Search Results (1,156)

With rapid socio-economic development, excessive anthropogenic consumption and the exploitation of natural resources have impaired the self-healing, supply, and carrying capacities of ecosystems. The assessment and prediction of ecosystem service values (ESVs) are crucial for the coordinated development of ecology and economy. This research examines the Xiangjiang River Basin and combines land use data from 1995 to 2020, Landsat images, meteorological data, and socio-economic data. These data are incorporated into the PLUS model to simulate land use patterns in 2035 under the following five scenarios: natural development, economic development, farmland protection, ecological protection, and coordinated development. Additionally, this research analyzes the dynamics of land use and changes in ESVs in the Xiangjiang River Basin. The results show that between 1995 and 2020 in the Xiangjiang River Basin, urbanization accelerated, human activities intensified, and the construction land area expanded significantly, while the areas of forest, farmland, and grassland decreased continuously. Based on multi-scenario simulations, the ESV showed the largest and smallest declines under economic development and ecological protection scenarios, respectively. This results from the economic development scenario inducing a rapid expansion in construction land. In contrast, construction land expansion was restricted under the ecological protection scenario, because the ecological functions of forests and water bodies were prioritized. This research proposes land use strategies to coordinate ecological protection and economic development to provide a basis for sustainable development in the Xiangjiang River Basin and constructing a national ecological security barrier, as well as offer Chinese experience and local cases for global ecological environment governance. Full article

A reliable precipitation dataset with high spatial resolution is essential for climate research in the Tarim Basin. This study evaluated the performances of four models, namely a random forest (RF), a long short-term memory network (LSTM), a support vector machine (SVM), and a feedforward neural network (FNN). FNN, which was found to be superior to the other models, was used to integrate eight precipitation datasets spanning from 1990 to 2022 across the Tarim Basin, resulting in a new monthly high-resolution (0.1°) precipitation dataset named MoHiPr-TB. This dataset was subsequently bias-corrected by the China Land Data Assimilation System version 2.0 (CLDAS2.0). Validation results indicate that the corrected MoHiPr-TB not only accurately reflects the spatial distribution of precipitation but also effectively simulates its intensity and interannual and seasonal variations. Moreover, MoHiPr-TB is capable of detecting the precipitation–elevation relationship in the Pamir Plateau, where precipitation initially increases and then decreases with elevation, as well as the synchronous variation of precipitation and elevation in the Tianshan region. Collectively, this study delivers a high-accuracy precipitation dataset for the Tarim Basin, which is anticipated to have extensive applications in meteorological, hydrological, and ecological research. Full article

This study investigates the spatiotemporal evolution of oil released during the Agia Zoni II shipwreck in the Saronic Gulf in 2017, employing the OpenOil module of the OpenDrift framework. The simulation integrates oceanographic and meteorological data to model the transport, weathering, and fate of spilled oil over a six-day period. Oil behavior is examined across key transformation processes, including dispersion, emulsification, evaporation, and biodegradation, using particle-based modeling and a comprehensive set of environmental inputs. The modeled results are validated against in situ observations and visual inspection data, focusing on four critical dates. The study demonstrates OpenOil’s potential for accurately simulating oil dispersion dynamics in semi-enclosed marine environments and highlights the significance of environmental forcing, vertical mixing, and shoreline interactions in determining oil fate. It concludes with recommendations for improving real-time response strategies in similar spill scenarios. Full article

With the wide application of airdrop technology in rescue activities in civil and aerospace fields, the importance of accurate airdrop is increasing. This work comprehensively analyzes the interactive mechanisms among multiple models affecting airdrops, including wind field distribution, drag force effect, and the parachute opening process. By integrating key parameters across various dimensions of these models, a multidimensional parameter dynamic evolution (MPDE) target prediction method for aerial delivery parachutes in radar-detected wind fields is proposed, and the Runge–Kutta method is applied to dynamically solve for the final landing point of the target. In order to verify the performance of the method, this work carries out field airdrop experiments based on the radar-measured meteorological data. To evaluate the impact of model input errors on prediction methods, this work analyzes the influence mechanism of the wind field detection error on the airdrop prediction method via the Relative Gain Array (RGA) and verifies the analytical results using the numerical simulation method. The experimental results indicate that the optimized MPDE method exhibits higher accuracy than the widely used linear airdrop target prediction method, with the accuracy improved by 52.03%. Additionally, under wind field detection errors, the linear prediction method demonstrates stronger robustness. The airdrop error shows a trigonometric relationship with the angle between the synthetic wind direction and the heading, and the phase of the function will shift according to the difference in errors. The sensitivity of the MPDE method to wind field errors is positively correlated with the size of its object parachute area. Full article

Soil moisture (SM) is a key variable in agricultural ecosystems and is crucial for drought prevention and control management. However, SM is influenced by underlying surface and meteorological conditions, and it changes rapidly in time and space. To capture the changes in SM and improve the accuracy of short-term and medium-to-long-term predictions on a daily scale, an LSTMseq2seq model driven by both observational data and mechanism models was constructed. This framework combines historical meteorological elements and SM, as well as the SM change characteristics output by the VIC model, to predict SM over a 90-day period. The model was validated using SMAP SM. The proposed model can accurately predict the spatiotemporal variations in SM in Jiangxi Province. Compared with classical machine learning (ML) models, traditional LSTM models, and advanced transformer models, the LSTMseq2seq model achieved R² values of 0.949, 0.9322, 0.8839, 0.8042, and 0.7451 for the prediction of surface SM over 3 days, 7 days, 30 days, 60 days, and 90 days, respectively. The mean absolute error (MAE) ranged from 0.0118 m³/m³ to 0.0285 m³/m³. This study also analyzed the contributions of meteorological features and simulated future SM state changes to SM prediction from two perspectives: time importance and feature importance. The results indicated that meteorological and SM changes within a certain time range prior to the prediction have an impact on SM prediction. The dual-driven LSTMseq2seq model has unique advantages in predicting SM and is conducive to the integration of physical mechanism models with data-driven models for handling input features of different lengths, providing support for daily-scale SM time series prediction and drought dynamics prediction. Full article

Thermal simulations of buildings play a critical role in optimizing energy efficiency, thermal comfort, and heating, ventilation and air conditioning (HVAC) systems design. Accurate weather data is essential for reliable simulations, as local weather and climate have a significant impact on energy requirements for space heating and cooling and thermal comfort. This study conducted a literature review regarding the sources, types, and uncertainties of weather data used for thermal simulations of buildings, including typical meteorological years (TMYs) and extreme weather files under current and future climates. Additionally, this paper evaluates methods for weather data processing, including interpolation, downscaling, and synthetic generation, to improve simulation accuracy. Finally, approaches are proposed for constructing weather files for the future and extreme conditions under a changing climate. This review aims to provide a guide for researchers and practitioners to enhance the reliability of thermal modeling through informed construction, selection, and application of weather data. Full article

Under the influence of global climate change, water conservancy projects located in the high-latitude cold regions of the world are facing severe challenges. This study addresses the contradiction between water supply stability and ecological flow during the dry season in cold regions. Taking Linhai Reservoir as the core, it integrates the HEC-RAS hydrodynamic model with multi-source data such as basin topography, hydro-meteorological data, and water conservancy project parameters to construct a multi-scenario water supply scheduling model during the dry season. The aim is to provide scientific recommendations for different reservoir operation strategies in response to varying frequencies of upstream inflow, based on simulations conducted after the reservoir’s completion. Taking into account winter runoff reduction characteristics and engineering parameters, we simulated the relationships between water level and flow, ecological flow requirements, and urban water shortages. The results indicate that in both flood and normal years, dynamic coordination of storage and discharge can achieve a daily water supply of 120,000 cubic meters, with 100% compliance for the ecological flow rate. For mild and moderate drought years, additional water diversion becomes necessary to achieve 93.5% and 89% supply reliability, respectively. During severe and extreme droughts, significantly reduced reservoir inflows lower ecological compliance rates, necessitating emergency measures, such as utilizing dead storage capacity and exploring alternative water sources. The study proposes operational strategies tailored to different drought intensities: initiating storage adjustments in September for mild droughts and implementing peak-shifting measures by mid-October for extreme droughts. These approaches enhance storage efficiency and mitigate ice blockage risks. This research supports the water supply security and river ecological health of urban and rural areas in Mudanjiang City and Hailin City and provides a certain scientific reference basis for the multi-objective coordinated operation of reservoirs in the same type of high-latitude cold regions. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Coastal regions are increasingly threatened by compound flooding due to the increasing intensities of storm surges and rainfall under climate change. However, relevant research has been limited because significant amounts of data, scenarios, and computations are often required to evaluate long-term variations in compound flood risk. In this study, a framework was proposed through efficient hydraulic simulations and a consequence-based statistical method using data projected under different general circulation models (GCMs). The analysis focuses on analyzing the interdecadal trends of compound flood risk for a coastal area in southwestern Taiwan across a baseline period and four future periods in the short-term (2021–2040), mid-term (2041–2060), mid-to-long-term (2061–2080), and long-term (2081–2100). Although discrepancies exist in the short term, the results show that the values of the annual maximum flood area exhibit an increasing pattern in the future for all GCMs by increasing about 27.8% on average at the end of the 21st century. This means that, under the same flood areas given in the baseline period, the return periods will decrease, and flood events will occur more frequently in the future. This framework can be extended to other regions to assess the impacts of compound flooding with different geographical and meteorological conditions. 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

With the availability of more ground-based remote-sensing meteorological equipment at Hong Kong International Airport, many more interesting features of terrain-disrupted airflow have been observed, such as the applications of short-range Doppler LIDAR. This paper documents a number of new features observed at the airport area, such as the hydraulic jump-like feature, vortex, and extensive mountain wake/reverse flow. The technical feasibility of using a numerical resolution weather prediction model to simulate such features is also explored. It is found that the presently available input data and numerical model may not be able to capture the fine features of the atmospheric boundary layer, and thus they are not very successful in reproducing many small-scale terrain-disrupted airflow features downstream of an isolated hill. On the other hand, more larger-scale terrain-disrupted flow features may be better captured, but there are still limitations with the available turbulence parameterization schemes. This paper aims at documenting the newly observed flow features at the Hong Kong International Airport, enhancing the understanding of low-level windshear, and evaluating the outputs of numerical resolution simulations for reproducing such observed features and its technical feasibility on short-term forecasting. Full article

This study was realized in the frame of an IAEA Coordinated Research Project for the evaluation of sediment dynamics, applying in-situ radiometric methods accompanied with a theoretical model. The in-situ methods were validated using lab-based high-resolution gamma-ray spectrometry. Sediment dynamics assessments were performed based on the measured and mapped activity concentrations of specific ²³⁸U progenies (²¹⁴Bi or ²¹⁴Pb), ²³²Th progenies (²⁰⁸Tl and ²²⁸Ac), and ⁴⁰K along the shoreline of the beach. The maps of the activity concentrations of natural radionuclides were produced rapidly using software tools (R language v4.5). The sediment dynamics of the studied area were also investigated through numerical simulations, applying an open source model considering land–sea interactions and meteorological conditions and the corresponding sediment processes. The assessments, which were conducted utilizing the detailed data from the natural radioactivity maps, were validated by the simulation results, since both were found to be in agreement. Generally, it was confirmed that the distribution of radionuclides reflects the selective transport processes of sediments, which are related to the corresponding processes that occur in the study area. Legrena Beach in Attica, Greece, served as a pilot area for the comparative analysis of methods and demonstration of their relevance and applicability for studying coastal processes. Full article

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. Full article

This study explores the optimization of solar energy harvesting in Truro City in the UK using PVSyst simulations integrated with real-time meteorological data. Focusing on panel orientation, tilt angle, shading, and albedo, the research aimed to enhance both energy efficiency and economic viability of photovoltaic (PV) systems in green buildings. A 100 kWp rooftop solar installation served as the case study. Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. Optimal tilt angles were identified between 35° and 39°, and the azimuth angle of 0° yielded the highest energy gain without requiring solar tracking. Fixed configurations with a 5 m pitch showed only a 10% shading loss, requiring 1680 m² of space and generating an average of 646.83 kWh/m² monthly. Compared to recent works, our integration of real-time climate data improved simulation accuracy by 6–9%, refining operational planning and decision-making processes. This included better timing of high-load activities and enhanced prediction for grid feedback. The study demonstrates that data-driven optimization significantly improves performance reliability and system design, offering practical insights for solar infrastructure in similar temperate climates. These results provide a benchmark for urban energy planners seeking to balance performance and spatial constraints in PV deployment. Full article

This study investigates the long-term impact of insulation degradation on building heating energy consumption, with a focus on extruded polystyrene (XPS) insulation. Year-by-year degradation in thermal transmittance was derived from long-term experimental data and applied to prototypical energy models of multifamily apartment buildings and office buildings. Simulations were performed using both Actual Meteorological Year (AMY) and Typical Meteorological Year (TMY) data for six cities representing Korea’s major climate zones. The results showed that insulation degradation led to a significant increase in heating energy consumption from 23.2% to 34.9% in AMY simulations and 23.5% to 36.2% in TMY simulations for multifamily apartment buildings over 15 years. The difference between the AMY and TMY estimates was within 4%, demonstrating the reliability of TMY for long-term performance assessments. Notably, the southern and Jeju zones exhibited higher sensitivity to degradation due to their relaxed insulation standards and lower initial thermal performance. Office buildings were less affected, with increases below 8%, attributed to smaller envelope areas and higher internal heat gains. These findings highlight the need for zone-specific insulation standards and differentiated energy-saving design strategies by building type to ensure long-term energy efficiency. Full article