Arabian Journal of Geosciences最新文献

This research work studies a problem associated with an axisymmetric torsion of an orthotropic layer by a circular rigid disc at the midplane. The orthotropic layer is sandwiched by two identical orthotropic half-spaces with two interfaced cracks. The layer and half-spaces are dissimilar in nature. The mixed boundary value problem is reduced to a system of dual integral equations by Hankel transformation, which are converted to Fredholm integral equations of the second kind. The integral equations are solved numerically by the quadrature rule. The stress intensity factors for crack and disc have been derived and are presented graphically for different thicknesses of orthotropic layer.

The aim of this study is to carry out a petrological and geotechnical study of the lateritic soils of Ngaoundal (Adamawa-Cameroon). Hence, geotechnical identification, X-ray difractometry, and chemical analysis (XRF) were used to characterize soils. Field investigations show that the average thickness of the lateritic layers is 1.3 m; the soils encountered are nodular with a silty/clay matrix. The dominant color is dark brown (7.5YR 5/6) with a lumpy structure and silty or clayey texture. Mineralogical analysis (XRD) and chemical data (XRF) show that these materials are made up of quartz (38.27%), goethite (13.98%), gibbsite (10.59%), kaolinite (8.62%), hematite (7.88%), magnetite (8.38%), anatase, and boehmite. These soils are silico-ferrugino-aluminous. Their silica/sesquioxide ratios correspond to those of true laterites. X-ray diffraction analysis of the soil samples revealed the absence of swelling clays. Geotechnical analyses indicate that these soils have specific weights between 2.580 and 2.648 g/cm³. The liquidity limits show an average of 54.5%, with an average plasticity index of 29.73%. According to the (HRB) classification, these soils belong to the class of silty/clayey gravels and sands known as A-2–7. The values for maximum dry density and optimum water content range from 2.040 to 2.188 g/cm³ and from 9.5 to 13.6% respectively. The CBR bearing capacity index shows values ranging from 78.0 to 95.1%, which proves that these materials belong to bearing capacity class S5. The geological and geotechnical data confirm that the Ngaoundal materials are suitable for road construction (sub-base and base layers).

The frequency of extreme hydrologic events such as floods, storm surges, droughts, heat waves, extreme precipitation, and other similar occurrences has been increasing in Bangladesh due to the impact of climate change. Therefore, the assessment of changes in future climates is essential for climate-induced risk management in the country to safeguard natural resources and human lives. The main purpose of the current study is to assess the trend of maximum temperature (T_max), minimum temperature (T_min), and precipitation for the north-west region of Bangladesh in seasonal and annual scales for three future periods, including 2025–2050, 2051–2080, and 2081–2100, respectively. In order to achieve this goal, a large-scale atmospheric dataset obtained from the well-known general circulation model (GCM), CanESM2, is downscaled to finer scales at the local level using the widely used statistical downscaling model (SDSM). The downscaling of local climate variables is carried out using daily observed climate data under three representative concentration pathways (RCP) scenarios, including RCP2.6, RCP4.5, and RCP8.5, respectively. Correlation matrices with p-values have been utilized to select the most suitable predictors from NCEP/NCAR reanalysis data. Both the calibration (0.87 < R2 < 0.98, 0.87 < EV < 0.99, 19.24 > SE < 0.12) and validation findings demonstrate that the model performs satisfactorily. The bias correction approach is also adopted to achieve more consistent results. Seasonally, the mean seasonal temperature and precipitation are projected to rise in all seasons (except winter for precipitation). Annually, T_max and T_min have grown by 0.49 °C and 1.36 °C, respectively, whereas precipitation has increased by 49% up to the next century considering the RCP8.5 scenario (worst case). Overall, the outcome of the current study is expected to be supportive to policymakers and water managers in planning climate-resilient agricultural and infrastructure development activities for managing climate-induced disastrous events in the north-west region of Bangladesh.

The present study using a 90-m resolution Shuttle Radar Topography Mission (SRTM), digital elevation model (DEM), and geospatial techniques and an in-depth study of geomorphic and structural characteristics has been carried out for the Cauvery River basin (CRB) of India. The study of terrain characteristics and their evolution has, in fact, been transformed in recent years by the combination of remote sensing data such as the SRTM, DEM with GIS technology. The analysis involves morphometric assessments of the drainage system, focusing on linear, aerial, and relief aspects. Additionally, the longitudinal profile analysis synthesizes the influence of lithology on the basin’s characteristics. Notably, Cauvery is identified as the 8th order drainage basin, covering a substantial area of 85,071.6 km². River basin structural control is found to be low to moderate, according to the bifurcation ratio study. Because of the hard rock lithology, there is less stream frequency (0.22) and drainage density (0.6 km/km²), which indicates that there may be more permeability in the strata below the surface, river’s mean stream length, which varies from 1.8 to 241.4 for all orders, and the stream length ratio, which varies from 0.38 to 0.65, which has a surface area of 85,071.6 sq. km and a circumference of roughly 2573.2 km. The vastness of the basin, as seen by its area and perimeter, emphasizes how important it is to the dynamics of peak flow and runoff in hydrological processes. The basin is extremely elongated, as indicated by the shape parameters, which makes managing flood control within the basin comparatively easier and river watershed management.

This study analyzed long-term hydro-metrological drought under climate change in the Kesem sub-basin, Middle Awash basin, Ethiopia. The comparative analysis was employed using three drought indices (the streamflow drought index, standard precipitation index, and reconnaissance drought index). These indices were evaluated using the ordinal by ordinal Spearman’s correlation, interval by interval Pearson, and kappa measure of agreement. The three drought indices have statistically significant (α < 0.01) strong correlation (> 0.78) and degree of agreement (0.2 fair agreement to 0.8 near-perfect agreement) tested at 99% confidence  interval. The potential evapotranspiration (PET) estimation shows an increase of + 25.9 mm (1.6%) from the base period to RCP 4.5 (2020) and + 26.7 mm (1.67%) to RCP 8.5 (2020), and + 55 mm (3.4%) to RCP 4.5 (2050) and + 56.8 mm (3.5%) to RCP 8.5 (2050). This increase in PET is an indication that the watershed is very susceptible to water deficit and drought in the coming periods. Mild to extreme hydro-metrological drought was experienced during the baseline period (1984–2010) and is projected to occur in the current (2011–2044) and future (2045–2075) periods under both RCP 4.5 and 8.5 emission scenarios at 6- and 12-month timescales. Droughts will likely become more frequent in the future in the study area. Currently, extreme droughts that last 6 and 12 months occur every 13 to 19 years. Under the RCP 4.5, these droughts could happen every 6–7 years by 2050. The RCP 8.5 suggests even more frequent extreme droughts every 14 years. These findings are substance information for the water users and development works in the basin including the Kesem dam reservoir.

The sediments from the Babouri-Figuil intracontinental Basin were investigated by major and trace elements geochemistry and paleontological analysis to infer their Paleoenvironment and paleoclimate evolution and metallogenic implication of an approximately 120-m thick sequence along the Mayo Louti River in this basin. The geochemical compositions revealed that the studied materials are mainly composed of Shales associated with Fe-sands, Wacke and Litharenite. These sediments show high ΣLREE/ΣHREE ratios (Σlight rare earth elements/Σheavy rare elements ratios: 5.16 to 10.49); weak negative and positive Eu anomaly (Eu/Eu* = 0.84 to 1.28) and Ce anomaly (Ce/Ce* = 0.93 to 1.10); Al₂O₃/TiO₂ ratios (17.02–28.16); Th/Co ratios (0.23–1.36). These features, together with the Ce vs La/Yb, Zr vs TiO2 and La/Sc vs Th/Co plots, indicate that the sediments are derived mainly from felsic rocks associated with intermediate composition rocks. The CIA and CIX (Chemical Index of Alteration: respectively 48.02 to 60.97 and 62.69 to 71.42) suggest that the source rocks have experienced low to moderate recycling and sorting and weathering. The SiO₂ vs. Al₂O₃ + K₂O + Na₂O, C-values, Sr/Ba, and Rb/Sr plots, and palynological content dominated by Gymnosperm pollens such as Classopollis sp. and Araucariacites sp., associated with single spores such as Cicatricosisporites sp indicate mainly semi-arid/humid to arid climate during the period of deposition, fluctuating from the bottom to the top of the sequence. The Sr/Ba values ranging from 0.78 to 12.23, suggest a fluctuating and sometimes high salinity (Hypersaline milieu). The presence of numerous tetrads to dyads and wood trunks indicates a lacustrine or swampy environment surrounded by vegetation, and the Ni/Co (1.75 to 4.14) and U/Th (0.10 to 0.64) ratios are consistent with oxic conditions. The discriminant function-based multidimensional tectonic diagrams show an arc-collisional setting, which is consistent with the Precambrian geological history of the study area, and indicate the unreworked and unsorted character of these sediments. The Al/Si ratio shows positive correlation with CIA (R² = 0.59), Th (R² = 0.37), Zr (R² = 0.47), Hf (R² = 0.51), Na (R² = 0.39), K (R² = 0.73) and negative correlation with Ca (R² = 0.32) and Mg (R² = 0.30). The positive correlation with K, Hf, Na, Zr, and Th from detrital origin and negative correlation with Ca and Mg from chemical origin could suggest the geochemical composition control of grain size as in the Amazonian floodplain deposits. The studied samples are essentially terrigenous and very weakly metalliferous.

Reinforced soil has been widely used in civil engineering for various infrastructure applications, including retaining walls, bridge abutments, foundations, support structures for highways, and railways. This study focused on examining the fundamental behavior of mechanically stabilized earth (MSE) walls using numerical modeling technique in the PLAXIS 2D software. The investigation includes the impact of tiered configurations, such as single-tiered, two-tiered, and three-tiered wall systems. Increasing the number of tiers showed lower displacements about 22% and improved stability. The influence of offset distances between tiers is further explored for two-tiered and three-tiered walls. Increase in offset distances showed decrease in displacements about 23% and improved stability by reducing the axial force distribution by 51% in two-tiered walls and 26% in three-tiered walls compared to single-tiered wall. Furthermore, the study investigated the effects of reinforcement stiffness and spacing between reinforcements. Keeping the length of the reinforcement in accordance with load to be applied and based on the findings from these analyses, the geometry of the MSE walls is fixed with the obtained optimum parameters such as offset distance and stiffness of reinforcement for application of different magnitude of surcharge loads. Surcharge loads of more than 200 kN/m resulted in FOS less than 1.5 stating the feasibility of potential failure of even multi-tiered walls with reinforcement of larger stiffness and closer spacing.

Landslide hazards affect the security of human life and property. Landslide hazard maps are essential for landslide prevention and mitigation. In this study, the success of machine learning and statistical methods in predicting landslide hazards in and around the district center of Maden, Elazığ province, within the borders of Turkey, was analyzed, and their performances were compared. The Random Forest method correctly predicted 1.398 of the 1.425 landslide points in the training dataset, but was incorrect on 27 points. The same method predicted 1942 of the 2075 landslide-free points in the training dataset, but incorrectly predicted 133 points as landslide-exposed. As a result of the study, it is evident that the Random Forest and M5P Rule Tree methods yield more successful results than the Frequency Ratio method. In the study area, the landslide hazard is concentrated in areas close to the East Anatolian Fault and in areas with steep slopes. Lithology, slope, and seismicity have been identified as important triggering factors for landslides in the region. It is expected that machine learning methods, which operate with high levels of accuracy, will make a significant contribution to the prediction of landslide hazards.

Predicting crop yields before harvest is important for making and carrying out policies about food safety, transportation costs, import-export, storage, and selling of agricultural goods. The weather is a key factor in crop growth and its development. Therefore, models that include meteorological variables can predict reliable forecasts for crop output; however, selecting the appropriate model for use in agricultural production forecasting can be challenging. This study investigates the development of wheat yield prediction models using various multivariate analysis techniques and weather indices derived from meteorological data collected over 22 years in Punjab, India. Five different modeling approaches, including stepwise multiple linear regression (SMLR), LASSO, elastic net (ELNET), artificial neural network (ANN), and ridge regression, were employed and compared for their effectiveness in predicting wheat yield. The models were calibrated using data from 17 years (2000–01 to 2016–17) and validated using data from the subsequent 5 years (2017–18 to 2021–22). Evaluation metrics such as R², root mean square error (RMSE), normalized root mean square error (NRMSE), mean biased error (MBE), and modeling efficiency (EF) were utilized to assess model performance. The results indicate varying degrees of performance across districts and modeling techniques. ANN demonstrated the highest performance during both calibration and validation periods, followed closely by LASSO and ELNET. However, certain districts showed discrepancies in model fit, with some models performing better than others depending on the specific district. Overall, ANN emerged as the most reliable approach for wheat yield prediction in Punjab followed by ELNET and LASSO, offering valuable insights for agricultural planning and management. This comprehensive analysis provides valuable contributions to the field of crop yield prediction, enhancing understanding of the complex interactions between weather variables and agricultural outcomes.

This study aims to evaluate the influence of current and changing rainfall dynamics on the start and end dates of the rainy seasons in Yaoundé town. Thus, classical statistical tests, trend detection, and break tests were applied for the analysis of different daily rainfall parameters recorded between 1964 and 2020. The results show that annual rainfall fluctuates between 1083 and 2196.7 mm for an interannual average of 1562 ± 271 mm without a significant break, with a downward trend of about − 3.352 mm/year. The increasing order of magnitude of seasonal precipitation is as follows: Small Dry Season < Great Dry Season < Small Rainy Season < Great Rainy Season. The Small Rainy Season, Great Rainy Season, and Great Dry Season show decreasing trends and magnitudes in the order of − 2.181 mm/year, − 1.741 mm/year, and − 1.015 mm/year, respectively, but without influence on the delineation of the seasons. On the daily level, the number of rainy days per year, per month, per season, and the rainfall height classes also show decreasing trends with multiple breaks except for the class [5.1–15 mm]. The analysis of the start and end dates of the rainy seasons shows that the Small Rainy Season extends from March 19 to June 21, and the Great Rainy Season is between August 20 and November 28. This information is of critical importance for activity planning.