Acta Geophysica最新文献

The non-inertia wave model in its linearized form with a space- and time-dependent lateral inflow is solved for a finite-length channel. The study is performed for two kinds of upstream boundaries with a stage hydrograph at the downstream end. The limit of convergence of the flow rate is found to be dependent on the observed location for the positions between the boundaries of the lateral inflow, while the same for the stage depends on the location of observation throughout the channel. The backwater effect caused by the lateral inflow decreases the flow rate in the upstream direction and increases the stage along the channel. The higher values of the stage are found either between the two boundaries of the lateral inflow or for the locations downstream of it. The location of the lateral inflow is more influential on the flow behavior than the distance between the two boundaries of the lateral inflow segment. When a stage hydrograph is imposed at both ends of the channel and as the downstream boundary effect reduces, the effect of the lateral inflow added in the upstream section becomes dominant over the lateral inflow added closer to the downstream end. Corresponding to a Péclet number greater than 2.5, the lateral inflow responses for the semi-infinite-length channel present a suitable approximation in predicting the flow rate in the finite-length channel at the locations nearer the upstream end, but the same cannot be termed appropriate for the locations near the downstream boundary.

Drought is a disaster that affects everything related to humans, particularly the economy. Therefore, predicting its effects before they occur is crucial. However, due to its nature, droughts are more challenging to detect than other natural disasters. This study aims to investigate the effect of decomposition techniques (VMD, DWT, EMD, and EEMD) on the drought forecasting performance of machine learning methods (network-based: MLP, KAN, RNN, BiLSTM, and BiGRU, as well as tree-based methods: RF, GB, XGB, AB, and M5P) in different climate types. To this end, the Standardised Precipitation Evapotranspiration Index (SPEI), which was calculated using 52 years of precipitation and temperature values from 1969 to 2020 for three meteorological stations in Türkiye with different Köppen-Geiger climate classifications, was employed. Drought predictions were made for three SPEI time scales: 3, 6, and 12 months. The results of the analysis revealed that decomposition increased the power of prediction compared to raw drought data, and VMD was the most effective decomposition technique. For instance, the NSE values, which was approximately 0.5 in SPEI-3, 0.7 in SPEI-6, and 0.9 in SPEI-12, increased to above 0.95 across all time scales following the implementation of the VMD method to different climate types. Besides, MLP, KAN, and M5P proved to be the most effective machine learning methods with this value above 0.98 in all data sets. Performance improved as the time scale increased in recurrent neural network-based methods (RNN, BiLSTM, and BiGRU). Consequently, irrespective of the climate region, models employing the decomposition method (VMD and DWT) exhibited considerably enhanced performance.

In recent years, the exploration and development of natural gas hydrates have become a global research hotspot. Geophysical logging, as an important means of gas hydrate reservoir identification, can be directly used to study the reservoir properties of in situ gas hydrates. Based on the exploration history of natural gas hydrates in the northern continental slope of the South China Sea and the Qilian Mountains permafrost zone, this paper summarizes the reservoir characteristics and occurrence types of natural gas hydrates in China. The reservoir characteristics of natural gas hydrates in China, logging acquisition items, logging response characteristics, logging data application, and other aspects are discussed. It has been found that natural gas hydrates are distributed in dispersed, tuberculous, strip, and stratiform forms in the reservoirs in the northern slope of the South China Sea and in the Qilian Mountains permafrost zone. The natural gas hydrates in China’s sea areas mainly occur in sediments such as unconsolidated diagenetic silt, clayey silt, and sandy clay. The natural gas hydrates in the permafrost regions of China mainly occur in the pores of coarse-grained rocks and fractures in fine-grained rocks that have been consolidated into rocks, and they are usually associated with carbonate rocks. The logging collection of natural gas hydrates in the northern slope of the South China Sea was mainly completed by Schlumberger using cable logging and logging while drilling. The logging collection in the permafrost zone in the Qilian Mountains in China was mainly carried out using conventional coalfield cable logging tools. The logging response difference between marine and permafrost gas hydrate reservoirs in China is mainly caused by the sedimentary environments of the reservoirs. The natural gas hydrate reservoirs are complex and cannot be studied using conventional oil and gas logging evaluation. In the classification evaluation, the distribution type of natural gas hydrates should be considered comprehensively based on the reservoir type. When natural gas hydrates only exist in dispersed form in the reservoir, the conventional oil and gas evaluation saturation method is applicable. However, the saturation evaluation requires further study when natural gas hydrates occur in many forms in a reservoir. We review the research progress in gas hydrate logging in China and discuss its development direction in order to provide a reference for evaluating gas hydrate reservoir logging in China and even similar types in other regions.

On 10 May 2024, a geomagnetic superstorm causing the largest geomagnetic disturbance since 2003 started because of the shock arrival at Earth from multiple coronal mass ejections at 17:05UT. In Greece, the magnetic disturbance was recorded by the Pedeli (PEG) magnetic observatory of the INTERMAGNET global network of observatories. At the same time, VAN telemetric network, installed in the 1980 s and 1990 s for earthquake prediction purposes, measured the electric field of the Earth at several field stations in Greece. Here, we report these measurements during this Mother’s Day or Gannon geomagnetic superstorm that lasted from 10 to 12 May 2024. We also show how the geoelectric data can be combined with the geomagnetic variations for an estimation of the resistivity at different locations in Greece. The present results are useful for the estimation of geomagnetically induced currents which constitute a major hazard for electric power networks.

Estimation of potential streamflow (PS) is an essential step for the reallocation of water resources to different water demands in a water resources system. However, observational data of PS are generally unavailable in catchments heavily affected by human activities, where the streamflow is influenced by water withdrawals, dam construction, and land use changes. Therefore, in this study, a novel and comprehensive methodological framework is developed for extracting land use maps, estimating the PS, and analyzing its trend in catchments with extensive irrigated agricultural lands. To apply this framework, a process-based river catchment model of atmosphere–water–soil–plant, i.e., SWAT, was developed and calibrated by incorporating climatic and anthropogenic factors, along with the required hydrological, crop, and land use data. PS was then simulated by removing human factors from the model. For this purpose, the Aji Chai catchment, which is one of the crucial sub-basins of the Lake Urmia basin in northwestern Iran, is selected. Results showed that the area of irrigated agricultural lands in the catchment increased by 41% during the period of 1987–2019. In addition, dam construction, inter-basin transfer, land use change, and agricultural expansion were identified as the most significant human factors influencing the streamflow. Hydrological simulations indicated that, due to human factors, the observed outflow is generally lower than PS across most sub-catchments. Over the study period, the average annual outflow at the catchment outlet decreased by 31%, relative to the corresponding PS. Moreover, in most sub-catchments where streamflow showed a significant decreasing trend, the rate of decrease in PS was typically greater than or at least comparable to that of the outflow. However, along the main river of Aji Chai, the cumulative effects of human interventions intensified downstream, resulting in a higher rate of decrease in the outflow compared to PS. This study provides a replicable framework for separating climatic and anthropogenic effects on river flows, which is crucial for sustainable water reallocation and management.

Frequent significant deviations of the observed magnitude distribution of anthropogenic seismicity from the Gutenberg–Richter relation require alternative magnitude–frequency models for probabilistic seismic hazard assessments. Five nonparametric kernel density estimation (KDE) methods are evaluated on simulated samples drawn from four magnitude distribution models: the exponential, concave and convex bi-exponential, and exponential-Gaussian distributions. The studied KDE methods include Silverman’s and Scott’s rules with Abramson’s bandwidth adaptation, two diffusion-based methods (ISJ and diffKDE), and adaptiveKDE, which formulates the bandwidth estimation as an optimization problem. Their performance is assessed for magnitudes from 2 to 6 with sample sizes of 400 to 5000, using the mean integrated square error of cumulative distribution (MISE_F) over 100,000 simulations. Their suitability in hazard assessments is illustrated by the mean of the mean return period (MRP) for a sample size of 1000. Among the tested methods, diffKDE provides the most accurate cumulative distribution function estimates for larger magnitudes. Even when the data are drawn from an exponential distribution, diffKDE performs comparably to maximum likelihood estimation when the sample size is at least 1000. Given that anthropogenic seismicity often deviates from the exponential model, using diffKDE for probabilistic seismic hazard assessments is recommended whenever a sufficient sample size is available.

Seasonal variability in the Mediterranean precipitation regime significantly affects vegetation cover, particularly due to frequent and severe drought conditions. In this study, the Leaf Area Index (LAI) was adopted as a key ecological indicator for assessing vegetation status. Monthly total precipitation and near-surface air temperature were used as predictor variables, while MODIS-based LAI data from 2007 to 2023 served as the response variable. A hybrid Wavelet–Artificial Neural Network (W-ANN) approach, in which Daubechies wavelet coefficients of meteorological variables were provided as inputs to a Levenberg–Marquardt backpropagation ANN, was compared to a conventional ANN model applied directly to the raw data. Four urban locations with contrasting Mediterranean climates—Antalya and Istanbul (Kandilli) in Türkiye, and Enna and Trieste in Italy—were selected for model evaluation. Using both approaches, LAI was forecasted for the period 2024–2030, and predictive performance was comparatively assessed. Results indicated that the W-ANN model outperformed the conventional ANN, yielding 15–85% higher accuracy, with mean squared error (MSE) values ranging from 0.01 to 0.04 on the test datasets. Scenario simulations revealed a declining trend in LAI for Antalya and Enna, and an increasing trend in Istanbul and Trieste. The proposed framework offers a transferable tool for vegetation monitoring and climate adaptation in semi-arid regions.

Climate and drought are attributes affecting plant growth and driving forest ecosystem processes. The growth of endemic black pine forests is strongly affected by changes in climatic and weather patterns, especially in the Mediterranean basin. This study aims to investigate the critical interactions between hydrometeorological factors and the annual growth of black pine forests along the Mediterranean, considering also the particular impact of extreme events, such as droughts. Annual growth data from 38 sites across the Mediterranean basin have been examined against various temperature related parameters (average, minimum, and maximum temperatures, diurnal temperature range, frequency of frost days, and cloud cover), water-related factors (precipitation, potential evapotranspiration, vapor pressure, and frequency of wet days), and drought indices (standardized precipitation index, SPI; agricultural standardized precipitation index, aSPI; reconnaissance drought index, RDI; and effective reconnaissance drought index, eRDI), across multiple time steps and longitudinal gradients. The results indicate a positive correlation between winter average temperatures and tree growth rates, whereas this relationship turns negative for summer temperatures. Summer water-related attributes, such as precipitation, the number of wet days, and the ratio of precipitation to potential evapotranspiration, have a strong positive effect on annual growth, while the relationship with the number of frost days in spring is negative. Summer droughts significantly impact annual growth rates in the basin. Furthermore, the analysis reveals significant differences in the response of black pines at different latitudes, with populations in the western and eastern parts of the Mediterranean basin being affected differently by various meteorological factors.

Identifying and predicting flood-prone areas is a crucial aspect of effective flood management. Over the past decades, various numerical and statistical techniques have been employed to assess inundation vulnerability across different regions. However, these traditional methods often suffer from limitations such as high computational costs, reliance on assumptions, and extensive simplifications. In contrast, artificial intelligence (AI) approaches have been widely adopted over the last twenty years to enhance flood susceptibility prediction in diverse contexts. This study aims to comprehensively review prior global research in this field. It collects and evaluates multiple facets, including prediction parameters, performance metrics, study areas, and data sources. Consequently, the most promising flood susceptibility methodologies are presented, alongside an analysis of key trends in their advancement. Effective strategies identified include hybrid modeling, data decomposition, model optimization, ensemble algorithms, the specificity of the study area, the type and quantity of input data, data source and temporal coverage, as well as evaluation criteria. This review revealed that use of ML models has increased significantly since 2018, while DL models have shown notable growth since 2020. The majority of research on flood susceptibility prediction has come from Asian nations, including Iran, China, India and Bangladesh. This indicates the region’s significant emphasis on managing water resources and reducing the risk of flooding. Additionally, satellite data has served as the main information source for many investigations. In terms of assessing model performance, it should be noted that because of its high discrimination ability and adaptability in classification tasks, the AUC-ROC evaluation index has been widely regarded as a crucial evaluation criterion in the majority of research. This research serves as a valuable reference for hydrologists in selecting the most suitable methods or models tailored to specific regions and flood prediction strategies. Additionally, it highlights existing research gaps and proposes directions for future investigations.

To address the limitations in deep seismic research on the Vietnamese continental margin, this study utilized high-resolution marine satellite gravity data, alongside sediment thickness and bathymetry data. Applying Parker’s (1972) 3D inverse method, we developed a crustal structure model of the eastern Vietnamese continental margin. Interpretation revealed Moho depths ranging from 8.5 km in the Southwest Sub-basin to 28–29 km in the coastal zone, demonstrating a Root Mean Square Error (RMSE) of 1.885 km (or 8.6%) compared to OBS data. Basement depths varied from 2.5 km near the Hoang Sa Archipelago to 12.5–13.5 km in the Red River Basin, with a RMSE of 0.373 km (or 6.3%) compared to OBS data. Consequently, the derived crustal thickness map showed significant variations, from 4 to 6 km in the Southwest Sub-basin to 25 km in coastal areas. Major NW–SE, NE-SW, and N-S fault systems were also identified using the maximum horizontal gradient method and its derivative. Based on modern rifted continental margin models, six distinct crustal domains were zoned, and importantly, their distribution showed a strong correlation with known oil and gas fields, affirming the pivotal role of Earth’s crustal structure in controlling hydrocarbon potential.