Search Results (34)

For the electricity system with a high proportion of new energy, the extreme weather events caused by climate change will make the new energy power supply present an extremely complicated situation, thus affecting the safe and stable operation of the power system. In order to solve the above problems, this study proposes a classification method of the extreme weather process based on the Progressive Layered Extraction (PLE) model considering the weather-sensitive factors with high impact on new energy. This method analyses the sensitive factors affecting the new energy output from the two perspectives of abnormal output and abnormal prediction error, defines the high-impact weather process, and divides the standard set. According to the standard set, a high-impact weather process identification model based on PLE is constructed to provide more accurate early warning information. The proposed method is applied to a new energy cluster in Jiangxi Province, China. Compared with the traditional classification task model, the accuracy of the proposed method is increased by 1.30%, which verifies the effectiveness of the proposed method. Full article

Bees are crucial for food production and biodiversity. However, extreme weather variation and harsh winters are the leading causes of colony losses and low honey yields. This study aimed to identify the most important features and predict Total Honey Harvest (THH) by combining machine learning (ML) methods with climatic conditions and environmental factors recorded from the winter before and during the harvest season. The initial dataset included 598 THH records collected from five apiaries in Lombardy (Italy) during spring and summer from 2015 to 2019. Colonies were classified into medium-low or high production using the 75th percentile as a threshold. A total of 38 features related to temperature, humidity, precipitation, pressure, wind, and enhanced vegetation index–EVI were used. Three ML models were trained: Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC). All models reached a prediction accuracy greater than 0.75 both in the training and in the testing sets. Results indicate that winter climatic conditions are important predictors of THH. Understanding the impact of climate can help beekeepers in developing strategies to prevent colony decline and low production. Full article

Ship–bridge collisions in inland waterways pose a serious threat to bridge infrastructure, often resulting in structural damage and jeopardizing safety. Despite the widespread deployment of collision warning systems, these systems fail to function effectively due to factors such as weather conditions, equipment malfunctions, and human error. Current alarming technologies, such as wavelet-based methods, are limited by poor real-time performance, high sensitivity to noise, and low localization accuracy, which hinder their practical application. This paper proposes an innovative Kalman filter–cointegration alarming (KFCA) technology, combining cointegration theory with Kalman filtering to achieve precise and real-time collision detection. Through numerical simulation, KFCA is validated, with the results summarized as follows: (i) KFCA effectively recognizes ship–bridge collisions under an SNR of 60, 70, and 80 dB; and (ii) it accurately identifies impact locations on the bridge based on sensor arrangement indices. Compared to existing methods, KFCA offers significant advantages in real-time response, noise resistance, and localization accuracy. This technology provides an efficient solution for bridge management departments, enabling the timely and accurate detection of ship–bridge collisions, thereby enhancing bridge safety and reducing secondary disasters. Full article

The assessment of urban heat resilience has become crucial due to increasing extreme weather events. This study introduces the Running Activity Z-score (RAZ) index based on running activity trajectory data to evaluate heat resilience. Through a case study of an August 2022 heatwave in Beijing, we examined the index’s sensitivity to extreme heat and explored its spatial relationships with key built environment factors, including plot ratio, green coverage, population density, and blue space proximity. Our results reveal two key findings: (1) the RAZ index serves as an effective real-time, high-precision indicator of urban heatwave impacts, as evidenced by extremely low RAZ values consistently coinciding with heatwave periods, and (2) the RAZ index offers valuable insights for identifying potential low heat resilience areas and supporting planning decisions, as demonstrated by its significant correlations with built environment factors that align with previous studies while uncovering more detailed spatial relationships. Although RAZ effectively complements traditional measurement methods, its application requires careful consideration of external factors such as social dynamics and climate variability. Full article

The accurate prediction of heat demand in retrofitted residential buildings is crucial for optimizing energy consumption, minimizing unnecessary losses, and ensuring the efficient operation of heating systems, thereby contributing to significant energy savings and sustainability. Within the framework of this article, the dependence of the energy consumption of a thermo-modernized building on a chosen set of climatic factors has been meticulously analyzed. Polynomial fitting functions were derived to describe these dependencies. Subsequent analyses focused on predicting heating demand using artificial neural networks (ANN) were adopted by incorporating a comprehensive set of climatic data such as outdoor temperature; humidity and enthalpy of outdoor air; wind speed, gusts, and direction; direct, diffuse, and total radiation; the amount of precipitation, the height of the boundary layer, and weather forecasts up to 6 h ahead. Two types of networks were analyzed: with and without temperature forecast. The study highlights the strong influence of outdoor air temperature and enthalpy on heating energy demand, effectively modeled by third-degree polynomial functions with R² values of 0.7443 and 0.6711. Insolation (0–800 W/m²) and wind speeds (0–40 km/h) significantly impact energy demand, while wind direction is statistically insignificant. ANN demonstrates high accuracy in predicting heat demand for retrofitted buildings, with R² values of 0.8967 (without temperature forecasts) and 0.8968 (with forecasts), indicating minimal performance gain from the forecasted data. Sensitivity analysis reveals outdoor temperature, solar radiation, and enthalpy of outdoor air as critical inputs. Full article

No form of economic activity is unaffected by climate change, which has emerged as a new risk factor impacting financial market stability and sustainable development. This study examines the impact of extreme weather on the stock returns of A-share listed companies in China. Utilizing a decade-long dataset, we construct monthly proportions of extreme high-temperature days and extreme humid days using a percentile comparison approach. The findings reveal a significant negative impact of extreme weather on stock returns. Specifically, each standard deviation increase in the monthly proportion of extreme high-temperature days and extreme humid days corresponds to a decrease in annualized returns by 0.09% and 0.15%, respectively. The mediation analysis suggests that extreme weather primarily affects stock returns through its influence on investor sentiment, impacting economic decision making, with minimal direct effects on corporate performance. Additionally, the sensitivity of stock returns to extreme weather varies notably among different types of companies. Larger, more profitable, and less risky firms show lower sensitivity to extreme weather. The impact is observed not only in heat-sensitive industries but also in non-heat-sensitive industries and remains significant even after excluding company announcement days. This study offers new insights and relevant recommendations for businesses and policymakers on sustainable development and financial stability. Full article

Global climate change poses a significant threat to the sustainable development of human society, highlighting the critical importance of developing effective adaptation strategies in response to climate-related disasters. Public awareness and adaptive behaviors towards climate risks serve as crucial indicators of community concerns regarding climate change, laying the foundation for effective adaptation strategy design. For this study, we selected inland northwest Chinese residents, represented by Xi’an City, as the research subjects, to investigate their climate risk cognition and adaptation levels. Based on randomly sampled survey data, descriptive statistical analysis and multiple logistic regression models were used to study the public’s climate change awareness, climate risk sensitivity, and climate risk adaptability, as well as evaluation of climate risk adaptation measures in the public sector, and we also analyzed the impact mechanisms of factors such as gender, age, income, and education level on the related indicators. The study found that with the increasing urban heat island effect, residents of Xi’an are more likely to reach a higher level of belief in climate change regarding long-lasting weather events. However, there is still no collective consensus on the reasons for climate change. Residents are overly optimistic about the future impact of climate disasters, and there is high uncertainty in their ability to adapt to climate change risks. Additionally, specific demands were obtained from different groups of urban residents regarding measures in the public sector for climate risk adaptation. Full article

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure. Full article

Agriculture is a vulnerable sector to climate change due to its sensitivity to weather conditions. Changes in climatic parameters such as temperature and precipitation significantly affect productivity as well as the consumption of natural resources like water to meet irrigation water needs. There has been a large amount of research on regional climate change. However, this study placed specific crops at first place and considered their irrigation water needs that will arise due to evapotranspiration increase. The aim of this study was to estimate the future irrigation water needs of wheat, cotton, and alfalfa in the east part of Thessaly Plain in central Greece, where Lake Karla, a recently restored lake, is located. The Weather Research and Forecasting (WRF) model was applied as a high-resolution regional climate model to simulate temperature and precipitation for two 5-year periods, namely 2046–2050 (future period) and 2006–2010 (reference period). Simulations refer to the RCP8.5 emission scenario (worst-case). A methodology proposed by the Food and Agriculture Organization (FAO) of the United Nations was followed to estimate the reference crop evapotranspiration, the crop evapotranspiration based on each crop factor, which was determined for each crop, the effective rainfall, and finally, the irrigation water needs for each crop, for the two 5-year periods. Based on WRF simulations, temperature was projected to be 1.1 °C higher in the future period compared to the reference period, while precipitation and effective precipitation were projected to decrease by 32% and 45%, respectively. Based on the WRF projections, by 2025, the irrigation water needs of wheat and alfalfa are expected to increase by more than 16% and more than 11%, respectively, while irrigation water needs of cotton are expected to increase by 7%. An extension of wheat’s irrigation period for one month (i.e., December) was also identified. Good practices that could be applied in the frame of precision agriculture principles in order to save irrigation water were suggested. The results of this study could be exploited by water resources and land use managers when planning short and long-term strategies to adapt to climate change impacts. Full article

Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, $R^2$, while for sugar beet, the Neural Prophet model achieved superior $R^2$ levels of 0.99 and 0.98, respectively. Full article

Oil spills are one of the most hazardous pollutants in marine environments with potentially devastating impacts on ecosystems, human health, and socio-economic sectors. Therefore, it is of the utmost importance to establish a prompt and efficient system for forecasting and monitoring such spills, in order to minimize their impacts. The present work focuses on the numerical simulation of the drift and spread of oil slicks in marine environments. The specific area of interest is the Azemmour estuary, located on Morocco’s Atlantic Coast. According to the environmental sensitivity index (ESI), given its geographical location at the intersection of the World’s Shipping Lines of oil transport, this area, as with many other sites in Morocco, has been classified as a high-risk area for oil spill accidents. By taking into account a range of factors, including the ocean currents, the weather conditions, and the oil properties, detailed numerical simulations were conducted, using the hydrodynamic TELEMAC-2D model, to predict the behavior and spread of an oil spill event in the aforementioned coastal region. The simulation results help to understand the spatial–temporal evolution of the spilled oil, the effect of wind on the spreading process, as well as the coastal areas that are most likely to be affected in the event of an oil spill accident. The simulations were performed with and without wind effects. The results showed that three days after the oil spill only 31% of the spilled oil remained on the sea surface. The wind was found to be the main factor responsible for oil drifting offshore. The results indicated that rapid action is needed to address the oil spill before it causes significant environmental damage and makes the oil cleanup process more challenging and expensive. The results of the present study are highly valuable for the management and prevention of environmental disasters in the Azemmour estuary area. The findings can be used to assess the efficacy of various response strategies, such as containment and cleanup measures, and to develop more effective emergency response plans. Full article

With the aim of understanding the impact of air pollution on human health and ecosystems in the tropical Andes region (TAR), we aim to couple the Weather Research and Forecasting Model (WRF) with the chemical transport models (CTM) Long-Term Ozone Simulation and European Operational Smog (LOTOS–EUROS), at high and regional resolutions, with and without assimilation. The factors set for WRF, are based on the optimized estimates of climate and weather in cities and urban heat islands in the TAR region. It is well known in the weather research and forecasting field, that the uncertainty of non-linear models is a major issue, thus making a sensitivity analysis essential. Consequently, this paper seeks to quantify the performance of the WRF model in the presence of disturbances to the initial conditions (IC), for an arbitrary set of state-space variables (pressure and temperature), simulating a disruption in the inputs of the model. To this aim, we considered three distributions over the error term: a normal standard distribution, a normal distribution, and an exponential distribution. We analyze the sensitivity of the outputs of the WRF model by employing non-parametric and robust statistical techniques, such as kernel distribution estimates, rank tests, and bootstrap. The results show that the WRF model is sensitive in time, space, and vertical levels to changes in the IC. Finally, we demonstrate that the error distribution of the output differs from the error distribution induced over the input data, especially for Gaussian distributions. Full article

Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system. Full article

Trees can be used as archives of changes in the environment. In this paper, we present the results of the analysis of the impact of water stress and increase in air temperature on BAI and carbon stable isotopic composition and water use efficiency of pine. Dendrochronological methods together with mass spectrometry techniques give a possibility to conduct a detailed investigation of pine growing in four industrial forests in Silesia (Poland). Detailed analysis-based bootstrap and moving correlation between climatic indices (temperature, precipitation, and Standardized Precipitation-Evapotranspiration Index) and tree parameters give the chance to check if the climatic signals recorded by trees can be hidden or modified over a longer period of time. Trees have been found to be very sensitive to weather conditions, but their sensitivity can be modified and masked by the effect of pollution. Scots pine trees at all sites systematically increased the basal area increment (BAI) and the intrinsic water use efficiency (iWUE) and decreased δ¹³C in the last century. Furthermore, their sensitivity to the climatic factor remained at a relatively high level. Industrial pollution caused a small reduction in the wood growth of pines and an increase in the heterogeneity of annual growth responses of trees. The main factors influencing the formation of wood in the pines were thermal conditions in the winter season and pluvial conditions in the previous autumn, and also in spring and summer in the year of tree ring formation. The impact of thermal and pluvial conditions in the year of tree ring formation has also been reflected in the isotopic composition of tree rings and water use efficiency. Three different scenarios of trees’ reaction link to the reduction of stomata conductance or changes in photosynthesis rate as the response to climate changes in the last 40 years have been proposed. Full article

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models. Full article