Search Results (2,406)

The time and spatial variability of hail events limit the capability of diagnosing the occurrence and stones’ size in thunderstorms using weather radars. The bibliography presents multiple variables and methods with different pros and cons. The studied area, the Lleida Plain, is annually hit by different hailstorms, which have a high impact on the agricultural sector. A rectangular distributed hail pad network in this plain has worked operationally since 2000 to provide information regarding different aspects of hail impact. Since 2002, the Servei Meteorològic de Catalunya (SMC) has operated a single-pol C-band weather radar network that volumetrically covers the region of interest. During these years, the SMC staff has been working on improving the capability of detecting hail, adapting some parameters and searching for thresholds that help to identify the occurrence and size of the stones in thunderstorms. The current research analyzes a twenty-year period (2004–2023) to provide a good picture of the hailstorms occurring in the region of interest. The main research result is that VIL (Vertically Integrated Liquid) density is a better indicator for hailstone size than VIL, which presents more uncertainty in discriminating different hail categories. Full article

This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model’s effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology. Full article

This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised extreme value distribution ($GEV{D}_r$). Over the past couple of decades, the academic literature has transitioned from conventional statistical time series models to embracing EVT and machine learning algorithms for the modelling of environmental variables. This study adds value to the literature and knowledge of modelling wind speed using both EVT and machine learning. The primary aim of this study is to forecast wind speed in the Limpopo province of South Africa to showcase the dependability and potential of wind power generation. The application of CNN showcased considerable predictive accuracy compared to the Vanilla LSTM, achieving 88.66% accuracy with monthly time steps. The CNN predictions for the next five years, in m/s, were 9.91 (2024), 7.64 (2025), 7.81 (2026), 7.13 (2027), and 9.59 (2028), slightly outperforming the Vanilla LSTM, which predicted 9.43 (2024), 7.75 (2025), 7.85 (2026), 6.87 (2027), and 9.43 (2028). This highlights CNN’s superior ability to capture complex patterns in wind speed dynamics over time. Concurrently, the analysis of the $GEV{D}_r$ across various order statistics identified $GEV{D}_{r=2}$ as the optimal model, supported by its favourable evaluation metrics in terms of Akaike information criteria (AIC) and Bayesian information criteria (BIC). The 300-year return level for $GEV{D}_{r=2}$ was found to be 22.89 m/s, indicating a rare wind speed event. Seasonal wind speed analysis revealed distinct patterns, with winter emerging as the most efficient season for wind, featuring a median wind speed of 7.96 m/s. Future research could focus on enhancing prediction accuracy through hybrid algorithms and incorporating additional meteorological variables. To the best of our knowledge, this is the first study to successfully combine EVT and machine learning for short- and long-term wind speed forecasting, providing a novel framework for reliable wind energy planning. Full article

Climate change is a global phenomenon impacting ecosystems, economies, and livelihoods. This research carried out in Bol in the Lake Province of Chad, a region heavily affected by climate change, aims to analyze the gender-differentiated impacts of this phenomenon. It was carried out using the rapid analysis and participatory planning (RAPP) method and structural analysis for social systems (SAS2). Meteorological and socioeconomic data were collected through interviews, household surveys, and focus groups. The results indicate variability in rainfall, with a slight downward trend and an increase in temperature. The women identified an increase in the cost of living, human and material losses, warmer housing, and health problems as socioeconomic socioeconomic consequences of climate change. Their coping strategies include community self-help, humanitarian aid, and welfare activities. Obstacles to full participation in the search for solutions include access to education, low decision-making power, and political representation. This research enriches our understanding of the interactions between gender, climate change, adaptation, and inclusive policy importance. Full article

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change. Full article

The application of intelligent process-based crop model parameter optimization algorithms can effectively improve both the model simulation accuracy and applicability. Based on measured values of soil N₂O emission flux in wheat fields from 2020 to 2022, and meteorological data from 1971 to 2022, five parameters of the N₂O emission flux module in the APSIM model were optimized using the variable step Fruit Fly algorithm (VSS-FOA). The optimized parameters were the soil nitrification potential, the range of concentrated KNH₄ of ammonia and nitrogen at semi-maximum utilization efficiency, the proportion of nitrogen loss to N₂O during the nitrification process, the denitrification coefficient, and the Power term P for calculating the denitrification water coefficient. Contrasting the optimized parameters using the VSS-FOA algorithm versus the default values supplied with the model substantially improved the goodness-of-fit to field measurements with the overall R² increasing from 0.41 to 0.74, and a decrease in NRMSE from 17.1% to 11.4%. This work demonstrates that the VSS-FOA algorithm affords a straightforward mechanism for the optimization of parameters in models such as APSIM to enhance the accuracy of model N₂O emission flux estimates. Full article

Nightly images offer a special data source for monitoring fishing activities. This study used images from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) to analyze the early development of lit fisheries in the open South China Sea (SCS), which mainly occurred around the Zhong Sha and Xi Sha Islands. Based on peak detection and a fixed threshold, lit fishing positions were extracted well from filtered, high-quality DMSP/OLS images. The results indicated that fisheries experienced an apparent rise and fall from 2005 to 2012, with the numbers of lit fishing boats rising to a maximum of ~60 from 2005 to 2008, almost disappearing in 2009, peaking at ~130 from 2010 to 2011, and starting to decline in 2012. The fish price of major fishing targets declined by ~60% in 2009, which obviously impacted the year’s fishing operations. The reason for declined fishing operations in 2012 was that most of the lit fishing operations shifted farther south to fishing grounds around the Nan Sha Islands. We also explored factors shaping the distribution patterns of lit fisheries by using MaxEnt models to relate fishing positions to environmental variables. Major environmental factors influencing the distribution of lit fishing boats varied with years, of which water depth was the most important factor across years, with an optimal depth range of 1000–2000 m. In addition to depth, the distribution of lit fisheries was also influenced by SST, especially for the years 2005–2008, and a suitable SST was found between 26 and 28 °C. This study fills the knowledge gaps of the inception of lit fisheries and their dynamic changes in the SCS. Full article

This article examines the impact of climate change on the hydrometeorological indicators of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Two meteorological variables’ annual and seasonal trends at three weather stations in 1986–2023 were analyzed. The non-parametric Mann–Kendall and Sen’s slope methods were used to determine the presence of a positive or negative trend in weather data and their statistical significance. Hydrometric indicators were studied using the ArcGIS 10.8 program from 1995 to 2023. The results indicate an increasing average spring air temperature, with an annual rise of 0.08–0.09 °C. A significant trend in increasing average annual precipitation was observed in Saumalkol, with a rise of 4.7 mm per year. In contrast, no significant trends were found in the annual and seasonal precipitation data for Sergeyevka. It was also found that the area of Lake Saumalkol increased by 1.6% due to a rise in annual precipitation. In contrast, the area of Lake Kopa decreased by 6.04% because of an increase in the annual average temperature. Full article

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. Full article

The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an appropriate distribution function significantly affects the actual wind data, directly influencing the estimated energy output. While the Weibull function is commonly used to describe wind speed at various locations worldwide, the variability of weather information across wind sites varies significantly. Probabilistic forecasting offers comprehensive probability information for renewable generation and load, assisting decision-making in power systems under uncertainty. Traditional probabilistic forecasting techniques based on machine learning (ML) rely on prediction uncertainty derived from previous distributional assumptions. This study utilized a Bayesian Recurrent Neural Network (BNN-RNN), incorporating prior distributions for weight variables in the RNN network layer and extending the Bayesian networks. Initially, a periodic RNN processes data for wind energy prediction, capturing trends and correlation characteristics in time-series data to enable more accurate and reliable energy production forecasts. Subsequently, the wind power meteorological dataset was analyzed using the reciprocal entropy approach to reduce dimensionality and eliminate variables with weak connections, thereby simplifying the structure of the prediction model. The BNN-RNN prediction model integrates inputs from RNN-transformed time-series data, dimensionality-reduced weather information, and time categorization feature data. The Winkler index is lower by 3.4%, 32.6%, and 7.2%, respectively, and the overall index of probability forecasting pinball loss is reduced by 51.2%, 22.3%, and 10.7%, respectively, compared with all three approaches. The implications of this study are significant, as they demonstrate the potential for more accurate wind energy forecasting through Bayesian optimization. These findings contribute to more precise decision-making and bring sustainability to the effective management of energy systems by proposing a Bayesian Recurrent Neural Network (BNN-RNN) to improve wind energy forecasts. The model further enhances future estimates of wind energy generation, considering the stochastic nature of meteorological data. The study is crucial in increasing the understanding and application of machine learning by establishing how Bayesian optimization significantly improves probabilistic forecasting models that would revolutionize sustainable energy management. Full article

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. Full article

The Arabian Peninsula (AP) is notable for its unique meteorological and climatic patterns and plays a pivotal role in understanding regional climate dynamics and dust emissions. The scarcity of ground-based observations makes atmospheric data essential, rendering reanalysis and satellite products invaluable for understanding weather patterns and climate variability. However, the accuracy of these products in the AP’s desert environment has not been extensively evaluated. This study undertakes the first comprehensive validation of reanalysis products—the European Centre for Medium-Range Weather Forecasts’ European Reanalysis version 5 (ERA5) and ERA5 Land (ERA5L), along with Clouds and Earth’s Radiant Energy System (CERES) radiation fluxes—against measurements from the Liwa desert in the UAE. The data, collected during the Wind-blown Sand Experiment (WISE)–UAE field experiment from July 2022 to December 2023, includes air temperature and relative humidity at 2 m, 10 m wind speed, surface pressure, skin temperature, and net radiation fluxes. Our analysis reveals a strong agreement between ERA5/ERA5L and the observed diurnal T2m cycle, despite a warm night bias and cold day bias with a magnitude within 2 K. The wind speed analysis uncovered a bimodal distribution attributed to sea-breeze circulation and the nocturnal low-level jet, with the reanalysis overestimating the nighttime wind speeds by 2 m s⁻¹. This is linked to biases in nighttime temperatures arising from an inaccurate representation of nocturnal boundary layer processes. The daytime cold bias contrasts with the excessive net radiation flux at the surface by about 50–100 W m⁻², underscoring the challenges in the physical representation of land–atmosphere interactions. Full article

Accurate water demand forecasting is crucial for the effective operation and management of water distribution networks. Predicting future water demand empowers utilities to optimally operate system components. Various data-driven methodologies have been proposed for water demand forecasting, including artificial neural networks and econometric models. Recently, Long Short-Term Memory (LSTM) was shown to be particularly relevant for this application. Nevertheless, few studies have utilized multivariate-LSTM (M-LSTM) models for water demand forecasting. This study introduces an M-LSTM model incorporating historical water demands, meteorological data, and social variables to forecast short-term water demand. The proposed M-LSTM model performance was tested by applying it to the ten district metered areas (DMAs) case study of the Battle of Water Demand Forecasting (BWDF). The results demonstrated the model’s ability to accurately predict the hourly water demand one week in advance. The mean absolute error of the predictions ranged between 0.5 and 2.2 l/s (2.8% to 12.9% of the average demand). The results also showed a strong correlation between the prediction error and the variability of the water demand data. Full article

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced. Full article

Background: Diarrheal disease remains a significant public health issue, particularly affecting young children and older adults. Despite efforts to control and prevent these diseases, their incidence continues to be a global concern. Understanding the trends in diarrhea incidence and the factors influencing these trends is crucial for developing effective public health strategies. Objective: This study aimed to explore the temporal trends in diarrhea incidence and associated factors from 1990 to 2019 and to project the incidence for the period 2020–2040 at global, regional, and national levels. We aimed to identify key factors influencing these trends to inform future prevention and control strategies. Methods: The eXtreme Gradient Boosting (XGBoost) model was used to predict the incidence from 2020 to 2040 based on demographic, meteorological, water sanitation, and sanitation and hygiene indicators. SHapley Additive exPlanations (SHAP) value was performed to explain the impact of variables in the model on the incidence. Estimated annual percentage change (EAPC) was calculated to assess the temporal trends of age-standardized incidence rates (ASIRs) from 1990 to 2019 and from 2020 to 2040. Results: Globally, both incident cases and ASIRs of diarrhea increased between 2010 and 2019. The incident cases are expected to rise from 2020 to 2040, while the ASIRs and incidence rates are predicted to slightly decrease. During the observed (1990–2019) and predicted (2020–2040) periods, adults aged 60 years and above exhibited an upward trend in incidence rate as age increased, while children aged < 5 years consistently had the highest incident cases. The SHAP framework was applied to explain the model predictions. We identified several risk factors associated with an increased incidence of diarrhea, including age over 60 years, yearly precipitation exceeding 3000 mm, temperature above 20 °C for both maximum and minimum values, and vapor pressure deficit over 1500 Pa. A decreased incidence rate was associated with relative humidity over 60%, wind speed over 4 m/s, and populations with above 80% using safely managed drinking water services and over 40% using safely managed sanitation services. Conclusions: Diarrheal diseases are still serious public health concerns, with predicted increases in the incident cases despite decreasing ASIRs globally. Children aged < 5 years remain highly susceptible to diarrheal diseases, yet the incidence rate in the older adults aged 60 plus years still warrants additional attention. Additionally, more targeted efforts to improve access to safe drinking water and sanitation services are crucial for reducing the incidence of diarrheal diseases globally. Full article