Acta Geophysica最新文献

Precipitation is a crucial component of the hydrological cycle, with significant implications for agriculture, water resources, and environmental sustainability, particularly in climate-sensitive regions such as Pakistan. To enable informed decision-making and long-term planning, precipitation variability must be well understood and precisely modeled. We collected and evaluated seasonal precipitation data from numerous meteorological stations in Punjab, Pakistan. The precipitation concentration index (PCI) was calculated seasonally at each sampling station to analyze the concentration and timing of rainfall over the winter, spring, summer, and autumn seasons. To simulate the geographical distribution of seasonal PCI, we used four geostatistical methods: ordinary kriging, universal kriging, Bayesian ordinary kriging, and Bayesian universal kriging. As far as the proposed study is the first to use and compare both conventional and Bayesian kriging methods in mapping the seasonal precipitation concentration index (PCI) in the Punjab area. The seasonal orientation of PCI instead of an annual one gives us a complete knowledge of the intra-annual time distribution of precipitation. The evaluation of the temporal variability across seasons provides new information on spatial prediction accuracy and spatial variability, which can serve important functions in the planning of agricultural activities, as well as the water resource management and adapting to climate change within this climate-sensitive area. Before spatial interpolation, representative PCI values were prepared using the Gibbs sampling approach. Comparative performance according to the root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) indicated that Bayesian ordinary kriging was more effective than ordinary kriging in most of the seasons, and Bayesian universal kriging was more reliable and accurate than universal kriging. The results show that Bayesian geostatistical techniques can enhance the spatial modeling of seasonal precipitation indicators. The study’s findings are relevant to the Pakistan Meteorological Department and can serve as a scientific foundation for policymakers to develop improved water management, agricultural planning, and climate resilience measures.

Satellite data play a crucial role in understanding and monitoring numerous environmental processes, and their increasing accessibility has led to their use in various scientific disciplines, particularly those related to the hydrosphere. This includes the hydrosphere, with a wide range of applications related to lakes. In Poland, where there are several thousand lakes, they have become a subject of significant interest. The aim of this article is to review the current state of lake research in Poland using satellite data. The results indicate that data from the Landsat and Sentinel-2 satellite families have been most commonly used in research. The satellite-based research has covered a range of topics, including lake evolution and morphometry, water quality, water temperature, biodiversity, ice phenomena, and water levels. To broaden the use of satellite data in the future, it will be necessary to coordinate in situ studies, such as hydrological monitoring (water levels and temperature) and environmental monitoring (water quality and ecological status), with satellite overpasses. Considering the rapid development of satellite technology, this methodology is expected to gain importance, expanding the scope of knowledge into previously inaccessible areas.

Predicting crop yield is complex due to its dependence on multiple meteorological variables. Remote sensing (RS) has become the prime source of climate parameters due to detailed spatial and temporal resolutions; however, its product needs further quality enhancement due to associated errors. The primary aim of this study is to incorporate the EEMD, as a signal modification technique, with the LSTM model, aiming to anticipate crop yield of four strategic crops, namely barley, lentils, pea, and wheat in all provinces of Iran. The annual crop yield of these crops was extracted from Iran’s Ministry of Agriculture data over 15 years, spanning from 2005 to 2020. In the context of EEMD-LSTM prediction, four influential climate parameters, including minimum and maximum temperature, precipitation, and the SPEI, were considered as the input variables. The analysis shows that applying EEMD generally enhances the predictive performance of the LSTM model for most agricultural products. For barley, lentils, and peas, EEMD substantially improves accuracy compared with the baseline model. Although its impact on wheat is not statistically significant, EEMD still reduces RMSE by approximately 42%. For lentils, the method yields notable improvements, with reductions of 15.7, 13.8, and 8% in MAE, RMSE, and PCC, respectively. Additionally, when noisy wheat data are removed, the error distribution slightly increases, indicating that wheat exhibits the least improvement among all evaluated crops. The spatial assessment reveals clear geographic differences in the effectiveness of EEMD. The method substantially reduces prediction errors for barley in the northwestern region. A similar, though smaller, improvement is observed for lentils in the same area. Conversely, EEMD increases prediction errors for chickpea production in both the northwest and eastern regions, highlighting strong spatial variability in the model’s performance across the study area. The utilization of EEMD, as a noise removal tool, evidently leads to a reduction in model errors and a notable enhancement in the performance of the estimation model. The findings of this study can be utilized in the development of food security policies and enhancement of agricultural product performance across various locations, ultimately increasing productivity.

A set of new empirical relationships between modified Mercalli intensity (MMI) and synthetic peak ground acceleration (PGA) is developed for shallow crustal earthquakes in central and north-west Mexico. Few strong-motion recordings of shallow crustal earthquakes in these regions have led to uncertainties in estimating seismic risk even though they comprise some of the most densely populated urban sites in the country. We present relationships in which MMI is a function of PGA and its inverse form. No relationship for this type of events has been developed, although these earthquakes represent a high-risk potential for nearby highly populated urban regions. Ground motion data from 18 moderate-to-large earthquakes (4.5 < M_W < 7.5) that took place in the Basin and Range, Sierra Madre Oriental Fold-thrust belt, and Trans-Mexican Volcanic Belt provinces and the corresponding 531 MMI information reports were employed. Synthetic PGA data were generated using the finite-fault stochastic method, assuming different rupture scenarios to extend the limitations of the dataset. Linear and bilinear regression techniques were used, considering a binning averaging procedure and the whole dataset, respectively. As the first approach, a set of MMI predictive equations independent of moment magnitude (M_W) and hypocentral distance (R) was derived. Despite weak dependencies of the residuals on M_W and R terms, we also developed complementary predictive relationships that include these parameters as independent variables. The conversion equations between PGA and MMI, including all terms, show slightly less variability than simple linear equations in predicting intensity values. The proposed predictive equations are consistent with similar relationships in other regions of the world. The discrepancies among the different relationships may reflect variations in input data, particularly concerning the macroseismic intensity assignments, which are inherently subjective, and the tectonic regime. The conversion relationships that we have developed can be used to generate maps of estimated shaking intensities based on ground motion observations for crustal earthquakes in Mexico.

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.