Search Results (790)

Recent years have seen frequent heavy rainfall events in Japan, often linked to Baiu fronts and typhoons. These events are exacerbated by global warming, leading to an increased frequency and intensity. As floods represent a serious threat to sustainable urban development and community resilience, this study contributes to sustainability-focused risk reduction through integrated analysis. This study focuses on the 2 June 2023 heavy rain disaster in Toyohashi City, Japan, which caused extensive damage due to flooding from the Yagyu and Umeda Rivers. Using numerical models, this study accurately reproduces flooding patterns, revealing that high tides amplified the inundation area by 1.5 times at the Yagyu River. A resident questionnaire conducted in collaboration with Toyohashi City identifies key trends in evacuation behavior and disaster information usage. Traditional media such as TV remain dominant, but younger generations leverage electronic devices for disaster updates. These insights emphasize the need for targeted information dissemination and enhanced disaster preparedness strategies, including online materials and flexible training programs. The methods and findings presented in this study can inform local and regional governments in building adaptive disaster management policies, which contribute to a more sustainable society. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h⁻¹, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h⁻¹), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches. Full article

This study presents AD-MRCNN, an advanced deep learning framework for automated atmospheric front detection that addresses two critical limitations in existing methods. First, current approaches directly input raw meteorological data without optimizing feature compatibility, potentially hindering model performance. Second, they typically only provide frontal category information without identifying individual frontal systems. Our solution integrates two key innovations: 1. An intelligent adapter module that performs adaptive feature fusion, automatically weighting and combining multi-source meteorological inputs (including temperature, wind fields, and humidity data) to maximize their synergistic effects while minimizing feature conflicts; the utilized network achieves an average improvement of over 4% across various metrics. 2. An enhanced instance segmentation network based on Mask R-CNN architecture that simultaneously achieves (1) precise frontal type classification (cold/warm/stationary/occluded), (2) accurate spatial localization, and (3) identification of distinct frontal systems. Comprehensive evaluation using ERA5 reanalysis data (2009–2018) demonstrates significant improvements, including an 85.1% F1-score, outperforming traditional methods (TFP: 63.1%) and deep learning approaches (Unet: 83.3%), and a 31% reduction in false alarms compared to semantic segmentation methods. The framework’s modular design allows for potential application to other meteorological feature detection tasks. Future work will focus on incorporating temporal dynamics for frontal evolution prediction. Full article

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. Full article

Stormwater carries significant amounts of pollutants—including metals, microorganisms, organic micropollutants, and nutrients—from land surfaces into nearby water bodies, leading to water quality deterioration and threats to both human health and ecosystems. The removal of these contaminants is essential not only for environmental protection, but also to enable the reuse of treated water for various beneficial applications. Common treatment methods include bioretention systems, biofiltration, constructed wetlands, rain gardens, swales, and permeable pavements. To improve pollutant removal efficiency, adsorbent materials are often incorporated into the soil substrate of these treatment devices. However, most research on adsorbents has focused on their effectiveness against one or two specific pollutants and has been conducted under static, short-term laboratory conditions rather than dynamic, field-relevant scenarios. Column-based dynamic filtration type studies, which are more informative for field applications, are limited. In one study, a combination of two or more adsorbents with contrasting properties that matched the affinity preferences of the different pollutants to the substrate media removed 77–100% of several heavy metals that occur in real stormwater compared to 38–73% removal with only one adsorbent. In another study, polycyclic aromatic hydrocarbon removal with zeolite was only 30–50%, but increased to >99% with 0.3% granular activated carbon addition. Long-term dynamic column-based filtration experiments and field studies using real stormwater, which contains a wide range of pollutants, are recommended to better evaluate the performances of the combined adsorbent systems. Full article

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article

The analysis of the vulnerability of vernacular buildings to climatic hazards is nowadays a subject of significant importance due to the consequences of climate change. This study assesses the vulnerability of vernacular buildings to three climatic hazards (heavy rains, strong winds and high heat) in the Kara region to identify the vulnerable parts of these constructions that require reinforcement. It is based on PTVA (Papathoma Tsunami Vulnerability Assessment), a multi-hazard analysis methodology, which uses vulnerability indicators. It focuses on the Kabiyè and Nawdeba peoples, who are the major ethnic groups in the region. Focus groups with the population, interviews with professionals and a series of surveys of 125 households in the visited territories enabled us to identify, firstly, the types of vernacular constructions in the region, the climatic hazards that occur there and the indicators that affect the vulnerability of the constructions. Secondly, we calculated the vulnerability index for each type of construction to the three climatic hazards. The vulnerability index of Kabiyè vernacular architecture (KVA) to heavy rain, high heat and strong wind is 0.379, 0.403 and 0.356, respectively. The Nawdéba vernacular architecture (NVA) vulnerability score is 0.359 for heavy rain, 0.375 for high heat, and 0.316 for strong wind. The index of vulnerability to heavy rain, high heat and strong wind for contemporary architecture (CA), as we term the current state of evolution of these two forms of architecture, is 0.499, 0.522 and 0.456, respectively. This study reveals that contemporary architecture (CA) in the Kara region, regardless of the type of hazard considered, is the most vulnerable construction model in the region. It also highlights the indicators that accentuate the vulnerability of vernacular constructions. Regardless of the type of construction, special attention must be paid to features such as roof style (roof slope, shape and material) and building style (form and state of maintenance of the building) to increase the resilience of buildings to climatic hazards. Full article

Acid rain alters soil chemistry significantly and is a key driver of heavy metal pollution. This study investigates the environmental impact of acid rain-induced heavy metal migration in the Siding Lead–Zinc mining area in south China. Tailings, surrounding soils, and riverbed sediments were examined through simulated acid rain soil column leaching experiments. Leachate parameters—including pH, redox potential (Eh), total dissolved solids (TDSs) and heavy metal concentrations—were used to develop machine learning models (Random Forest and Support Vector Machine) to quantify the influence of environmental factors on metal migration. The results showed that leachates were generally alkaline and reductive after leaching, with Cd, Pb, and Zn as the dominant migrating metals. Leachates from tailings and nearby soils exceeded safe drinking water standards, with significantly higher cumulative metal release than other samples. The RF model outperformed the SVM model in predicting heavy metal concentrations. Feature importance analysis revealed that, beyond sample characteristics, pH and Eh were critical factors driving metal migration. Zn and Cd showed strong sensitivity to these parameters, with pH and Eh contributing over 80% to their migration. The findings highlight that acid rain can enhance the solubility and migration of heavy metals, posing a serious threat to the quality of surrounding water and underscoring the requirement for effective mitigation strategies to protect the ecological environment in mining areas. Full article

As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes. Full article

This work underlines an analogy between urban and biological systems. The dialogic approach of systems biology showed us that parts constitute a whole and, in turn, the whole constitutes the parts. The development of a biological system such as an animal or a plant does not unfold by means of an autonomous internal program. Rather, it stems from the interaction of the organism’s internal response pattern and its external environment. The wide scientific literature on the genome–environment interaction confirms this. Nevertheless, the scientific community still tends to consider the environment as a mere external factor which simply modulates the organism’s program. On the contrary, the environment has a key role in development. For example, when a seed germinates after heavy rain, it does not simply react to an external signal indicating favorable conditions for germination. Rather, it interacts directly with rainwater, which becomes a developmental factor no less important than the seed coat proteins. Similar to what happens during the development of an organism, the interface between any complex system and its environment determines its structural and functional fate. We argue that large cities have blurred the interface with their natural environment and depend on delocalized global sources. They are like organisms kept alive by external devices. Hence, we propose to regenerate a vital interface between cities and their rural and natural environment as the main and promising path towards future urban civilization. Full article

Rainfall variability in the sensitive Kalahari semi-desert in Southern Africa, a region of strong climatic gradients, has not been much studied and is poorly understood. Here, anomalies in rainfall totals and moderate and heavy rain day frequencies are examined for both the summer half of the year and three bi-monthly seasons using CHIRPS rainfall data and ERA5 reanalysis. Peak rainfall occurs in January–February, with anomalously wet summers marked by a significant increase in the number of rainy days rather than rainfall intensity. Wet summers are linked to La Niña events, cyclonic anomalies over Angola, and a weakened Botswana High, which enhances low-level moisture transport and convergence over the region as well as mid-level uplift. Roughly the reverse patterns are found during anomalously dry summers. On sub-seasonal scales, ENSO and the Botswana High (the Southern Annular Mode) are negatively (positively) significantly correlated with early summer rainfall, while in mid-summer, and for the entire November–April season, only ENSO and the Botswana High are correlated with rainfall amounts. In the late summer, weak negative correlations remain with the Botswana High, but they do not achieve 95% significance. Full article

LiDAR is a technology that uses laser pulses to measure an object’s distance, an essential technology for Advanced Driver Assistance Systems (ADASs). However, it can be affected by adverse weather environments that may reduce the safety of ADASs. This paper proposes a convolutional neural network that utilizes lightweight network nodes with multiple repetitions instead of the traditional large-scale model. The proposed approach reduces the parameter size, and a consistent pre-processing method is designed to control the input parameters of the network. This process reduces the data size while retaining sufficient features for neural network training. The method was tested on a LiDAR system, demonstrating its ability to run on simple embedded systems and be deployed in heavy rain environments for real-time processing. The proposed convolutional neural Repetitive Lightweight Feature-preserving Network (RLFN) for LiDAR noise filtering demonstrates significant potential for generalization across various adverse weather conditions and environments. This paper discusses the theoretical and practical aspects of the model’s generalization capabilities. By theoretical justification, the design of our model incorporates several key features that enhance its ability to generalize: (1) Adaptive pre-processing—The adaptive pre-processing method standardizes input sizes while preserving essential features. This ensures that the model can handle varying data distributions and noise patterns, making it robust to different types of adverse weather conditions. (2) Inception and residual structures—The use of inception modules and residual connections allows the model to capture multi-scale features and maintain gradient flow, respectively. These structures are known for their robustness and ability to generalize well across different tasks and datasets. (3) Lightweight network design: The lightweight nature of the network, combined with repetitive loops, ensures efficient computation without sacrificing performance. This design is particularly beneficial for deployment on embedded systems, which often have limited computational resources. As verified by testing on a dataset, WADS, the RLFN demonstrated 98.53% accuracy, with a 96.31% F1 score. Full article

To thoroughly investigate the distribution of heavy rain disaster risks in the Beijing–Tianjin–Hebei region, this paper analyzes the spatiotemporal evolution characteristics of heavy rain disaster-inducing factors. Based on disaster system theory, we constructed a heavy rain disaster risk assessment framework from four dimensions. We improved the application of model-agnostic meta-learning (MAML) in hyperparameter optimization for the random forest (RF) algorithm, thereby developing the MAML-RF heavy rain disaster risk assessment model. This model was compared with the SCV-RF model, which is based on random search and cross-validation (SCV), to determine which model had higher accuracy. Then we introduced the SHAP (Shapley additive explanations) interpretability algorithm to quantify the impact of each risk factor. The results indicate that (1) the annual characteristics of heavy rain days and rainfall amounts show a significant upward trend over the past 17 years; (2) the MAML-RF model improved the accuracy and precision of heavy rain disaster risk simulation by 4.44% and 3.71%, respectively, and reduced training time by 27.95% compared to the SCV-RF model; and (3) the SHAP interpretability algorithm results show that the top five influential factors are the number of heavy rain days, rainfall amount, slope, drainage pipe density, and impervious surface ratio. Full article