Search Results (5,708)

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

Soil erosion affects agricultural and environmental sustainability and needs to be addressed. The Cagayan de Oro River Basin (CDORB), one of the major river basins in the Philippines, provides economic, social, and environmental services to the city and municipalities inside the basin. More than 70% of the area of the river basin is devoted to various forms of agricultural production. Land cover critically influences erosion dynamics as vegetation reduces rainfall impact, enhances infiltration, and limits sediment transport. This study employs the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) integrated with the Modified Universal Soil Loss Equation (MUSLE) to evaluate soil erosion under different rainfall return periods (5, 10, 25, 50, 100 years) and four land cover scenarios: No Reforestation Intervention (NI), Maximum Forest Cover (MF), Slope-Based Land Use (SB), and Reforestation on Public Domain (PD). Model results showed that soil loss increased with rainfall intensity, with NI yielding the highest average erosion of 1443 t ha⁻¹. Conservation scenarios reduced erosion by up to 53% compared to NI. Among the conservation scenarios, MF, SB, and PD yielded average erosion of 21, 716, and 1304 t ha⁻¹, respectively. While the MF scenario had the least soil loss, no space was assigned for economic production. On the other hand, the SB approach offered the best balance, halving erosion across all rainfall return periods, but at the same time has sufficient space available for economic production. These findings demonstrate the scientific value of integrating HEC-HMS and MUSLE for event-based erosion modeling and highlight how comparing multiple land-cover scenarios can inform data-driven land use planning and policy formulation for sustainable watershed management. Full article

Hamatocaulis vernicosus is a pleurocarpous moss of conservation concern, listed in Annex II of the EU Habitats Directive due to its significant and ongoing decline across Europe. H. vernicosus is also listed as ‘Vulnerable’ on the Red List of Romanian Bryophytes. Despite its protected status, the species remains under-recorded in Romania, where many potentially suitable habitats have yet to be surveyed. The ecosystems, classified as Transition mire and quaking bog (NATURA 2000 code: 7140), are wet peatlands with oligo- to mesotrophic conditions and a pH of 5.0–7.5. H. vernicosus is recorded in 58 Romanian locations (10 confirmed by us, 5 new), spanning the Continental and Alpine bioregions. Models showed good performance (AUC 0.79–0.83; TSS 0.54–0.59), with distribution mainly shaped by mean annual temperature and temperature range, and secondarily by precipitation. The species favors cold, stable climates with high seasonal rainfall. Even though the number of localities reported for this species has increased in recent years, this does not indicate an improvement in its conservation status, but rather is an effect of recent recording efforts. To support targeted conservation planning, an ensemble species distribution model was developed in order to predict the suitable habitats of H. vernicosus across Romania. Both climate models project major range losses for the varnished hook-moss: ~30% by 2050 and ~40–60% by 2100, depending on the scenario. Losses are gradual under SSP245 but more abrupt under SSP585, with increased fragmentation, especially between the Eastern and Southern Carpathians. By integrating field observations with predictive climate change modeling, our study brings critical insights applicable to the conservation of H. vernicosus and the unique peatland ecosystems it relies on. Full article

The implementation strategy of a nowcasting methodology can be crucial to pursue skillful results in an operational context to obtain reliable short forecasts with as much as possible reduced errors. In this work, a spectral nowcasting algorithm was considered to carry out rainfall prediction at the Italian national scale, instead of the traditional “single-piece area” approach; strategies were tested to dynamically split the precipitation zone into smaller sub-regions by identifying connected components within the precipitation area. These strategies rely on image-processing techniques, and they were tested over a long period of time which includes several events with a variety of rainfall typologies (stratiform, thunderstorms, persistent rainfall). Traditional standard skill scores widely used in hydro-meteorology are exploited to quantify the improvements. The strategy that leads to the best performance is the one that results in smaller spatial calculation domains; this demonstrates the importance of correctly modeling and interpreting the different types of rain structures that may be present simultaneously in the rain field across a large domain. Full article

UV-Visible spectroscopy provides qualitative and quantitative information on colored dissolved organic matter (CDOM) that can be used as a proxy for dissolved organic carbon (DOC). We developed an absorbance-based linear model of DOC for the San Sebastian River estuary in NE Florida. We compared linear and mixed models, with and without salinity as an additional fixed effect. All models exhibited strong correlations (R² = 0.88–0.97) with measured DOC values for the training dataset. The model with the strongest performance on the testing dataset was a linear model containing the absorption coefficient at 254 nm, the spectral slope at 275–295 nm, and salinity. The range of measured DOC was 0.5 to 52.3 mg/L, and the model was able to predict DOC concentrations for an independent testing dataset with a relative mean absolute error of 17%. Incubation experiments indicated that aging and photolysis altered absorption coefficients and spectral slopes, which negatively affected the model performance, particularly for photolysis. However, predicted DOC was still well-correlated (R² > 0.9) with measured DOC, even for photolyzed samples. Spectral slope ratios indicate that DOM in the San Sebastian River is mainly terrigenous, and that hydrologic variability, possibly associated with freshwater inflow from rainfall, influences DOC/CDOM concentration and composition. Full article

This paper advances machine learning (ML)-based streamflow prediction by strategically selecting rainfall events, introducing a new loss function, and addressing rainfall forecast uncertainties. Focusing on the Iowa River Basin, we applied the stochastic storm transposition (SST) method to create realistic rainfall events, which were input into a hydrological model to generate corresponding streamflow data for training and testing deterministic and probabilistic ML models. Long short-term memory (LSTM) networks were employed to predict streamflow up to 12 h ahead. An active learning approach was used to identify the most informative rainfall events, reducing data generation effort. Additionally, we introduced a novel asymmetric peak loss function to improve peak streamflow prediction accuracy. Incorporating rainfall forecast uncertainties, our probabilistic LSTM model provided uncertainty quantification for streamflow predictions. Performance evaluation using different metrics improved the accuracy and reliability of our models. These contributions enhance flood forecasting and decision-making while significantly reducing computational time and costs. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article

This paper investigates an integrated sensing and communication (ISAC) system operating in a rainfall scenario, where a base station (BS) simultaneously serves multiple communication users and performs rainfall detection. Specifically, considering the fading characteristics of the millimeter-wave (mmWave) channel and the impact of rainfall on the signal propagation link, we adopt the Weibull distribution as the channel model between the nodes. Based on the above, the received signal-to-noise ratio (SNR), channel capacity, bit error rate (BER), and outage probability of the users within the system are analyzed to characterize the communication performance. Furthermore, the sensing capability of the BS is demonstrated through the analysis of the probability of rainfall. Simulation results reveal that increasing the distance between the BS and users significantly degrades their communication performance. Furthermore, the performance is highly sensitive to the rainfall intensity. Specifically, compared to storm conditions, light rain yields an improvement of 16.9 dB in the average user SNR, a 7.2 bps/Hz increase in channel capacity, and a 40.2% reduction in the outage probability. Additionally, an increase in the complex dielectric constant of raindrops substantially reduces the backscattering coefficient at the ISAC BS. Full article

Estimating design hydrographs in small and ungauged basins remains a significant challenge, primarily due to limited hydrometeorological data and the operational complexity of advanced modelling tools. This study presents an interactive digital twin platform to support hydrological modelling in such contexts. The aim of the proposed platform is to integrate three hydrological models—EBA4SUB (event-based rainfall–runoff model), COSMO4SUB (continuous rainfall–runoff model), and Virtual Rain (stochastic rainfall generator)—and automates key pre-processing tasks, including watershed delineation, Curve Number estimation, and rainfall input generation. Built on a three-tier architecture, the system comprises an interactive front end, a back-end database with spatial and meteorological data, and a suite of computational routines developed in Python and C#. The platform was deployed across the Lazio Region (Italy) for basins with contributing areas smaller than 400 km². Users can interactively select watersheds via a map-based interface, obtain preliminary hydrological characterizations, and export model-ready inputs and outputs. The proposed platform offers several advantages: it reduces model preparation time, facilitates access to advanced modelling tools, standardizes input data at the regional level, and ensures reproducible pre-processing workflows. By lowering the technical and time barriers of hydrological modelling, the digital twin provides an effective framework for bringing science-based tools closer to real-world practice. Full article

Climate change has intensified torrential rainfall and floods, causing frequent floodplain inundation with erosion and deposition. Large-scale waterfront facilities such as park golf courses are highly vulnerable, requiring systematic hydraulic safety evaluation. We simulated a recent flood in the Musim Stream using a two-dimensional FaSTMECH model to assess floodplain safety. The model showed excellent reproducibility (RMSE = 0.0176 m, NSE = 0.95 for depth; RMSE = 0.016 m/s, NSE = 0.87 for velocity). Flood risk indices—flood intensity (FI) and flood hazard rating (FHR)—and erosion–deposition indices—transient erosion and deposition index (TEDI) and steady erosion and deposition index (SEDI)—were applied. FI values were in the range of 0.3–6.4 (median 2.8) and FHR was in the range 0.7–10.2 (median 3.0), indicating that most floodplain areas exceeded the “high” to “extreme” risk range. TEDI was in the range of 0.004–4.15 (mean = 0.60), while SEDI was in the range of 0.001–5.59 (mean = 2.12). High TEDI values (0.6–0.9) occurred in curved and contracted sections, corresponding to observed erosion zones, whereas high SEDI values (0.8–1.0) were concentrated in the main channel. These results demonstrate that the indices effectively quantify and visualize floodplain risk, providing a practical basis for the design, placement, and maintenance of floodplain facilities. Full article

This paper, based on the CFD-DPM model coupled with sliding grid technology, constructs a simulation analysis method for the aerodynamic effects of propellers and wings under heavy rainfall. The mechanism of the influence of raindrops on the aerodynamic characteristics of this configuration is deeply analyzed, and the influence of the laws of different rainfall parameters is explored. The conclusion indicates that the local attack angle of the propeller decreases due to the influence of the falling speed of raindrops, resulting in a decrease in blade thrust and a maximum loss of 2.35%. The torque increases due to the increase in the rotational drag of the propeller. The maximum torque increment reaches 2.15%. With a decrease in the local angle of the attack and the effects of raindrop impact, film covering, and splashing, the maximum lift loss is 1.84%, and the drag increases by more than 12%. Raindrops will further influence the pitching, rolling, and yawing moment variation effect, combined with the rotation of the propeller. The greater the terminal velocity, diameter, and rainfall are, close to the surface of the propeller–wing combination configuration, the more severe the deterioration of the blade performance, and the stronger the lift reduction, drag increase, and moment variation effects of the wing. Full article

Under the dual pressures of global climate change and accelerated urbanization, the impacts of flood disasters on urban systems are becoming increasingly pronounced. Enhancing regional resilience has emerged as a critical factor in achieving sustainable urban development. Compared with existing methods such as CRITIC–Entropy, PCA–AHP, or SWMM-based resilience evaluations, grounded in urban resilience theory, this study takes Fangshan District in Beijing as empirical research to construct a post-flood disaster resilience evaluation index system spanning five dimensions (ecological, social, engineering, economic, and institutional) and leverages the integrated DEMATEL-TOPSIS-AISM model to synergistically identify key drivers, evaluate performance, and uncover internal hierarchies, thereby overcoming the limitations of existing research approaches. The findings indicate that the DEMATEL analysis identified the frequency of heavy rainfall (a12 = 0.889) and the proportion of flood disaster information databases (c51 = 1.153) as key driving factors. The TOPSIS assessment reveals that Fangshan District exhibits the strongest resilience in the economic dimension (Relative Closeness C = 0.21200), while the institutional dimension is the weakest (C = 0.00000), the AISM model constructs a hierarchical topology from a cause–effect priority perspective, elucidating the causal relationships and transmission mechanisms among factors across different dimensions. This study pioneers a novel perspective for urban resilience assessment, thereby establishing a theoretical foundation and practical references for enhancing flood resilience and advancing resilient city development. Full article

Ensuring the quality of hydrological data is critical for effective flood forecasting, water resource management, and disaster risk reduction, especially in regions vulnerable to typhoons and extreme weather. This study presents a framework for quality control and outlier detection in rainfall and water level time series data using both supervised and unsupervised machine learning algorithms. The proposed approach is capable of detecting outliers arising from sensor malfunctions, missing values, and extreme measurements that may otherwise compromise the reliability of hydrological datasets. Supervised learning using XGBoost was trained on labeled historical data to detect known outlier patterns, while the unsupervised Isolation Forest algorithm was employed to identify unknown or rare outliers without the need for prior labels. This established framework was evaluated using hydrological datasets collected from Lao PDR, one of the member countries of the Typhoon Committee. The results demonstrate that the adopted machine learning algorithms effectively detected real-world outliers, thereby enhancing real-time monitoring and supporting data-driven decision-making. The Isolation Forest model yielded 1.21 and 12 times more false positives and false negatives, respectively, than the XGBoost model, demonstrating that XGBoost achieved superior outlier detection performance when labeled data were available. The proposed framework is designed to assist member countries in shifting from manual, human-dependent processes to AI-enabled, data-driven hydrological data management. Full article

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions. Full article

Rainfall retention and runoff detention are the key hydrological processes that reduce runoff from green roofs. This study aims to quantify and model the hydrological response of nine combinations of growing substrates and drainage layers for extensive green roofs. Retention and detention capacities were evaluated using laboratory column experiments under two extreme initial moisture conditions—air-dried (D) and field capacity (W)—and three rainfall intensities (30, 60, and 100 mm h⁻¹). Regardless of the substrate–drainage combination, retention capacity, W_R, was significantly higher under dry conditions than under wet ones. Under wet conditions and rainfall intensity of 30 mm h⁻¹ (30 W tests), the mean W_R value (5.2 mm) was significantly lower than those recorded at higher intensities (14.3 and 14.2 mm, for 60 W and 100 W tests, respectively). Detention capacity, W_D, was less influenced by initial moisture and rainfall intensity, with mean values ranging from 7.4 to 10.9 mm. The distinct hydrological responses of green roof columns in the two antecedent moisture conditions were attributed to contrasting infiltration mechanisms: capillary flow dominated under dry conditions, while gravity-driven preferential flow prevailed under wet conditions. The application of a simple reservoir-routing model revealed that the AgriTerram (AT)—expanded perlite (EP) combination achieved the greatest reduction in total outflow volume and peak runoff. Under wet initial conditions, no single configuration clearly outperformed the others. This study highlights how the combined use of simulated rainfall experiments and a reservoir-routing model enables the identification of the most effective combination of substrate and drainage system to improve the hydrological performance of green roofs. Full article