Extreme Scenarios Research Articles

Risky lane-changing behavior recognition based on stacking ensemble learning on snowy and icy surfaces

Risky lane-changing (LC) behavior adversely affects traffic safety, especially on snowy and icy surfaces. However, due to the particularity of the snowy and icy surfaces and the scarcity of data, research on risky lane-changing behavior (RLCB) under extreme scenarios is insufficient. Therefore, this study presents a novel research framework aimed at selecting key risk characterization indicators (RCIs) and identifying RLCB on highways using driving simulation data on snowy and icy surfaces. A highway LC scenario was established on snowy and icy surfaces using a driving simulator, and 1200 sets of LC sample data were extracted. From the perspectives of parameter importance and correlation, 12 key RCIs with high importance and low inter-correlation were selected using the C4.5 decision tree algorithm and Pearson correlation analysis method. The RLCB recognition model was developed using the Stacking ensemble learning method and then compared with traditional recognition algorithms. The results show that the accuracy of the recognition model based on the Stacking ensemble learning model is significantly better than that of traditional algorithms, with a recognition accuracy of 98.33%. This finding can provide the basis for developing LC warning systems for intelligent connected vehicles on snowy and icy surfaces.

Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions

We present a data-driven feedforward control to attenuate large transient lift experienced by an airfoil disturbed by an extreme level of discrete vortex gust. The current analysis uses a nonlinear machine-learning technique to compress the high-dimensional flow dynamics onto a low-dimensional manifold. While the interaction dynamics between the airfoil and extreme vortex gust are parametrized by its size, gust ratio and position, the wake responses are well captured on this simple manifold. The effect of extreme vortex disturbance about the undisturbed baseline flows can be extracted in a physically interpretable manner. Furthermore, we call on phase-amplitude reduction to model and control the complex nonlinear extreme aerodynamic flows. The present phase-amplitude reduction model reveals the sensitivity of the dynamical system in terms of the phase shift and amplitude change induced by external forcing with respect to the baseline periodic orbit. By performing the phase-amplitude analysis for a latent dynamical model identified by sparse regression, the sensitivity functions of low-dimensionalized aerodynamic flows for both phase and amplitude are derived. With the phase and amplitude sensitivity functions, optimal forcing can be determined to quickly suppress the effect of extreme vortex gusts towards the undisturbed states in a low-order space. The present optimal flow modification built upon the machine-learned low-dimensional subspace quickly alleviates the impact of transient vortex gusts for a variety of extreme aerodynamic scenarios, providing a potential foundation for flight of small-scale air vehicles in adverse atmospheric conditions.

Temperature-dependent responses and trophic interaction strengths of a predatory marine gastropod and rock oyster under ocean warming

Predator-prey interactions are important in shaping ecosystem structure. Consequently, impacts of accelerating global warming on predators will have notable implications. Effects are likely to be particularly marked for tropical organisms which are anticipated to be sensitive to further thermal stress. Here, we investigated effects of future ocean warming on the predatory dogwhelk Reishia clavigera and its predation of Saccostrea cucullata. Mortality of the predators rapidly increased under the extreme elevated temperature, while those exposed to moderate elevated temperature displayed similar mortality as the ambient. Predators that survived moderate temperature increases altered their oxygen consumption patterns, increased average feeding rates, and functional responses, although condition index and energy reserves were unchanged. Overall, we show extreme ocean warming scenarios can remove predators and their consumption of prey from an ecosystem, whereas moderate warming can intensify predator-prey interactions. Such temperature-dependent alterations to predator-prey interactions would lead to fundamental changes of ecosystem structure as the ocean warms.

Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs.

Modeling the distribution of hemlock woolly adelgid under several climate change scenarios

The hemlock woolly adelgid (HWA), Adelges tsugae Annand, has invaded eastern North America and caused significant mortality to eastern hemlock, Tsuga canadensis (L.) Carrière. In eastern North America, HWA’s range is theorized to be limited by minimum winter temperatures. As climate change reduces the severity of winter, the risk of northward expansion of HWA increases. This study used maximum entropy species distribution modeling in conjunction with HWA occurrence records and future climate projections to model habitat suitability for HWA throughout the range of eastern hemlock. Species distribution models were created for present and future climatic conditions using both historical climatic data and future climatic emissions scenarios for mid- and late-century. In addition, present climatic condition reference models for western North America and Asia were generated for comparison with HWA’s current range and earlier predictions of the potential range in eastern North America. Under a low emissions scenario, HWA will be capable of invading almost the entire range of eastern hemlock by the end of the century. More extreme warming scenarios result in a more rapid northwards shift by mid-century. The consequences for eastern hemlock are significant, with infestations likely to become more widespread and severe due to climate change.

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing.

Reconstructing the past of observed fluids has been known as an ill-posed problem due to both numerical and physical challenges, especially when observations are distorted by inevitable noise, resolution limits, or unknown factors. When employing traditional differencing schemes to reconstruct the past, the computation often becomes highly unstable or diverges within a few backward time steps from the distorted and noisy observation. Although several techniques have been recently developed for inverse problems, such as adjoint solvers and supervised learning, they are also unrobust against errors in observation when there is time-reversed simulation. Here we present that by using physics-informed neural computing, robust time-reversed fluid simulation is possible. By seeking a solution that closely satisfies the given physics and observations while allowing for errors, it reconstructs the most probable past from noisy observations. Our work showcases time rewinding in extreme fluid scenarios such as shock, instability, blast, and magnetohydrodynamic vortex. Potentially, this can be applied to trace back the interstellar evolution and determining the origin of fusion plasma instabilities.

Unraveling The Dynamics Of Foreign Direct Investment And Economic Activity In Brazil: A Copula-Based Analysis

This study analyzes the relationship between FDI and the IBC-Br from 2003 to 2022. Our objective is to examine the effect of FDI on Brazil´s economic activities. Thus, we look also for a connection between the Brazilian Central Bank’s leadership in each period and the FDI effect on IBC-Br. To explore this, we employ an approach based on the Granger Causality Test and copula models that encompass Gaussian, t-Student, Gumbel, Frank, and Clayton functions. Our findings indicate that in the short term, FDI exerts some influence on IBC-Br, while the reverse relationship is not observed. Furthermore, using the Clayton Copula function, we identify the dependency of causal relationships between the variables, particularly in extreme economic scenarios. These discoveries contribute to a more comprehensive understanding of the dynamics between FDI and economic activity in the different periods of the Brazilian Central Bank’s leadership.

A distributed coordinated control strategy for isolated AC microgrids based on consensus algorithm considering communication delay

AbstractIn the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions.

YOSMR: A Ship Detection Method for Marine Radar Based on Customized Lightweight Convolutional Networks

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability.

Creating a renewable energy-powered energy system: Extreme scenarios and novel solutions for large-scale renewable power integration

The large-scale integration of renewable power sources, such as wind and solar, is pivotal in achieving climate change mitigation goals and reducing dependency on fossil fuels within energy systems. This manuscript explores the integration of substantial wind and solar power capacities into Luxembourg's energy framework, addressing challenges like variability, intermittency, and curtailment. It delves into scenarios that include power-to-heat (P2H) and vertical farming (VF) technologies to improve system flexibility and promote the incorporation of renewable energy. Utilising a mixed-integer linear programming (MILP) algorithm, an energy balance model with hourly resolution optimises the system. This algorithm guides the operation of individual plants, ensuring optimal performance based on a variety of criteria. Notable findings underscore the significant influence of an appropriate mix of renewable power resources, indicating a potential increase in renewable power consumption by up to 50% within the system. Furthermore, curtailment management solutions are shown to enhance renewable power integration by as much as 30%, highlighting the importance of strategic, system-wide approaches. Additionally, vertical farming emerges as a promising avenue for significantly augmenting the utilisation of surplus solar power, potentially by up to 100%. While the integration of large-scale renewable power is demonstrably viable, it necessitates a meticulously crafted strategy that considers technological innovations and strategic planning.

Variation of nitrate sources affected by precipitation with different intensities in groundwater in the piedmont plain area of alluvial-pluvial fan

A substantial reservoir of nitrogen (N) in soil poses a threat to the quality and safety of shallow groundwater, especially under extreme precipitation that hastens nitrogen leaching into groundwater. However, the specific impact of varying precipitation intensities on the concentration and sources of nitrate (NO3−) in groundwater across diverse hydrogeological zones and land uses remains unclear. This study aims to elucidate the fluctuations in NO3− concentration, sources, and controlling factors in shallow groundwater under different intensities of precipitation (extreme heavy precipitation and continuous heavy precipitation) in a typical alluvial-pluvial fan of the North China Plain by using stable isotopes (δ2H–H2O, δ18O–H2O, δ15N–NO3-, δ18O–NO3-), hydrochemical analyses and the SIAR model. Affected by extreme heavy precipitation the depleted isotopes of δ2H–H2O and δ18O–H2O in groundwater of the entire area suggested the rapid recharge of fast flow by precipitation. The enriched isotopes of δ2H–H2O and δ18O–H2O of north part in alluvial fan after continuous heavy precipitation showed the recharge of translatory flow of soil water. NO3−concentrations increased to 78.9 mg/L after extreme heavy precipitation and increased to 105.3 mg/L after continuous heavy precipitation when compared to those in normal year (56.8 mg/L) of north part of the alluvial fan. However, NO3− concentrations had slight variation after continuous heavy precipitation of south part of the fan due to the deep vadose zone. The contribution ratio of sources of NO3− in groundwater by using SIAR analysis revealed manure & sewage (MS) as the primary NO3− source (accounting for 59.7–78.1%) before extreme heavy precipitation, chemical fertilizer (CF) making a minor contribution (6.9–17.3%). Different precipitation events and land use types lead to changes in NO3− sources. Affected by extreme heavy precipitation, the contribution of MS decreased while CF increased, particularly in vegetables (26.2–28.1%) and farmland (29.2–34.7%). After continuous heavy precipitation, MS increased again, particularly in vegetables (50.0%) and farmlands (20.4–66.4%), with CF either increasing or remaining steady. This indicated that continuous heavy precipitation accelerated the leaching of nitrogen (organic manure application) stored in deep soil to groundwater and it has a larger influence on the increasing of NO3− concentrations of groundwater than extreme heavy precipitation which carried nitrogen (chemical fertilizer application) in shallow soil to groundwater by fast flow. These findings underscore the importance of considering soil chemical N stores and their implications for groundwater contamination mitigation under future extreme climate scenarios, particularly in agricultural management practices.

Variations of compound warm, dry, wet, and cold climate extremes in India during 1951 to 2014

The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from the India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events has increased at 1.8 % per decade, while cold/wet events decreased by 1.1 % over India. The warm/wet events have shown an increased trend of about 0.3 %, and cold/dry events at modest rise of 0.7 % per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30 to 45, 15–30, 20–30, and 25–45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983–2014) with 31 years compared to the base period (1951–1982) which had approximately 24 years for a spatial extent exceeding 5 %. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.

Visual inertial localization method assisted by pedestrian motion features

Visual-inertial SLAM (Simultaneous Localization and Mapping) is a reliable and effective positioning method in indoor and outdoor environments. However, low-cost sensors and extreme environments (such as low light and weak textures) severely impact the nonlinear optimization within the sliding window, loop closure detection, and graph optimization. To address this, the Pedestrian Dead Reckoning (PDR) algorithm, capable of providing robust pose estimation over a certain time period, is introduced as an auxiliary to enhance overall performance. However, its heading is susceptible to inaccurate readings due to variations in magnetic field anomalies.The paper proposes integrating the velocity, attitude, and stride information provided by PDR into the visual-inertial SLAM optimization, which can improve the localization accuracy by 60%. In extreme scenarios, incorporating the PDR pose information into loop closure detection and graph optimization can improve the localization accuracy by over 30%. Furthermore, utilizing the heading information from visual-inertial SLAM to correct the PDR heading offset caused by magnetic anomalies can improve the localization accuracy by over 20%.

A Data‐Driven Approach to Assess the Impact of Climate Change on a Tropical Mangrove in India

AbstractAs a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem‐atmosphere CO2 exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel‐2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R2 = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R2 = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1‐1.26 and SSP5‐8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%–20.3% from 2050 to 2060 and of 4.82%–28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018).

Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics

This paper proposes a multi-electrolyzer switching scheduling strategy based on the Pelican Optimization Algorithm (POA) to enhance the efficiency of large-scale wind power hydrogen production systems. To validate the effectiveness of the proposed strategy, we analyzed wind power data from three typical days in northern Hebei, China, with a 2.5 MW wind turbine output. We designed three strategies for comparative analysis: a simple start-stop strategy, a rule-based multi-electrolyzer switching strategy, and a POA-based multi-electrolyzer switching strategy. The study results demonstrate that the POA-based strategy exhibits higher hydrogen production efficiency and system stability under various wind conditions. Particularly, in extreme wind scenarios, this strategy significantly reduces the start-stop cycles of electrolyzers, thereby lowering operational costs and improving overall system performance. The main contribution of this study lies in the novel optimization algorithm and its validation through real-world data, demonstrating its effectiveness in large-scale wind power hydrogen production systems. Our findings provide new insights for enhancing the utilization of renewable energy and the economics of hydrogen production systems.

Listening to Stakeholders III: Potential Users Evaluate Product Content and Design for Subseasonal Extreme Precipitation Forecasts

Abstract Extreme precipitation over a 2-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal time frame may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over 6 years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study, experimental subseasonal-to-seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S time frame. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification that allows for more flexibility in spatial uncertainty might be beneficial for evaluating S2S forecasts. These results help to create a foundation for the design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers.

Multi-source coordinated low-carbon optimal dispatching for interconnected power systems considering carbon capture

The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fuzzy set of wind power forecasting error probability distribution based on the Wasserstein distance was first established, and the approximate radius of the fuzzy set was corrected under extreme scenarios. By introducing joint chance constraints, the inequalities of uncertain variables were established at the lowest confidence level to improve the reliability of the model. Next, a two-stage distributed robust optimal scheduling model for source-load coordination was developed. In the first stage, wind power forecasting information was fully utilized to schedule the electrolytic aluminum load and optimize unit commitment. In the second stage, the uncertainty of wind power output was considered to schedule the ferroalloy load and optimize unit output. The model was approximately transformed into a mixed-integer linear programming problem and solved using a sequential algorithm. The IEEE 24-bus system was used for case validation. The validation results show that the model can effectively improve wind power absorption capacity, reduce overall operating costs, and achieve a balance between low carbon emissions and robustness.

Comprehensive Framework for Distribution Network Multi-Investment Expansion Planning: Emissions, Uncertainty, and Resource Remuneration Integration

Changes are currently unfolding within electric distribution networks as renewable energy integration grows. This paper introduces a novel stochastic approach for expansion planning in distribution networks with high renewable energy penetration. The model addresses uncertainties, daily and seasonal impacts, distributed generation remuneration, carbon emissions, and optimal energy storage deployment. Risk aversion techniques such as value-at-risk and conditional value-at-risk are employed to prepare networks for impactful events. Emphasizing multi-period investment over single-period, the analysis considers confidence levels and risk aversion’s effects on reliability indexes, energy storage placement, generation patterns, network topology, and costs. Using a realistic 180-bus distribution network in Portugal spanning 30 years, the method’s applicability and robustness are demonstrated. Results illustrate the benefits of multi-period investment, with cost increases of 6.09 % and 2.97 % for single- and multi-period investments at a 90 % confidence level, respectively, when both are compared to a situation with no risk aversion. Higher confidence levels amplify the trade-off between extreme scenario cost reduction and total cost increase, with a 99 % confidence level reducing scenario costs by 19.01 % while increasing total costs by 5.64 %.

Observation and modelling of infragravity waves at a large meso-tidal inlet and lagoon

The role of infragravity waves (IG waves) in beach and dune erosion or in flood hazard has been extensively studied on open beaches. In contrast, the detailed characterization of IG waves and their contribution to the Total Water Level (TWL) along the shore of inlets received little attention so far. In such environment, there is a real lack of in situ observations of waves and hydrodynamics conditions at appropriate spatial and temporal coverage to study the role of infragravity (IG) waves (long waves of frequency typically ranging between 0.004 Hz and 0.04–0.05 Hz) on coastal hazards. This contribution is based on field observations collected at the Arcachon Lagoon, a shallow semi-enclosed lagoon connected to the ocean by a large tidal inlet, located in southwest France. Analyses combine observations made at several locations during storm events within the inlet and the lagoon with numerical simulation with the XBeach surfbeat model to explore the spatial variability of IG waves and simulate observed, historical, and idealized storm conditions. The results show that IG waves are substantial during typical winter storms at the inlet and range from Hm0 = 0.8 to over 1 m across the ebb delta and about 0.4–0.6 m in the inner part of the inlet. At the lagoon entrance, IG waves remain substantial (about 0.1–0.2 m) and decrease to a few centimeters at the lagoon shore. The spatial variability and magnitude of IG waves along the inlet coast, simulated for the historical storms, are quite comparable to those observed during classical winter, and do not increase linearly with offshore wave energy. However, both observations and simulations reveal local amplifications of IG waves in the inner part of the inlet, especially along the sheltered coast were IG waves dominate the variance of free surface elevation, reaching about 0.6–0.7 m during common storms and more than 1 m for an extreme storm scenario. A numerical experiment indicates that IG wave reflection from one coast to the other contributes up to 35–40% of the measured IG wave height at a hot spot located along the sheltered coast. Finally, the contribution of IG waves to TWL at the shore on both sides of the inlet has been estimated to be about 0.4–0.6 m for a common storm and 0.6–0.9 m for an extreme scenario, locally peaking at 0.74 and 1.1 m respectively and overpassing the contribution of wave-induced setup. This work provides new insights into the contribution of IG waves to TWL and its implications for overtopping flooding hazard and overwash processes at large inlets, highlighting the need to consider IG waves in Early Warning Systems or hazard mapping for flood prevention plans in these environments.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.