Acta Geophysica最新文献

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.

The Gutenberg–Richter law describes an exponential relationship between earthquake magnitude and frequency, with the b value as a key parameter for quantifying the relative occurrence of large versus small earthquakes. Accurate estimation of the b value is important for various seismological applications, particularly in identifying asperities, i.e., areas of strong coupling between subducting and overriding plates in the subduction zones. The challenge in mapping spatiotemporal variations of the b value and resolving their spatial heterogeneity is small data sets for which the estimation error is large. To balance the two opposite goals, the smallest estimation error and the largest spatial resolution, we propose the overlapping window algorithm. This method partitions seismic data into overlapping windows, allowing for a more detailed characterization of spatial variations in the b value. We applied the algorithm to the 1998–2024 seismicity in the Japan subduction zone, revealing significant b value patterns that can be interpreted in terms of the locked asperity distributions before and after the largest earthquakes. These patterns suggest that the b value changes reflect plate coupling preceding and following major seismic events. The changing shapes of the small b value patches suggest areas where a strong earthquake is likely to occur. In particular, a future m9 earthquake initiated off the coast of Hokkaido and extending to the area off the coast of Honshu seems likely. Our results demonstrate that this approach, when combined with other methods, provides insight into stress redistribution and locked asperity locations, offering a tool for estimation of location and size of possible future large events.

The objective of this paper is to verify a research hypothesis that there exist certain types of atmospheric circulation for which short-term hydrologic ensemble predictions, with lead times ranging from a few minutes to a few hours, are skilful. In the aftermath of the scrutiny, the forecaster will have the information on what skills to expect in case of a given atmospheric circulation type. Herein, atmospheric circulation is not considered as an input variable in the process of issuing short-term hydrologic forecasts, but it characterises favourable and unfavourable meteorological background for these prognoses to perform well. Short-term (up to 3 h into the future) multimodel ensemble predictions of water levels in the upper Nysa Kłodzka river basin (southwestern Poland) were computed using HydroProg, one of the hydrologic ensemble prediction systems. Nash-Sutcliffe efficiency (NSE) statistics of those forecasts was computed and juxtaposed with 40 types of atmospheric circulation. The latter types were based on the combination of direction of advection, the cyclonality index and the humidity type. The experiment was conducted between 1 September 2013 and 3 December 2016, when the HydroProg system was working in real time. The hydrologic multimodel ensemble prediction was based on up to six ensemble members. Forecasts were issued at 11 sites within the basin, had 12 intermediate 15-minute steps (lead time ranged from 15 to 180 min) and were updated every 15 min. It was found that the most skilful short-term water level predictions, classified according to a widely accepted classification into good or satisfactory prognoses (NSE (ge) 0.36), were associated with the wet humidity type, with prevailing northerly advection of air masses. Although meteorological conditions, during which short-term multimodel ensemble predictions perform well, were identified, the relation was not quantitative (statistical) but rather qualitative (based on prediction ranking in relation to circulation types). It presents some meteorological background for good or satisfactory short-term hydrologic prognoses, but cannot be used as a variable for refining these forecasts.