Search Results (299)

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

Precipitation hardening is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. In particular, predicting yield strength (YS) based on microstructure is experimentally complex and costly because its key variables, such as precipitate radius, spacing, and volume fraction (VF), are difficult to measure. Physics-based models have emerged to tackle these complications utilising advancements in simulation environments. Nevertheless, pure physics-based models require numerous free parameters and ongoing debates over governing equations. Conversely, purely data-driven models struggle with insufficient datasets and physical interpretability. Moreover, the complex dynamics between internal model variables has led both approaches to adopt heuristic optimisation methods, such as the Powell or Nelder–Mead methods, which fail to exploit valuable gradient information. To overcome these issues, we propose a gradient-based optimisation for the Kampmann–Wagner Numerical (KWN) model, incorporating CALPHAD (CALculation of PHAse Diagrams) and a strength model. Our modifications include facilitating differentiability via smoothed approximations of conditional logic, optimising non-linear combinations of free parameters, and reducing computational complexity through a single size-class assumption. Model calibration is guided by a mean squared error (MSE) loss function that aligns the YS predictions with interpolated experimental data using L2 regularisation for penalising deviations from a purely physics-based modelling structure. A comparison shows that the gradient-based adaptive moment estimation (ADAM) outperforms the gradient-free Powell and Nelder–Mead methods by converging faster, requiring fewer evaluations, and yielding more physically plausible parameters, highlighting the importance of calibration techniques in the modelling of 6xxx series precipitation hardening. Full article

Arid mountainous regions are vulnerable to extreme hydrological events such as floods and droughts. Providing accurate and continuous rainfall records with no gaps is crucial for effective flood mitigation and water resource management in these and downstream areas. Satellite data and geospatial interpolation can be employed for this purpose and to provide continuous data series. However, it is essential to thoroughly assess these methods to avoid an increase in errors and uncertainties in the design of flood protection and water resource management systems. The current study focuses on the mountainous region in northern Oman, which covers approximately 50,000 square kilometers, accounting for 16% of Oman’s total area. The study utilizes data from 279 rain gauges spanning from 1975 to 2009, with varying annual data gaps. Due to the limited accuracy of satellite data in arid and mountainous regions, 51 geospatial interpolations were used to fill data gaps to yield maximum annual and total yearly precipitation data records. The root mean square error (RMSE) and correlation coefficient (R) were used to assess the most suitable geospatial interpolation technique. The selected geospatial interpolation technique was utilized to generate the spatial distribution of annual maxima and total yearly precipitation over the study area for the period from 1975 to 2009. Furthermore, gamma, normal, and extreme value families of probability density functions (PDFs) were evaluated to fit the rain gauge gap-filled datasets. Finally, maximum annual precipitation values for return periods of 2, 5, 10, 25, 50, and 100 years were generated for each rain gauge. The results show that the geostatistical interpolation techniques outperformed the deterministic interpolation techniques in generating the spatial distribution of maximum and total yearly records over the study area. Full article

As shallow mineral resources become increasingly depleted, the search for deep-seated orebodies has emerged as a crucial focus in modern gold exploration. This study investigates Vein 171 in the Linglong gold field, Jiaodong Peninsula, using 3D attribute modeling for deep mineralization prediction and precise orebody delineation. The research integrates surface and block models through Vulcan 2021.5 3D mining software to reconstruct the spatial morphology and internal attribute distribution of the orebody. Geostatistical methods were applied to identify and process high-grade anomalies, with grade interpolation conducted using the inverse distance weighting (IDW) method. The results reveal that Vein 171 is predominantly controlled by NE-trending extensional structures, and grade enrichment occurs in zones where fault dips transition from steep to gentle. The grade distribution of the 171₁ and 171_sub-1 orebodies demonstrates heterogeneity, with high-grade clusters exhibiting periodic and discrete distributions along the dip and plunge directions. Key enrichment zones were identified at elevations of –1800 m to –800 m near the bifurcation of the Zhaoping Fault, where stress concentration and rock fracturing have created complex fracture networks conducive to hydrothermal fluid migration and gold precipitation. Nine verification drillholes in key target areas revealed 21 new mineralized bodies, resulting in an estimated additional 2.308 t of gold resources and validating the predictive accuracy of the 3D model. This study not only provides a reliable framework for deep prospecting and mineral resource expansion in the Linglong Goldfield but also serves as a reference for exploration in similar structurally controlled gold deposits globally. Full article

The precise estimation of precipitation is key to understanding and mitigating the effects of extreme weather conditions, especially in areas susceptible to Mediterranean cyclones. This work assesses the performance of the integrated multi-satellite retrievals for GPM (IMERG) precipitation products during the extreme Mediterranean cyclone “Medicane Daniel” that affected the Thessaly Plain in Central Greece in early September 2023. Three IMERG versions (final run (FR), early run (ER), and late run (LR)) were inter-compared with gauge-based interpolated rainfall estimates using inverse distance weighting (IDW) and ordinary kriging techniques. Pixel-wise and categorical verification metrics, such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS), were calculated for rainfall thresholds between 50 mm and 400 mm. It was found that the IMERG final run agreed most with the ground observations, with a correlation coefficient (R) of 0.87, RMSE of 138.8 mm, and CSI up to 0.995 at the 100 mm threshold when the IDW interpolation was used. Kriging produced slightly better spatial accuracy overall, as indicated by a lower RMSE (14.5 mm) and higher correlation (R = 0.99). The results indicate the benefit of combining satellite precipitation data with ground-based observations through spatial interpolation for the enhanced monitoring of extreme weather events over complex terrain. Kriging is suggested when greater spatial reliability is needed, while IMERG-FR is found to be a reliable satellite product for quick response analysis during heavy precipitation events. The study emphasizes the importance of blending satellite precipitation estimates and ground observations via spatial interpolation methods, i.e., kriging and IDW, allowing for a more localized and precise validation of intense weather events. Full article

Land bonitation, or land rating, is a core instrument in agricultural policy used to evaluate land productivity based on environmental and climatic indicators. However, conventional Bonitation Coefficient (BC) methods are often rigid, require complete indicator sets, and lack mechanisms for handling missing or forecasted data—limiting their applicability under data scarcity and climate variability. This paper proposes a GIS-integrated, modular framework that couples classical BC computation with machine learning-based temporal forecasting and spatial generalization. Specifically, we apply deep learning models (LSTM, GRU, and CNN) to predict monthly precipitation—one of the 17 indicators in the Romanian BC formula—using over 61 years of data. The forecasts are spatially interpolated using Voronoi tessellation and then incorporated into the bonitation process via an adaptive logic that accommodates both complete and incomplete datasets. Results show that the ensemble forecast model outperforms individual predictors, achieving an $R^2$ of up to 0.648 and an RMSE of 18.8 mm, compared to LSTM ($R^2=0.59$), GRU ($R^2=0.61$), and CNN ($R^2=0.57$). While the case study focuses on precipitation, the framework is generalizable to other BC indicators and regions. This integration of forecasting, spatial generalization, and classical land evaluation addresses key limitations of existing bonitation methods and lays the groundwork for scalable, AI-enhanced land assessment systems. The forecasting module supports BC computation by supplying missing climate indicators, reinforcing that the primary aim remains adaptive land bonitation. Full article

Asphalt pavements are highly sensitive to climatic conditions, and their performance and longevity are significantly affected by temperature fluctuations, precipitation, and extreme weather events. With increasing climate variability, the development of refined and adaptive climate zoning systems for pavement engineering has become essential. This study reviews the evolution, methodologies, and applications of asphalt pavement climate zoning in China. First, it delineates the historical progression of climate zoning into three stages, from general natural zoning to the specialized three-indicator model and performance grade (PG) system, and finally to refined spatial processing based on meteorological data. Notably, 48% of provinces have conducted localized zoning studies, with South and Northeast China as key focus areas. Second, this study classifies existing zoning models into three major categories: the traditional three-indicator model (based on high temperature, low temperature, and precipitation), the hydrothermal coefficient model tailored to hot, humid climates, and clustering models incorporating spatial interpolation and multivariate analysis. While the three-indicator model remains the most widely applied due to its simplicity, it may result in coarse divisions in climatically diverse regions. The hydrothermal model offers general guidance but limited accuracy, whereas clustering methods provide high-resolution, adaptive zoning results at the cost of increased computational complexity. Third, the application of climate zoning results to the PG system for asphalt binder classification is analyzed. Although SHRP, LTPP, and C-SHRP formulas are commonly used, C-SHRP tends to overestimate pavement temperatures by 6.0–8.6 °C in China. Approximately 68.8% of studies rely on existing formulas, while 31.2% propose localized conversions to improve PG grading accuracy. Overall, this review identifies both the methodological diversity and key challenges in China’s climate zoning practices and provides a scientific foundation for more performance-oriented, climate-resilient pavement design strategies. Full article

To accurately estimate Zenith Tropospheric Delay (ZTD) for high-precision positioning of the Global Navigation Satellite System (GNSS), this study proposes a modeling method of ZTD based on the UNB3m model and Kriging spatial interpolation, in which the optimal spatial interpolation parameters are determined based on the errors corresponding to different combinations of the interpolation parameters, and the spatial distribution of the GNSS modeling stations is determined by the interpolation errors of the randomly selected GNSS stations for several times. To verify the accuracy and reliability of the proposed model, the ZTD estimates of 132,685 epochs with 1 h or 2 h temporal resolution for 28 years from 1997 to 2025 of the global network of continuously operating GNSS tracking stations are used as inputs; the ZTD results at any position and the corresponding observation moment can be obtained with the proposed model. The experimental results show that the model error is less than 30 mm in more than 85% of the observation epochs, the ZTD estimation results are less affected by the horizontal position and height of the GNSS stations than traditional models, and the ZTD interpolation error is improved by 10–40 mm compared to the GPT3 and UNB3m models at the four GNSS checking stations. Therefore, this technology can provide ZTD estimation results for single- and dual-frequency hybrid deformation monitoring, as well as dense ZTD data for Precipitable Water Vapor (PWV) inversion. Since the proposed method has the advantages of simple implementation, high accuracy, high reliability, and ease of promotion, it is expected to be fully applied in other high-precision positioning applications. 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

The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling. Full article

Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. To investigate the underlying factors contributing to these accidents, we developed a multifactorial dataset integrating 250,000 accident records from 2015 to 2023 with spatially interpolated weather data and sociodemographic indicators. We employed Kriging interpolation to convert point-based weather station data into continuous surfaces, enabling the attribution of location-specific weather conditions to each accident. Following comprehensive preprocessing and spatial analysis, we generated visualizations—including heatmaps and choropleth maps—that revealed distinct regional patterns at the county level. Our preliminary findings suggest that accident occurrence and severity are driven by different underlying factors: while temperature and vehicle counts strongly correlate with total accident numbers, humidity, precipitation, and road infrastructure quality show stronger associations with fatal outcomes. This integrated dataset provides a robust foundation for Bayesian and time-series modeling, supporting the development of evidence-based road safety strategies. Full article

Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids is challenging due to the uneven distribution of monitoring stations, data confidentiality restrictions, and the limitations of existing interpolation methods. In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. (CSPGRI) during the winter of 2023. Our method integrates static geographic information, dynamic meteorological factors, and ice_kriging values derived from parameter-optimized Empirical Bayesian Kriging Interpolation (EBKI) to create a spatiotemporally matched, multi-source fused icing thickness grid dataset. We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. The stacking model performed best, achieving an R-value of 0.634 (0.893), RMSE of 3.424 mm (2.834 mm), CSI of 0.514 (0.774), MAR of 0.309 (0.091), FAR of 0.332 (0.161), and fbias of 1.034 (1.084), respectively, when comparing predicted icing values with actual measurements on pylons. Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. Meteorological factors such as relative humidity (RH), 10 m wind speed (WS₁₀), 2 m temperature (T₂), and precipitation (PRE) demonstrated a range of positive and negative contributions consistent with the observed growth of icing. Thus, our multi-source remote-sensing data-fusion approach, combined with the stacking model, offers a highly accurate and interpretable solution for generating real-time icing grid fields. Full article

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article