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We offer a perspective on the major challenges that are confronting the management of reproduction in sheep and cattle in Australia and New Zealand, over the next two decades. An important context is the dominance of grazing systems in which large flocks or herds are managed over large areas where it is challenging to manage reproduction with precision. Consequently, the variable forage supply usually dominates reproductive outcomes, a problem that will be exacerbated by global heating. Thus, in extensive grazing systems, there is a great need for technological solutions to improve the management of nutrition. Global heating will also exert direct effects on reproductive function. Therefore, for the foreseeable future, reproduction will remain a focus for industry. In addition, as the industries develop, we foresee continued societal pressure to reduce medication, mitigate environmental damage, and improve animal well-being. Management solutions for extensive grazing systems must involve minimal interventions with the animals and be applicable to diverse genotypes and environments. Clearly, genetics and breeding will be at the heart of solutions and elegant strategies will be needed that focus on developing animals that are robust, if perhaps a little less productive. A high rate of genetic gain is the main reason for pursuing reproductive technologies, but highly advanced reproductive technology is not likely to be the best fit in extensive management systems. Even for AI, the simplest technology, uptake is limited and lateral thinking is needed to find ways to improve the rates of genetic gain. We conclude that there are many opportunities for improving reproductive performance in sheep and cattle in Australia and New Zealand. As we gain deeper understanding of the processes involved, we should be able to make progress in fertility and fecundity, embryo survival, and postnatal survival. Improvements in reproductive performance will increase productivity, and should also be associated with significantly improved animal well-being and a reduction in methane emissions intensity. To capture these benefits, the development of new management options will require lateral thinking about reproductive technology for extensive grazing systems, and a transdisciplinary approach that brings together the systems biology of grazing animals with an understanding of the barriers to adoption by farmers. • The context for management of reproduction is large flocks or herds grazing over large areas. • Nutrition-reproduction is key – feed supply is variable, becoming more so with climate change. • Extreme weather events caused by climate change might directly affect reproduction. • Reproduction is an industry focus, but transdisciplinary problem-solving will be needed. • Intensive ART not likely for genetic strategies so lateral thinking is needed. [ABSTRACT FROM AUTHOR]

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies. [ABSTRACT FROM AUTHOR]

The continuous growth in global energy demand has led to extensive burning and overconsumption of fossil fuels, resulting in a rapid increase in greenhouse gas emissions, primarily carbon dioxide. On one hand, the frequency and intensity of extreme weather events have increased dramatically due to global climate change. On the other hand, renewable energy sources are rapidly developing. Therefore, CO 2 electrochemical reduction technology has attracted considerable attention as a sustainable solution for converting CO 2 into valuable chemicals. Copper-based catalysts can efficiently reduce CO 2 directly to high-value chemicals, making them one of the focal points in CO 2 electrochemical reduction (eCO 2 RR) research. Many factors influence the activity and selectivity of eCO 2 RR, and this paper systematically reviews the recent advances in CO 2 electrochemical reduction using Cu-based catalysts. Firstly, the fundamental principles and reaction mechanisms of CO 2 electrochemical reduction are outlined. Subsequently, the discussion delves into Cu catalyst structures and composition synergistic control strategies, covering monometallic copper catalysts, multimetallic copper-based catalysts, copper oxides and their derived catalysts, and copper-organic composite catalysts. Finally, we also analyzed the challenges faced by Cu-based catalyst research and provided an outlook on the prospects of CO 2 electroreduction technology in conjunction with emerging AI technology. • Summarized the mechanisms of C 1 and C 2+ products in eCO 2 RR. • Summarized the application of five types of copper-based catalysts in eCO 2 RR. • Discussed the development prospects and strategies of eCO 2 RR in combination with AI. [ABSTRACT FROM AUTHOR]

Climate change is intensifying heat events, and local governments are working to absorb and mitigate the associated costs. To develop effective responses, local data on the relationship between climate and health are crucial. This study investigates the impact of heat events on unexpected mortality, focusing on deaths investigated by the Medical Examiner in Los Angeles County. A retrospective observational study. We estimate the associations between the National Weather Service's HeatRisk index and deaths investigated by the Medical Examiner in Los Angeles County using negative binomial count models with controls for time trends and seasonality. In subgroup analyses, we explore how these effects vary for those who are homeless or living in care facilities. Compared to days with no HeatRisk, days with moderate, major, or extreme HeatRisk were associated with death increases of 6.7% [CI: 1.9–11.7%], 15.3% [CI: 2.9–29.1%], and 65.5% [CI: 34.9–102.1%], respectively. Effects were more pronounced for individuals who were homeless or in care facilities. Major or extreme heat days were associated with a 59.3% [CI: 19.8–109.4%] increase in deaths among homeless individuals and a 91.4% [CI: 19.0–198.6%] increase in deaths among those in care facilities. Heat events have a significant impact on mortality investigated by the Medical Examiner, especially among vulnerable groups. Local governments may consider using the warning tools provided by the National Weather Service to focus their resources on the most intense heat events, especially to target those living in care facilities or who are homeless. [ABSTRACT FROM AUTHOR]

Earth's temperature has risen by an average of 0.11°F per decade since 1850 and experts predict continued global warming. Studies have shown that exposure to extreme temperatures is associated with adverse health outcomes. Missed primary care visits can lead to incomplete preventive health screenings and unmanaged chronic diseases. This study examines the associations between extreme temperature conditions and primary care utilization among adult Philadelphians. A total of 1,048,575 appointments from 91,580 patients age ≥ 18 years enrolled in the study at thirteen university-based outpatient clinics in Philadelphia from January 1, 2009 to December 31, 2019. Statistical analysis was performed from June to December 2023. Data on attended and missed appointments was linked with measures of daily maximum temperature and precipitation, stratified by warm and cold seasons. Sociodemographic variables and associations with chronic disease status were explored. Rates of missed appointments increased by 0.72% for every 1°F decrease in daily maximum temperatures below 39°F and increased by 0.64% for every 1°F increase above 89°F. Individuals ≥ 65 years and those with chronic conditions had stronger associations with an increased rate of missed appointments. Temperature extremes were associated with higher rates of missed primary care appointments. Individuals with chronic diseases were more likely to have missed appointments associated with extreme temperatures. The findings suggest the need for primary care physicians to explore different modes of care delivery to support vulnerable populations, such as making telemedicine during extreme weather events a viable and affordable option. [ABSTRACT FROM AUTHOR]

[Display omitted] Vegetation phenology serves as a crucial indicator of ecosystem dynamics and its response to environmental cues. Against the backdrop of global climate warming, it plays a pivotal role in delving into global climate change, terrestrial ecosystem dynamics, and guiding agricultural production. Ground-based field observations of vegetation phenology are increasingly challenged by rapid global ecological changes. Since the 1970 s, the development and application of remote sensing technology have offered a novel approach to address these challenges. Utilizing satellite remote sensing to acquire phenological parameters has been widely applied in monitoring vegetation phenology, significantly advancing phenological research. This paper describes commonly used vegetation indices, smoothing methods, and extraction techniques in monitoring vegetation phenology using satellite remote sensing. It systematically summarizes the applications and progress of vegetation phenology remote sensing at a global scale in recent years and analyzes the challenges of vegetation phenology remote sensing: These challenges include the need for higher spatiotemporal resolution data to capture vegetation changes, the necessity to compare remote sensing monitoring methods with direct field observations, the requirement to compare different remote sensing techniques to ensure accuracy, and the importance of incorporating seasonal variations and differences into phenology extraction models. It delves into the key issues and challenges existing in current vegetation phenology remote sensing, including the limitations of existing vegetation indices, the impact of spatiotemporal scale effects on phenology parameter extraction, uncertainties in phenology algorithms and machine learning, and the relationship between vegetation phenology and global climate change. Based on these discussions, the it proposes several opportunities and future prospects, containing improving the temporal and spatial resolution of data sources, using multiple datasets to monitor vegetation phenology dynamics, quantifying uncertainties in the algorithm and machine learning processes for phenology parameter extraction, clarifying the adaptive mechanisms of vegetation phenology to environmental changes, focusing on the impact of extreme weather, and establishing an integrated "sky-space-ground" vegetation phenology monitoring network. These developments aim to enhance the accuracy of phenology extraction, explore and understand the mechanisms of surface phenology changes, and impart more biophysical significance to vegetation phenology parameters. [ABSTRACT FROM AUTHOR]

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs), typically comprising 5–30 amino acids, can deliver impermeable cargo molecules across membranes into the cytosol of eukaryotic cells. CPPs can transport a broad spectrum of molecules, including peptides, proteins, nucleic acids, and small molecules. Delivery can be accomplished by covalently attaching a CPP to the target cargo or by the formation of a noncovalent complex between the CPP and cargo. CPPs penetrate cells via two distinct mechanisms: energy-dependent endocytic pathways or energy-independent direct translocation across the plasma membrane. CPPs could potentially reduce dependence on chemical fertilizers and pesticides, boost crop yields and resilience to extreme weather conditions, improve nutrient uptake from the soil, and enhance nutrient contents in plants. Cell-penetrating peptides (CPPs) are short (typically 5–30 amino acids), cationic, amphipathic, or hydrophobic peptides that facilitate the cellular uptake of diverse cargo molecules by eukaryotic cells via direct translocation or endocytosis across the plasma membrane. CPPs can deliver a variety of bioactive cargos, including proteins, peptides, nucleic acids, and small molecules into the cell. Once inside, the delivered cargo may function in the cytosol, nucleus, or other subcellular compartments. Numerous CPPs have been used for studies and drug delivery in mammalian systems. Although CPPs have many potential uses in plant research and agriculture, the application of CPPs in plants remains limited. Here we review the structures and mechanisms of CPPs and highlight their potential applications for sustainable agriculture. [ABSTRACT FROM AUTHOR]

• Graduated driver licensing can allow for flood safety education of learner drivers. • We audited publicly available learner driver handbooks for flood safety information. • We found no information in some jurisdictions; inconsistent information in others. • Auditing flood-related visual signage yielded significant gaps for learner drivers. • Learner driver handbooks represent a missed opportunity to educate on flood safety. Introduction: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. Methods: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. Results : Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. Discussion : Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. Conclusions : The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. Practical applications : We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive. [ABSTRACT FROM AUTHOR]

Weather effects are long known to significantly impact human health, especially in the context of climate change and their associated extreme weather conditions. Both healthcare systems and individuals are undoubtedly negatively affected by these phenomena. Thus, our study takes a dual approach towards leveraging Machine Learning for adaptation and prevention. The proposed solution consists of a regression model for hospital admissions and a classification model for risk prediction considering weather conditions and comorbidities for three diseases: heart failure, cerebral infarction and respiratory failure. We relied on meteorological data from official weather stations in Romania and hospitalization data from the five most populated Romanian cities (Bucharest, Iasi, Constanta, Cluj-Napoca, and Timisoara). We analyzed the effectiveness of various Machine Learning algorithms for both tasks. To evaluate the influence of environmental and health factors on critical incidents, we explored three experimental configurations: all features, excluding weather parameters, and excluding medical information. For both demand forecasting and risk evaluation, Extreme Gradient Boosting delivered the best metrics upon the analyzed features: 11.2% mean absolute percentage error for hospital admissions forecasting and recall of 94.1% for heart failure, 86.5% for cerebral infarction, and 65.1% for respiratory failure. The main conclusion is that selected weather parameters considered have no major significance for the prediction outputs for the selected diseases, especially in comparison with other features such as geographical and temporal information in case of admission prediction, and age, pre-existing medical conditions of an individual in case of disease risk estimation.1 [ABSTRACT FROM AUTHOR]

Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method's potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies. [ABSTRACT FROM AUTHOR]

This study mainly focuses on the prediction of the variability of the Total Electron Content (TEC) affecting space and ground-based technological infrastructures vulnerable to extreme space weather events. The impact of solar wind conditions (SWC) can be quantified by measuring the ionosphere's TEC. We used a machine learning (ML) algorithm based on the Support Vector Machine (SVM), Long Short-Term Memory (LSTM) Neural Network (NN), and International Reference Ionosphere 2016 (IRI 2016) models to predict the hourly TEC over Ethiopia during quiet and storm geomagnetic conditions. We considered TEC data from the Global Positioning System (GPS) stations at ADIS, CURG, and NEGE during the years 2013 to 2016. The results indicated that the SVM model predicted the hourly TEC variation with a root mean square error (RMSE) between 2.136 and 7.923 Total Electron Content Unit (TECU) under different ionospheric conditions. For the LSTM model, the RMSE between the predicted and observed TEC lies between 1.483 and 2.527 TECU. But the empirical IRI 2016 models' RMSE from GPS TEC is in the range of 4.777 and 14.519 TECU. The statistical study points out the LSTM has the highest prediction accuracy and is the most robust for accurate prediction of ionospheric TEC in comparison to the SVM and IRI 2016 models throughout both storm and quiet periods. The result can be further improved with a lower RMSE (high correlation) by utilizing auxiliary data and increasing the number of stations employed in the study. This research adds fresh insights to deep learning applications in space weather forecasting in the region of interest. [ABSTRACT FROM AUTHOR]

As extreme weather becomes more prevalent, it has serious implications for the insurance industry, real estate owners, and historic buildings. First of all, for the extreme weather, we summarize extreme weather into three parts: extreme temperature, extreme precipitation, and extreme humidity, and we process the data by combining the actual situation and applying the improved Critic method, and we demonstrate the data processing by taking the three data of Lhasa and Luxor as examples. anomalies. Subsequently, a future extreme climate prediction model for a region based on the Bi-directional LSTM method is developed. A time-weather model was fitted based on the collected data, with the highest and lowest points as the anomalies. At the same time, we simplify the formula to derive the insurance company's revenue, thus modeling the relationship between the degree of extreme weather hazard and insurance revenue, and derive that the insurance company will not be able to underwrite when the combined hazard is greater than the ratio of the insurance claim cost to the insurance payment. We applied the resulting model to Lhasa and Luxor, and plotted the combined hazard level of the actual data and the predicted data to help the insurance company visualize the combined hazard level of the region each month, and then choose whether to underwrite the policy in that region or not. [ABSTRACT FROM AUTHOR]

Extreme weather occurrences provide issues that necessitate the development of technology capable of accurate analysis and exact prediction in order to successfully reduce their effects. Artificial neural networks (ANNs) have appeared in the last few years as a promising weather forecasting technology because of their capacity to manage intricate and nonlinear weather factors. The use of ANNs in weather element prediction has shown considerable improvements in forecasting precision and accuracy. Factors influencing an ANN model's efficacy include input Data Type and Volume for Training, Hidden Layer Neuron Count, network architecture, Activation Functions, and training algorithms. In this paper, we will present a thorough review of the applications of Artificial Neural Networks (ANNs) in weather forecasting, specifically focusing on temperature and rainfall prediction over the past 15 years. To enhance reader comprehension, The work of numerous researchers in this topic is methodically analyzed and compared in in tabular format. The authors aim to facilitate future research decisions by offering an organized review, aiding in the determination of suitable input features, transfer functions, and training algorithms for accurate temperature and rainfall predictions. [ABSTRACT FROM AUTHOR]

Despite ample evidence on the labor productivity impact of heat shocks, little is known about how heat shocks affect labor adjustment over time. This paper uses administrative employer–employee linked data from Brazil to examine margins of labor market adjustment to extreme heat. Tracking workers over time and across job spells, I find that quarterly heat shocks increase the probability of formal manufacturing layoffs. In the medium run, there is limited intersectoral and interregional reallocation, as well as a significant failure rate to reallocate back to formal sectors, indicating job transitional costs. Finally, the employment impact of heat shocks is larger for workers in more routine manual task intensive, less abstract occupations. [ABSTRACT FROM AUTHOR]

Climate policy uncertainty (CPU) raises concerns about financial instability and creates several macroeconomic challenges for the United States. Therefore, hedging against climate policy uncertainty risk is essential for formulating appropriate policies for efficient risk management. This study explores the relationship between climate policy uncertainty and green bonds, evaluating whether green bonds can hedge climate policy uncertainty risk and enhance the stability of the United States' climate policy environment. Against this background, bootstrap full-sample and subsample rolling-window Granger causality tests are employed to investigate the mutual influences between climate policy uncertainty and green bonds (measured by GBI). The positive impact of climate policy uncertainty on green bonds highlights that green bonds can mitigate uncertainty risks. However, this perspective cannot be confirmed through negative influences, as the green bond market is also determined by public confidence and speculative bubbles. Therefore, the characteristics of instability reveal that green bonds should not always be used to hedge against climate uncertainty risks in the United States. These results are supported by the general equilibrium model, which underscores a certain impact of climate policy uncertainty on green bonds. In turn, the positive influence of green bonds on climate policy uncertainty indicates that the green bond market can provide insights into predicting climate policy uncertainty. Additionally, using the quantile-on-quantile (QQ) method reveals a mutual influence between climate policy uncertainty and green bonds in short, medium, and long term. Under severe extreme weather conditions and complex economic environments, investors and government can benefit from the green bond market to optimize their investments. However, they should mitigate potential risks associated with green bond volatility to avoid significant losses and reduce climate policy uncertainty. [ABSTRACT FROM AUTHOR]

We constructed an Extreme Weather Index to measure extreme weather risks in China. Analyzing macroeconomic data through a structural vector auto-regression model suggests that a negative weather shock leads to persistently low GDP and credit obtained by non-financial firms. In our regression analysis of a panel of firms listed in China, the negative effects of weather shocks on firm level loans were statistically and practically significant. Further analysis suggests that credit risk and expectations are two important impact channels. A high existing credit risk or low confidence among firm managers, amplifies the negative effects of extreme weather on loans. [ABSTRACT FROM AUTHOR]

• We considered the solar activity-magnetospheric-ionospheric disturbance relationship. • 1D MHD module solved solar wind-magnetosphere shock for several solar wind speeds. • We included the resulting electric field to the F-layer of ionization model. • The excess satellite drag and plasma wake size were accordingly estimated. This study introduced a simplified, integrated computational model to estimate the drag coefficient and surface charging within the plasma wake of Low Earth Orbit (LEO) spacecraft influenced by various solar activities. The model constituted four core modules: a solver for the solar wind-magnetosphere interaction, a magnetosphere-ionosphere coupling for electric field mapping, an ionization model for the F-layer of the ionosphere, and an estimation model for excess satellite drag and plasma wake size. The initial module adopted a simplified 1D ideal Magnetohydrodynamic (MHD) model while incorporating magnetosphere-ionosphere interactions to derive electric field data near Earth's boundary. A simplified ionospheric model for the F-layer was implemented to compute the O+ and electron densities. Subsequently, the spacecraft drag coefficient and the plasma wake size were determined for varying solar wind speed inputs corresponding to quiet, intense, and extreme space weather conditions. In the case of extreme conditions with a solar wind speed of 1000 km/s, the results indicated that the excess satellite drag coefficient and plasma wake size increased to around ten percent. [ABSTRACT FROM AUTHOR]

Moving a world leading numerical weather prediction system that runs on a dedicated, bespoke, high performance computing cluster and supporting infrastructure, into the heart of a digital twin for climate change adaptation and extreme weather events has been a challenging and exciting journey. In this paper we describe this journey with a focus on those aspects required to leverage the pre-exascale EuroHPC systems that have been made available to Destination Earth (DestinE) to run its computational representation[1]. EuroHPC systems can be effectively used for DestinE and are in fact key assets to deliver the computational power required for Earth system digital twins at global km-scale resolution. At the same time, EuroHPC systems were newly installed and procedures to run them efficiently are evolving. We find that each of these systems is operated by a national hosting entity that implements its own procedures, e.g. for identity and access management, specific system configuration like schedulers, filesystems, software management systems, and specific, sometimes vendor associated, toolchains, tooling, and container runtimes. In particular, the different scheduling policies encountered, required us to adapt our workflows for each site. We found that having dedicated resources available, which was trialed in a period from 16th February to 14th April on LUMI, allowed to achieve high occupation rates, with 92% on the reserved GPU allocation and greater than 97% efficiency on the CPU reservation. Also a stronger focus on federation of these systems, with a focus not only on federation of identities and accounts, but also in the areas of data ownership/transfer, observability, services and service accounts, maintenance coordination and performance portability is required. In general, it should become much easier to transfer a workload, or a digital twin system, from one EuroHPC site to the next and run and maintain them across several sites concurrently. [ABSTRACT FROM AUTHOR]

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and dificult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository: https://github.com/TeodorChiaburu/RIWWERTimeSeries. [ABSTRACT FROM AUTHOR]

Weather forecasting is a critical tool for many different applications, from agriculture and transportation to disaster preparedness and response. While weather forecasts are not always perfect, they provide valuable information that can help people make informed decisions and take appropriate actions to protect themselves and their property from the impacts of extreme weather events. In this paper, to forecast the weather we are using the SARIMA model as RMSE is 1.24 and analyse how it forecast the weather with high accuracy. Note that the SARIMA model is a machine learning (ML) technique used to forecast time series data that has both trend and seasonal components. It is an extension of the ARIMA model, which stands for Autoregressive Integrated Moving Average. To account for periodic changes in the data, the SARIMA model augments the ARIMA model with a seasonal component. The SARIMA model is trained on historical weather data and can be used to predict future weather patterns. It is a powerful tool for weather forecasting as it can accurately predict both short-term and long-term weather trends. The quality of the input data determines how accurate the SARIMA model will be, and the model can be adjusted to perform better. In conclusion, the model known as SARIMA is a ML based approach that has been successfully used to predict weather. It is a powerful tool that can handle non-stationary data and seasonal components and can accurately predict short-term and long-term weather trends. [ABSTRACT FROM AUTHOR]

We investigate the determinants of people's attention to climate change in Italy. We propose a Twitter-based indicator consistent with traditional survey data but potentially available at a higher frequency and spatial granularity. We show that attention to climate change is heterogeneous across the country and correlated with socioeconomic characteristics and social capital. Mass media and social movements have a relevant impact on people's attention about climate change while the effect of private experience – proxied by the occurrence of extreme weather events – is more limited. • In recent years, attention to climate change has urged, accompanied by a worsening of the overall sentiment about the issue • Attention to climate change correlates with socioeconomic characteristics, media coverage, and social movement initiatives • Heat waves draw attention to climate change, while cold waves merely shape weather discussions • Experiencing an extreme weather event has a limited, but still significant, impact on the attention to climate change [ABSTRACT FROM AUTHOR]

With increasing extreme weather events and urban development, drinking water source areas (DWSAs) face rising water quality pollution incidents. Timely and precise pollution incident early warning is critical. This study integrated the hydrodynamic model and water quality early warning model developed for the Three Gorges Reservoir Area (TGRA)'s DWSA, establishing a water pollution incident early warning system for the TGRA. Assuming a heavy metal zinc (Zn) pollution incident in the TGRA's Heshangshan drinking water source area (HDWSA), the study analyzed pollutant Spatio-temporal variations and the duration of early warning exceedance during different water periods. Results indicated varying durations for pollutant levels to return to background values across the four water periods: 123 min (dry), 115 min (falling), 106 min (storage), and 96 min (flood). Level I early-warning durations ranged from long to short: 68 min (dry), 63 min (falling), 57 min (storage), and 50 min (flood), respectively. The early warning system is scientifically reliable and supports decision-making for DWSA water pollution incidents. [ABSTRACT FROM AUTHOR]

• Combining the three pillars of susatainability with the transportation companies which impact climate. • The literature analysis focuses on a specific industry which influences climate change and is influenced by the climate change itself. • Literature Review has no observation period limitation. • Literature Analysis focuses not only on the non-financial effects of the climate change on the transportation companies, but also on possible economic effects. This research examines the intricate interactions between climate change (CC) and transportation companies (TC). It situates itself within the growing dialogue on environmental sustainability, acknowledging the dual role of TC as both contributors to and victims of CC phenomena. These companies are recognised as significant sources of greenhouse gas emissions while concurrently facing operational challenges due to climate-induced events such as extreme weather conditions. The methodology for this study incorporates a bibliometric and systematic literature review, drawing upon the frameworks established by Bartolini et al. (2019) and Badassa et al. (2020). This approach facilitates an understanding of the relationship between CC and TC. A significant contribution of this study is its broadened perspective on the economic repercussions of CC on the transportation sector, addressing a gap in existing literature. It identifies a disproportionate focus in current research, which tends to emphasise the effects of climate change on TC more than the impact these companies have on CC. This observation highlights the pressing need for sustainable practices within the transportation industry, extending beyond conventional CC discourse. Furthermore, this study enhances the academic understanding of the CC-TC relationship's broader systemic and economic implications. It provides new perspectives on the potential for TC to adapt and move towards sustainability and resilience. The research underscores the necessity of innovative and sustainable approaches for these companies, emphasising their role in reducing environmental impact and adapting to the ongoing changes brought about by CC. In conclusion, this study is a vital guide for future research and policy development. It underscores the complex challenges facing the TC in the context of CC and proposes a direction for proactive and informed action within this field. [ABSTRACT FROM AUTHOR]

• Treat a problem of predicting the demand for staple food and feed grain. • A novel fractional discrete dynamic multivariate grey model is proposed. • The model considers the effects of linear correction term & random disturbance term. • Sailfish optimizer is used to optimize the fractional order accumulation parameter. • Empirical analysis shows the proposed method performs better than similar methods. Increasing geopolitical conflicts, extreme weather and other emergencies are continuously affecting global agriculture and food systems, threatening national and regional food security. Therefore, it is particularly important to accurately predict national or regional grain demand. In this paper, a novel fractional discrete dynamic multivariate grey model is proposed to predict grain demand. Based on the discrete modeling idea, the model considers the impacts of the change trend of the related factor sequences, adds the linear correction term and the random disturbance term, and applies the fractional order accumulation strategy, which improves the accuracy and robustness. An algorithm based on sailfish optimizer is introduced to optimize the fractional order accumulation parameter of the model. Taking the Yangtze River Economic Belt as an example, the fitting and prediction results of the novel and the existing three models are compared. The novel model can better fit and predict the demand for staple and feed grain, which is superior compared to other models. The predictions show that demand for staple food and urban feed grain in the Yangtze River Economic Belt will increase to varying degrees, while rural feed grain demand will remain stable overall. This paper will help the government to better grasp the changes in the structure and quantity of staple food and feed grain demand in the Yangtze River Economic Belt and formulate efforts to ensure food security. [ABSTRACT FROM AUTHOR]

• Characteristics of PM 2.5 -bound metals and carbon were investigated. • Biomass burning accounted for 50% in La Niña year and 79% in El Niño year. • Climate phenomenon affected levels of PM 2.5 and their metal and carbon compositions. • Open burning increases in El Niño year causing serious air pollution in the region. • Less open burning in La Niña year made soil resuspension and traffic prominent. There have been a very limited number of systematic studies on PM 2.5 compositions and their source contribution in Southeast Asia. This study aims to explore the characteristics of PM 2.5 composition collected in Chiang Mai (Thailand) during La Niña and El Niño years and to apportion their sources during smoke haze and non-haze periods. The average PM 2.5 concentration of smoke haze episode in 2019 (El Niño) was much higher than in 2017 (La Niña). The ratios of organic carbon (OC) to elemental carbon (EC), as well as K (biomass burning (BB) tracer) to PM 2.5 , were higher during smoke haze episodes in 2019 than in 2017 indicating a significant influence from BB. The ratios of secondary organic carbon (SOC) levels to primary organic carbon (POC) levels during smoke haze episodes were higher than those in non-haze period, which indicated greater SOC contributions or more photo-oxidation of precursors in haze episodes with high ambient temperatures. However, the ratios of soil markers (Ca and Mg) during non-haze period were high implying that soil source contributed more to PM 2.5 concentrations when there less BB occurred. The positive Matrix Factorization (PMF) model revealed that the source of BB, characterized by high K fractions, was the largest contributor during smoke haze episodes accounting for 50% (2017) and 79% (2019). Climate conditions influence meteorological patterns, particularly during incidences of extreme weather such as droughts, which affect the scale and frequency of open burning and thus air pollution levels. [Display omitted] [ABSTRACT FROM AUTHOR]

Concrete has experienced significant improvements in recent decades, resulting in lower Portland cement consumption as well as a lower carbon footprint through CO2 injection while keeping comparable fresh, mechanical, and durability performance. The use of CO2 injection in concrete (CarbonCure) in the United States and Canada has enabled advances such as green concrete under normal and severe conditions. While incorporating novel developments, this study focuses on CarbonCure concrete modification in the extreme weather of the Arabian Gulf region. This is a relatively new option that has entered the field in the Middle East. The materials, process, and fresh and hardened properties of the CarbonCure concrete made and field cured in the Arabian Gulf region will be considered here and evaluated in detail. Along with adding CO2 to the mix, the cementitious material was lowered without impacting the quality or performance of the structural concrete. The developed green mix included up to 0.2% CO2 by mass of cementitious material. Because of the benefit of early carbonation, which strengthened the concrete product even with less cementitious material, this green concrete mix retained a 28% increase in slump compared to the standard concrete mix without CO2, a 28-day compressive strength of 48 MPa, low water absorption of 1.3%, and resistance to aggressive chemicals, all within the limits defined by the standard codes. Due to these benefits, and because this green concrete was tested against the severe coastal climate, it would be ideal for maritime applications. [ABSTRACT FROM AUTHOR]

Historical auroral reports extend our knowledge of solar eruptions and long-term solar variability in the millennial time scale beyond the chronological coverage of instrumental observations, in the decadal to centennial time scales. Such chronological extensions benefit the scientific community, increasing the number of case studies on extreme space weather events with lower frequency but higher potential impact on modern technological infrastructure. So far, the earliest known datable reports of candidate aurorae have reached back to the 7th century BCE. Beyond this time series, we have analysed a celestial report in the Chinese Bamboo Annals that has attracted little scientific interest, probably owing to the controversial interpretations for the physical identity and the chronology of the event. Our philological analysis establishes its probable auroral nature. The textual description can be compared with early modern accounts of visual auroral observations for the multi-colour aurorae. We have located the observational site around Hàojīng (N34°14′, E108°46′) and dated the event to 977 ± 1 or 957 ± 1 BCE. On this basis, we have computed the equatorward extension of the auroral visibility as ≤39.0° in magnetic latitude and reconstructed the equatorward boundary of the auroral oval as ≤45.5° in invariant latitude. Our investigations empirically associate this candidate aurora with an extreme geomagnetic storm. We have compared this space weather event with proxy-based reconstructions of long-term solar variability and characterised it as a unique space-weather reference before the Neo-Assyrian Grand Minimum (alternatively Homeric Grand Minimum) in ca. 810 – 720 BCE. [ABSTRACT FROM AUTHOR]

Dairy cows are highly motivated to access pasture and have a partial preference for alternative forms of outdoor access (e.g., deep-bedded outdoor sand or wood-chip packs). In addition, Canadians value the provision of outdoor access to dairy cows as they perceive it as important for good cow welfare. In contrast to Europe, Oceania, and the United States, little data exist on the use of outdoor access on Canadian dairy farms. Therefore, our objective was to assess current outdoor access practices for dairy cows in Canada. An online questionnaire was used to determine housing and outdoor access practices for lactating cows, dry cows, pregnant heifers, and weaned, nonpregnant heifers on Canadian dairy farms. The questionnaire was distributed by the 10 provincial milk boards between November 2020 and August 2021, resulting in an 8.9% response rate (n = 903 completed questionnaires). In total, 75% (n = 675) of respondents provided some form of outdoor access to at least 1 cattle class on their farm. Pasture was the most frequently used form of outdoor access for all cattle classes. Based on a weighted average, a total of 29% and 48% of Canadian dairy farms provided lactating and dry cows, respectively, access to pasture; for youngstock, these numbers were 48% and 27% for pregnant heifers and weaned, nonpregnant heifers, respectively. Herd size (for each cow class), indoor housing system, and region were all associated with the provision of pasture. Farms with larger lactating herds less often provided access to pasture; larger herd sizes in terms of weaned, nonpregnant heifers, pregnant heifers, and dry cows were also associated with a lower likelihood of access to pasture. Farms using indoor bedded pack housing for their lactating cows more often provided pasture to this cattle class than farms with freestall or tiestall housing; this likelihood did not differ between farms with tie or freestall housing for this cattle class. Dry cows or pregnant heifers housed in a tiestall were more often provided pasture than freestall-housed dry cows or pregnant heifers. Housing type for weaned, nonpregnant heifers was not associated with the likelihood of pasture provision. Farms in British Columbia or on Canada's East Coast (i.e., Nova Scotia, New Brunswick, and Prince Edward Island) more often provided lactating cows pasture compared with farms in other regions. For the other 3 cattle classes, farms on the East Coast of Canada more often provided pasture than farms in other parts of Canada. These results will inform future decisions regarding outdoor access for Canadian dairy cattle and may also aid in identifying future areas of research. For example, our results may aid in designing housing systems that facilitate outdoor access in larger herds and in areas that are subject to more extreme weather conditions. [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency of extreme weather events and compounded natural disasters. Heat, wildfires, flooding, and pollen are already threatening public health and disproportionately affecting individuals in susceptible situations and vulnerable locations. In this theme issue of the Journal of Allergy and Clinical Immunology , we address what is known and not known about the biologic as well as clinical upstream and downstream effects of climate change on asthma and allergy development and exacerbation. We present potential actions that individuals can take at the family, neighborhood, community, health care system, and national and international levels to build climate resilience and protect their own health and the health and welfare of others. We emphasize the importance of actions and policies that are context specific and just. We emphasize the need for the health care system, which contributes between 3% and 5% of global greenhouse gas emissions, to reduce its carbon footprint and build resiliency. Health care providers play a pivotal role in helping policymakers understand the effects of climate on the health of our patients. There is still a window to avoid the most serious effects of climate change on human health and our planet. [ABSTRACT FROM AUTHOR]

Human epigenetic variation is associated with both environmental exposures and allergic diseases and can potentially serve as a biomarker connecting climate change with allergy and airway diseases. In this narrative review, we summarize recent human epigenetic studies examining exposure to temperature, precipitation, extreme weather events, and malnutrition to discuss findings as they relate to allergic and airway diseases. Temperature has been the most widely studied exposure, with the studies implicating both short-term and long-term exposures with epigenetic alterations and epigenetic aging. Few studies have examined natural disasters or extreme weather events. The studies available have reported differential DNA methylation of multiple genes and pathways, some of which were previously associated with asthma or allergy. Few studies have integrated climate-related events, epigenetic biomarkers, and allergic disease together. Prospective longitudinal studies are needed along with the collection of target tissues beyond blood samples, such as nasal and skin cells. Finally, global collaboration to increase diverse representation of study participants, particularly those most affected by climate injustice, as well as strengthen replication, validation, and harmonization of measurements will be needed to elucidate the impacts of climate change on the human epigenome. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) offers the benefits of high spatiotemporal resolution logging-consistency and all-weather operability. Estimation of water vapor weighted mean temperature (T m) is a crucial part and a major error source in extracting GNSS PWV from atmospheric delay. Empirical modelling of T m is a convenient approach, especially in cases where, the meteorology community suffers from inefficient and inconsistent logging of temperature and humidity profiles etc., in the existing meteorological observing systems, particularly for harsh weather conditions. In a first, a novel site specific model (T m-SSM) is developed for the atmospheric weighted mean temperature (T m) for 13 CORS (continuously operating reference stations) in the Survey of India (SOI) network, recently established in the hilly state of Uttarakhand (UK) using the ERA5 reanalysis data from 2016 to 2018. GNSS observations at 10 CORS are then used to observe the spatiotemporal characteristics of PWV from T m-SSM during the heavy rainfall event in October 2021. The newly developed T m-SSM performs better with the mean bias error (MBE) and root mean square error (RMSE) of 0.498 and 2.9 K, respectively, when compared with other globally accepted models. The T m-SSM PWV estimates are well correlated with the precipitation data from AWS stations. The newly developed T m-SSM is well conditioned and highly stable, with the mean stability indicator value as low as 1.043%. Further, a close relationship can be observed from the spatiotemporal characteristics of GNSS PWV during heavy rainfall event, with the in-situ precipitation measurements. It is apparent from the results that PWV steadily builds up before the arrival of rain, with sharp peaks in PWV observed just before rainfall event. It then, follows a downward slope through which it decreases to a stable value of around 30 mm when the rainfall ceases. The fluctuations in the PWV from T m-SSM and the passage of the severe mesoscale convective system in a complex terrain such as UK were shown to have high spatial and temporal correlation, which is significant for the monitoring and forecasting such extreme weather. The findings imply that GNSS PWV represents the compendious signature of the water vapor dynamics during severe weather, demonstrating the potential to improve the early detection and sensing of severe weather. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose improved variational mode decomposition by dingo optimization algorithm. • Propose improved least squares support vector machine by African vultures optimization algorithm. • Propose improved back propagation by hunter-prey optimizer. • Introduce error correction to improve the performance of the hybrid model. • Propose a hybrid forecasting model of carbon emissions. Global carbon emissions have increased dramatically in recent years, leading to a spate of extreme weather events worldwide. So, it is very important to establish a scientific carbon emissions forecasting model for environmental protection and human health. Aiming at the nonlinear and nonstationary of carbon emissions, a forecasting model with optimized variational mode decomposition (VMD) and error correction, including neural network estimation time entropy (NNetEn), VMD optimized by dingo optimization algorithm (DVMD), back propagation optimized by the hunter-prey optimizer (HPOBP), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), error correction (EC) and least squares support vector machine optimized by African vultures optimization algorithm (AVOALSSVM), named DVMD-NNetEn-CEEMDAN-HPOBP-AVOALSSVM-EC, is proposed. To simplify the description, the name of the proposed model will be abbreviated as DNCHAE. Firstly, original data is decomposed into intrinsic mode functions (IMFs) by DVMD, and then IMFs are divided into low-complexity components and high-complexity components by NNetEn. Secondly, the sum of high-complexity components is decomposed by CEEMDAN, and HPOBP is used to forecast the decomposition results. Then, AVOALSSVM is used to forecast the low-complexity components, and all the forecasting results are combined to obtain the preliminary forecasting results. Finally, error correction is done to reduce the error, and final forecasting results are obtained. Carbon emissions from China and Russia are forecasted to validate the model's forecasting effect. Experiments show that DNCHAE has a good forecasting effect on carbon emissions. The MAE, RMSE and MAPE of China are 0.1624, 0.2048 and 0.0055 respectively, and the forecasting effect is far better than that of other forecasting models. [ABSTRACT FROM AUTHOR]

Affected by extreme hot and dry weather events, the power supplied by hydropower is seriously insufficient in Sichuan province during the summer of 2022, causing severe power shortage and incalculable economic losses for the society. Therefore, a novel multi-objective scheduling model based on grid-connected hydro-wind-solar-battery energy resources combining flexible reserves is proposed to collaboratively guarantee the power supply reliability of both the contracted long-distance power transmission and the local electricity demand under extreme hot and dry weather. The intermittent nature of water inflow, wind speed, solar irradiance, and load is considered, and the multi-scenarios are produced using Latin Hypercube Sampling method. In this work, thermal plants are regarded as flexible reserves for easing the load shedding level of local load. The McCormick's Envelope method is implemented to linearize the power production model of the hydropower station. A multi-objective model, including minimum penalty cost of load shedding and carbon dioxide emission, is proposed to achieve the economic-environmental equilibrium of the power system. The proposed multi-objective optimal hybrid energy complementary strategy is transformed into a single objective model via ε -constraint method, and the fuzzy satisfying method is introduced to select the best compromise solution in the Pareto Front. The meteorological information of the summer of 2022 is collected to verify the effectiveness of this proposed mixed-integer-linear-programming energy complementary model based on a hydropower station on the Jinsha River in Sichuan province, China. Numerical experiments demonstrate that the proposed model is efficient and can enhance the reliability of real-world power systems under extreme hot and dry weather. A set of policy suggestions have been provided for Sichuan province to develop the power grid to face the extreme weather effect. [ABSTRACT FROM AUTHOR]

It is a challenging task for pedestrians to cross a road with multiple traffic lanes and busy traffic. Many footbridges and underpasses have been built in the urban area of metropolitan cities such as Hong Kong to resolve the problem of vehicle-pedestrian conflict. To maximize the utilization and benefit of the installation of such crossing facilities, it is crucial to understand the choice behaviour of pedestrians. Although many studies have examined pedestrian walking behaviour and preference towards crossing facilities, the influence of ratio of perceived values between waiting and walking time on the choice of crossing is not explored. In addition, individual perception and choice may vary with the environmental conditions, which has not been fully accounted for in existing studies. Exposure to extremely hot weather, crowded walkways, and roadside traffic emissions are not favoured. In this study, a stated choice experiment is developed to examine the relationship between possible influencing factors and the crossing choices of pedestrians in Hong Kong. In addition, a regret-based panel mixed multinomial logit approach is adopted to model the choice, accounting for the effects of unobserved heterogeneity and panel data. The results indicate that the choice decision of pedestrians is more sensitive to an increase in waiting time at signalized crossings than to an increase in walking time to access footbridges and underpasses. These findings shed light on future urban and transport planning strategies to improve the walking environment and promote walkability. • A stated choice experiment is developed to examine pedestrian crossing preferences. • Effects of perceived waiting time, walking time and personal characteristics are examined. • Influences of pedestrian flow, traffic and weather conditions are considered. • Regret-based panel mixed multinomial logit approach is used to model pedestrian choices. • Extreme hot weather and raining modify the association between pedestrian characteristics and crossing preferences. [ABSTRACT FROM AUTHOR]

• By 2050, 70–80 million tonnes of EoL-PV waste may be expected. • A novel approach to upcycle EoL-PV as building material is proposed. • India's housing needs and SDGs are benefited from this approach. • Reduction in cost, embodied energy & carbon emission expected with this approach. • This approach promotes jobs in PV and construction sector benefiting society. Solar photovoltaic (PV) installations worldwide continue to grow, and in 2022, global PV installations crossed 1 TW of solar PV. Various factors like extreme weather events, higher temperatures, humidity, and other operational factors bring down the life of PV panels. A poor functional and (or) economic viability leads PV panel to its End-of-Life (EoL). By the end of 2050, 70–80 million tonnes of EoL-PV waste is anticipated. Recycling technology is currently not economically viable. A hitherto untried solution is proposed to use EoL-PV panels as a building material. The rising need for building materials in developing countries is imminent, and EoL-PV would be a low-cost, low embodied energy building material. Our objectives are to evaluate the potential benefits of such strategy in terms of reduction in embodied energy, carbon emission offset, and cost benefit. Following that, projections on number of EoL-PV panels and number of households in demand in every state and union territory of India are made. A projection at this level will show the potential sources of End-of-Life PV panels and number of places where the houses in demand exist. Embodied Energy, Cost, and carbon emissions of a typical EoL-PV integrated building is lower by 30.7%, 38.4% and 33.3% compared to conventional buildings of the same size, respectively. This is a promising result for a preliminary investigation and calls for further assessment. Even if 6.9% of the households (demand) in the country use EoL-PV panels as the choice of building material during 2030-2035, all the EoL-PV panels generated may be fully utilized in India ending the possible landfill option. The avoided carbon emissions, in this case, is around 2% (or 8 million tonnes). This study strongly suggests the use of EoL-PV in buildings by integrating as a building element rather than applying on the wall/roof of the building, simply to enjoy the benefits of cost, embodied energy and emissions. The estimations made in this study may be replicated for the case of other countries where solar PV waste issue is anticipated. This approach makes EoL-PV no longer a liability to the solar power plant but an asset that can be traded for another application. [ABSTRACT FROM AUTHOR]

Climate change and the resulting extreme weather events bring significant economic losses and many unstable factors. It is an important means to actively seek environmental protection and climate governance countermeasures to meet severe challenges. As an environmental protection tool widely used by the Organization for Economic Cooperation and Development (OECD), environmental taxes are expected to play an important role in combating climate change and reducing extreme weather events. Based on public goods theory, collective cooperation theory, externality theory and double dividend theory of environmental taxes, this paper takes 36 OECD countries as research samples and empirically tests the role of environmental taxes in mitigating climate change process and reducing extreme weather events through the establishment of panel Auto-Regressive Distributed Lag (ARDL) model. The empirical results show that the environmental taxes on the whole is conducive to reducing the frequency of abnormal temperature weather and plays a positive role in reducing carbon dioxide emissions in OECD countries. Further research shows that environmental budget and environmental technologies play a positive role in the process of environmental taxes to delay climate change and reduce extreme weather events. In other words, we should attach importance to the investment of environmental protection funds and the research and development of environmental protection technology while carrying out environmental taxes. The robustness test shows that the main regression results of this paper have strong robustness. The research in this paper enriches theories and existing literature related to environmental taxes and climate change, and provides new research ideas and policy guidance for delaying the process of climate change and responding to extreme weather events. [ABSTRACT FROM AUTHOR]

The Southern Ocean has warmed substantially, and up to early 21st century, Antarctic stratospheric ozone depletion and increasing atmospheric CO 2 have conspired to intensify Southern Ocean warming. Despite a projected ozone recovery, fluxes to the Southern Ocean of radiative heat and freshwater from enhanced precipitation and melting sea ice, ice shelves, and ice sheets are expected to increase, as is a Southern Ocean westerly poleward intensification. The warming has far-reaching climatic implications for melt of Antarctic ice shelf and ice sheet, sea level rise, and remote circulations such as the intertropical convergence zone and tropical ocean-atmosphere circulations, which affect extreme weathers, agriculture, and ecosystems. The surface warm and freshwater anomalies are advected northward by the mean circulation and deposited into the ocean interior with a zonal-mean maximum at ∼45°S. The increased momentum and buoyancy fluxes enhance the Southern Ocean circulation and water mass transformation, further increasing the heat uptake. Complex processes that operate but poorly understood include interactive ice shelves and ice sheets, oceanic eddies, tropical-polar interactions, and impact of the Southern Ocean response on the climate change forcing itself; in particular, limited observations and low resolution of climate models hinder rapid progress. Thus, projection of Southern Ocean warming will likely remain uncertain, but recent community effort has laid a solid foundation for substantial progress. [ABSTRACT FROM AUTHOR]

Background: Future predictions of the world population and the demand for agricultural products per capita suggest that we have to increase food production at least two-fold by 2050 despite negative forces such as climate change with more frequent extreme weather conditions, degradation of land, increasing land scarcity, shortness of water, desertification, fooding and the loss of biodiversity for ecosystem services. One pathway how to achieve this challenge is sustainable intensification, which formulates a coarse goal rather than a detailed guideline. A heterogeneous compound of strategies is necessary and a central aspect involves examining the potential of AI technology to boost production efficiency while mitigating negative environmental consequences. Methodology: To comprehensively examine this topic, two systematic literature searches were conducted. The first aimed to gather information on sustainable agriculture and the environmental costs of conventional practices. The second focused on identifying explicit AI applications and their impact. Results: Numerous examples demonstrated how sustainable AI development drives agriculture towards a more sustainable future. The main contribution of this study is the Data-Model-Purpose matrix (DMP matrix) and the derived Bayesian matrix for a comprehensive analysis of several AI applications in agriculture and their relations to data sources and algorithms. [ABSTRACT FROM AUTHOR]

The composition of seasonal pasture-produced milk is influenced by stage of lactation, animal genetics, and nutrition, which affects milk nutritional profile and processing characteristics. The objective was to study the effect of lactation stage (early, mid, and late lactation) and diet on milk composition in an Irish spring calving dairy research herd from 2012 to 2020 using principal component and predictive analytics. Crude protein, casein, fat, and solids increased from 2012 to 2020, whereas lactose concentration peaked in 2017, then decreased. Based on seasonal data from 2013 to 2016, forecasting models were successfully created to predict milk composition for 2017 to 2020. The diet of cows in this study is dependent upon grass growth rates across the milk production season, which in turn, are influenced by weather patterns, whereby extreme weather conditions (rainfall and temperature) were correlated with decreasing grass growth and increasing nonprotein nitrogen levels in milk. The study demonstrates a significant change in milk composition since 2012 and highlights the effect that seasonal changes such as weather and grass growth have on milk composition of pasture-based systems. The potential to forecast milk composition at different stages of lactation benefits processers by facilitating the optimization of in-process and supply logistics of dairy products. [ABSTRACT FROM AUTHOR]

While direct influences of the environment on population growth and resilience are well studied, indirect routes linking environmental changes to population consequences are less explored. We suggest that social behavior is key for understanding how anthropogenic environmental changes affect the resilience of animal populations. Social structures of animal groups are evolved and emergent phenotypes that often have demographic consequences for group members. Importantly, environmental drivers may directly influence the consequences of social structure or indirectly influence them through modifications to social interactions, group composition, or group size. We have developed a framework to study these demographic consequences. Estimating the strength of direct and indirect pathways will give us tools to understand, and potentially manage, the effect of human-induced rapid environmental changes. The environment directly influences individual fitness in many species. The environment influences the adaptive value of sociality both directly and indirectly, influencing individual fitness. Social relationships are sensitive to anthropogenic environmental changes that include the response to extreme weather, fire, human-created chemicals, etc. We have developed a framework that permits the study of both direct and indirect pathways by which environmental changes (both natural and artificial) impact fitness and hence population viability. The framework can be used to identify important targets of management in an increasingly human-altered world. [ABSTRACT FROM AUTHOR]

Engineered landfill capping systems consist of geosynthetics and soil layers, which often experience inconsistent and extreme weather events throughout their service life. Complex moisture dynamics in the capping layers can be created by these weather events in combination with other field conditions and can be detrimental to the system's integrity. The limited data on the hydraulic performance of landfill capping systems is a major challenge that hinders the development, validation, and calibration of models that can be used for realistic forecasting of these dynamics. Using the field-level data collected at the Bletchley landfill site, UK, this study develops a data-driven forecasting approach employing a non-linear autoregressive neural network with exogenous inputs (NARX). The data includes precipitation and volumetric water content (VWC) of the capping soil overlaying different geosynthetic layers recorded from Nov 2011 to July 2012. The NARX network was trained using the VWC data as inputs and precipitation data as the exogenous input. Also, the accuracy of NARX predictions was compared against that of a state-space statistical model. NARX-predicted VWC values for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. In the majority of prediction windows, NARX approach outperforms the state-space model. For all NARX prediction periods, RMSEr has been less than 10% for the cuspated core geocomposite. Comparatively, RMSEr values increased to approximately 15% and 19% for the non-woven needle-punched geotextile and the non-woven needle-punched geotextile with band drains, respectively. • NARX neural network predicted the volumetric water content (VWC) in a landfill cap experiencing precipitation and dry events. • Field data (e.g.,VWC) came from a capping system comprised of veneer soil and different geosynthetic drainage products. • The NARX model VWC forecasts are compared against the state-space statistical model predictions to validate the robustness. • NARX-predicted VWCs for a period of 21-days ahead are distributed with a mean error of 0.05 and a standard deviation of 0.2. • The accuracy of NARX predictions was found to be far superior to that of the state-space statistical model. [ABSTRACT FROM AUTHOR]

Proliferated low Earth orbit constellations are becoming more common both in reality and in planning. The resilience of these constellations to the radiation effects of adverse solar weather is of interest given that some of them pass over polar regions. This paper stochastically simulates solar weather events over the course of solar cycle 25 in order to quantify radiation effects on a model of Planet Lab's Flock constellation under various design and programmatic decisions. The Flock constellation is a highly proliferated, commercial, and polar low Earth orbit earth observation architecture. Lifecycle and radiation failures are simulated along with a reconstitution system in order to quantify architectural performance as a function of constellation population. This time-based measure of performance is then used to quantify the constellation resilience and allow for programmatic and design variables to be altered in order to observe the effects on the resilience of proliferated constellations against adverse solar weather radiation effects. A sensitivity analysis is performed in order to assess which decisions most improve or degrade resilience. Broadly, the results show that commercial off the shelf components and programmatic decisions such as launch and manufacturing schedules may improve constellation resilience against adverse solar weather effects. More specifically, the results also show that commercial off the shelf components with even basic shielding levels provide high resilience to cumulative total ionizing dose effects and single event upset radiation effects. However, any further reductions to shielding would have significant impacts on single event upset rates whereas total ionizing dose would remain negligible. Furthermore, manufacturing time for Flock satellites is sufficient to maintain a resilient reconstitution strategy. Finally, results show that in order to improve the resilience afforded by a reconstitution strategy, improvements to manufacturing and launch times should be made in parallel, rather than one or the other in isolation. • COTS and programmatics may improve LEO constellation resilience to solar weather. • LEO constellations are highly resilient to even extreme solar weather radiation. • Shielding and launch times are significant for resilience to LEO solar radiation. • Manufacture and launch times should improve parallelly for constellation resilience. • Cumulative radiation in LEO is negligible, primary concern is single event effects. [ABSTRACT FROM AUTHOR]

• Compound flooding. • 3D resiliency visualization platform. • Wave surge. • Pluvial flooding. • Community flood resiliency. • Thailand. Compound flooding refers to the complex interactions among oceanographic, inland (catchment) hydrological, and meteorological processes with anthropogenic factors such as land use changes due to urbanization. To-wit, the impact of higher tide levels, storm surge, and high intensity rainfall events over the inland catchment collectively may result in more extensive flooding than the individual processes acting separately. In the context of climate change and more frequent extreme weather events, coastal cities, particularly those in the ASEAN region, are increasingly vulnerable to compound flooding, yet there is no convenient and user-friendly modelling approach available that would enable the planning community and decision-makers to envision compound flooding as part of resiliency-oriented urban plan development. We addressed this gap by developing the3D Resiliency Visualisation Platform (3DRVP), within which linked features are established using Python scripts to seamless integrate a 2D mixed land use fluvial/pluvial catchment model (PCSWMM) with a 2D/3D coastal hydrodynamic model (Delft3D) to simulate the dynamics of compound floods for the assessment of coastal inundation. This platform aims to assist planners, urban design professionals, and engineers with a realistic visualization tool to picture the urban infrastructure planning alternatives as well as to facilitate the real-time operational decision-making and evacuation activation with flood control strategies. The integrated platform theory is developed first and the platform then is trialled for a developing coastal area in south Bangkok, Thailand. Similar to many cities of the global south, data availability to calibrate models is limited and as such we used a mixed methods approach to explore model accuracy. The Delft3D model was calibrated successfully using water level data from a nearby gauge in the Gulf of Thailand for Typhoon Linda. The catchment model (PCSWMM) was validated using observed flood areas as reported by the local municipality. A 100-year design rainstorm was subsequently modelled and linked with the Typhoon Linda surge levels with results indicating the combination of rainfall flooding and storm surge would increase the flooded area by 25.6% over the system components modelled individually. [ABSTRACT FROM AUTHOR]

Extreme weather conditions in marine environments significantly increase the risk of sliding for submersible platforms, making skirted edges design crucial for the platform's anti-sliding performance. However, the impact of skirted edges size parameters and arrangements on anti-sliding performance requires further investigation. This study employs the Coupled Eulerian-Lagrangian (CEL) method to establish a comprehensive simulation process for platform sliding. It analyses the effects of skirted edges length, thickness, and arrangements on anti-sliding performance. The findings indicate that increasing skirted edges length within a certain range improves anti-sliding performance, but when the length L equals 0.007 D , 0.043 D , or 0.06 D (where D is the side length of the mat), anti-sliding performance sharply declines. Furthermore, an increase in skirted edges thickness T reduces the contact area with the soil, leading to a decrease in anti-sliding performance. By defining seven anti-sliding performance indicators, the study identifies the optimal skirted edges length L as 0.057 D , thickness T as 0.0013 D , and the best arrangement as orthogonal. This research reveals the influence of skirted edges shape on the anti-sliding performance of submersible platforms and establishes the optimal shape, providing important theoretical and design guidance for enhancing platform stability and ensuring safe operation. • A CEL method-based process is suggested for simulating submersible platforms sliding and analysing skirt shape's effect on anti-sliding. • An improved Mohr-Coulomb constitutive model is used to reflect the soil deformation during the platform sliding process. • The impact of skirt size and arrangements on anti-sliding performance is identified, determining the best skirt shape. [ABSTRACT FROM AUTHOR]

In this study, the issue of centrifugal pumps used in marine engineering is addressed, as they are susceptible to malfunction owing to long-term operation in highly corrosive seawater and extreme weather conditions, resulting in operational interruptions and safety risks. We propose a high-precision intelligent fault-diagnosis method for multiple fault types based on deep learning. In this method, continuous wavelet transform is firstly employed to extract signal time–frequency domain features. Subsequently, the Swin transformer model is used to process the wavelet time–frequency images converted from signals. Finally, multilabel classification methods are combined to diagnose various complex faults. The effectiveness of the proposed method is validated using a dataset obtained from simulation experiments pertaining to centrifugal-pump faults. The results show that the proposed method achieves 100% accuracy in diagnosing 27 types of faults and provides excellent diagnosis even under limited compound-fault samples, thus offering an efficient and practical method for fault diagnosis in centrifugal pumps used in marine engineering. • Highlight 1: A comprehensive method is proposed that integrates CWT, the Swin transformer model, and multilabel classification techniques for centrifugal-pump fault diagnosis. • Highlight 2: The performance of the proposed method is discussed under various extremely complex conditions and its efficiency and robustness are verified in complex mechanical fault-diagnosis tasks, particularly in cases involving data imbalance. • Highlight 3: An experimental simulation of a fault test rig for centrifugal pumps rig is designed and implemented, fault samples for model training and validation are acquired successfully, and the authenticity and reliability of the experimental data are verified via spectral analysis. [ABSTRACT FROM AUTHOR]

A Frontal Identification System (FIS), initially designed to track Galernas in the Bay of Biscay, has been adapted to monitor cold fronts across Australia using wind shifts derived from ERA5 hourly reanalysis data. This high-resolution system tracks shallow, cloud-free fronts during the warm season, which can trigger bushfires, dust storms, extreme heat, and coastal weather extremes like Southerly Busters (SB) on the east coast. SB episodes, marked by sudden, squally south winds, pose hazards for boating and aviation. Our analysis of 35 SB events from 1994 to 2020 indicates that SBs originate from frontogenesis in Bass Strait (40 %) or from prefrontal troughs crossing the strait (60 %). Preceding synoptic conditions involve a Southern Ocean cold front driving cool maritime winds into the Australian thermal low, creating shallow convergence fronts (∼1000 m deep) facing warm continental winds. Onshore acceleration into the thermal low sharpens these new fronts (Type 2 fronts of the southern coast) and weakens the trailing primary ocean front, which may disappear due to high-pressure wave propagation, cold advection, and subsidence over the sea. These fronts can penetrate deep inland (Central Australian fronts) and initiate SBs on the southeast coast after interacting with the Great Dividing Range. All 35 SB events show active shallow front frontogenesis/frontolysis affecting the southern coast and inland regions. Upper-level reversed pressure gradients between the thermal low over the continent and the ocean depression maintain a wind shear region over the shallow inland cold advection. Intense warm north-westerlies south of the surface front, with wind speeds of 35–50 m s−1 between 700 and 550 hPa, contribute to mesofront formation preceding SB episodes. This jet also sustains strong cross-mountain winds over the Great Dividing Range, causing the lee trough at the coastal strip that precedes all SB episodes on the eastern coast. Understanding these dynamics can help predict and manage these events more effectively. • Active shallow front frontogenesis/frontolysis detected during all SB episodes • A vast region of vertical wind shear unveiled in the middle-lower troposphere • The region holds a NW jet (35–50 m s−1) within the highest temperature gradients • Shown the thermal low role in shallow front and wind shear region formation • Initiation of Busters concurrent with highest wind shear location and NW jets [ABSTRACT FROM AUTHOR]

With climate change intensifying, forests globally are becoming more susceptible to extreme weather events, such as windstorms, which account for a significant share of Europe's economic losses. The Vaia windstorm of late autumn 2018, striking Italy's North-East alpine ecosystem, highlighted this vulnerability, toppling over 8.5 million cubic meters of timber and sparking debates on forest management's role in mitigating such disasters. This study aims to evaluate the impact of structural and topographical characteristics on the damage caused by Vaia, using Airborne Light Detection And Ranging (LiDAR) data collected before the storm, in four heavily affected forest areas in the Italian Alps (Carezza in the Province of Bolzano-Bozen, Predazzo, Manghen, and Primiero in the Province of Trento). We analyzed structural metrics like forest height heterogeneity (HH), forest mean height, and density, alongside topographical features such as aspect, slope, and altitude, to discern their influence on the storm's severity. Our results revealed that the most significant difference between affected and unaffected areas is forest mean height that was found higher in areas hit by the storm. Forest density played a lesser but important role, with denser areas experiencing more severe damage, though this was only significant in certain areas. Contrary to common assumptions, our analysis revealed that forest height heterogeneity (HH) did not have a significant effect on damage levels. The findings, consistent with previous research, revealed a significant association between specific aspects, particularly the South-East orientation, which aligned with the predominant wind direction during the Vaia storm, and an increased likelihood of damage. Both structural and topographical factors interact in complex ways to influence the outcome of such extreme events. The study emphasizes the dominant impact of the Vaia windstorm, noting that while managing forest height and density may help, the diverse topography complicates these efforts. Our study explicitly tested the effectiveness of using Airborne LiDAR data to explore forest structural and topographical factors that influenced Vaia storm damage. The achieved results demonstrate that LiDAR serves as a useful tool to field data, offering valuable insights for broader applications in this domain. • LiDAR data reveals significant structural changes following the Vaia storm. • Study confirms tree height and density as key factors in windstorm damage. • Complex topography significantly influences forest susceptibility to storms. • Height Heterogeneity showed limited impact on wind damage resistance. • Remote sensing with LiDAR provides critical data for forest vulnerability analysis, [ABSTRACT FROM AUTHOR]

Forests provide important ecosystem functions such as carbon sequestration and climate regulation, particularly in countries with high forest cover. Climate change-induced extreme weather events have a negative impact on many forest ecosystems. In Germany, for instance, the drought of the years 2018 until 2020 resulted in signs of damage in almost 80% of trees. This decline in forest vitality has additionally led to severe bark beetle infestations and widespread tree mortality, posing significant challenges to forest managers to obtain a complete picture of the state of their forests. Since a completely ground-based monitoring of forest condition is not feasible due to the forests' vast extent, remote sensing and particularly multispectral satellite image time series (SITS) analysis were suggested as efficient alternatives. Transformers, a state-of-the-art Deep Learning (DL) architecture, have shown promising results in the classification of multivariate SITS for other applications. Here, we use Transformers in combination with Sentinel-2 (S2) time series data to test if they can improve forest disturbance detection capabilities in comparison to conventional methods by automatically extracting relevant information from background variability throughout the whole time series. To match the large training data needs of Transformers, we use a two-step approach including pre-training and finetuning. During pre-training, we use outputs of earlier presented SITS approaches, while during finetuning, we use detailed reference data of known disturbances covering between 10 and 100% of a Sentinel-2 pixel as extracted from aerial images. We test three setups: DL base using ten S2 bands, DL IND using ten vegetation indices (VIs), and DL +IND utilising both as model input. F1-scores across all of our six study sites range between approx. 0.65 (DL +IND) and 0.72 (DL base) in a binary classification (undisturbed vs. disturbed) when considering both full and partial disturbances. DL base outperforms the other setups in forest disturbance detection, and detects disturbance extents as small as 40 m2 within pixels of 100 m2 size. Given the best performance of DL base, handcrafted vegetation indices (VIs) do not improve the model. Our model is competitive with existing approaches and slightly outperforms most earlier reported results, even though a direct comparison is challenging. Considering the option to further refine our trained model if additional reference data becomes available over time, we conclude that a combination of Transformers and Sentinel-2 time series can be developed into an effective tool for forest disturbance monitoring of Central European forests at fine spatial grain. • we present a Transformer-based forest disturbance detection. • our model is trained with >1.6 million Sentinel-2 time series. • commission errors are approx. 3% in binary classification. • omission errors are approx. 34% incl. Disturbances on sub-pixel level. • minimum detectable forest disturbance area is around 40 m2. [ABSTRACT FROM AUTHOR]

The terrestrial water storage anomaly (TWSA), derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-on (GRACE-FO) satellite, presents a remarkable opportunity for extreme weather detection and the enhancement of environmental protection. However, the practical utility of GRACE data is challenged by an 11-month data gap and several months of missing data. To address this limitation, we have developed an innovative transformer-based deep learning model for data gap-filling. This model incorporates a self-attention mechanism using causal convolution, allowing the neural network to capture the local context of GRACE time series data. It takes into account various factors such as temperature (T), precipitation (P), and evapotranspiration (ET). We trained the model using a global dataset of 10,000 time series pixels and applied it to fill all the time gaps. The validation results demonstrate its robustness, with an average root mean square error (RMSE) of 6.18 cm and Nash-Sutcliffe efficiency (NSE) of 0.906. Notably, the Transformer-based method outperforms other state-of-the-art approaches in arid regions. The incorporation of T, P, and ET has further enhanced the accuracy of gap filling, with an average RMSE decrease of 7.5 %. This study has produced a reliable gap-filling product that addresses 11-month data gaps and 24 isolated gaps, ensuring the continuity of GRACE data for various scholarly applications. Moreover, our Transformer approach holds important potential for surpassing traditional methods in predicting and filling gaps in remote sensing data and gridded observations. • A novel Transformer-based deep learning method is introduced for remote sensing data gap filling. • New proposed method with attention mechanisms successfully fills the GRACE data gap. • The newly Transformer method has enhanced the accuracy of prediction and gap filling in arid regions. • Including precipitation and evapotranspiration variables further enhanced the simulation ability in arid regions. [ABSTRACT FROM AUTHOR]