Impact Of Rain Research Articles

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Damage evolution simulation and lifetime prediction for composite blades under continuous droplet impacts

Offshore wind power generation is a promising technology in renewable energy applications due to its high reserves of wind energy in sea areas. To improve the energy transformation efficiency, the blade length of the offshore wind turbines has become larger and larger, and it has made rain erosion be one of the most frequent failures during the turbine operation. As the natural rainfall is stochastic in spatial and time domains, it is difficult to depict the damage evolution process caused by rain impact exactly. Therefore, regular and continuous droplet impact simulation and experiments present an alternative methodology for this issue. With the composite structure of the blade and deformability of the liquid droplet in consideration, a fluid solid interaction model will be established to investigate the impact response and subsequent damage evolution. In which, the Smooth Particle Hydrodynamics (SPH) model is utilized to depict the constitutive relationship within the droplet, and Finite Element Method (FEM) is used to construct the Representative Volume Element (RVE) model of the blade leading edge. The impact process is simulated first to obtain the impact pressure distribution at the contact center and velocity field in the droplet. Furthermore, the stress wave propagation in the blade multilayer structure can be analyzed. Owing to the multiaxial fatigue feature of the continuous droplet impact, the continuum damage mechanics is integrated with the fatigue criterion and the Jump-in-Cycle procedure is used to simulate the high-cycle fatigue process. The damage factor distribution on the blade coating surface and its influence on mechanical properties are analyzed. Thereafter, the droplet impact fatigue life can be accumulated based on Miner’s linear rules. The theoretical achievements are validated by experimental data provided by Rain Erosion Testing (RET), which shows a good agreement between each other. As a result, V-N curves and D-N curves, i.e. quantitative relationship between droplet falling conditions and impact fatigue life, are established. The achievements in this study can provide an effective tool for rain erosion mechanism analysis and life prediction in industrial applications.

Sound of natural and artificial rain impact on ETFE membranes

Thin membranes are known to produce a drum-like effect when subjected to rainfall. Rain noise affects acoustic comfort and might disrupt communication, speech intelligibility and cognitive task performance. Therefore, assessment of the characteristics of noise produced by impact of rain on membranes in different architectural settings is important. Given inherent disparities between natural and artificial precipitation-primarily in terms of droplet size distribution and terminal velocities-underscores the importance of conducting evaluations using natural rain noise. The focus of this study is on the comparative assessment of the sound generated by natural rainfall versus the sound generated by the impact of individual droplet in a semi-anechoic room on single Ethylene tetrafluoroethylene (ETFE) membranes. The study assesses the extent of spectral variations of the rain sound produced in both approaches and the corresponding absolute value of noise intensity.

Assessing and Mitigating Dwelling Collapse Risk due to Extreme Precipitation: A Comprehensive Study Using CNN-RF and GeoDetector

Dragon boat rain, the most common extreme precipitation form in South China from May to June with more similar spatial distribution, caused serious loss of people's lives and property. The dwelling collapse is one of the main losses. Previous studies have paid little attention to the dwelling collapse risk caused by dragon boat rain (DCRDBR), the coupling model with CNN and RF applied to its assessment, and the influence of the precipitation process and interaction of natural and social factors on it. To fill these gaps, the CNN-RF was used to calculate the DCRDBR and the DCRDBR map was drawn. The Geodetctor was used to identify the main influencing factors and influencing factor interactions of DCRDBR, due to the spatial stratified heterogeneity of DCRDBR and the ability to obtain the determinant power of single factor and factor interaction. The results show that the F1 score and the AUC value of CNN-RF are 0.96 and 0.81, respectively. The spatial distribution of DCRDBR obtained by CNN-RF is high in the northeast and low in the southwest Guangdong Province. The total precipitation has the strongest determinant power (q=0.54) followed by Slope (q=0.52). The average determinant power of factors describing the precipitation process is 0.25. The combination of total precipitation and GDP/capita has the strongest determinant power of all combinations of natural and socio-economic factors (q=0.72) followed by the total precipitation and ratio of urban population (q=0.71). This study demonstrates the ability of CNN-RF applied to the DCRDBR assessment due to the integration of feature extraction and anti-overfitting ability, and identifies the influence of precipitation processes and the interaction of natural and socio-economic factors on the DCRDBR. It provides a solid scientific basis for crafting strategies to mitigate the impact of dragon boat rain and is conducive to the city's sustainable development.

Mitigating acid rain stress and enhancing aquaponics lettuce quality through root protection

Aquaponics is a sustainable eco-agricultural technology that combines aquaculture with horticulture. However, there is still limited research on the effects and preventive measures of acid rain on aquaponics vegetables. Herein, we conducted an experiment to decipher the combined effect of acid rain (pH 5.0, 4.0, and 3.0) and the protecting treatments (AR and AG) on the lettuce, in which plant growth, photosynthetic system, antioxidant system, nutrition and mineral elements were investigated. The results indicated that acid rain was the main factor affecting the growth of lettuce. Without acid rain, the various indicators of lettuce did not alter under AR and AG treatments. As pH decreased, lettuce yield and quality gradually decreased. Acid rain stimulated the antioxidant system and reduced root vitality and nutrient absorption. AG treatment mitigated the harm of acid rain on lettuce root vitality, soluble sugars, nitrate, and ascorbic acid content. At pH 4.0, AG treatment had the greatest mitigating effect. While at pH 3.0, AG treatment had no effect on the SOD activity, total phenolic and soluble sugar content of lettuce. These findings indicated that protecting the roots can mitigate the impact of acid rain on the edible and nutritional value of aquaponics lettuce. As pH decreased, this mitigating effect gradually diminished. In summary, the detrimental effect of acid rain on aquaponics lettuce was inevitable, yet protective root treatments offered a viable strategy to mitigate this stress. This research established a foundation for the enhancement of aquaponics systems resilience to acid rain.

Light rain exacerbates extreme humid heat

Humid heat waves pose significant risks to human health and the ecosystem. Intuitively, rainfall often alleviates extreme humid heat. However, here we show that light rain often accompanies extreme humid heat, exacerbating its frequency and intensity, especially over arid and semi-arid regions compared to no rain and moderate-to-heavy rain cases. This is because light rain does not dramatically reduce solar radiation but increases near-surface humidity through enhanced surface evaporation. The water replenishment from light rain as well as a shallower planetary boundary layer is crucial for consecutive extremes where there are commonly sporadic drizzle days amidst several rain-free days. These extremes last longer than rain-free extremes. Current global climate models (GCMs) overestimate light rain. After reducing this bias in a GCM, underestimations of humid heat waves in energy-limited regions and overestimations in water-limited regions are largely alleviated. These findings underscore the underappreciated impact of light rain on extreme humid heat.

An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k–ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation.

What Can Hurricane Sam (2021) Tell Us About Extreme Ocean Waves Under Tropical Cyclones?

AbstractWe investigate the ocean wave field under Hurricane Sam (2021). Whilst measurements of waves under Tropical Cyclones are rare, an unusually large number of quality in situ and remote measurements are available in that case. First, we highlight the good consistency between the wave spectra provided by the Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China‐France Oceanography SATellite, the in situ spectra measured by National Data Buoy Center buoys, and a saildrone. The impact of strong rains on SWIM spectra is then further investigated. We show that whereas the rain definitely affects the normalized radar cross section, both the innovative technology (beam rotating scanning geometry) and the post‐processing processes applied to retrieve the 2D wave spectra ensure a good quality of the resulting wave spectra, even in heavy rain conditions. On this basis, the satellite, airborne and in situ observations are confronted to the analytical model proposed by Kudryavtsev et al. (2015, https://doi.org/10.1002/2015JC011284). We show that an extended fetch mechanism may be invoked to explain the large significant wave height observed in the right front quadrant of Hurricane Sam.

Crash Risk Monitoring Method for Highways in Rain Conditions

The slick road surface during rainy days necessitates an increased braking distance, posing prominent safety hazards for driving. Consequently, this paper researched on the monitoring method of collision risk posture of highway in rainy days. The impact of rain on the driving risk of vehicles is mainly the braking distance. In this paper, the ratio μ, defined as the quotient of stopping sight distance (St) and the spacing (Sr) between the front of a trailing vehicle and the rear of a leading vehicle, was adopted as the collision risk assessment metric. Employing the Expressway A-Rain segment from the Citysim dataset as the subject of investigation, traffic risk probability statistics diagrams and heatmaps had been generated. The results showed that the ratio μ>1 has accounted for nearly 10% of the roadway, the traffic risk of this roadway is high, and the risk is mainly distributed on the middle lane of the freeway and entrance ramp.

POLICY FORMULATION FOR ANTICIPATING THE IMPACT OF ACID RAIN ON PADDY PLANTS USING NORMATIVE JURIDICAL ANALYSIS

Law Number 32 of 2009 Article 57, paragraph 4C that one of the preservations of atmosphere functions regulate efforts to protect against acid rain. One of the effects of acid rain is soil damage to plants, such as what happened to paddy plants. In Indonesia, paddy is one of the priority food sources, but every year, it decreases in several regions. Aims: This study aimed at acid rain's anticipated impact on paddy plants in this case study Bandung Regency. Methodology and results: The approach method in this study is a normative juridical approach, using primary data sources obtained through interviews and journal literature. Then, secondary data from legal literature were analyzed descriptively and qualitatively. The study results show that the Bandung Regency Environmental Service has enforced the law. However, violations still occur by industry and society through vehicle emissions, coupled with the increase in population. One of the efforts to formulate policies to anticipate acid rain in paddy plants is to establish sanctions for violators who still pollute the environment, apply catalytic converters to vehicle exhausts that have the potential to remove NOx, carry out measurements at groundwater measurement stations, water quality information systems (manual or real-time). In addition, the irrigation policy for river water flowing on agricultural land with existing laws is being improved. Conclusion, significance, and impact study: Anticipating the impact of acid rain on rice requires various approaches, both technical and non-technical, as well as further analysis from various policy sectors.

A Passive-Active Anti/Deicing Coating Integrating Superhydrophobicity, Thermal Insulation, and Photo/Electrothermal Conversion Effects.

Anti/deicing coatings that combine active and passive methods can utilize various energy sources to achieve anti/deicing effects. However, poor photothermal or electrothermal performance and inevitable heat loss often reduce their anti/deicing efficiency. Herein, copper sulfide loaded activated biochar (AC@CuS) as photo/electric material, polydimethylsiloxane as hydrophobic component, thermally expandable microspheres as foaming agent, and an anti/deicing coating integrating thermal insulation, superhydrophobicity, photo/electrothermal effects was successfully constructed. Benefiting from the synergistic effect of superhydrophobicity and thermal insulation, the freezing time of water droplets on the coating surface is extended from 150 to 2140 s, showing excellent passive anti-icing performance. AC@CuS exhibits photo/electrothermal effects, and porous expanded microspheres reduce heat loss, which endows the coating with desirable photo/electrothermal conversion performance. Under the conditions of 0.2 W/cm2 electric power density (EPD) and 0.1 W/cm2 optical power density (OPD), the temperature of the coating increases from 24 to 96.4 and 113 °C, respectively. Interestingly, with a coheating of 0.05 W/cm2 weaker OPD and 0.05 W/cm2 lower EPD, the ice on the coating surface can be quickly melted in 2.5 min, showing synergistic deicing performance. In addition, the WCA of the prepared coating remains above 150° after mechanical damage, rain impact, UV irradiation, chemical corrosion, and high-temperature treatment, and good superhydrophobic durability ensures the anti/deicing durability of the coating.

Investigated Signal-to-Noise Ratio for Terrestrial Free Space Optical based on Libya Cities Data

Free Space Optical (FSO) communication links offer high-speed data transmission but can be affected by weather conditions like rain, haze, fog, and snow. This study investigates the impact of rain on FSO link availability in the Libyan cities of Garian, Elkhoms, and Tripoli. An empirical model based on the ITU Carb. (France) Model is proposed to predict FSO link availability for ranges up to 5 km. Rain data shows that Garian has the highest rainfall, followed by Elkhoms and Tripoli. The study examines the effects of rainfall on FSO link performance for these three cities over a 5 km link distance. The results show that signal-to-noise ratio (SNR) and received power at the FSO link are influenced by both rainfall and path length. For a 2 km link distance, the SNR ranges from 16 dB in Tripoli to 10 dB in Garian. Under the influence of rain, the SNR varies from 45 dB to 5 dB over a link distance of 1 km to 5 km.

Clutch Size, but Not Egg Volume, Increases with Rainfall in an Arid-dwelling Bird.

Understanding how animals maximize reproductive success in variable environments is important in determining how populations will respond to increasingly extreme weather events predicted in the face of changing climates. Although temperature is generally considered a key factor in reproductive decisions, rainfall is also an important predictor of prey availability in arid environments. Here, we test the impact of weather (i.e., rainfall and temperature) on female reproductive investment in an arid-dwelling bird (i.e., clutch size and egg volume) and tradeoffs between the two. We predicted that female chestnut-crowned babblers (Pomatostomus ruficeps), endemic to the arid region of Australia, would increase clutch size at the expense of egg volume in response to variation in rainfall and temperature. We found that over 14 breeding seasons, clutch size decreased with increasing temperature, but increased following more rain. Egg volume, on the other hand, became larger as temperatures increased and, although not related to the amount of rain, was related to the number of days since the last rainfall. Finally, egg size decreased as clutch size increased, indicating a tradeoff between the two reproductive parameters. Our results suggest that chestnut-crowned babblers breed reactively in response to variable environments. We expect that clutch size variation in response to rain may reflect the impact of rain on arthropod abundance, whereas the effect of temperature may represent an established decline in clutch size observed in other seasonal breeders. As the tradeoff between clutch size and egg volume was modest, and clutch sizes were modified to a greater extent than egg volumes in response to rainfall, we suggest selection is more likely to increase offspring number than quality, at least in favorable years. Our results support the idea that reproductive investment is variable in fluctuating environments, which has implications for species living in habitats experiencing more extreme and less predictable weather as the global climate changes.

Artificial Rainfall on Grain Quality and Baking Characteristics of Winter Wheat Cultivars in Korea.

Wheat (Triticum aestivum L.) stands as a significant cereal crop globally, including in Korea, where its consumption reached 35.7 kg per capita in 2023. In the southern regions of Korea, wheat cultivation follows paddy rice, with harvesting typically occurring during the rainy season in mid-June. This timing, coupled with the high humidity and unpredictable rainfall, often leads to pre-harvest sprouting and subsequent deterioration in flour quality. To assess the impact of rain on flour quality, an artificial rain treatment was administered 45 days after heading in an open field greenhouse, followed by flour quality analysis. The color measurement revealed an increase in the L* parameter, indicative of enhanced kernel vitreousness, attributed to endosperm starch degradation via alpha-amylase activation induced by water absorption. Moreover, significant changes were observed in ash content and the gluten index within the wetted group, resulting in decreased dough strength and stability, ultimately leading to a reduction in loaf volume. Consequently, it is recommended that wheat be harvested 4-7 days after reaching the physiological maturity stage to avoid the rainy season and ensure the production of high-quality wheat.

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives.

With the emergence of autonomous functions in road vehicles, there has been increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor, detecting the positions of obstacles by representing them as clusters of points in three-dimensional space. LiDAR performance degrades significantly when a vehicle is driving in the rain as raindrops adhere to the outer surface of the sensor assembly. Performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradation is highly dependent on the interface material properties. This subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of a LiDAR assembly coated with four classes of material-hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to the different droplets of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis. The results are validated with physical optical tests, using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations of LiDAR performance degradation in rain are presented from both material and optical perspectives. These can aid component selection and the development of signal-enhancing strategies for the integration of LiDARs into vehicle designs to minimize the impact of rain.

The impact of urban rain on the changes of bare and artificially patinated bronze during 9-year exposure

Atmospheric pollutants in the air form acid rain which interacts with bronze surfaces exposed in urban outdoor environment. In this study, different types of patinas on bronze were investigated during and after 9 years of exposure to urban environment in moderately polluted continental city. Natural bronze patina and artificial brown sulphide, green chloride, and green–blue nitrate patinas were investigated. Visual assessment was carried out at defined periods. After 9 years of exposure, an electrochemical study was performed to investigate the electrochemical activity of the patinas in artificial urban rain. Additionally, the patinas were characterised using a variety of techniques, including metallographic examination, scanning electron microscopy/energy dispersive X-ray spectroscopy, Raman spectroscopy, X-ray diffraction analysis, X-ray-photoelectron spectroscopy, and time-of-flight secondary ion mass spectrometry to analyse the surface morphology, chemical composition, and stratigraphic features of the patinas. Evolution of the patinas was shown to be a result of both, the composition of the acid rain and the hydrophobicity of the patinated surfaces.

Effective Method of Improving the Performance Properties of Wall Products On the Basis of the Gypsum Binder

The basis of the conducted research was the goal of modifying composite gypsum-containing binders for the manufacture of wall products with increased water resistance, strength, frost resistance, and durability by developing a hydrophobizing solution for processing products based on gypsum binder. The article presents the results of research on the modification of artificial gypsum stone with a developed hydrophobizing chemical solution based on calcium polysulfide and titanium dioxide. Comparative indicators of water absorption of original and solution-impregnated samples, which were exposed to the frontal influence of water, are given. The water absorption of the modified samples is within 9...21% and does not change after the frontal impact of rain. Indicators of chemical resistance of impregnated samples in solutions of magnesium sulfate, urea, acetic and oxalic acids, which are characteristic components for aggressive environments of livestock complexes, food and chemical industry, are presented. The test results of the samples showed that the developed chemical solution provides a chemical resistance factor of 0.7, that is, they are chemically stable in such environments. It is shown that, depending on the number of applied layers of the solution or the time of impregnation, hydrophobization gives high indicators of the softening coefficient of the products, which is in the range of 0.6...0.82. The research results showed that thanks to the developed hydrophobizing solution, the construction and technical characteristics of the artificial gypsum stone are significantly increased. Wall products made of this material have high operational characteristics, and it is advisable to use them both indoors and outdoors.

Impact of Ice Rain on Forests of Russky Island

Impact of Ice Rain on Forests of Russky Island

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015–2020) to 56 ± 12 cm yr−1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Research on highway rain monitoring based on rain monitoring coefficient.

The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain.