Search Results (835)

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

Commercial Microwave Links (CMLs) have emerged as one of the most widely utilized opportunistic sensors for rainfall monitoring. However, rainfall retrieval using microwave links continues to face significant challenges in terms of accuracy, particularly for shorter path lengths. In recent years, a statistical approach has been demonstrated to effectively enhance retrieval accuracy. Concurrently, studies have shown that the selection of localized parameters can further optimize CML retrieval results. In this study, we evaluate and calibrate the probabilistic–statistical retrieval method proposed in a previous study for the Chinese region. Following their framework, we replace the global parameters with a Gamma rainfall distribution derived from local rain gauge observations, making the method more suitable for local climatic conditions. To validate the effectiveness of the improved method, we deployed three experimental microwave links with path lengths ranging from 420 m to 3.50 km and simultaneously recorded path attenuation along with rainfall data from surrounding rain gauges. The results show that the coefficient of determination and correlation coefficient between the proposed method and rain gauge observations reach 0.85 and 0.86, respectively, indicating a significant improvement over traditional models. The calibrated method performs particularly well during high-intensity rainfall events, demonstrating the importance of parameter localization for improving retrieval accuracy. Full article

Multimodal fusion methods leveraging various sensors provide strong support for 3D object detection. However, under adverse weather conditions such as rain, fog, snow, and intense glare, complex environmental factors can degrade sensor data quality, leading to increased false positives and missed detections. In addition, sensor modalities (e.g., LiDAR and cameras) inherently vary in information density, and directly fusing them can cause critical details in high-density data to be diluted by low-density data, thereby increasing errors. To address these issues, we propose a Semantic-Enhanced Bidirectional Multimodal Fusion (SeBFusion) framework. By introducing a semantic enhancement mechanism and a bidirectional fusion strategy, SeBFusion mitigates the impact of noise under adverse weather and alleviates information dilution in multimodal fusion. Specifically, SeBFusion first employs a virtual point generation and camera semantic injection module to selectively map image semantic features into 3D space, producing semantically enhanced LiDAR features to compensate for the sparsity of the raw LiDAR point cloud. Then, during cross-modal interaction, we design a bidirectional cross-attention fusion module. This module estimates the confidence of each modality and adaptively reweights the bidirectional information flow, thereby reducing the risk of noise propagation across modalities and improving the robustness and accuracy of 3D object detection in complex environments. Experiments on adverse-weather versions of datasets such as KITTI-C and nuScenes-C validate the effectiveness and superiority of the proposed method. On the nuScenes-C dataset, it achieves 66.2% mAP and 66.6% mAP under fog and snow conditions, respectively. Full article

LiDAR serves as the primary sensor for acquiring environmental information in intelligent driving systems. However, under adverse weather conditions, point cloud signals obtained by LiDAR suffer from intensity attenuation and noise interference, leading to a decline in segmentation accuracy. To address these issues, this paper designs a lightweight semantic segmentation system based on the Gated Visual State Space Model (VMamba), named RainMamba. Specifically, the system utilizes spherical projection to transform point clouds into 2D sequences and constructs a physical perception feature embedding module guided by the Beer–Lambert law to explicitly model and suppress spatial noise at the source. Subsequently, an uncertainty-weighted cross-modal correction module is employed to incorporate RGB images for dynamically calibrating the degraded point cloud data. Finally, a VMamba backbone is adopted to establish global dependencies with linear complexity. Experimental results on the SemanticKITTI dataset demonstrate that the system achieves an inference speed of 83 FPS, with a relative mIoU improvement of approximately 7.2% compared to the real-time baseline PolarNet. Furthermore, zero-shot evaluations on the real-world SemanticSTF dataset validate the system’s robust Sim-to-Real generalization capability. Notably, RainMamba delivers highly competitive accuracy comparable to the state-of-the-art heavy-weight model PTv3 while requiring a significantly lower parameter footprint, thereby demonstrating its immense potential for practical edge-computing deployment. Full article

Existing research on driving under the influence (DUI) crashes predominantly employs independent modeling frameworks that overlook the interdependency between injury outcomes of impaired and sober drivers, potentially leading to biased parameter estimates and an incomplete understanding of crash mechanisms. This study develops a copula-based bivariate ordered response modeling framework to investigate how injury severities of DUI and non-DUI drivers are interdependent and how this dependency varies systematically across weather conditions. Using crash data from the U.S. Crash Report Sampling System (2016–2022), we analyze 3773 two-vehicle crashes involving one alcohol-impaired and one sober driver under clear, rain/snow, and fog conditions. Three key findings emerge from our analysis. First, injury severities between DUI and non-DUI drivers exhibit significant dependency, with both the strength and structure of this association varying systematically across weather conditions. Dependency intensity increases progressively from clear weather (Kendall’s τ = 0.2717) to rain/snow (0.2966) and peaks under fog (0.3239). Moreover, the optimal dependency structure differs by weather conditions. Second, DUI and non-DUI drivers demonstrate markedly differentiated response patterns to risk factors, with the same factor often producing opposite-direction or substantially different magnitude effects on the two parties. Third, weather conditions play a critical moderating role, with most risk factors exhibiting significant amplification effects on crash injury severity under adverse weather. For example, on curved roadways under fog compared to clear weather, severe/fatal injury risk increases from 4.45% to 5.81% for DUI drivers and from 7.99% to 11.36% for non-DUI drivers. These findings highlight the importance of joint dependency modeling in alcohol-related crash research and provide evidence-based insights for weather-sensitive DUI enforcement and targeted safety interventions. Full article

In the context of global warming, the prediction of extreme precipitation events faces great challenges, especially the ensemble forecast of convective-scale heavy precipitation. Taking the heavy rainstorm in Zhengzhou on 20 July 2021 as an example, this paper aims to explore the performance of the convective-scale ensemble forecasting system based on the local breeding model cultivation method (LBGM) in extreme precipitation forecasting, and reveal the key physical mechanisms affecting the quality of forecasting. The traditional scoring (TS, Bias), neighborhood FSS and Contiguous Rain Area (CRA) methods were used to systematically evaluate the precipitation forecast, and the superior and inferior forecast members were diagnosed and analyzed by combining physical quantities such as isentropy vortex, relative vorticity, and water vapor flux divergence. The results show that: (1) the LBGM-EPS system can better capture the spatial distribution and intensity of heavy precipitation, which is better than the single deterministic forecast; (2) The CRA method is better than the traditional score in describing the spatial structure and intensity of precipitation, and can effectively identify the good and bad members of the forecast. (3) The reason why the dominant forecast members perform better is that the simulation of the dynamic-thermal structure of the mesoscale convective vortex is more reasonable, especially the coupling mechanism of the downward transmission of the high-level vortex and the convergence of water vapor at the lower level is better. The preliminary application of convective-scale ensemble forecasting based on the LBGM in this study has reference value for improving the prediction ability of extreme precipitation. Full article

Rainfall variability strongly governs vegetation dynamics in the Semi-Arid Tropics (SAT) of Sub-Saharan Africa (SSA). Yet the impacts of heavy rainfall are less well quantified than those of drought. This study proposes a modified heavy rainfall index (mR95pT) to enable robust comparison of extreme rainfall signals across seasons and regions. The index mitigates the strong seasonal background signal inherent to constant-threshold approaches and highlights episodic heavy rainfall events more clearly. Using CHIRPS precipitation (1981–2022, to derive long-term climatological means) and MODIS NDVI (2003–2022) aggregated to 0.05° and 16-day intervals, we computed the cumulative precipitation, the original ETCCDI-based index (R95pT), and mR95pT across three subregions (Sahel, Southern Africa, and Eastern Africa) and examined event-scale detectability. mR95pT reduced spurious concentration around climatological wet-season peaks and more clearly captured episodic events (e.g., cyclone-related extremes). The vegetation stress (VS) responses were quantified based on the Vegetation Condition Index (VCI) and a probabilistic framework conditioned on background wetness (SPI-3) and heavy rainfall intensity (mR95pT). Under near-normal wetness (SPI-3 ≈ 0), the baseline VS probability was 18% in Eastern Africa and 13% in the other regions. Conditioning on heavy rainfall increased VS probability (relative to the SPI-3 ≈ 0 baseline) by +0.8 to +38% (Eastern Africa), +0.6 to +24% (Southern Africa), and +11 to +39% (Sahel), with the additional effect diminishing under very wet conditions. Overall, mR95pT and the proposed probabilistic framework provide a scalable pathway to monitor both drought- and heavy-rain-related vegetation risks over data-sparse semi-arid regions. Full article

A vulnerability surface modeling method based on dual intensity metrics is proposed to assess the impact of typhoons and heavy rainfall disasters on the distribution pole-tower conductor system. A three-dimensional finite-element model is developed for a typical “three-pole four-conductor” distribution line, considering the uncertainties in both load-side and structural-side parameters. A spatially coherent turbulent wind field is generated using the Davenport spectrum and harmonic superposition method, while an equivalent rain load is derived based on raindrop spectrum integration. Nonlinear dynamic time-history analysis is then conducted under multiple combinations of basic wind speeds and rainfall intensities, extracting engineering demand parameters such as conductor axial tension and pole-base bending moments. Based on probabilistic demand analysis, the relationship between engineering demand parameters and dual intensity measures is regressed in the logarithmic domain to construct bivariate fragility surfaces for both the conductors and the poles. Critical failure curves are obtained by intersecting the fragility surfaces with the 10% exceedance probability level, enabling rapid classification of structural risk under the joint effects of wind and rain. The results show that the regression model provides a high fit, effectively revealing that wind speed is the dominant control factor, while rainfall intensity serves as a secondary amplifying factor. The resulting critical failure curves can be directly used as operation and maintenance warning thresholds and can be coupled with observed and forecast meteorological data for time-varying risk assessment. These findings provide methodological support and engineering guidance for risk assessment, operation and maintenance decision-making, and resilience enhancement of distribution networks under multi-hazard coupling. Full article

Rain-on-snow events in cold regions frequently trigger slope failures. This study elucidates the instability mechanism of low-permeability silty clay slopes under combined rainfall and snowmelt conditions. A refined numerical model was established based on the sequential coupling of SEEP/W and SLOPE/W, utilizing the Morgenstern-Price method for stability analysis. A rigorous mesh sensitivity analysis confirmed that a locally refined mesh of 0.2 m with exponential time-stepping is essential to eliminate numerical dispersion at the wetting front. Simulation results indicate a significant time-lag effect in stability response; the critical failure time lags behind rainfall cessation (e.g., ~8 h for moderate rain) due to gravity-driven moisture redistribution. Spatially, the slope toe reaches saturation first, generating excess pore-water pressure and suggesting a tendency toward retrogressive instability. Furthermore, snowmelt superposition functions as a continuous hydraulic load, creating a base flow effect that advances the acceleration phase of failure by 1–2 h and further reduces the minimum safety factor. These findings highlight the critical role of the slope toe saturation and the necessity of considering snowmelt intensity in landslide early warning systems for cold regions. Full article

Combined sewer overflow (CSO) pollution has consequently become a critical challenge, yet its formation depends on tightly coupled dry- and wet-weather processes. This study aims to integrate high-resolution field monitoring with statistical analysis to characterize the full “accumulation–transport–discharge” cycle of CSO pollution in a representative combined sewer catchment located in the Yangtze River basin, China. A dynamic analytical framework was established, combining multiple pollution media and linking dry-weather accumulation with rainfall-driven transport, enabling quantitative source apportionment of pollutant contributions. Results indicated that during dry periods, domestic sewage exhibited strong enrichment, with concentrations of total inorganic nitrogen (TIN), chemical oxygen demand (COD), and total phosphorus (TP) being 2.1-, 2.3-, and 1.9-fold higher, respectively, than the Chinese secondary discharge standards (GB 18918-2002). Surface sediment showed pronounced spatial heterogeneity, with greater loads in residential than transportation areas and substantial fine-particle accumulation on roofs (particle size < 150 μm, accounting for 73% by mass). Sewer sediments, dominated by coarse inorganic particles (over 77% by mass), represented the main pollutant reservoir. Rainfall produced distinct hydrodynamic and water quality responses. Light rain following long antecedent dry periods generated a high-concentration but low-load regime with a strong first flush, whereas moderate rain yielded lower concentrations but higher loads. Overflow occurred when rainfall exceeded ~14 mm, with pollutant peaks lagging rainfall by 20–45 min in the studied area. TIN and TP peaked sharply at rainfall event onset, and first-flush intensities followed TIN > TP > COD > suspended solids (SS). Source apportionment identified sewer sediments as the dominant CSO source, followed by surface runoff and domestic sewage. These findings clarify the mechanisms linking dry-weather accumulation to wet-weather transport and support targeted CSO pollution control and urban water quality management. Full article

LiDAR sensors are becoming more common and are going to be widely adopted in vehicles in the future by reducing the production cost of the time-of-flight units. Manufacturers are uncertain about the placement, cover material, and shape of the assembly to achieve the optimal performance of the LiDAR, especially in rainy conditions. Although there are existing methodologies for evaluating the visibility and signal intensity of point clouds, there are no indexing approaches available since they would require a broad and comprehensive dataset and realistic and repeatable conditions to perform parametric studies. A matrix of rain conditions with quantified raindrop distribution characteristics is simulated using a wind tunnel via the wind-driven rain concept to produce the realistic impact of raindrops onto the sensor assembly surface at various wind speeds. This paper presents a performance prediction model method for LiDAR sensors and showcases the capability of such a model to provide insights quantitatively when comparing variations. The model is 3-dimensional, including rain conditions perceived by a moving vehicle at different speeds, material properties of surface wettability, and LiDAR visibility in rain compared to dry conditions. The observed LiDAR signal degradation follows an exponential manner, for which this study provides experimentally derived coefficients, enabling quantitative prediction across materials, topologies, rain, and driving speed conditions. Full article

Studying the precipitation characteristics and extreme precipitation events in arid and semi-arid regions is of significant baseline value for optimizing water resource allocation and utilizing precipitation resources. Utilizing multi-scale ERA5 precipitation data from 1960 to 2023, this study focuses on the typical arid and semi-arid region of Ordos as the research area. Precipitation exceeding the 90th percentile was defined as extreme precipitation, and three indices—extreme precipitation amount (EPA), extreme precipitation frequency (EPF), and extreme precipitation proportion (EPP)—were used to investigate its characteristics in the study area. Additionally, three typical extreme precipitation events in recent years were analyzed to study the precipitation process of these typical events. The main results are as follows: The annual average precipitation in the study area ranges from 170.3 to 606.1 mm, with an average of 378.5 mm, which has been on a declining trend over the years, with an average annual decrease of 1.2 mm. Overall, 70% of the precipitation is concentrated in the months of June to September. The daily average of extreme precipitation in Ordos is 18.7 mm and the annual average number of extreme precipitation days ranges from 8 to 13 days, with an average annual number of extreme precipitation days being 11. Extreme precipitation accounts for more than 50% of the total precipitation. Among all areas analyzed, Jungar Banner demonstrates the greatest vulnerability to intense rainfall events. Typical extreme precipitation events in Ordos are characterized by short-duration heavy rainfall, with the rain peak ratio coefficients of the three events ranging from 0.62 to 0.72, exhibiting a distinct “post-peak” pattern. These findings provide scientific support for water resource management and disaster prevention strategies in arid and semi-arid regions. Full article

Since early 2023, severe to exceptional drought has developed in southern coastal Australia, with dam levels falling as stream flows plummet. The wet season, April to September, reflects the most equatorward position of the mid-latitude westerly wind regime that brings rain-bearing systems to southern coastal Australia. Climatologically, an upper-level tropospheric split-jet is present in the Australia–New Zealand region. This is evident in the subtropical jet (STJ) location when the 1965 to 1995 u-component of the 250 hPa wind anomaly, relative to 1991 to 2020, is located above northern tropical Australia, and the weaker polar-front jet (PFJ) branch anomaly spans the mid-latitudes south of Australia. Permutation testing revealed a statistically significant decrease in the 2016 to 2025 wet season mean precipitation across southern Australia. Compared with the 1965 to 1995 u-component wind anomaly at 250 hPa, the 2006 to 2015 decadal anomaly still shows the split jet with the STJ branch over northern tropical Australia and the PFJ in the mid-latitudes of the Australia–New Zealand region. However, there is a dramatic change in position and structure of the STJ branch of the split jet, between the 1965 to 2015 and the 2016 to 2025 anomalies. The split jet structure has shifted approximately 10° poleward, causing rain-producing systems to track south of the Australian continent. The reduced precipitation can generate more frequent and intense droughts, with greatly reduced stream flows and dam levels. Historically, the low precipitation warm season follows from October to March when heatwaves, combined with pre-existing dry conditions, often create catastrophic bushfire conditions. 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

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