Journal of Earth System Science最新文献

A preliminary lithospheric magnetic anomaly map of the Indian subcontinent (LAMI-1) was generated utilising seven years (April 2014–December 2020) of data recorded by the Swarm satellite constellation. To obtain a high-resolution lithospheric anomaly map, the fields originating from sources other than lithospheric sources are eliminated sequentially. The external field component is minimised by selecting the days of low geomagnetic activity (Kp < 2 and –20 < Dst < 20) within night-time hours. The main and the remaining external field sources are removed using data from CHAOS model. The model of residual data is achieved directly through the damped least square inversion technique by expanding the Legendre polynomial of order n = 6–50. The resulting satellite-derived lithospheric magnetic anomaly mostly reflects intermediate to long wavelength deep geological phenomena, with diverse tectonic provinces exhibiting discrete magnetic fingerprints/impressions with amplitudes ranging from high to low. Various tectonic blocks of the Indian subcontinent show distinct signature in the derived lithospheric anomaly map. The Himalayas and the Deccan Volcanic Province are associated with low magnetic signatures. The Central Indian Tectonic Zone and the Arakan Yoma Fold belt appear to have positive magnetisation. The division of Dhawar Craton into Western and Eastern Dharwar by Chitradurga Boundary shear is clearly evident in the anomaly map. A possible track of the Reunion hotspot is depicted as a north–south oriented high within the Marwar block. The comparison between the vertical (Z) component of LAMI-1 with the vertical component of MAGSAT data and MF7 lithospheric model from Champ satellite data indicates the LAMI-1 model shows far less noise and sharper anomalies with tectonic blocks better resolved compared to the other models.

Due to the complexity of the sedimentary environment and the heterogeneities and similarities between logging data, lithology identification is difficult. Taking the Permian in Shunbei area of Tarim Basin as an example, based on a complete understanding of the target reservoir characteristics, used the crossplot method, BP (backpropagation) neural network method and RBF (radial basis function) neural network method to identify three types of volcanic rocks: tuff, andesite, and basalt. At the same time, the crossplot method was used to select four logging curves that are sensitive to lithofacies changes as important indicators for identifying volcanic rocks, such as the natural gamma ray (GR), compensated density (DEN), compensated neutron (CNL) and spontaneous potential (SP) logs. Then, the sensitive curves were preprocessed by standardization, and suitable learning samples were selected. Two types of neural network prediction models were established, and the mapping relationship between the lithology and logging curves was used to identify the lithofacies of the key wells in the study area. Finally, by comparing the recognition results of the three methods, it was found that the RBF network not only achieved higher accuracy in the prediction results but also had fewer learning iterations than the BP network could more accurately identify volcanic rocks, and has certain popularization and application values, while the crossplot method had the worst recognition results.

Genesis of Phanerozoic Andean andesite rocks is related to the subduction of the oceanic Nazca plate beneath the South American continental plate along the west coast of South America. Exploratory data analytics is done on whole-rock geochemical data of 3311 andesite rock samples collected from the Andes orogenic belt, extending from Argentina, Chile, Bolivia, Peru, and Ecuador to Colombia. Python programming has been used for the visualisation and interpretation of large volumes of geochemical data, and data-driven tectonomagmatic inferences for volcanism extending across the South American continent have been drawn with the help of data analytics. Interelemental diagrams with Zr as a fractionation index reveal relative immobility and incompatibility of several major elements, large ion lithophile elements, high field strength elements, and rare earth elements. The interelemental diagrams, correlation matrix and heat maps drawn for major elements reveal that except K₂O, TiO₂ and SiO₂, all major elements decrease with fractionation. Major element variation trends indicate that plagioclase and pyroxene were the major fractionating phases. The density plots give insight into the range of variation and data density of major and trace elements. Petrogenetic study reveals calc-alkaline, basaltic andesite to andesite, and back-arc tectonomagmatic environment for these volcanic rocks. Mantle source of Andean magma was enriched to primitive upper mantle. Around 25–30% partial melting of the upper mantle led to the genesis of the most primitive Andean magma.

Land surface characteristics may influence the planetary boundary layer atmosphere and climate through exchanges of moisture, energy, and momentum near the surface. We attributed the impact of multitemporal landsat-derived land use/land cover change (LULCC) on temperature and precipitation variability in eastern South Africa using reanalysis data and satellite-derived estimates from 1979 to 2020. Landsat images were classified into different land cover classes using a machine learning random forest pixel-based supervised algorithm within the cloud-based Google Earth Engine. Time series analysis was employed to analyze cycles and trends in LULCC and hydrometeorological variables, whilst the variable importance model determined the most sensitive variable. The impacts of LULCC on the boundary layer climate were attributed via multiple linear regression. An uninterrupted rapid expansion of urban areas was observed, resulting in the transformation of grasslands, water bodies, forests, and croplands. Statistically significant changes in moisture and energy fluxes, and hydrometeorological variables were observed across the study period. Latent heat flux (LHF), as well as rainfall decreased, while maximum temperature, sensible heat, and potential evapotranspiration (PET) increased significantly. We found that LULCC is significantly impacting the boundary layer climate, with urban and bare land, grasslands, forests, and croplands influencing temperature positively while negatively influencing rainfall. Rainfall was most sensitive to changes in LHF, whilst the key driver of temperature variability was PET. Our results reinforce the significance of LULCC and associated feedbacks to understanding boundary layer processes, climate variability, and change.

The groundwater crisis is spreading across India at an alarming rate, and it poses a serious threat to the existence of the living world. However, the nature of scarcity is uneven and spread to some unexpected locations, such as the Darjeeling Himalayan region, where rainfall is adequate. Darjeeling Himalaya, one of the most attractive tourist destinations in India, is facing a water crisis due to adverse topographical characteristics, climatic issues, and over-exploitation along with rapid urbanization, booming tourism industry, excessive deforestation, and climate change, which are making the situation progressively worsening. Consequently, a huge imbalance is generated between the requirements and availability of water, which promotes the black-marketing of water and has flourished in the entire region. For this reason, the assessment of the groundwater potential zone and its distribution in the Darjeeling Himalayan region become very important. In this regard, eight thematic maps are prepared with the help of geospatial techniques and integrated with frequency ratio and analytical hierarchy methods to prepare a groundwater potential map of the study area. The final map is reclassified into five potential zones, i.e., very low, low, moderate, high, and very high classes, respectively. The results of this analysis show that the success rate and prediction rate of the FR model are 87 and 83%, respectively. On the other hand, success and prediction rates for AHP methods are 84 and 79%, which indicates the performance of the FR method is slightly better than the AHP method. The overall result may be helpful to planners in better management or sustainable use of groundwater resources.

Atmospheric aerosols partly compensate for the warming due to greenhouse gases by perturbing the radiation balance of the Earth–Atmosphere system. In this study, the impacts of aerosols on surface temperature are examined over the Indo-Gangetic Plain (IGP) and Himalayan Tibetan Plateau (HTP), where diverse aerosol and climatic conditions prevail. Both regions have significant impacts on the regional climate and hydrological cycles in South Asia. The IGP experiences high aerosol loading throughout the year and is expected to affect surface temperature significantly. In contrast, the HTP exhibits relatively pristine or lower aerosol loading, whose effects on surface temperature are highly uncertain due to snow albedo feedback. Climate model simulations are used to decompose the surface temperature changes due to aerosol forcing to its radiative and non-radiative components over the IGP and HTP. The shortwave cooling due to aerosols is mostly compensated by the decrease in sensible heat over the IGP. On the other hand, HTP experiences surface cooling due to the direct effects and surface warming due to aerosol-induced snow-darkening effects (deposition of absorbing aerosols on snow). The net effect of aerosols on shortwave radiation is further redistributed into non-radiative heat fluxes. This study provides a better understanding of aerosol-induced surface temperature change and its partitioning into radiative and non-radiative components.

The present study deals with the Fe–Ti oxides, rock magnetic and anisotropy of magnetic susceptibility (AMS) carried out to determine the magneto-mineralogical characteristics and the nature of the magnetic fabrics of the high-grade metamorphic rocks from Chilka Lake area, Eastern Ghats Mobile Belt (EGMB), India. Petrography depicted the presence of both primary and secondary magnetite and titano-magnetite as the chief magnetic minerals. Different generations of magnetite were found in these rocks corresponding to different conditions of temperature that prevailed during their oxidation, having tectonic implications depicting the crustal upliftment. Thermomagnetic analysis, isothermal remanent magnetization (IRM), hysteresis loops and backfield IRM demagnetisation show the presence of ferrimagnetic minerals of different origin. The magnetic domain is determined by Day plot, using the remanence and coercivity values from hysteresis curves, dominated by stable single domain (SSD), which reveals the potentiality to record the ancient magnetic field. AMS studies unravelled the nature of the magnetic fabrics in the region. The magnetic susceptibility ellipsoids are dominantly oblate as revealed from the P_j–T_j shape plots. The magnetic lineation is highly variable which states the multiple phased tectono-metamorphic conditions. The similarity between the magnetic and mesoscopic fabrics in the granulite is significant, whereas the anorthosites result from felsic magmatism, which occurred after the deformational phases and thus did not record any mesoscopic tectonic significance.

Wildfires are serious hazards for the environment, and WFSD (Wildfire Smoke Detection) is a challenge for ensuring optimal response and mitigation efforts. Hence, this study suggests an attention-based YOLOv5 (You Only Look Once) network for detecting smoke instances within video frames, particularly ECA (Efficient Channel Attention), GAM (Global Attention Module) and CA (Coordinate Attention). Here, an open-source wildfire smoke dataset divided into train, validation and test set is used for experimentation. The comprehensive research and evaluations show that the incorporation of attention mechanisms successfully enhances the accuracy and robustness of the YOLOv5 model for WFSD. In the training among the attention modules, GAM appears as the most effective, attaining an improved 95% F1 score on the dataset. This research provides the impact of attention mechanisms on object detection in the context of wildfire smoke. The findings of the research paper contribute to improving the capabilities of deep learning models for emergency response and environmental monitoring. The proposed methodology not only outperforms regular YOLOv5 but also sets up a benchmark for future research of WFSD.

Land use land cover (LULC) changes hugely influence the ecological balance of an ecosystem, which adversely affects the inhabitants, making them more vulnerable to natural calamities. The LULC change studies are therefore carried out to analyze the impact of these changes on the overall ecology of an area and are very helpful in policy framing and proper management of the available natural resources. In this study, changes in the land use and land cover for a three-decade period spanning from 1992 to 2020 have been monitored in the valley of Kashmir using remotely sensed satellite data obtained from USGS/NASA’s Landsat repository. Considerable changes in the LULC patterns were observed with a significant reduction in the area covered by water (18.21%), forest (13.56%), snow/glacial cover (29.32%) and agriculture (22.37%) during the past three decades. Concurrently, expansion in the land covered by urban areas (22.33%), barren land (37.32%), plantation (14.53%) and marshes (13.21%) were noted. The calculated Normalized Difference Water Index (NDWI) confirmed an overall reduction of 51.1% in the water and glacial cover of the study area. Significant changes in the form of forest, water and glacial cover transforming into urban, marshy and barren areas can be largely accredited to increased human interference that may have serious repercussions on the environment.

Heat waves (HWs) are currently one of the most dangerous natural catastrophes both globally and in India, particularly upsurged in urban areas. Bhubaneswar, the capital city of Odisha in India, experiences heatwaves (HWs) each year from the pre-monsoon season to the onset of the summer monsoon. The manifest increase in intensity and frequency of HWs over Bhubaneswar leads to a higher death toll, and increasing vulnerability demands accurate prediction in advance over HWs-prone zones. Numerical weather prediction models are capable of predicting these HWs, subject to the customization of suitable physical parameterization schemes. In this context, the role of five planetary boundary layer (PBL) schemes such as Yonsei University (YSU), Asymmetric Convection Model version 2 (ACM2), Medium Range Forecast (MRF), Mellor–Yamada–Janjic (MYJ), and Bougeault Lacarrere (BouLac) are assessed in predicting six HW events over Bhubaneswar city using a very high-resolution (500 m horizontal resolution) Weather Research and Forecast (WRF) model. The model simulated results are verified against the Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis of high-resolution gridded hourly datasets. The performance of the PBL schemes varies with the meteorological parameters that have a physical relationship with HWs. The composite of statistical analysis shows that the ACM2 scheme performs better for the maximum temperature with lesser root mean square error (RMSE) by 1.67°C. The BouLac shows a lesser RMSE of 1.25°C for the early morning temperature. ACM2 and BouLac schemes have replicated the zonal (meridional) wind with an RMSE of 1.47 and 1.79 m/s (2.86 and 2.81 m/s), respectively. Both the BouLac and ACM2 performed well in representing PBL height and relative humidity. The aggregated rank analysis reveals that BouLac and ACM2 are suitable for the prediction of HW over Bhubaneswar city. The city is underwarming during the HW period, and the UHI is about 0.77°C. PBL schemes are overestimating the UHI, and a possible reason might be representations in fluxes and land-atmosphere interactions. The spatial and temporal distribution of energy fluxes simulates the same over built-up areas irrespective of the PBL schemes used in the WRF model.