Search Results (4,973)

Esca disease, the most widespread grapevine trunk disease in Europe, is characterized by both chronic and acute forms. In both cases, alterations in the plant’s physiological processes are significant and lead to yield losses and/or plant death. Studies have highlighted the effects of a mixture of foliar fertilizers and seaweeds in reducing foliar symptoms and improving both the quantity and quality of yield. These effects have now been evaluated on additional cultivars and in other vineyard areas. Furthermore, for the first time, the activity of the fertilizer mixture in reducing apoplexy and the resulting vine mortality has been assessed. During the 2022–2023 biennium, in four vineyards of the Lambrusco cultivar in the Province of Reggio Emilia, Northern Italy, affected by both chronic and acute forms of the disease, foliar applications of the mixture were carried out at 10-day intervals starting from the “nine leaves unfolded” BBCH (Biologische Bundesanstalt, Bundessortenamt and Chemical industry) stage 19 up to the “berries developing color” BBCH stage 83. The results confirmed the activity of the fertilizer mixture in reducing chronic symptoms, which appeared particularly pronounced in 2022, when rainfall quantity and distribution allowed regular development of phenological stages. In that year, in all vineyards, a reduction of approximately 50% and 60% in the incidence and severity of chronic leaf symptoms was recorded. Under these optimal growth conditions, treated vines generally showed superior yield and quality. Conversely, in 2023, characterized by heavy rains, smaller effects on foliar symptoms and no improvements in yield were observed. Applications of the mixture resulted in a significant reduction in apoplexy and, consequently, vine mortality, as verified in 2024. This effect did not appear to be influenced by climatic conditions. This study confirms that applications of the mixture aimed at reducing symptom expression and yield damage are a valid addition to the few available control practices. The positive effects observed on the acute form for the first time require further investigation. Full article

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article

Rapid urbanization has led to increasing structural modification of river catchments through dam construction and concrete-lining of natural channels as flood management measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow responses to short-duration, high-intensity rainfall events in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis conducted on five key model parameters showed that parameters controlling surface runoff generation, particularly curve number, exerted the strongest influence on simulated peak flows, while routing-related parameters had a secondary effect. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = −0.68% during calibration and R² = 0.95, NSE = 0.75, PBIAS = −2.49% during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated localized instantaneous maximum channel velocities of up to 20 m/s, increased flood depths by up to 11%, decreased lag times and expanded flood inundation widths by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood inundation widths by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation. Therefore, the study provides event-scale insights to support flood-risk management and infrastructure planning in rapidly urbanizing, data-scarce catchments. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

This study, conducted in the framework of the LIAISE field campaign in NE Spain (May–September 2021), investigates how near-surface relative humidity influences early-stage rainfall characteristics when precipitation is most affected by temperature and relative humidity before rainfall onset. Two instrumented sites were examined, using disdrometers, Micro Rain Radar (MRR), C-band weather radar data, and automatic weather stations. Rainfall events were first classified as stratiform or convective using weather radar data based on a texture analysis of the reflectivity field. Then, only stratiform events were selected and further classified into dry and moist categories according to the upper and lower terciles of near-surface (2 m) relative humidity at the rainfall onset (dry < 54%; moist > 72%). Results show that during dry events, the time delay between the detection of precipitation at ~750 m above ground level (AGL) (by MRR or C-band radar) and its arrival at the surface (measured by the disdrometer) is consistently longer than during moist events, indicating possible evaporation of raindrops during their descent. Surface drop size distributions also differ: dry cases have generally fewer small drops (with diameters < 0.8 mm) but relatively more large drops, leading to higher radar reflectivity values despite similar surface rainfall amounts. However, reflectivity observed aloft by C-band radar and MRR does not present the dependence on relative humidity found at ground level. Findings reported here increase our understanding of the impact of low-level conditions on precipitation characteristics and microphysical associated processes and may contribute to improve correction schemes in operational weather radar quantitative precipitation estimates. Full article

Image deraining is a crucial preprocessing task for enhancing the robustness of high-level vision systems under adverse weather conditions. However, most of the existing methods are limited to a single RGB color space, and it is difficult to effectively separate high-frequency rain streaks from low-frequency backgrounds, resulting in color distortion and detail loss in the restored image. Therefore, a rain removal network that combines dual-color space and frequency domain priors is proposed. Specifically, the devised network employs a dual-branch Transformer architecture to extract color and structural features from the RGB and YCbCr color spaces, respectively. Meanwhile, a Hybrid Attention Feedforward Block (HAFB) is constructed. HAFB achieves feature enhancement and regional focus through a progressive perception selection mechanism and a multi-scale feature extraction architecture, thereby effectively separating rain streaks from the background. Furthermore, a Wavelet-Gated Cross-Attention module is designed, including a Wavelet-Enhanced Attention Block (WEAB) and a Dual Cross-Attention module (DCA). This design enhances the complementary fusion of structural information and color features through frequency-domain guidance and bidirectional semantic interaction. Finally, experimental results on multiple datasets (i.e., Rain100L, Rain100H, Rain800, Rain12, and SPA-Data) demonstrate that the proposed method outperforms other approaches. Full article

This study explores water as memory and as method in African thought. It shows how rivers, rain, and oceans act not only as sources of life but also as teachers who carry a story, restore balance, and reveal moral truth. Drawing from Yoruba, Akan, Igbo, southern African, Kenyan and Afro-Atlantic traditions, this paper presents water as archive and as oracle, holding the past while speaking to the present. This article develops the idea of hydro epistemology, understood here as a way of knowing through flow, renewal, and relationship. In this framework, knowledge is created through ritual engagement with water, transmitted through oral memory and ecological observation, tested against environmental response and revised when conditions change. Water is treated as a witness, mediator and guide, rather than a passive resource. By setting these traditions alongside global discussions on water governance, nature-based ecological care and decolonial environmental ethics, this paper argues that African water imagination offers more than symbolism. It proposes a practical philosophy in which caring for water and caring for life are the same act. To listen to water is to remember, to restore and to recover a way of living that renews both people and land. Full article

Suburban residential streets have long been criticised for their multiple short-comings, including traffic-related injury, increased stormwater runoff, and lack of aesthetic values. Research suggests that Integrated Green-Grey Infrastructure (IGGI) is likely to play a role in mitigating these problems. IGGI refers to infrastructure that consists of both natural materials (such as plants, soil) and human-made structures (such as concrete, pipes). However, IGGI’s definition remains vague, and little is known about its implementation in suburban street retrofitting, and how effective it is. Using a systematic literature review method, this paper analyses peer-reviewed journal articles published over a period of ten years between 2014 and 2023. The objective was to understand IGGI’s definition, integration, and effectiveness in implemented residential street retrofitting projects. Through a rigorous screening process, 15 papers were selected for qualitative analysis. Clusters developed in analysing the results consist of IGGI’s concepts, components, integration and effectiveness. The most notable subject area is system-scale integration, shared by 14 papers. Findings regarding the effectiveness of IGGI suggest strong empirical evidence related to stormwater management and road user behavioural change; however, there were mixed perceptions toward the aesthetic values of rain gardens. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

The growing threat posed by climate change and extreme weather events necessitates the adoption of advanced solutions for crop protection, such as agrotextile nets. The use of anti-rain (AR) and anti-insect (AI) nets is essential to safeguard production, but their effectiveness varies significantly. AR nets offer rain protection but can compromise ventilation, while AI nets ensure a better microclimate but offer poor resistance to precipitation. Given the lack of a standardized index, this study aims to use the rainwater permeability index (Φ_rw) to provide an objective parameter for evaluating and comparing the performance of different agrotextiles. Laboratory tests were conducted on eight different nets (three AR and five AI) using a rainfall simulator. The Φ_rw index, defined as the ratio between the mass of water passing through the net and the total mass of water applied, was evaluated as a function of rainfall intensity (39, 80, and 170 mm/h), net inclination (10°, 20°, and 30°), and the orientation of the warp relative to the slope. The results confirmed that AR nets are most suitable in protecting crops from extreme rainfall, because it becomes clear that AI nets are much more permeable than AR nets. In this sense, the plots show that AI nets usually have a higher permeability than AR nets, between 15% and 25%, depending on rainfall intensity and net inclination. In fact, the AR1 net showed the best performance, with Φ_rw values stabilizing between 40% and 50% under the most common installation conditions. Conversely, AI nets generally exceed 60% permeability, with the AI1 net reaching Φ_rw above 90%, confirming their inadequacy for rain protection alone. In general, AR nets show Φ_rw between 33% and 92%, while Φ_rw for AI nets ranges from 45% and 98%. The research allowed for the comparison of eight agricultural nets with different characteristics and the identification of those that perform best in terms of protection against three different levels of rainfall intensity. The introduction of the Φ_rw index constitutes a significant contribution, providing a quantifiable standard for the selection of agrotextiles in terms of protection from rainfall, regardless of manufacturers’ claims. The data obtained underscore the need to develop future hybrid and multifunctional nets capable of balancing the low water permeability of AR nets with the high ventilation and insect protection of AI nets, thereby ensuring an optimal microclimate and comprehensive crop protection. Full article

Wind turbines operate in harsh environments where leading-edge blade erosion from particulates like sand, rain, and insects is prevalent, significantly degrading aerodynamic performance and reducing power output. To counteract this, this study proposes a novel flow-control method using detached micro-cylinders placed upstream of the leading edge of eroded S809 (a wind turbine blade profile) airfoils. The approach is inspired by the concept of symmetry recovery in disturbed flows, where strategically introduced perturbations can restore balance to an asymmetric separation pattern. The aerodynamic performance of the S809 airfoil was numerically investigated under three leading-edge erosion depths (0.2%, 0.5%, and 1% of chord length, *c*) with a fixed micro-cylinder diameter of 1% *c* positioned at fifteen different locations. Findings reveal that the strategic placement of micro-cylinders ahead of the leading edge or on the pressure side markedly enhances the aerodynamic efficiency of airfoils with 0.2% and 0.5% erosion, achieving a maximum improvement of 148.7% in the lift-to-drag ratio (L/D) difference function for the 0.5% eroded airfoil. This performance recovery is interpreted as a partial restoration of flow symmetry, disrupted by erosion-induced separation. The interaction between the cylinder wake and the spill-over stall vortex originating from the erosion groove was identified as the primary mechanism, injecting high-energy fluid into the boundary layer to suppress flow separation. This study systematically parametrizes the effect of erosion depth and cylinder placement, offering new insights for mitigating erosion-induced performance loss through controlled asymmetry introduction. Full article

Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane ($Z_e)$ and differential reflectivity ($Z_{dr})$. This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. Full article

The November 2025 extreme rainfall event associated with Tropical Cyclone Ditwah caused catastrophic flooding and landslides across Sri Lanka. This study presents a national-scale statistical and Intensity–Duration–Frequency (IDF)-based assessment of the event using long-term rain gauge observations, extreme value analysis, and climate scenario-based projections. The 24-h rainfall data from 46 stations were analyzed for 1-, 2-, and 3-day durations. Historical annual maximum series were extracted and compared with the 2025 event to identify record-breaking extremes. Rainfall volumes were also estimated and compared with the island’s Average Annual Rainfall (AAR) and volumes from major flood events in 2010 and 2016. The November 2025 event exceeded historical maxima at 14 stations, with estimated return periods frequently surpassing 1000 years. The cumulative rainfall volume from 26–28 November accounted for 15.8% of Sri Lanka’s AAR. Updated IDF curves incorporating the event showed marked upward shifts, with intensities at some locations matching or exceeding projections under high-emission climate scenarios. The results highlight the inadequacy of existing design standards in capturing emerging extremes and the need for urgent updates to Sri Lanka’s national IDF relationships to support climate-resilient flood risk management and infrastructure planning. Full article