Acta Geophysica最新文献

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.

For risk assessment and management, accurate solute transport modeling is essential because groundwater contamination affects drinking water and ecosystems. In this study, a solute transport model incorporating adsorption, dispersion, and homogeneous flow across different geological formations is developed and compared to improve groundwater contamination prediction accuracy. Under realistic boundary circumstances, solute transport is examined with a uniform source concentration at one end of the geological formation and zero mass flux at the other. Analytical solutions are produced via the Laplace transform, whereas numerical solutions are produced by finite difference methods. The model’s performance is evaluated using the Normalized Root Mean Square Error (NRMSE), Global Performance Indicator (GPI), and statistical tests including two-way ANOVA and t-tests. The unique temporal and spatial concentration patterns found in gravel, silt and clay are effectively represented by the model. Significant variations in solute behavior among geological formations were confirmed by statistical studies and NRMSE values varied from 0.004 to 0.05 across formations. The impact of hydrological conditions on solute distribution is illustrated graphically; clay exhibits higher retention and slower migration than silt and gravel. The model accurately forecasts solute transport and emphasizes the essential function that geological characteristics play in pollution retention. In addition to offering helpful suggestions for groundwater monitoring, pollution prevention, and sustainable water management, it offers insightful information for further reactive transport study.

This study presents a theoretical and numerical investigation into the role of the vertical gravity gradient (VGG) in the analytical downward continuation of surface gravity data for geoid determination. Several VGG computation techniques, including integral-based, FFT-based, and integrated second vertical derivative (ISVD)-based methods, are evaluated using synthetic, noise-contaminated gravity disturbances at (1'times 1') and (2'times 2') resolutions. Among these, ISVD-based methods consistently demonstrate better numerical stability and accuracy. A new iterative downward continuation approach is proposed, which uses VGG values computed on the reference ellipsoid. Its performance is compared to the conventional Taylor series expansion and Poisson integral methods. While the Poisson integral achieves slightly better accuracy (2.1 mGal) when optimally regularized, the proposed iterative method attains comparable accuracy (2.4 mGal) using a simpler regularization strategy- fixed truncation after the second iteration. The proposed method also outperforms the Taylor approach under the same regularization conditions. The results further indicate that higher-resolution input data do not necessarily improve downward continuation accuracy; instead, they can amplify high-frequency noise beyond the signal bandwidth. These findings offer practical guidance for selecting VGG computation methods, regularization strategies, and spatial resolutions in geoid modeling applications.

This study examines the structural characteristics of the Cerro Prieto and Indiviso Faults within the Colorado River Delta, Baja California, México, through seismic reflection, seismicity, magnetic, and gravimetric analyses. The Cerro Prieto Transform Fault, a critical component of the Pacific-North American plate boundary, traverses the Mexicali Valley and northern Gulf of California, within the study area. A key objective is to determine whether the Indiviso Fault existed prior to or originated during the 2010 Mw 7.2 El Mayor–Cucapah earthquake. Results confirm the Indiviso Fault as a pre-existing structure reactivated during the event. Gravimetric and magnetic anomalies and seismicity delineate their obliquity to the Cerro Prieto Fault and intersection with the Wagner Basin, challenging prior models of the Cerro Prieto Fault's trajectory. Tectonic activity along the La Mesa and Santa Clara Faults correlates with significant subsidence beneath the Ciénega de Santa Clara. Seismic profiles reveal buried faults, such as Yurimori and Pangas Viejas, that lack surface expression. Post-earthquake deformation transitioned from the Cerro Prieto Fault to the Indiviso Fault, resembling slip-transfer processes observed in the San Andreas Fault system. Declining seismicity along the Cerro Prieto Fault contrasts with diffuse regional activity, underscoring the role of transform faults in accommodating interplate motion. Aftershocks were concentrated beneath sedimentary lowlands, while surface ruptures predominantly occurred in mountainous areas, influenced by lithostatic stress conditions. The primary rupture initiated along the northern Indiviso Fault zone, where differential stress reactivated pre-existing structures. Another aspect supporting this interpretation is that historical seismicity also shows a trend along the Indiviso Fault. The relocation of these identified two significant events that occurred in 1934 (Mw 6.5 and 6.3), as well as the 1935 event (Mw 5.7), distributed along this structure. This evidence indicates that the Indiviso Fault already existed and was tectonically active since that time. However, it is noteworthy that, prior to the EMC event, virtually no seismic activity was recorded in the region. These findings contribute to the understanding of fault reactivation, transform fault dynamics, and regional seismic hazard assessments.

This paper presents a comprehensive comparative analysis of Global Navigation Satellite System (GNSS) module performance in both static and dynamic Unmanned Aerial Vehicle (UAV) environments, focusing on four major satellite navigation systems: GPS, BeiDou, NavIC, and Galileo. Two separate test scenarios is conducted here. In the dynamic test, the M8N GPS and 7Semi L89-based BeiDou modules is simultaneously mounted on a drone and evaluated using predefined waypoints in Mission Planner software. Results showed that the GPS module exhibited more stable and responsive roll, pitch, and yaw tracking during high-speed maneuvers and BeiDou module provides superior horizontal positioning accuracy due to its utilization of multiple orbital planes (MEO, GEO, IGSO). In the static evaluation, NEO M8 (GPS), PX1125S-01D (NavIC), and 7Semi L89-based Galileo modules were assessed. The NavIC-enabled PX1125S-01D demonstrated the highest positioning accuracy and the most consistent signal strength in an open-field setup in the Indian region. Galileo offeres low DOP values and excellent accuracy in multi-satellite environments, while GPS maintained reliable performance with broader global coverage. To address the limitation of short-duration trials, an extended 3-h static experiment was carried out, which confirmed NavIC’s superior stability and further emphasized the effect of long-term observation on evaluating constellation reliability. Key performance metrics such as HDOP, VDOP, GDOP, CEP, and 3DRMS are measured for assessment. The findings indicate that no single system is optimal for all use cases: NavIC and BeiDou excel in precise navigation under favorable signal conditions, while GPS provides dependable performance in fast-changing and globally diverse environments. A hybrid GNSS configuration that integrates the strengths of multiple systems could significantly enhance UAV navigation accuracy and stability across varying operational contexts.