Search Results (3,511)

Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

Hydrological modeling is critical for assessing water availability and guiding sustainable resource management, particularly in monsoon-dependent, data-scarce basins such as the Chalakudy River Basin (CRB) in Kerala, India. This study integrated the Soil Conservation Service Curve Number (SCS-CN) method within the Google Earth Engine (GEE) platform, making novel use of multi-source, open access datasets (CHIRPS precipitation, MODIS land cover and evapotranspiration, and OpenLand soil data) to estimate spatially distributed long-term runoff (2001–2023). Model calibration against observed runoff showed strong performance (NSE = 0.86, KGE = 0.81, R² = 0.83, RMSE = 29.37 mm and ME = 13.48 mm), validating the approach. Over 75% of annual runoff occurs during the southwest monsoon (June–September), with July alone contributing 220.7 mm. Seasonal assessments highlighted monsoonal excesses and dry-season deficits, while water balance correlated strongly with rainfall (r = 0.93) and runoff (r = 0.94) but negatively with evapotranspiration (r = –0.87). Time-series analysis indicated a slight rise in rainfall, a decline in evapotranspiration, and a marginal improvement in water balance, implying gradual enhancement of regional water availability. Spatial analysis revealed a west–east gradient in precipitation, evapotranspiration, and water balance, producing surpluses in lowlands and deficits in highlands. These findings underscore the potential of cloud-based hydrological modeling to capture spatiotemporal dynamics of hydrological variables and support climate-resilient water management in monsoon-driven and data-scarce river basins. Full article

The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series forecasting models for rainfall prediction in Barranquilla, Colombia. To this end, five models were applied, namely, Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (ES), and multiplicative and additive Holt–Winters models, using 139 monthly precipitation records from the IDEAM database covering the period 2013–2025. Model accuracy was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE), and nonlinear optimization techniques were applied to estimate smoothing and weighting parameters for improved accuracy. The results showed that optimization significantly enhances model performance, particularly in the multiplicative Holt–Winters model, which achieved the lowest errors, with a minimum MAE of 75.33 mm and an MSE of 9647.07. The comparative analysis with previous studies demonstrated that even simple models can yield substantial improvements when properly optimized. Furthermore, forecasts optimized using MAE were more stable and consistent, whereas those optimized with MSE were more sensitive to extreme variations. Overall, the findings confirm that seasonal models with optimized parameters offer superior predictive capacity, making them valuable tools for hydrological risk management. Full article

Hypophosphatemia, defined as serum phosphate levels below 2.5 mg/dL, is a common yet underrecognized electrolyte disturbance in critically ill patients, with prevalence estimates reaching up to 80%. This review explores the intricate bidirectional relationship between hypophosphatemia and hyperventilation, emphasizing its profound implications for respiratory function and critical care management. Hypophosphatemia impairs oxygen delivery by depleting 2,3-diphosphoglycerate (2,3-DPG), disrupts central respiratory drive, and weakens respiratory muscles, leading to hyperventilation, ventilatory failure, and prolonged mechanical ventilation. Conversely, hyperventilation exacerbates hypophosphatemia through respiratory alkalosis, triggering intracellular phosphate shifts and metabolic cascades that rapidly deplete serum levels. This cycle creates significant challenges for ventilator weaning and increases morbidity and mortality. Underlying mechanisms include impaired ATP synthesis, altered chemoreceptor sensitivity, and systemic inflammatory responses. Hypophosphatemia-induced hyperventilation manifests as unexplained tachypnea and respiratory alkalosis, often misdiagnosed as anxiety or pain, while hyperventilation-induced hypophosphatemia contributes to diaphragmatic dysfunction and poor ventilatory performance. Common precipitating factors include refeeding syndrome, diabetic ketoacidosis, continuous renal replacement therapy, and malnutrition. Complications extend beyond respiratory dysfunction to include cardiac depression, immune dysfunction, prolonged ICU stays, and increased healthcare costs. Current diagnostic approaches rely on serum phosphate measurements, which poorly reflect total body stores due to significant intracellular shifts. Emerging biomarkers such as fibroblast growth factor 23 (FGF23) and advanced monitoring technologies, including continuous phosphate tracking, may enhance recognition. Treatment strategies emphasize targeted phosphate repletion based on severity, with intravenous supplementation and ventilatory support tailored to minimize complications. Preventive measures, including risk stratification, prophylactic supplementation, and ventilator management, are critical for high-risk populations. Despite advances, knowledge gaps persist in optimizing monitoring and repletion protocols, understanding genetic variations, and identifying ideal phosphate targets for improved respiratory outcomes. This review provides a comprehensive framework for recognizing and managing hypophosphatemia’s impact on respiratory dysfunction in critically ill patients. Adopting evidence-based interventions and leveraging emerging technologies can significantly improve clinical outcomes, reduce ICU complications, and enhance recovery in this vulnerable population. Full article

Microplastics released from synthetic garments pose a complex challenge to society and the environment. Textiles contribute to microplastic pollution throughout their entire lifecycle—from design and production to washing and use to their disposal—and can enter the environment through wastewater, soil, and air. The detachment of fibre fragments and their fate in the environment has received attention in the recent literature but lacks a harmonised research methodology and a holistic approach to the topic. This work presents a model to estimate the flows of microplastic fibres and synthetic garments in geographical Europe, expressed in tonnes per year. It was developed through a search of the literature to provide an estimate of synthetic fibres entering the environment and to identify the connections between the stakeholders involved. A first-level multicriteria decision analysis was conducted to recognise relevant pollution flows: the study revealed significant but poorly understood pathways, such as the flow of microplastics in the indoor and outdoor air during garment wear. Also, the flow of microplastics from the combined sewer overflow of untreated water during heavy precipitation and the flow to the agricultural land from the application of sewage sludge result in relevant pathways to water and soil, respectively. By fostering collaboration across multiple actors, the transition toward sustainable textile practices can significantly reduce fibre pollution. Full article

This study develops an integrated drought assessment framework based on trivariate copula modeling to simultaneously evaluate three key drought characteristics: duration, severity, and peak intensity. Meteorological data from stations across 23 meteorological stations in Northeastern Thailand, covering the period of 2007–2025, were analyzed. The Standardized Precipitation–Evapotranspiration Index (SPEI) was employed to characterize multidimensional drought conditions. The trivariate copula approach provides a flexible and robust statistical framework, enabling the separation of marginal distributions from dependence structures, capturing nonlinear and tail dependencies more effectively than traditional methods. Results demonstrate that this modeling framework significantly improves the accuracy of drought risk estimation and enables the calculation of joint return periods for extreme drought events. These findings offer valuable insights with respect to designing adaptive water resource management strategies, enhancing agricultural resilience, and strengthening early warning systems under future climate variability. Full article

To enhance the operational efficiency of the CMA-BJ3.0 regional numerical model and address the issue of short-term precipitation overforecasting caused by assimilating estimated saturated water vapor, this study investigates the assimilation of radar reflectivity mosaic data by optimizing the configuration of retrieved water vapor in the indirect assimilation scheme. A 1D (one-dimensional) Bayesian method was employed to retrieve and constrain water vapor from reflectivity observations, generating retrieved water vapor for assimilation to mitigate overforecasting biases. A case study of precipitation on 1 August 2022 was analyzed, with particular focus on comparing the innovation vector statistics, spatial patterns of analysis increments, and physical mechanisms underlying forecast differences across multiple data assimilation configurations. Results showed that an observation-background (O-B) statistical distribution closer to a Gaussian unbiased state indicated a better balance between observations and the background field. The optimized scheme corrected systematic positive biases in water vapor, curbed excessive increments, and effectively resolved the overforecasting issue by refining the initial water vapor field. Batch experiments quantitatively demonstrated that assimilating 1D Bayesian-retrieved water vapor significantly improved precipitation forecast scores, particularly for higher magnitudes (≥25.0 mm/3 h), and reduced the over-forecast within the first 6 h. While the study focused on improving short-term precipitation accuracy without considering hydrometeor impacts or convective dynamics, the 1D Bayesian method, despite its background-dependency, proved effective in correcting water vapor biases, making it a promising assimilation scheme. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

Extreme precipitation events have become a central focus of the scientific community due to their increased occurrence in recent years. This study aims to analyze the variability and trends in aspects associated with the rainy seasons in the South American Monsoon System (SAMS) area from 1979 to 2022. The dates for the onset and demise of the rainy season (ONR and DER, respectively) were determined using antisymmetric outgoing longwave radiation (OLR) data relative to the equator (AOLR) for the clustered regions defined in a previous work. Based on these dates, the duration of the rainy seasons and the total precipitation for each rainy season were also calculated. The main advantage of this study is the analysis of trends within homogeneous regions derived from cluster analysis, which enables a more reliable assessment of precipitation patterns across the spatially heterogeneous SAMS domain. The non-parametric Mann–Kendall test and Sen’s slope estimator were applied to the ONR, DER, rainy season length, and total precipitation time series for each group over the 1979–2022 period. Quartile analysis was performed on the total precipitation time series to identify the most and least intense rainy seasons in the SAMS’s regions. These analyses revealed a trend of shortening of the SAMS rainy season over the 44 years of analysis, with a positive trend in the ONR dates and a negative trend in the DER dates, which is further confirmed by the decreasing trends in rainy season length and accumulated precipitation in most analyzed regions. The most (above the third quartile) and least (below the first quartile) intense rainy seasons were found to be concentrated at the beginning and end of the study period, respectively, for all monsoon regions. After removing the linear trend, the distribution of events appeared more uniform over time, yet the major droughts that occurred after 2010 remained clear. The results of this study contribute to a better understanding of the precipitation characteristics in the SAMS area, and these findings may assist climate forecasting and monitoring centers in improving regional precipitation assessments. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article

Largescale vegetation reconstruction projects in the western and northern parts of China, along with climate change and increased humidity, have significantly boosted fractional vegetation cover (FVC) in the Mu Us Desert. However, this increase may impact the area’s vulnerability to drought stress. Here, we assessed the area’s susceptibility to hydrometeorological drought by analyzing the maximum correlation coefficients (MCC) derived from the spatiotemporal relationships between FVC and estimates of standardized precipitation evapotranspiration index (SPEI) for the area. The results of the study were as follows: (1) FVC exhibited an increasing trend throughout the growing seasons from 2003 to 2022. Although the region experienced an overall wetting trend, drought events still occurred in some years. MCC-values were predominantly positive across all timescales, suggesting that vegetation generally responded favorably to drought conditions. (2) The order of response of land covertype to drought, from greatest to lowest, was grassland, cultivated land, forestland, and sand land. Cultivated land and grassland exhibited heightened sensitivity to short-term drought; forestland and sand land showed greater sensitivity to long-term drought. (3) With a high FVC, the response of grassland and sand land to drought was significantly enhanced, whereas the response of cultivated land and forestland was less noticeable. (4) Low FVC grassland and sand land have not yet reached the VCCSW threshold and can support moderate vegetation restoration. In contrast, forestland and cultivated land exhibit drought sensitivity regardless of FVC levels, indicating that increasing vegetation should be approached with caution. This research offers a method to evaluate the impact of drought stress on ecosystem stability, with findings applicable to planning and managing vegetation cover in arid and semiarid regions globally. Full article

The Athabasca River Basin (ARB) is the location of the Canadian oil sands industry and 70.8% of global estimated bitumen deposits. The Athabasca River is the water source for highly water-intensive bitumen processing. Our objective is to project ARB temperature, precipitation, total runoff, climate moisture index (CMI), and standardized precipitation evapotranspiration index (SPEI) for 2011–2100 using the superior modelling skill of seven regional climate models (RCMs) from Coordinated Regional Climate Downscaling Experiment (CORDEX). These projections show an average 6 °C annual temperature increase for 2071–2100 under RCP 8.5 relative to 1971–2000. Resulting increases in evapotranspiration may be partially offset by an average 0.3 mm/day annual precipitation increase. The projected precipitation increases are in the winter, spring, and autumn, with declines in summer. CORDEX RCMs project a slight increase (0.04 mm/day) in annual averaged runoff, with a shift to an earlier springtime melt pulse. However, these are countered by projected declines in summer and early autumn runoff. There will be significant decreases in annual and summertime CMI and annual SPEI. We conclude that there will be increasingly stressed ARB water availability, particularly in summer, doubtless resulting in repercussions on ARB industrial activities with their extensive water allocations and withdrawals. Full article