Search Results (135)

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

The pilot flame serves as the primary anchor for global flame stabilization in a centrally staged combustor. In engineering practice, it typically operates in the diffusion mode. The fuel non-uniformity and diffusion kinetics of the pilot flame may have a significant impact on the flow and flames within the combustor. The flame structure and flame–flow interaction in a centrally staged burner featuring a diffusion pilot flame are investigated in the present paper, using high-frequency CH₂O planar laser-induced fluorescence (CH₂O-PLIF), CH^* chemiluminescence, and particle image velocimetry (PIV) measurements. The stratified flame (S-flame) and the lifted flame (L-flame) are identified under two-stage conditions. The S-flame and L-flame correspond to the separated flow and the merged flow of the two stages, respectively. Significant radial oscillation of the pilot stage airflow is also found. Extensive tests demonstrate that the pilot equivalence ratio (Φp) plays an important role in flame mode switching. Silicone droplets with extremely fine sizes are introduced into the pilot fuel to trace its transportation. When the oscillating pilot stage airflow rushes towards the lip in an instant, it can entrain the pilot fuel to reach the inner side of the main stage outlet. With a low pilot fuel supply and relatively low injection velocity, the pilot fuel and the hot radicals are more likely to be entrained and accumulate in larger amounts at the inner side of the main stage outlet. Consequently, the main stage premixed mixture can be ignited at the main stage outlet, forming the S-flame. The flame mode switches from S- to L-flame when the equivalence ratio increases to the point where the corresponding velocity ratio of pilot fuel to air (V_fp/V_ap) approaches 1.0, with a reduced entrainment of the pilot fuel and radicals. Simultaneous CH₂O-PLIF and flow field results show that when the main stage is ignited downstream, hot products cannot recirculate to the pilot stage outlet, causing the extinction of the pilot flame root. This paper reveals that the fuel diffusion characteristics of the pilot stage can dramatically change the flame structure. To achieve the ideal designed flame shape, the interaction between the pilot fuel and pilot air requires very careful treatment in practical centrally staged combustors. Full article

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones. Full article

This paper presents a comprehensive statistical analysis of extremely/very low-frequency (ELF/VLF) whistler-mode waves observed within magnetic ducts (B-ducts) using data from NASA’s Magnetospheric Multiscale (MMS) mission. A total of 687 events were analyzed, comprising 504 occurrences on the dawn-side flank of the magnetosphere and 183 in the nightside magnetotail, to investigate the spatial distribution and underlying mechanisms of wave–particle interactions. We identify distinct differences between these regions by examining key parameters such as event width, frequency, plasma density, and magnetic field extrema within B-ducts. Using an independent two-sample t-test, we assess the statistical significance of variations in these parameters between different observation periods. This study provides valuable insights into the magnetospheric conditions influencing B-duct formation and wave propagation, offering a framework for understanding ELF/VLF wave dynamics in Earth’s space environment. Full article

Given the global rise in magnitude and frequency of extreme events due to climate change, accurately determining these values—typically through frequency analysis—is especially important. The article analyzes the particular aspects of three probability distributions of 4 and 5 parameters in flood frequency analysis (FFA) using the L-moments as a parameter estimation method. Aspects regarding the behavior of the five-parameter Wakeby, four-parameter generalized Pareto and four-parameter Burr distributions are highlighted in generating the maximum flow values in the area of low annual exceedance probabilities characteristic of rare and very rare events. After applying these distributions to four case studies, it was found that for the 10,000-year return period event, the relative error between multi-parameter distributions is under 20%—a more than acceptable margin given the extremely low exceedance probability. However, its importance depends on the use of the generated values, which in some cases can lead to excessive costs in establishing structural flood protection measures (urban planning), which can be avoided. It also highlights possible negative consequences (material and human lives) regarding the risk associated with these analyses that can lead to an under-dimensioning of this infrastructure. Full article

Coastal cities and low-lying areas are increasingly vulnerable, and accurate data is needed to identify where interventions are most required. We compared 53 cities affected by a 1 m increase in land levels and a 2 m rise in sea levels. The geographical scope of this study covered selected coastal cities in Europe and northern Africa. Data were sourced from the European Environment Agency (EEA) in the form of prepared datasets, which were further processed for analysis. Statistical methods were applied to compare the extent of urban flooding under two sea level rise scenarios—1 m and 2 m—by calculating the percentage of affected urban areas. To assess social vulnerability, the analysis included several variables: MAPF65 (Mean Area Potentially Flooded for people aged 65 and older, indicating elderly exposure), Age (the percentage of the population aged 65+ in each city), MAPF (Mean Area Potentially Flooded, representing the average share of urban area at risk of flooding), and Unemployment Ratio (the percentage of unemployed individuals living in the areas potentially affected by sea level rise). We utilized t-tests to analyze the means of two datasets, yielding a mean difference of 2.9536. Both parametric and bootstrap confidence intervals included zero, and the p-values from the t-tests (0.289 and 0.289) indicated no statistically significant difference between the means. The Bayes factor (0.178) provided substantial evidence supporting equal means, while Cohen’s D (0.099) indicated a very small effect size. Ceuta’s flooding value (502.8) was identified as a significant outlier (p < 0.05), indicating high flood risk. A Grubbs’ test confirmed Ceuta as a significant outlier. A Wilcoxon test highlighted significant deviations between the medians, with a p << 0.001, demonstrating systematic discrepancies tied to flood frequency and sea level anomalies. These findings illuminated critical disparities in flooding trends across specific locations, offering essential insights for urban planning and mitigation strategies in cities vulnerable to rising sea levels and extreme weather patterns. Information on coastal flooding provides awareness of how rising sea levels affect at-risk areas. Examining factors such as MAPF and population data enables the detection of the most threatened zones and supports targeted action. These perceptions are essential for strengthening climate resilience, improving emergency planning, and directing resources where they are needed most. Full article

In the field of space gravitational wave detection, high-precision temperature measurement with a resolution at the micro-Kelvin level in the milli-Hertz frequency range is required to mitigate the interference caused by temperature fluctuations around the core components. This is a very challenging task due to resistance thermal noise and the inherent 1/f noise of electronic components. To overcome this problem, this paper proposes a low-noise, high-resolution temperature measurement method based on an inductive voltage divider and an alternating-current (AC) bridge. The proposed method has the following three characteristics: (1) it employs an AC excitation signal to drive the temperature measuring bridge to overcome the influence of 1/f noise in electronic components; (2) it uses as few resistance components as possible in the AC bridge and signal detection circuit to reduce the impact of resistance thermal noise on the measurement results; (3) it adopts a frequency-domain data processing algorithm based on discrete Fourier transform to improve the accuracy of the temperature measuring result. Using this method, a circuit board is designed and tested. The results show that the noise floor level of the designed temperature measurement circuit is below $7\times {10}^{-6}\mathrm{K}/\sqrt{\mathrm{Hz}}$ in a frequency range of 0.005~1 Hz. This demonstrates that our proposed method is able to detect extremely weak temperature change signals and meets the temperature measurement requirements of space gravitational wave detection. Full article

Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency and intensity, driven by factors such as climate change and urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable management of flood risks by integrating the analytical hierarchy process-weighted linear combination (AHP-WLC) and fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, an arid and flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), and distance to rivers (DTR), were processed using the collected data. The AHP-WLC method classified the region into flood susceptibility zones: very low (10.23%), low (23.14%), moderate (29.61%), high (17.54%), and very high (19.48%). The FOWA technique ensured these findings by introducing optimistic and pessimistic fuzzy scenarios of flood risk. The most extreme scenario indicated that 98.79% of the area was highly sensitive to flooding, while less than 5% was deemed low-risk under conservative scenarios. Validation of the IFAA approach demonstrated its reliability, with the AHP-WLC method achieving an area under curve (AUC) of 0.83 and an average accuracy of ~75% across all fuzzy scenarios. Findings revealed elevated flood dangers in densely populated and industrialized areas, particularly in the northern and southern regions, which were influenced by proximity to rivers. Therefore, the study also addressed challenges linked to sustainable development goals (SDGs), particularly SDG 13 (climate action), proposing adaptive strategies to meet 60% of its targets. This research can offer a scalable framework for flood risk management, providing actionable insights for hydrologically vulnerable regions worldwide. Full article

This paper investigates evaluation methodologies and machine reasoning schemes to analyze dynamic electromagnetic spectrum. We research practical and scalable classification of radio frequency signals across high frequency, very high frequency, ultra high frequency and super high frequency bands. The multi-band software defined radio, software defined mobile networks, and global navigation system receivers accomplish reconfigurable communication. Resilient communication, as well as high-fidelity analysis of extreme and congested electromagnetic spectra, are open problems due to challenges in classification of interference, distortions and adaptive jamming from spatially distributed transmitters and jammers. This paper documents high-fidelity characterization and dynamically reconfigured machine reasoning schemes to ensure the cognitive capabilities of communication systems. Low-fidelity experimental studies substantiate the spectrum evaluation methodology and demonstrate results. Full article

The recent improvements in streamflow measurement approaches have boosted the reliability and accuracy of river flow measurement. In this study, long-term measurements of river discharge in the Tokoro River, Japan, were conducted. The key objective of this work is to investigate the extent of river flow measurement in a very shallow and narrow silt stream using the fluvial acoustic tomography system (FAT). Despite the preliminary nature of the measurement results, the recorded data were subject to analysis from three different outlooks. First, examinations were performed under very shallow and high-water conditions. Second, examinations were performed using double acoustic frequency. Third, examinations were performed using multiple independent flow datasets. As a new achievement in terms of advanced monitoring capabilities, it was documented that the measurement by the FAT was possible even in extremely shallow conditions. However, the minimum water depth along the measured cross-section must be ≥9 cm. Moreover, the FAT system demonstrated its capability to monitor streamflow in high water levels. In addition, it was found that using high transmission frequency can provide shorter wavelengths, permitting better spatial resolution and higher velocity resolutions and hence desirable measurement accuracy. Nevertheless, measurements in the presence of high suspended sediment particles were lacking. Alternatively, a lower transmission frequency offers a longer wavelength, which might be less sensitive to small-scale variations and results in an imprecise degree of measurements. Nonetheless, measurements can be accomplished even during the mobilization of a high concentration of suspended sediment matters. Finally, using multiple independent streamflow measurement records, the results proved that the flow measured by the FAT system was in very good agreement with the records acquired using sophisticated measurement approaches such as HADCP and STIV with a very low range of uncertainty. Full article

The growing intensity and frequency of bushfires across the globe pose serious threats to building safety when it comes to the vulnerability of glass windows. During bushfires, extreme heat can cause significant damage to these windows, creating openings that allow embers, radiant heat, and flames to enter buildings. This study investigated the effectiveness of various construction materials, including thin steel sheets, glass fibre blankets, aluminium foil layers, and intumescent layers on glass fibre blankets, as bushfire-resistant shutters for protecting windows in bushfire-prone areas. The shutters were tested under two scenarios of radiant heat exposure: rapid and prolonged exposures of 11 and 47 min, respectively. Heat transfer models of the tested shutters were developed and validated using fire test results, and then comparisons of the performance of materials were made through parametric studies for bushfire radiant heat exposure. The results show that a 0.4 mm glass fibre blanket with aluminium foil performed best, with very low glass temperatures and ambient heat fluxes due to the reflective properties of the foil. Similarly, a thin steel sheet (1.2 mm) also effectively maintained low glass temperatures and ambient heat fluxes. Additionally, graphite-based intumescent coating on a glass fibre blanket reduced the ambient heat flux. These results highlight the importance of bushfire-resistant shutters and provide valuable insights for improving their design and performance. Full article

Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers’ experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall. Full article

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position. Full article

The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, including ultra low frequency (ULF), extremely low frequency (ELF), and very low frequency (VLF) ranges. The electric current source due to lightning near the location of the Hunga Tonga volcano eruption has a wide-band frequency spectrum determined in this paper based on a data-driven approach. The spectrum is monotonous in the VLF range but has many significant details at the lower frequencies (ULF, ELF). The decreasing amplitude tendency is maintained at frequencies exceeding 0.1 Hz. The density of effective lightning current in the ULF range reaches the value of the order of 10⁻⁷ A/m². A combined dynamic/quasi-stationary method has been developed to simulate ULF penetration through the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system. This method is suitable for the ULF range down to 10⁻⁴ Hz. The electromagnetic field is determined from the dynamics in the ionosphere and from a quasi-stationary approach in the atmosphere, considering not only the electric component but also the magnetic one. An analytical/numerical method has been developed to investigate the excitation of the global Schumann resonator and the eigenmodes of the coupled Schumann and ionospheric Alfvén resonators in the ELF range and the eigenmodes of the Earth–ionosphere waveguide in the VLF range. A complex dispersion equation for the corresponding disturbances is derived. It is shown that oscillations at the first resonance frequency in the Schumann resonator can simultaneously cause noticeable excitation of the local ionospheric Alfvén resonator, whose parameters depend on the angle between the geomagnetic field and the vertical direction. VLF propagation is possible over distances of 3000–10,000 km in the waveguide Earth–ionosphere. The results of simulations are compared with the published experimental data. Full article