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Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

This paper presents a novel theoretical framework for incorporating climate risks and adaptation investments into infrastructure debt pricing. Utilizing the Capital Asset Pricing Model (CAPM), the framework extends the conventional modeling of infrastructure project revenues and costs to include climate risk considerations. It proposes three climate-informed revenue and cost formulations: adjustmentment of mean and standard deviation, incorporation of extreme climate events via Pareto and Poisson distributions, and a climate-informed cost model that includes adaptation investment. The paper demonstrates the application of this model in pricing a loan for a Light Rail Transit project in Costa Rica, introducing the concepts of "flood risk premium" and "adaptation curves". This study not only offers a novel lens through which to view infrastructure investment under climate uncertainty but also sets the stage for transformative policy and practice in financial risk assessment, encouraging a shift towards more sustainable and resilient infrastructure development. [ABSTRACT FROM AUTHOR]

The increasing intensity and frequency of extreme climate events have made improving the adaptability to extreme climate events a strategic imperative for manufacturing companies. This paper investigates whether manufacturing enterprises increase green technology innovation affected by different extreme climate events. Based on panel data of Chinese listed manufacturing enterprises, we show that extreme precipitation events can positively promote green technology innovation, yet extreme temperature events do not. Heterogeneity analyses suggest that the effect of extreme precipitation events on green technology innovation is more significant for non-state-owned enterprises, poor performance enterprises, and high R&D intensity enterprises than other enterprises. Furthermore, the facilitating effect of extreme precipitation events on green technology innovation is merely temporary. [ABSTRACT FROM AUTHOR]

Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze-thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable the time-lapse ERT surveys in Antarctica, however, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it in the Crater Lake CALM-S site at Deception Island, Antarctica to collect quasi-continuous ERT measurements. To efficiently process a large number of obtained A-ERT datasets, we developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted modeling results shows that the A-ERT system can detect the active-layer freezing and thawing events with very high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost one decade at this site. The evolution of thaw depth exhibits also a similar pattern in both years, with the active layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT, combined with the new processing workflow that we developed, is an efficient tool for studying permafrost and active layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the real-time progression of thaw depth, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and work towards establishing an early warning system for the consequences of climate change. [ABSTRACT FROM AUTHOR]

Purpose: This research paper aims to empirically explore how stock market investors' perceptions are affected by extreme climatic events like El Nino and floods in Malaysia. Design/methodology/approach: This study uses structural equation modelling (SEM) to analyse the empirical data gathered through a questionnaire survey involving 273 individual investors from Bursa Malaysia between January and June 2019. Findings: Results reveal that companies' efforts, especially for agriculture and plantation-based industries, to adapt to climate change risk at the production, business and stock market levels significantly impact investors' behaviour and investment decisions. Moreover, stock market investors' climate change knowledge shows a significant moderating effect on corporate climate change adaptation initiatives and investors' decisions to invest in Malaysian agricultural and plantation industry stocks. Practical implications: This research has significant implications for practice and policy, as it measures the stock market investors' level of awareness about climate change events and explores the companies' strategies to reduce climatic risks to their business model. Social implications: This study shows the way to adjust the climate change information in the stock market investment decision to improve market efficiency and sustainable stock exchanges initiative. Originality/value: To the best of the authors' knowledge, this paper is the pioneer one to provide a comprehensive link between climate change events and business performances at production level, business level and stock market levels by drawing inferences from empirical data on investors' behaviours. This study also added value in investment theories and financial literature by observing the climate change as an important factor to determine the investors' decisions in the stock market. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale. Plain Language Summary: A significant correlation between the North Atlantic Oscillation (NAO) and the North Atlantic tripole sea surface temperature (SST_tri) is widely recognized. However, this study found that the correlation between the two was significantly weakened in late winter during 1980–2001 while no such change was detected in early winter. Therefore, this paper will focus on the late winter and discuss the role of NAO spatial structure in the above correlation changes. When the NAO south center is located over the North Atlantic, both observations and the climate models indicate that the corresponding North Atlantic SST shows a significant tripole pattern. However, during 1980–2001, the NAO south center shift significantly eastward from the North Atlantic toward Western Europe (up to 20° longitude). At the same time, the NAO north center expanded eastward significantly. The eastward shift of the NAO results in the significant eastward shift of the turbulent heat flux and wind stress anomalies. Shift in the region of strong air‐sea interaction led to the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale and also provide implications for subseasonal‐seasonal predictions of Eurasian climate extremes. Key Points: The link between NAO and North Atlantic tripole SST weakened obviously during 1980–2001 late winter, which was not detected in early winterThe eastward shift of ∼20° longitude in NAO south center forced the strong air‐sea interactions region to shift in observations and CMIP6NAO's spatial shift in late winter caused the changes in the link between NAO and North Atlantic tripole SST [ABSTRACT FROM AUTHOR]

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse. [ABSTRACT FROM AUTHOR]

Settlements operate across a wide range of densities and do so for every socio-economic mode of life from those based on hunter-gatherer economies to those which are based on industrial production. Human beings also live across a range of residential densities from very high to very low. Why they do so is a function of many factors, especially differing socio-cultural ways of managing interaction and communication and the associated social and political practices of the communities. Settlement forms are seen as a derivative of many factors because they are. But they are not thereby an epiphenomenon - especially as they become larger, more durable, and bulkier. That gives them inertia and, as a consequence, they become an agency in their own right which produces outcomes with consequences for the communities, which inhabit them. They are not a neutral background. Instead, their materiality, their sizes, and their densities have an impact on the viability of social life. This paper considers the outcomes generated by the regional networks of low-density, urban settlements larger than 100 sq km in extent. The implications of what happened to agrarian-based low-density urban settlements, like Greater Angkor and the Classic Maya settlements, such as Caracol, are of consequence for the risk faced by the regional networks of present-day, low-density urban giants - the megalopoleis and desa-kota. A further perspective is provided by placing these great cities of the past and the present in the larger context of the trajectories and outcomes of smaller low-density settlements over the previous six millennia. The concern is the implications for the viability of low-density urbanism in contexts of the rapid, extreme climate change we are now beginning to experience. The implications are ominous, yet the past also indicates that social and cultural systems are robust, that human beings can survive, and that they retain and continue to remake their social traditions as they adjust to seriously changing circumstances. [ABSTRACT FROM AUTHOR]

Pacific Islands, many relatively remote and small, have been occupied by people for more than 3000 years during which time they experienced climate‐driven environmental changes (both slow and rapid onset) that challenged human survival and led to the evolution of place‐based coping strategies expressed through traditional knowledge (TK). In today's globalized Pacific Islands region, into which western worldviews and global adaptation strategies have made significant inroads, most plans for coping with climate‐changed futures are founded in science‐based understandings of the world that undervalue and sideline TK. Many such plans have proved difficult to implement as a consequence. This paper reviews the nature of extant Pacific TK for coping with climate change, something that includes TK for anticipating climate change (including climate variability and climate extremes) as well as ancillary TK associated with food and water security, traditional ecological knowledge, environmental conservation, and settlement and house construction that represent coping strategies. Much of this TK can be demonstrated as being effective with precedents in other (traditional) contexts and a compelling plausible scientific basis. This study demonstrates that Pacific Islands TK for coping with climate change has value and, especially because of its place‐based nature, should be central to future climate‐change adaptation strategies to enhance their uptake, effectiveness and sustainability. To this end, this paper proposes specific ways forward to optimize the utility of TK and ensure it has a realistic role in sustaining Pacific Island communities into the future. This article is categorized under:Climate, History, Society, Culture > Ideas and KnowledgePaleoclimates and Current Trends > Modern Climate ChangeAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

The diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates. Plain Language Summary: The future flood hazard status over the major UK urban region of Bristol (domain size ∼746 km2) is evaluated using the latest 12‐member UK Climate Projections Local ensemble rainfall data set at the hourly and kilometer scale. A total of 30,098 rainfall events from an equivalent 720 years of climate data are identified and used for flood modeling. Impact of changing precipitation pattern and soil moisture dynamics on surface water floods are evaluated, and the interaction between precipitation, inundation, infiltration, and soil wetness are handled simultaneously at a spatial resolution of ∼30 m or less using an improved hydrodynamic model (LISFLOOD‐FP). Our findings highlight a shift toward a later seasonal occurrence of extreme flooding under global warming. Specifically, a large proportion of future extreme flood hazards is projected to manifest in December, contrasting with historical trends where most extreme events occurred in November. Extreme precipitation can be magnified in rainy seasons due to amplified moisture convergence, while in dry periods limited moisture availability may offset precipitation increases. Despite an overall increase in the total annual rainfall volume, the future summer is anticipated to be much drier due to limited rainfall availability, leading to a further reduction in soil moisture availability. Key Points: Shifts to a later seasonal occurrence of extreme flooding is identified under global warming in the UKIncrease in annual total rainfall leads to a disproportionate rise in the resultant flood volumeImpact of the antecedent soil moisture on flood generation shows seasonality [ABSTRACT FROM AUTHOR]

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks). [ABSTRACT FROM AUTHOR]

Average monthly air temperatures in Bosnia and Herzegovina (BiH) exhibit a notable rise during summer, ranging from 0.4 to 0.8 °C per decade, while precipitation experiences a significant decrease of up to 8 mm per decade. Climate models, across various RCP scenarios, project an increase in air temperature, that is most pronounced in the summer season. Additionally, there is a projected frequency and intensity of heavy precipitation during autumn. In BiH, agricultural production faces substantial risks, including droughts, spring and autumn frosts, hail, and floods. Recent years have witnessed extreme hydrological events, notably the 2012 drought and the 2014 floods. Strategic documents highlight the critical importance of addressing floods and droughts for agriculture, as well as their implications for the environment, households, and industry. To assess the severity of extreme hydrological events and their impact on agriculture, with a specific emphasis on autumn and summer in Bosnia and Herzegovina, average and peak values of the Standardized precipitation evapotranspiration index (SPEI) were calculated separately for the periods 1961-1990 and 1991-2020, focusing on October and August. Compared to the reference climatic period the current climate is characterized by shifts between intense wet and dry periods, with very few years exhibiting stable and expected weather conditions. Identified as extremely wet and flood-prone years, SPEI2 October values for 1974 (2.42), 1996 (2.13), 2001 (2.24), and 2014 (2.05) stand out, with only one extremely dry year in 1985 (-2.21). SPEI2 August indicates extremely dry years, notably 2012 (-2.35) and 2017 (-2.25). [ABSTRACT FROM AUTHOR]

Annual maxima of daily precipitation sums can be typically described well with a stationary generalized extreme value (GEV) distribution. In many regions of the world, such a description does also work well for monthly maxima for a given month of the year. However, the description of seasonal and interannual variations requires the use of non-stationary models. Therefore, in this paper we propose a non-stationary modeling strategy applied to long time series from rain gauges in Germany. Seasonal variations in the GEV parameters are modeled with a series of harmonic functions and interannual variations with higher-order orthogonal polynomials. By including interactions between the terms, we allow for the seasonal cycle to change with time. Frequently, the shape parameter ξ of the GEV is estimated as a constant value also in otherwise instationary models. Here, we allow for seasonal–interannual variations and find that this is beneficial. A suitable model for each time series is selected with a stepwise forward regression method using the Bayesian information criterion (BIC). A cross-validated verification with the quantile skill score (QSS) and its decomposition reveals a performance gain of seasonally–interannually varying return levels with respect to a model allowing for seasonal variations only. Some evidence can be found that the impact of climate change on extreme precipitation in Germany can be detected, whereas changes are regionally very different. In general, an increase in return levels is more prevalent than a decrease. The median of the extreme precipitation distribution (2-year return level) generally increases during spring and autumn and is shifted to later times in the year; heavy precipitation (100-year return level) rises mainly in summer and occurs earlier in the year. [ABSTRACT FROM AUTHOR]

The present paper aims to study the Wachau Valley in Austria as a representative Cultural Landscape under threat from extreme hydrometeorological hazards linked to climate change. The primary objective is to investigate the impacts and assess the vulnerability associated with the events of heavy rain and flooding. The methodology employed consists of an investigation of recorded past events impacting the Wachau; a vulnerability ranking system; a climate time series analysis based on earth observation products; and future hazard maps at territorial level, developed with outputs from regional and global climate models. The investigation we carried out provides a vulnerability assessment of two terraced areas with a surface of about 10,000 m2 in total, characterized by the presence of dry stone walls, with different state of conservation in the Municipality of Krems (Wachau). In addition, climate projections at territorial level for the extreme climate indices R20mm, R95pTOT, and R×5day—selected for investigating the likelihood of increases/decreases in events of heavy rain and large basin flooding—are provided, with a spatial resolution of ~12 km for the near and far future (2021–2050; 2071–2100) under stabilizing (RCP 4.5) and pessimistic (RCP 8.5) scenarios. The results indicate a general increase for the three indices in the studied areas during the far future under the pessimistic scenario, suggesting a heightened risk of heavy rain and flooding. These findings aim to inform policymakers and decision-makers in their development of strategies for safeguarding cultural heritage. Furthermore, they serve to assist local stakeholders in enhancing their understanding of prioritizing interventions related to preparedness, emergency response, and recovery. [ABSTRACT FROM AUTHOR]

Green roofing technologies have been developed and implemented worldwide as they bring with them thermal, hydrological, and environmental benefits, especially in urbanized areas. Many studies have been conducted to investigate the thermal performance of green roofs over reinforced cement concrete (RCC) structural bases but there is a lacuna of experimental evaluation of the thermal performance of green roofs on sloping galvanized iron (GI) sheet truss type roof structures. In this paper, the thermal performance of a retrofitted extensive green roof is quantified; this is challenging in subtropical climates because of extreme seasonal and diurnal variations. The thermal performance of the green roof system was assessed using a life-size experiment setup followed by a whole-building simulation using Design Builder, which runs on the EnergyPlus simulation engine. Two prototype roofs were analyzed and the performance of the retrofitted extensive green roof was compared with that of a conventional RCC roof solution. To validate the results, two prototype buildings were constructed at a university campus and their thermal and energy performance analyzed for two observation periods, one during a typical summer week and the other during a typical winter week. The extensive retrofitted green roof displayed enhanced thermal and energy performance at a nominal additional cost. The findings of this study can be deployed in the development of a retrofitted extensive green roof module for sloped GI sheet roofs for better thermal performance, and hence reduced energy loads, and meet the sustainable development goals. [ABSTRACT FROM AUTHOR]

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the " Hollowing " scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community's microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment. [ABSTRACT FROM AUTHOR]

This paper introduces and summarises the main outcomes of the 19th workshop of the International Association for Phytoplankton Taxonomy and Ecology held in Tiszafüred, Hungary, Tisza Balneum Hotel, from 23 to 30 September 2022. The selected theme of the workshop was "Trait-Based Approaches in Micro-Algal Ecology". The discussions presented during the workshop sessions resulted in the 18 articles contained in this Special Issue. There are 6 main thematic aspects developed by the participants: 1. Shape and size: are these traits easy to measure? 2. Spatial scales: when and where to look for microalgae? 3. Climate and extremes of ecological gradients: hot topics of this century. 4. Metaphyton and metaphytic habitats: life beyond plankton and benthos. 5. Microalgae in water management: phycology in practice. 6. Traditional and new methods: perspectives and comments. Trait-based approaches in microalgae ecology, although requiring further investigation and methodological development, represent a valid tool for refining the analysis of environmental variability in aquatic ecosystems. The papers presented in this Special Issue demonstrate that these approaches are extremely useful not only in the study of planktic algae but constitute a thoughtful method for the analysis of benthic and metaphytic microalgae in a wide variety of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [ABSTRACT FROM AUTHOR]

In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions. [ABSTRACT FROM AUTHOR]

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models. Plain Language Summary: In this paper we propose a new data‐driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co‐occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Key Points: Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak‐over‐threshold models in most regions [ABSTRACT FROM AUTHOR]

Aims: We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods: We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results: The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions: These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications: The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia. This paper explores the relationships between the major forces influencing Australian weather and climate, and the chance of severe fire seasons. The findings could be valuable in decision making and preparation for upcoming fire seasons to avoid more seasons with devastating outcomes such as the 2019–2020 Black Summer. This article belongs to the Collection Fire and Climate. [ABSTRACT FROM AUTHOR]

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Climate extremes are threatening transportation infrastructures and hence require new methods to address their vulnerability and improve their resilience. However, existing studies have yet to examine the climate impacts on transportation networks systematically rather than independently assessing the infrastructures at a component level. Therefore, it is crucial to configure alternative shipping routes from a systematic perspective to reduce climate vulnerabilities and optimise the resilience of the whole shipping network. This paper aims to assess the global shipping network focusing on climate resilience by a methodology that combines climate risk indicators, centrality analysis and ship routing optimisation. The methodology is designed for overviewing the climate vulnerability of the current and future scenarios for comparison. First, a multi-centrality assessment defines the global shipping hubs and network vulnerabilities. Secondly, a shipping model is built for finding the optimal shipping route between ports, considering the port disruption days caused by climate change (e.g. extreme weather) based on the climate vulnerability analysis result from the first step. It contributes to a new framework combining the global and local seaport climate vulnerabilities. Furthermore, it recommends changing shipping routes by a foreseeable increase in port disruptions caused by extreme weather for climate adaptation. [ABSTRACT FROM AUTHOR]

Extreme climate change is an existential threat to humanity and infrastructure development. At the same time, the construction and operation of carbon-intense public–private partnership (PPP) infrastructure such as road transport, water, public houses, energy supply and sanitation unleash most of the greenhouse gas emissions that impacts negatively on the climate. Increasingly, there is a heightened interests in the development and financing of climate-smart PPP solutions to promote resilient and sustainable public infrastructures. Therefore, this article aims at identifying the critical solutions to the provision of climate-smart PPP infrastructure projects together with the driving factors and challenges of its implementation in public facilities. The paper utilized a systematic literature review method where data were sourced from prominent academic databases of Scopus, Web of Science, Google Scholar, and PubMed. The outcomes of the review demonstrate that the adoption of climate finance, renewable energy, and maintaining resilient infrastructures are prominent solutions to attain low-carbon infrastructure development. Key drivers such as the global call to reduce huge emissions from construction projects and transition to sustainable green construction management account for the shift toward climate-smart PPP projects. The barriers identified include poor and unconcerted practice and policy directions to resolve emission problems in the construction industry. The outcomes of this article provide incentives for the development and management of climate-smart public projects. Researchers can harness the results to investigate and develop adaptation and mitigation strategies for low-carbon PPP projects. [ABSTRACT FROM AUTHOR]

Monoculture systems in South East Asia are facing challenges due to climate change-induced extreme weather conditions, leading to significant annual production losses in rice and oil palm. To ensure the stability of these crops, innovative strategies like resilient agroforestry systems need to be explored. Converting oil palm (Elaeis guineensis) monocultures to rice (Oryza sativa)-based intercropping systems shows promise, but achieving optimal yields requires adjusting palm density and identifying rice varieties adapted to changes in light quantity and diurnal fluctuation. This paper proposes a methodology that combines a model of light interception with indoor experiments to assess the feasibility of rice–oil palm agroforestry systems. Using a functional–structural plant model of oil palm, the planting design was optimized to maximize transmitted light for rice. Simulation results estimated the potential impact on oil palm carbon assimilation and transpiration. In growth chambers, simulated light conditions were replicated with adjustments to intensity and daily fluctuation. Three light treatments independently evaluated the effects of light intensity and fluctuation on different rice accessions. The simulation study revealed intercropping designs that significantly increased light transmission for rice cultivation with minimal decrease in oil palm densities compared with conventional designs. The results estimated a loss in oil palm productivity of less than 10%, attributed to improved carbon assimilation and water use efficiency. Changes in rice plant architecture were primarily influenced by light quantity, while variations in yield components were attributed to light fluctuations. Different rice accessions exhibited diverse responses to light fluctuations, indicating the potential for selecting genotypes suitable for agroforestry systems. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa, of which Kenya is part, is considered the most vulnerable region to climate change impacts due to its dependence on rain-fed agriculture and natural resources. Since the rural livelihood systems in Kenya are expected to bear some of the worst effects of climate change, it is imperative to assess rural households' vulnerability to climate change impacts to facilitate the development of adaptation strategies. Therefore, this paper determines the level and sources of households' livelihood vulnerability to climate variability in the Migori River watershed, Kenya. The Livelihood Vulnerability Index (LVI) framed within the Intergovernmental Panel on Climate Change (IPCC) vulnerability framework (LVI-IPCC) was applied. A cross-sectional household survey conducted on 318 randomly selected households was used to assess how vulnerability differs across three watershed zones, upstream, midstream, and downstream. The LVI-IPCC scores were −0.047, −0.003, and 0.008 for the upstream, midstream, and downstream zones, respectively, with significant differences noted in the scores (ANOVA, p < 0.05). Findings indicate that while the livelihoods in all three zones showed moderate vulnerability to environmental and socio-economic stressors, there are notable variations between them. The downstream households exhibit the highest vulnerability, attributed to their lower adaptive capacity, increased exposure, and heightened sensitivity. Conversely, the upstream households demonstrate the least vulnerability compared to the other zones, owing to their lower sensitivity and exposure, as well as better adaptive capacity. Policy recommendations for reducing households' exposure to climate risks and for strengthening their adaptive capacity are discussed. [ABSTRACT FROM AUTHOR]

Rodents are one of the most abundant and diverse groups of mammals in arid zones. Their population and community dynamics are closely linked to climatic factors and pulses of primary productivity activation. In the future, climatic conditions in arid ecosystems could become more extreme as a consequence of climate change, which could affect the species that inhabit these regions. In this paper, we review the literature on the ecological response of rodents in the world's arid zones, at the population and community levels, to climatological factors (temperature and precipitation), climatic events and the possible impact of climate change. We used the PRISMA protocol to systematically search the literature, and the “vote‐counting” method to count positive, null or negative responses of the rodents. At the population level, rodent density and abundance responded positively to precipitation, while biomass responded negatively and reproduction responded positively to increasing temperature. At the community level, rodent density, biomass and diversity increased after rainfall. El Niño events were positively related to rodent population increases. In contrast, intense storms affected the survival of some species. Rodents with physiological adaptations suited to living in arid areas, for example, the heteromyids, responded positively to precipitation and could suffer fewer negative ecological consequences in the future when exposed to increased temperature and changes in precipitation. [ABSTRACT FROM AUTHOR]

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain. [ABSTRACT FROM AUTHOR]

This study investigated food security governance in relation to taming the endemic starvation for peace and sustainable development in Africa. Food and nutrition security remain a multi-dimensional concept to ensure sustainability of the food system by 2030. It is a prerequisite to meaningful development as starving people are not capable of learning, innovating, nor prioritizing sustainable development. The COVID-19 preventive measures such as lockdowns and curfews, and persistent conflict in Africa, plunged deteriorating food insecurity into steep descent that necessitates collaborative efforts and partnerships to change the trajectory. The partnerships will restore livelihoods, and eliminate starvation, disease, abject poverty, wars and eventually restore peace. Data for the study were collected through systematic review of scholarly reviewed publications obtained from Access to Global Online Research in Agriculture (AGORA) database, University of Nairobi and Google search engines. The objectives of this paper, therefore, are to analyse underlying causes of persistent starvation in Africa, examine the factors that contribute to food security issues degenerating into conflict, and highlight the role of governance in finding lasting solutions to persistent starvation in the continent. However, it was revealed that, there is congruence that conflict and disease (COVID-19) may require more attention, with unanimous calls for the adoption of multilateralism in food governance as strategy to handle the nexus issues on food, water, energy, climate, economy, conflict and disease. Attention is also required to be focused on innovations supporting small-scale farmers, especially women farmers who are the most vulnerable, to benefit from autonomous measures (such as climate-smart agriculture practices) and adaptation to climate extremes. Conclusions are drawn on the best possible strategies available to mitigate endemic starvation in Africa, the urgent need for joint efforts to eliminate conflict – induced food insecurity, and emphasis on a systems approach to tackle nexus issues (Food-Water-Energy-Climate-Economy-Conflict and Disease) to ensure sustainable development. [ABSTRACT FROM AUTHOR]

This article discusses the importance of integrating water management into urban planning to mitigate flooding risks and build resilient cities. It presents a case study of the city of Duran in Ecuador, where a successful partnership model has been implemented to address disaster risk reduction and climate change impact. The study emphasizes the need for interdisciplinary work, community involvement, and the use of early warning systems to effectively respond to potential threats. The article acknowledges the gap between global agendas and their implementation at the local level, particularly in low- and middle-income countries. The authors propose a co-production model that involves a transdisciplinary team of experts from various fields to study the urban basin and understand the city's vulnerability to flooding. They collected data from different sources and developed detailed flood risk maps using technology and available data. The implementation of an early warning system for floods was completed in 2023 and proved invaluable during the El Niño/Southern Oscillation event in 2023-2024. The collaborative approach between the community and local authorities has been effective in responding to potential threats and protecting against future disasters. The authors emphasize the importance of interdisciplinary work, evidence-based policy recommendations, and digital transformation to empower communities and local authorities in building resilience. The collaboration between the government of Duran and ESPOL (a public university) resulted in a research paper that examined the vulnerability of Duran to floods and proposed policy recommendations for climate event prevention, response, and adaptation. The COVID-19 pandemic led to adjustments in the project, including the establishment [Extracted from the article]

Enhancing community capacity towards resilience is key to reducing climate disaster risk, especially in Black immigrant communities in Canada. While there are many extreme climate change events occurring, such as hailstorms, floods, snowstorms, forest fires, droughts, and heat waves in western Canada, there is no known study that has explored resilience within sub‐Saharan African immigrant communities to climate disaster risks in western Canada. All these extreme climate change events have devastated Black populations threatening their ability to cope with disaster risks. Following a decolonial phenomenology methodological framework research approach; our study explores sub‐Saharan African immigrant communities' adaptation strategies to address climate disaster risk in western Canada. In this research, our main purpose was to investigate whether community resilience strategies implemented by the two provinces (Saskatchewan and Alberta) meet the unique needs of sub‐Saharan African Immigrants. By exploring local communities' perspectives on climate change, we highlighted the relevance of inclusivity in climate capacity building to reduce disaster risk and cope with climate change‐related disasters in the localities. Our findings revealed that personal experiences with climate change risks significantly influenced communities' strength and resilience and contributed to their resilience strategies. We view this paper as a first step in developing a community‐led climate change resilience research agenda that will have a practical application for the community in the face of climate change in Canada. [ABSTRACT FROM AUTHOR]

Emerging economies (EMEs) often ignore effective mitigation strategies for persistent climate changes to prioritise growth acceleration. This paper shows that they cannot sustain their economic growth due to the adverse impact of temperature rise on total factor productivity (TFP). Using a standard growth model, it demonstrates how temperature rise and variation for growing carbon emissions reduce capital productivity along with the damage to ecosystem services and labour productivity, adversely impacting total factor productivity (TFP) of an individual economy. A cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses the issues of endogeneity, heterogeneity and cross-sectional dependence with stochastic trends, has been applied to 21 EMEs over the period from 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. Although EMEs have heterogeneous impacts across the countries depending upon their climatic zones and income levels, one degree increase in temperature, on average, has decreased the TFP by approximately 3.22 per cent. It is much higher in the extreme climatic zones and less developed economies. • Temperature rise and its variation for raising carbon emission reduce capital productivity along with ecosystem services and labour productivity, adversely impacting total factor productivity (TFP). • The cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses endogeneity, cross-sectional dependence and heterogeneity, has been applied to 21 emerging markets and developing economies for 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. • One-degree increase in temperature, on average, reduces the TFP by approximately 3 per cent. • The temperature rise has heterogeneous and non-linear impacts on TFP across countries in geo-climatic locations. • Two extre-climatic zones are worse-affected than the moderate-climatic region. [ABSTRACT FROM AUTHOR]

Natural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia. [ABSTRACT FROM AUTHOR]

Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that, as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme look more and more like one another: they converge towards a typical, i.e. most probable, trajectory called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics to understand the physical mechanisms driving extreme events in climate models. [ABSTRACT FROM AUTHOR]

This paper investigates how variations in sediment supply, grain size distribution and climate change affect channel morphology and flood inundation in the Nakkhu River, Kathmandu, Nepal. Climate change‐induced extreme rainfall is expected to increase flood intensity and frequency, causing severe flooding in the Kathmandu basin. The upper reaches of the Nakkhu River are susceptible to landslides and have been impacted by large‐scale sand mining. We simulate potential erosion and deposition scenarios along a 14 km reach of the Nakkhu River using the landscape evolution model CAESAR‐Lisflood with a 10 m digital elevation model, field‐derived sediment grain size data, daily discharge records and flood forecast models. In a series of numerical experiments, we compare riverbed profiles, cross‐sections, flood extent and flow depths for three scenarios (1.2‐, 85‐ and 1000‐year return period floods). For each scenario, the model is first run without sediment transport and then with sediment transport for three grain size distributions (GSDs) (observed average, finer and coarser). In all cases, the inclusion of sediment led to predicted floods of a larger extent than estimated without sediment. The sediment grain size distribution was found to have a significant influence on predicted river morphology and flood inundation, especially for lower magnitude, higher probability flood events. The results emphasise the importance of including sediment transport in hydrological models when predicting flood inundation in sediment‐rich rivers such as those in and around the Himalaya. [ABSTRACT FROM AUTHOR]

With the increasing prominence of extreme climate events and the construction and development of scenic areas, strengthening the management of rainwater resources in scenic areas has become an urgent need to enhance the capability to resist rain-induced flooding disasters and utilize rainwater resources for building safe, ecological, and sustainable scenic areas. In this paper, the Yesanpo scenic area was taken as the research object. Based on topographic and hydrometeorological data, the soil conservation service (SCS) model and ArcGIS spatial analysis tools were used to simulate the inundation areas resulting from an extreme daily rainfall event that 100-year return period within the research region, and comprehensively analyze the inundation areas. Drawing inspiration from the concept of resilience in ecology, this study proposed the construction of rainwater resource management strategies at three levels of ecological resilience, facility resilience, and social resilience. These strategies aimed to facilitate the transformation of stormwater from disaster to resource, thereby enhancing the systemic resilience of the Yesanpo scenic area against floods and water scarcity issues. At the same time, these strategies promoted the construction of resource-saving and environmentally friendly scenic spots, committed to treat rainwater as a valuable resource, and achieved the goal of rainwater recycling, providing a new perspective for promoting the sustainable development of scenic spots. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Unprecedented climate extremes will likely become a new 'normal'. Urban political ecology is thus confronted with the challenge of exploring and theorising emerging geographies of unprecedented climate change. This raises questions on the extent and ways in which past socionatures can be mobilised for this task. It also urges a critical reflection on what forms of knowledge are needed to meet political ecology's normative aspirations of transforming emerging and future socionatures of climate change. I argue that a critique of past and present socionatures alone is not enough to meet these theoretical and normative goals. The main objective of this paper, therefore, is to lay the foundations of a future-oriented urban political ecology that approaches geographies of climate change more experimentally and speculatively. To this aim, I examine the explanatory potential of two (of many possible) experiments, collaboratively developed by a team of hydrologists, climatologists, and political ecologists. Both experiments depart from past and present logics of colonial violence, racial capitalism, and climate change, but explore competing notions of unprecedented futures. The first experiment consists of a critical-realist scenario approach that examines how power and variability in the exercise of agency might shape outcomes of future unprecedented climate extremes. The second involves a model that speculatively brings about a better world in response to climate extremes and lays the foundation of a political ecology of possibility. The paper thereby serves as a demonstration of how critique can be mobilised to explore and reimagine urban futures. [ABSTRACT FROM AUTHOR]

Taking China's provincial panel data from 2010 to 2020 as an example, this paper verifies that climate change, especially extreme climate change, can destabilize China's financial stability and explores the transmission path of climate risks with meteorological disaster losses as the channel variable. Our empirical results demonstrate that: (a) only after the extreme climate change breaks through a certain threshold, thus causing a significant impact on the financial system; (b) we cannot ignore the improvement of disaster prevention and reduction ability brought by the improvement of social and economic development level; (c) the impact of extreme climate change will spread to the financial system through the economic losses caused by meteorological disasters; (d) there is obvious regional heterogeneity in the spread of meteorological disaster losses to the financial system. Our results are robust to a wider battery of checks and have important policy implications. • This paper empirically tests the impact of climate change on financial stability. • Extreme climate change will damage China's financial stability. • The economic losses caused by climate disasters will spread to the finance. • The transmission of such losses has obvious regional heterogeneity. • This paper provides policy implication for coping with climate risks. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [ABSTRACT FROM AUTHOR]

The mechanism and effectiveness of urban greenery in mitigating urban heat islands, regulating microclimate, and enhancing thermal comfort has been extensively studied during the last decades. While sporadic empirical evidence has been generated, the trends and patterns of existing scholarship pertinent to urban greenery's cooling effect have been rarely summarized and synthesized. To bridge this knowledge gap, the present paper systematically reviewed 310 relevant publications in the Web of Science database (1998–2022) and conducted a bibliometric analysis to depict a comprehensive profile of urban greenery's cooling effect, focusing on global research trends, prevalent research topics, and future prospects. Our analytical results reveal (1) a steady increase in publications, active journals, and knowledge-generating institutions since 2008 that might be attributed to the free accessibility of diverse remote sensing data; (2) a significant increasing trend of transdisciplinarity and interdisciplinarity, expanding from Environmental Science and Ecology to various subjects such as Engineering, Remote Sensing, Construction & Building Technology, Urban Forestry, and Urban Studies; (3) four influential publication outlets including Urban Forestry & Urban Greening , Science of the Total Environment ; Building and Environment , and Sustainable Cities and Society ; (4) core research themes focusing on the association of urban greenery's biophysical characteristics with cognate cooling effect, urban heat island mitigation, and land surface temperature; and (5) several new research themes that have not yet well-developed in the extant literature, including the integration of various analytical approaches to up-scale empirical studies from micro-scale to meso- and global scales, extending urban greening-thermal comfort to public health and social thermal justice, and coupling urban greenery's cooling effect with other environmental/ecological benefits to inform the design of urban greenery for biodiverse, climate-resilient and sustainable cities. Findings of this synthetic review offer a reference for the research focusing on urban greenery's cooling effect, and provide clear direction for further development of cognate scholarship that is urgently needed facing more frequent urban climate extremes along with global warming. [ABSTRACT FROM AUTHOR]

Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side. [ABSTRACT FROM AUTHOR]

With the accelerating deployment of renewable energy, photovoltaic (PV) and battery energy storage systems (BESS) have gained increasing research attention in extremely cold regions. However, the extreme low temperatures pose significant challenges to the performance and reliability of such systems. This paper reviews the current progress in PV-BESS technologies and applications in extreme cold climates. The major technical challenges are discussed, including the impacts of low temperatures on PV and battery efficiency, snow and icing effects, and difficulties in system modeling and optimization. Recent solutions are analyzed, such as advanced PV module designs, battery thermal controls, anti-icing coatings, and sensors. Case studies of existing PV-BESS demonstration projects in cold regions are presented, and key lessons learned from real-world operation data are summarized. The economic feasibility and policy incentives for promoting PV-BESS in cold areas are also examined. Finally, recommendations for further research and development are provided to overcome the limitations and knowledge gaps. This review covers the current state-of-the-art in PV-BESS systems suited for extreme cold environments, providing insights for researchers and engineers working on advancing renewable energy in these challenging climates. Key findings indicate that despite technical hurdles, PV-BESS systems show promising potential in extreme cold regions through continued system optimization, cost reduction, and supportive policies. • We summarize the state-of-the-art in PV-BESS technologies suited for extreme cold climates. • Field experiences and lessons learned from existing PV-BESS demonstration projects and installations in extreme cold regions are analyzed. • We provide recommendations for further research and development towards advancement. [ABSTRACT FROM AUTHOR]

Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes identified beforehand. An efficient alternative forecasting approach is based on the data-driven models. Despite their potential ability in modeling spatio-temporal ocean data, they ignore the fact that the ocean variables in different spatial regions and time periods typically have ever changing influences on each other, thus cannot yield satisfactory prediction results. In this paper, we develop a novel attention based dynamic graph for the ocean variable forecasting problem, which captures both the spatial and temporal dependencies. Specifically, we employ joint self-attention to incorporate information from the spatial graph over the target region, and model the graph evolution across long-range time steps. The performance of the proposed prediction model has been examined in the Indian Ocean based on ocean grid data products datasets. Experimental results demonstrate that this model has significant forecasting capability within 12 months, compared with the numerical methods and the state-of-the-art spatio-temporal embedding baselines. [ABSTRACT FROM AUTHOR]

Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic. Moist, warm air from mid-latitudes lead to a speed-up of glacial ice loss in Novaya Zemlya, High Russian Arctic. Foehn winds enable especially high melt rates. These conditions became more frequent over the past decade. [ABSTRACT FROM AUTHOR]