of 10 probability distribution functions—including exponential, normal, two- and three-parameter log-normal, gamma, Gumbel, log-Gumbel, Pearson type III, log-Pearson type III, and SQRT-ET max distributions—to assess annual maximum 24 h rainfall series obtained over a long period (1972–2022) from three nearby meteorological stations. Goodness-of-fit analyses including Kolmogorov–Smirnov and chi-square tests reveal the inadequacy of exponential and normal distributions in capturing the complexity of the data sets. Subsequent frequency analysis and multi-criteria assessment enable us to discern optimal functions for various scenarios, including hydraulic engineering and sediment yield estimation. Notably, the log-Gumbel and three-parameter log-normal distributions exhibit superior performance for high return periods, while the Gumbel and three-parameter log-normal distributions excel for lower return periods. However, caution is advised regarding the overuse of log-Gumbel, due to its high sensitivity. Moreover, as our study considers the application of mathematical and statistical methods for the detection of extreme events, it also provides insights into climate change indicators, highlighting trends in the probability distribution of annual maximum 24 h rainfall. As a novelty in the field of functional analysis, the log-Gumbel distribution with a finite sample size is utilised for the assessment of extreme events, for which no previous work seems to have been conducted. These findings offer critical perspectives on extreme rainfall modelling and the impacts of climate change, enabling informed decision making for sustainable development and resilience. [ABSTRACT FROM AUTHOR]

The changing global climate, characterized by rising surface air temperatures, shifting precipitation patterns, and heightened occurrences of extreme weather events, is anticipated to profoundly impact the environment, economy, and society worldwide. This impact is particularly acute in African nations like Angola, where crucial sectors, such as agriculture, rely heavily on climate variability and exhibit limited adaptive capacity. Given that the majority of Angola's agriculture is rain-fed and serves as a vital source of livelihood for the populace, the country is especially vulnerable to climate change, particularly in its southern region. Climate change has caused severe damage in Angola, especially in the southern part of the country, where the worst droughts in decades have affected over 3.81 million people, resulting in food and water shortages. Between 2005 and 2017, climate-related disasters cost the country about 1.2 billion US dollars, further exacerbating the economic and social challenges faced by the population. This study presents a systematic review of the effects of climate change on agriculture in Angola, with a focus on the southern region. Employing the PRISMA2020 methodology, the review examined 431 documents from databases such as Scopus and Web Science, spanning from 1996 to 2023, with 63 meeting inclusion criteria. The review reveals a paucity of research on the short and long-term impacts of climate change on Angolan agriculture. Projections indicate a rise in temperatures and a general decrease in precipitation, with the southern region experiencing a more pronounced decline. Agricultural productivity may suffer significantly, with models suggesting a potential 7% reduction by 2050. [ABSTRACT FROM AUTHOR]

Human activities, global warming, frequent extreme weather events, and changes in atmospheric composition affect the solar radiation reaching the Earth's surface, affect mass and heat transfer at the air–water interface, and induce oscillations in wind-driven internal waves. This leads to changes in the spatiotemporal characteristics of thermal stratification in lakes, altering lake circulation patterns and vertical mass transfer. However, thermal stratification structures are often overlooked. The intensification of lake thermal stratification due to warming may lead to increased release of bottom pollutants, spreading through the dynamic behavior of the thermocline to the epilimnion. Moreover, the increased heat storage is beneficial for the growth and development of certain phytoplankton, resulting in rapid transitions of the original steady state of lakes. Consequently, water quality deterioration, ecological degradation, and declining biodiversity may occur. Conventional surface water monitoring may not provide comprehensive, accurate, and timely assessments. Model simulations can better predict future thermal stratification behaviors, reducing financial burdens, providing more refined assessments, and thus preventing subsequent environmental issues. [ABSTRACT FROM AUTHOR]

Rail transport is expected to become a key component in the development of a long-term sustainable transport system. The planning, construction, operation, and maintenance of railway infrastructure are crucial in this effort. Hence, it is essential to ascertain that the railway infrastructure withstands and is adapted to extreme weather conditions and climate change. This study focuses on evaluating climate adaptation measures for lateral track buckling in Sweden. Through a literature review and interview with an expert at Swedish Transport Administration, it is highlighted that the maintenance status of railway infrastructure plays a significant role in the occurrence of lateral track buckling. According to the expert, inadequate track maintenance is the primary cause of lateral track buckling rather than weather variables like air temperature. The interview also clarifies that the chain of events related to the handling of track buckling is mainly initiated by the observation of a discrete lateral irregularity by a train driver, whereupon the train dispatcher at the traffic management center stops traffic until the location in the track has been inspected by a track entrepreneur. During the inspection, up to half of the observed cases of track buckling turn out to be false. [ABSTRACT FROM AUTHOR]

This study analyzes daily temperature and precipitation data collected from 44 weather stations throughout New York, New Jersey, and Connecticut to assess and quantify the historical climatic changes within these states. The study conducts a detailed examination of spatial and temporal trends, focusing on specific stations that best represent the climatic diversity of each area. A critical analysis aspect involves comparing temperature trends in urban and suburban areas, mainly focusing on New York City. The findings reveal a significant upward increasing trend in average temperatures across all seasons, with urban areas, especially NYC, exhibiting the most marked increases. This trend is notably sharp in the spring, reflecting climate change's escalating influence. The study also observes an increase in the annual average temperatures and a concurrent decrease in the variability of temperature ranges, suggesting a stabilization of temperature fluctuations over time. Also, we identified a notable increase in heat wave frequency, more so in urban locales than in their suburban counterparts. Analysis of precipitation patterns, particularly in NYC, reveals a decline in snowfall days, consistent with the general warming trend. The results demonstrate significant trends in seasonal average temperatures, a decrease in the variability of temperatures, and a rise in heat wave occurrences, with urban areas typically experiencing warmer conditions. This comprehensive study highlights the need for a more in-depth analysis of spatial precipitation trends. It underscores the importance of continued research in understanding the multifaceted impacts of climate change, particularly in differentiating urban and rural experiences. [ABSTRACT FROM AUTHOR]

Climate change disrupts food production in many regions of the world. The accompanying extreme weather events, such as droughts, floods, heat waves, and cold snaps, pose threats to crops. The concentration of carbon dioxide also increases in the atmosphere. The United Nations is implementing the climate-smart agriculture initiative to ensure food security. An element of this project involves the breeding of climate-resilient crops or plant cultivars with enhanced resistance to unfavorable environmental conditions. Modern agriculture, which is currently homogeneous, needs to diversify the species and cultivars of cultivated plants. Plant breeding programs should extensively incorporate new molecular technologies, supported by the development of field phenotyping techniques. Breeders should closely cooperate with scientists from various fields of science. [ABSTRACT FROM AUTHOR]

Cultural heritage (CH) sites are frequently exposed to natural elements, and their exposure becomes particularly precarious with the onset of climate change. This increased vulnerability places these sites at risk of deterioration or complete destruction. Risks such as land deformation, floods, acid rain, and erosion significantly threaten historic monuments, while water-related hazards, significantly influenced by both climate change and human activities, present a particularly grave risk to these invaluable sites. Considerable research efforts have focused on safeguarding CH sites. However, there remains a deficiency in systemic approaches towards identifying and mitigating risks for CH sites. The TRIQUETRA project proposes a technological toolbox and a methodological framework for tackling climate change risks and natural hazards threatening CH in the most efficient way possible. It aims at creating an evidence-based assessment platform allowing precise risk stratification as well as a database of available mitigation measures and strategies, acting as a Decision Support System (DSS) towards efficient risk mitigation and site remediation. TRIQUETRA is a European project that brings together a diverse group of researchers with varied expertise, encompassing university research groups, research institutes, public entities, as well as small and medium-sized enterprises. In this article, TRIQUETRAs overall methodology is presented, and preliminary results concerning risk identification, TRIQUETRAs knowledge base, as well as novel sensors and coatings, are discussed. [ABSTRACT FROM AUTHOR]

Recent studies on China's arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent. [ABSTRACT FROM AUTHOR]

Simple Summary: Butterflies are in decline due to habitat conversion, climate change, and agricultural practices. Insects have a narrow range of habitable conditions, and extreme fluctuations in weather or climate can lead to population declines. Other studies have shown that butterfly populations are declining in western US; however, given the distinct climate of the southwest, with two distinct rainy seasons, we wanted to explore whether the general trends held true for Arizona. We used 40 years of community science data taken at 13 sites and in two seasons to assess population trends and relationships with climate variables (precipitation and temperature). We found that the declines seen in Arizona reflect the general trends found for western US. The preceding winter precipitation is a driver of both the number of species in the fall and spring surveys and the total number of unique butterflies in the spring surveys. The number of unique butterflies counted in the fall surveys was more affected by summer monsoon precipitations. Managers and policy makers may use these results, combined with climate projections, to consider implementing management actions to help counteract butterfly declines, such as creating more refugia for butterflies across the landscape. Butterfly populations are declining worldwide, reflecting our current global biodiversity crisis. Because butterflies are a popular and accurate indicator of insect populations, these declines reflect an even more widespread threat to insects and the food webs upon which they rely. As small ectotherms, insects have a narrow range of habitable conditions; hence, extreme fluctuations and shifts caused by climate change may increase insects' risk of extinction. We evaluated trends of butterfly richness and abundance and their relationship with relevant climate variables in Arizona, U.S.A., using the past 40 years of community science data. We focused on precipitation and temperature as they are known to be influential for insect survival, particularly in arid areas like southwestern U.S.A. We found that preceding winter precipitation is a driver of both spring and summer/fall butterfly richness and spring butterfly abundance. In contrast, summer/fall butterfly abundance was driven by summer monsoon precipitations. The statistically significant declines over the 40-year period were summer/fall butterfly abundance and spring butterfly richness. When controlling for the other variables in the model, there was an average annual 1.81% decline in summer/fall season butterfly abundance and an average annual decline of 2.13 species in the spring season. As climate change continues to negatively impact winter precipitation patterns in this arid region, we anticipate the loss of butterfly species in this region and must consider individual butterfly species trends and additional management and conservation needs. [ABSTRACT FROM AUTHOR]

Vertical greenery not only helps to cool the surfaces of buildings but, more importantly, it can also mitigate the Urban Heat Island effect. The growth of vertical greenery is highly dependent on ongoing maintenance, such as irrigation. Wind-driven rain serves as a natural source of irrigation for vertical greenery. Wind-driven rain simulation was conducted on a typical high-density and high-rise case in Hong Kong to first classify the wind-driven rain harvesting potential on the façade with very high, high, moderate, low, and very low levels. Then, Scenario 1 (very high potential), Scenario 2 (very high + high potential), and Scenario 3 (very high + high + moderate potential) regarding vertical greenery in locations with three levels of wind-driven rain harvesting potential were simulated in ENVI-met to assess its Urban Heat Island mitigation effect. The maximum temperature reduction on the street occurs between 12 p.m. and 3 p.m., indicating the greatest mitigation of the Urban Heat Island effect. Scenario 1, Scenario 2, and Scenario 3 achieve a maximum temperature reduction of 0.76 °C, 0.88 °C, and 1.06 °C, respectively, during this time period. [ABSTRACT FROM AUTHOR]

Spatial planning is broadly acknowledged for its pivotal role in local climate change adaptation. Nevertheless, variations persist among countries in their perceptions and practices concerning the effectiveness and feasibility of utilizing spatial planning for climate change adaptation. Chinese cities confront the challenges of global warming, rapid urbanization, and intensified extreme weather events. Climate change adaptation agreements, frameworks, and policies have predominantly emphasized the national scale, often overlooking local adaptation efforts. However, the ongoing reform and restructuring of China's territorial and spatial planning system encompasses the categorization, hierarchical management, control of comprehensive national land resource development, and conservation. This process is gradually establishing spatial planning strategies that synergize with the impacts of climate change, thereby offering an opportunity to integrate climate change adaptation objectives into spatial planning. This endeavor is supported by a series of regulatory standards and guidelines to ensure its effective implementation. The study scrutinizes the public draft of territorial and spatial master plans for 368 Chinese cities to assess the integration of climate change concerns at the local level and to identify disparities among cities. Employing the Awareness, Analysis, and Action Framework (3A Framework), which draws from prior scholarship and integrates relevant existing research, we identified and assessed 30 criteria for recognizing and appraising climate change adaptation strategies in territorial and spatial planning. The study's findings indicate that: (1) The role of territorial and spatial planning in integrating climate change issues at the city level requires enhancement, as reflected in the generally low "awareness" of climate change concerns, limited "analytical capacity" regarding climate change, and a certain foundation for "action response" to climate change adaptation; (2) Large cities, characterized by robust economies and population concentrations, generally outperform small and medium-sized cities. They exhibit a superior ability to delineate quantifiable climate adaptation indicators and measures. In conclusion, the study provides prospective recommendations concerning technical approaches, spatial governance, mechanisms, and actions to more effectively incorporate climate change adaptation objectives into local-level territorial and spatial planning. [ABSTRACT FROM AUTHOR]

We report the results of a structured expert elicitation to identify the most likely types of potential food system disruption scenarios for the UK, focusing on routes to civil unrest. We take a backcasting approach by defining as an end-point a societal event in which 1 in 2000 people have been injured in the UK, which 40% of experts rated as "Possible (20–50%)", "More likely than not (50–80%)" or "Very likely (>80%)" over the coming decade. Over a timeframe of 50 years, this increased to 80% of experts. The experts considered two food system scenarios and ranked their plausibility of contributing to the given societal scenario. For a timescale of 10 years, the majority identified a food distribution problem as the most likely. Over a timescale of 50 years, the experts were more evenly split between the two scenarios, but over half thought the most likely route to civil unrest would be a lack of total food in the UK. However, the experts stressed that the various causes of food system disruption are interconnected and can create cascading risks, highlighting the importance of a systems approach. We encourage food system stakeholders to use these results in their risk planning and recommend future work to support prevention, preparedness, response and recovery planning. [ABSTRACT FROM AUTHOR]

The climate is changing in Europe: average temperatures are increasing, and so is the frequency of extreme weather events. Climate change has a severe impact on areas suitable for growing certain crops and on food safety, for example, affecting the occurrence of the aflatoxin contamination of maize. The aim of this study was to obtain insights into the impact of climate change on possible changes in land use in Europe, particularly in areas suitable for maize cultivation, and on the probability of the mycotoxin contamination of maize in order to give directions for long-term adaptation to climate change. By combining a land use model and a mycotoxin prediction model, the suitability of land for maize cultivation and the probability of aflatoxin contamination were estimated for suitable areas in Europe, comparing the current climate with the 2050 scenario. In 2050, the occurrence of aflatoxin contamination in Europe is predicted to severely increase, especially in Central and Southern Europe. More northern regions, presently unsuitable for maize cultivation, will become suitable for maize cultivation in 2050. In the baseline scenario, most regions suitable for maize cultivation have a low probability of aflatoxin contamination, whereas in 2050, about half of the regions suitable for maize cultivation have a medium to high probability of aflatoxin contamination. Regions for safely growing maize for human consumption will shift from the southern to the northern half of Europe. [ABSTRACT FROM AUTHOR]

Agriculture is a fundamental aspect of our society, providing food and resources for a growing population. However, climate change is putting this sector at risk through rising temperatures, changing rainfall patterns and an increase in the frequency and intensity of extreme weather events. Our study highlights the need to address climate change in a differentiated way, taking into account the specificities of each agricultural sector, and therefore aims not only to organise and summarise current research but also to fill an important gap in the existing literature by focusing on the impact of climate change on vegetable crops. The topic was researched using the Web of Science and Scopus databases, where 219 publications were thoroughly reviewed and only those that fully addressed the impact of climate change on vegetable crops were selected. Of the 219 publications reviewed, only 53 focused exclusively on the effects of climate change on vegetable crops, indicating the need for more specialised research in this area, especially given the complex challenges that climate change poses not only in terms of yield but also non-trivial quality and food safety, and can be considered a future research prospect. [ABSTRACT FROM AUTHOR]

Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSDC (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability. [ABSTRACT FROM AUTHOR]