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A catastrophic volcanic explosion took place in Thera/Santorini island around 1613 BC, known as the ‘Minoan’ eruption. Many papers have dealt with the shape of the shoreline of the island before the eruption, but none with the shape of the shoreline exactly after it, assuming that it would be the same with the contemporary one. However, this is not correct due to the wave erosion. In this paper, a new DEM was constructed, covering both land and submarine morphology, then topographic sections were drawn around the island. Using these sections, the ‘missing parts’ (sea-wave erosion) were calculated, the shoreline was reconstructed as it was one day after the eruption and finally the erosion rate was calculated.

Abstract: In this paper, gravity dataset acquired from a recent ground survey spanning 3500 km2in parts of Mahakoshal–Vindhyan Basin, central India, has been analysed with an aim to evaluate gravity anomalies and assess structural attributes that could identify potential areas for mineral exploration. The Bouguer gravity anomaly exhibits a total variation of 41 mGal with a prominent high in ENE–WSW axis demarcating the Mahakoshal Fold belt, whereas, a low gravity anomaly (–48 to –59 mgal) is predominantly observed over Vindhyan basin. Various geophysical processing techniques identify absence of deformations in the Vindhyan basin. The data analysis shows the presence of ultramafic intrusives, a few anomalous zones corresponding to shear and fault zones in Mahakoshal, trending in multiple directions (E–W, ENE–WSW, NE–SW, and N–S). These tectonic features significantly impacted the development of the Mahakoshals and Vindhyans. Derivative-based anomaly maps reveal three distinguished high-gradient fault zones in the north, east and south of the Mahakoshal belt. In the southeast of the Mahakoshal belt, a prominent low gravity anomaly of shear zone, probably caused by rifting, is delineated. The depths of anomaly sources are estimated using spectral analysis, Euler solutions, Werner solutions, and analytical maxima solutions. Furthermore, 2D forward modelling of gravity data along a specific profile aid in constructing a schematic evolutionary model for the Mahakoshal and Vindhyan regions in the study area. Notably, the present work reveals distinct curvilinear sheared basement zones in the southeastern Mahakoshal belt, suggesting potential zones for future mineral exploration through integrated studies. Highlights:

The paper presents the geophysical anomalies of a ground gravity dataset acquired at a station density of 2.5 km2over 3500 km2in segments of Vindhyans and Mahakoshals.

Gravity data evaluation discerns the presence of high-density ultramafic rock as well as a distinctive anomalous shear zone controlled by supracrustal faults within the Proterozoic Mahakoshal Fold belt.

Based on the geophysical characteristics and structural assessments, three potential target zones have been identified for future mineral exploration.

A scored lithological map including 10 radiometric units is established through applying factor analysis approach to aerial spectrometric data of Area-1, Syrian desert, which includes Ur, eU, eTh, K%, eU/eTh, eU/K%, and eTh/K%. A model of four rotated factors F1, F2, F3, and F4 is adapted for representing 234,829 data measured points in Area-1, where 86% of total data variance is interpreted. A geological scored pseudo-section derived from the lithological scored map is established and analyzed in order to show the possible stratigraphic and structural traps for uranium occurrences associated with phosphate deposits in the studied Area-1. These identified traps presented in this paper need detailed investigation and must be necessarily followed and checked by ground validations and subsurface well logging, in order to locate the anomalous uranium occurrences and explore with more confidence and certitude their characteristics as a function of depth.

In the Earth’s atmosphere, greenhouse gases (GHGs) and reactive trace gases are essential components of chemistry–climate interactions. These trace gases are emitted from both natural and anthropogenic sources over terrestrial and marine regions. Air–sea exchange is the dominant process controlling the distribution of several important trace gases over remote marine regions. Although the ocean–atmosphere interface covers ~70% of the Earth’s surface, the quantitative air–sea exchange of reactive trace gases is estimated over the limited oceanic regions. The production and air–sea exchange of trace gases are controlled by physical conditions at both sides of the interface and ocean biogeochemistry. The northern Indian Ocean (NIO) experiences strong seasonal monsoon winds and intense tropical cyclones. Consisting of the Arabian Sea and the Bay of Bengal, it is one of the most biologically productive regimes of the world ocean and home to the intense oxygen minimum zone (OMZ) of the Arabian Sea with dissolved oxygen concentrations. Thus, the NIO offers a unique system to investigate the air–sea exchange processes of reactive trace gases. So far, most of the studies of air–sea exchange of trace gases is focused on the Atlantic and Pacific Oceans, while studies over the northern Indian Ocean are very limited and reported mainly for CH4, CO2and N2O. Although progress has been made in recent years, studies of air–sea exchange of reactive trace gases such as non-methane hydrocarbon (NMHCs), oxygen-, sulfur- and halogen-containing hydrocarbons remain scarce. This paper addresses the current understanding of air–sea exchange processes and fluxes of reactive trace gases, including NMHCs, dimethyl sulfide (DMS), oxygenated volatile organic compounds (OVOCs), halocarbons, carbon monoxide (CO) and ozone (O3) in the northern Indian Ocean. This review summarizes the studies on the air–sea exchange of trace gases over the northern Indian Ocean and common parametrization approaches used to estimate the air–sea flux of gases. Flux range for ethene (3–10.35 µmol m–2d–1), isoprene (0.215–0.172 µmol m–2d–1), acetaldehyde (–6.75–11.35 µmol m–2d–1), acetone (–9–9 µmol m–2d–1), DMS (0.03–41.4 µmol m–2d–1) and CO (1.4–5.4 µmol m–2d–1) over the NIO were summarized from various in-situand modelling studies. The paper addresses the importance of the northern Indian Ocean apropos the production and exchange of reactive trace gases, the knowledge gaps and the future scientific scope. Additionally, it emphasizes the need for a multidisciplinary study of oceanic reactive trace gas cycling and its impact on regional atmospheric chemistry over the northern Indian Ocean.

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τcand Pdmethods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (MJMA) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records.

The present work serves as a theoretical commentary over the Himalayan Re-gridded and Observational EXperiment (HiROX) and distributes the conceived framework grown under the two-phase experiment. HiROX is a scientific effort aimed at revising the existing precipitation knowledge over the Central Himalayas over the state of Uttarakhand, including the Upper Ganga Basin. The first phase of the study is experimenting with machine learning methods aimed at developing re-gridded precipitation datasets capable of incorporating selective precipitation features and distributing the developed machine learning framework to the research community. The model is designed for easy reproduction of re-gridded precipitation data and the ability to adapt to the available precipitation datasets subject to missing and/or modified inputs. The model is capable of ingesting gridded precipitation datasets at varying spatial resolutions, along with point data values, and can return gridded precipitation information at a spatial resolution of 5 × 5 km2. The second objective of the companion paper under HiROX is the realization of the features of the developed model and exercising it in the development of two high-resolution precipitation datasets at daily timesteps, namely HiROX-1 (~50 yrs) and HiROX-2 (~20 yrs). The present paper limits the discourse to robust development, and the produced datasets are discussed and distributed in the joint paper – Himalayan Re-gridded and Observational Experiment (HiROX): Part II – Application.

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.

Abstract: The December 26, 2004 tsunami waves caused significant destruction that changed geomorphic characteristics in India’s east coast. The article presents a comparative study of granulometric and granule trends of pre-tsunami (December, 2004) and post-tsunami (January, 2005) sediments with recent beach sediments (December, 2022) in eight locations, namely, Poompuhar, Chinnankudi, Kuttyandiyur, Chandrapadi, Kottucherimedu, Karaikal Beach, Vadakku Vanjiyur North, and Nagoor Beach. The study was conducted using R Programming and aimed to understand the sediment transport and depositional processes. The textural parameters of the sediments imply that they are becoming relatively finer, stating that the beaches are recovering and reclaiming their regular depositional environment after the disaster. The linear discriminate function plots of pre-tsunami sediments have the highest percentage of aeolian deposits, followed by post-tsunami. The recent sediments have the maximum deposition by the beach environment, and pre-tsunami sediments have the minimal. CM plots show that the transportation of sediments by rolling is more in recent days, whereas sediments were transported by bottom suspension and rolling and by graded suspension in pre- and post-tsunami environments. Further, this study compared sediment roundness between the pre- and post-tsunami periods and the recently deposited sediments. This paper highlights the sediment characteristics observed, especially in the aftermath of natural disasters such as tsunamis. Research highlights:

The study provides valuable insights into the origin of the debris, historical transportation, and depositional activities along the coast.

The study of pre-, post-tsunami and recent beach sediments along the coast from Nagoor to Poompuhar have been done, in which, the sediments have become relatively coarser from pre to post tsunami deposition condition and in recent deposition, the sediments are becoming relatively finer.

The pre-tsunami sediments have the highest percentage of aeolian deposits, followed by post-tsunami. In contrary, recent sediments have the maximum deposition by beach environment.

The transportation of sediments by rolling is more in recent days.

The beaches are recovering and reclaiming their regular depositional environment in recent times after the disaster which is evident by this study.

The paper describes the procedure employed for developing a new earthquake zone map of India as part of the seventh revision of the Indian Earthquake Standard IS 1893 (Part 1). This new zone map is based primarily on a probabilistic earthquake hazard analysis performed at a grid spacing of 0.1°×0.1° in longitudes and latitudes of the entire country. But, for grid locations with small probabilistic hazard estimates, a minimum level of hazard has been estimated deterministically for the most likely maximum magnitude of an earthquake on the nearest mapped fault. Based on the results, the Indian landmass is grouped into five zones, designated as ‘earthquake zones II, III, IV, V, and VI.’ The peak ground accelerations corresponding to a return period of 2475 yr in these zones are estimated as 0.15, 0.30, 0.45, 0.60, and 0.75g, which also include the site amplification effect. Common normalized response spectra are recommended for all five zones, one for each of the three different site soil conditions, as an interim measure.

The Cauvery Basin is one of the prolific hydrocarbon-producing basins of southern India. The pericratonic basin has five sub-basins separated by five basement and structural highs formed by granitic and gneissic rocks of the Archaean Southern Granulite Terrane. The sub-basins have excellent Cretaceous source rocks and hydrocarbon-bearing reservoirs of different geological ages. Commercial hydrocarbons are produced from the Archean basement highs, specifically from the Kumbakonam–Madanam palaeo-highs of the Cauvery basin. The paper addresses basement characterisation using conventional as well as advanced well logs for accurate characterisation of the basement reservoirs. Basement reservoirs are challenging in terms of sporadic porosity and permeability distribution. Most of the porosities and permeability are attributed to secondary generation by fracturing and weathering. The present work aims to understand and compare the petrophysical attributes of two kinds of basement reservoirs (fractured basement and weathered basement) on the Kumbakonam–Madanam High. Two wells from each type lying on either side of the Madanam High were selected. Well log data, including gamma-ray, resistivity, porosity (neutron and bulk density), acoustic logs and advanced logs, such as resistivity images and dipole acoustic and elemental capture spectroscopy logs, from these four wells were analysed for their petrophysical understanding. Furthermore, image and acoustic log data were integrated to characterise the fracture geometry and fracture permeability of the basement reservoirs. Conventional log suites showing low GR, low resistivity, high density, and high neutron porosity are indicative of intricate lithologies, possibly mafic rocks. A notable negative crossover in density and neutron log along with excess Si concentration indicates weathering. A higher fracture density with a crisscross fracture/mesh fracture pattern is indicated by the analysis of image logs, shear wave anisotropy, and Stoneley fractures. Thus, a comparison of the petrophysical attributes of both fields is attempted to understand the fractured and weathered basement reservoirs and their geological characteristics. In conclusion, the basement reservoirs of the Madanam High Field are of interest because of their hydrocarbon-producing ability, and proper synthesis of petrophysical attributes will help develop activities in the basement reservoirs.

Abstract: Glaciers are one of the most active erosional forces sculpting mountainous regions, and understanding the diversity of glacial valleys is a key to advancing knowledge of mountain evolution. ‘Inverted trapezoidal’ glacial valleys are an important type of glacial erosional landform, and research on their morphologic characteristics and causal mechanisms might contribute to a deeper comprehension of the variety of landforms formed by glacial erosion. In this paper, ALOS 12.5m DEM data and field geomorphologic surveys were used to quantitatively extract 171 transect parameters from 32 valleys in the study area and to quantitatively describe the morphologic characteristics of the inverted trapezoidal glacial valley transects based on the V-index model. The results show that the minimum V-index value for glacial valley cross-sections in the study area is 0.42, the maximum value is 0.86, and the mean value is 0.60. The most important factors influencing the diversity in the morphology of glacial valleys in the study area are the area of glacial action and climatic conditions. The formation of inverted trapezoidal glacial valleys may be attributed to the stable glacial action region of ice caps with gentle topography in Geladandong snow mountain. Research highlights: The minimum value of the V-index for glacial valley cross-sections in the study area was 0.42, the maximum value was 0.86, and the mean value was 0.60. The greatest number of cross-sections were concentrated in the 0.60 ~0.70 interval. The most important factor influencing the diversity in the morphology of glacial valleys in the study area are the area of glacial action and climatic conditions. The formation of inverted trapezoidal glacial valleys may be attributed to the stable glacial action region of ice caps with gentle topography in Geladandong snow mountain.

Abstract: This paper investigates the initiation and evolution of polygonal crack patterns in desiccating soil layers with varying thicknesses and grain sizes, both with and without a secondary saturated sand layer placed beneath the soil layer. Single-layer experiments involved soil samples within a specific range of grain sizes (0–100, 100–300, 300–500, and 500–850 μm), maintaining soil layer depths at 10, 20, 30, 40, 50, and 60 mm. In double-layer experiments, a saturated sand layer was introduced below the soil layer to check the role of relative layer thickness on crack patterns. Time-lapse photography captured surface crack development during desiccation, allowing for the measurement of geometric parameters like crack width, crack intensity factor (CIF), and intersection angle (CIA). The single-layer models indicate an increase in crack width and CIF with greater layer thicknesses, while CIA decreases with increased layer thickness. Additionally, experiments with finer grain sizes exhibit relatively wider cracks, along with higher CIF and CIA. In double-layer models with varying thicknesses of individual layers, crack growth is found to be independent of the upper soil layer's thickness. Instead, crack propagation is controlled by the lower sand layer, as the supply of water from the lower sand layer to the upper soil layer facilitates prolonged desiccation, resulting in larger CIF values. Using digital image processing and the box-counting method, we calculated the fractal dimensions (D) of the cracks were calculated. Ddemonstrates positive relationships with grain size in both single- and double-layer experiments, suggesting a self-similar evolution of crack patterns in the models with coarse-grained soil. Research highlights:

The role of basal water-rich sand layer in the growth of mud-cracks is investigated.

Layer thickness and grain size are two additional variables during the experiments.

Single mud-layer experiments were used to compare the double-layer experiments.

Basal wet-sand delays desiccation, resulting in a higher crack intensity factor.

Finer grains produce denser cracks irrespective of the imposed variables.

Low fractal dimensions in course-grained soils imply a self-similar crack pattern.

This paper discusses the upgraded data assimilation (DA) wave forecasting system at the Indian National Centre for Ocean Information Services (INCOIS). Significant wave height (SWH) observations from deep and shallow water buoys in the North Indian Ocean are incorporated into the assimilation system in conjunction with satellite observations from SARAL/AltiKa, Jason-3, Sentinel-3a, and Sentinel-3b. In deep water, satellite DA improved the SWH forecast by ~16%, and the inclusion of buoy observations enhanced it up to ~43% in the initial forecast hours. The impact of satellite DA persists throughout the forecast period of 5 days for swell height, with 11–27% improvement. Ingestion of buoy observations further improved it up to ~31.5% at the 3rdhr forecast. For the wind–sea height, buoy DA resulted in an improvement of 9–26% during the initial 12 hr. In general, the positive impact of buoy DA only lasts till the 30thhr forecast. In shallow water, the impact of satellite DA on SWH prediction is moderate, with positive improvements of 4–7.5%. Adding buoy observations significantly improved it to 10–40% in the initial 12 hr, especially for the low wave heights. Although the buoy DA improved the prediction of wave periods compared to the non-assimilated model, it tends to degrade the forecast compared to the model with satellite DA alone. This points to the limitation of the current assimilation approach in correcting the wave energy distribution in the background spectrum, particularly at higher frequencies.

Landslides are prominent geomorphological processes in active mountain belts hindering urban development and food production projects. Generally, heterogeneous geomorphological contexts are the most affected, particularly in the Mediterranean rim. On the other hand, landslides in homogenous contexts remain under-investigated. In such conditions, the monotony of certain causative factors may conceal the parameters controlling the landslide's magnitude and typological differences. In this paper, the frequency–size distribution of landslides was performed to investigate the effect of the main categories of the landslide causative factors to identify the key features connected to the magnitude of these gravitational processes. Results show that the main typological difference is related to the land use and lithological categories in a way that marls and cereal farming slopes promote small-size and hence shallow movements, while olive growing terrain for the land use and calcareous marls and colluvium for the lithological factor promote larger and deeper processes. Furthermore, the slope direction has also proved to be an influencing parameter on landslide typology, where slopes remaining in the shade during wet seasons (northern slopes) promote more shallow movements than those in the Sun. These findings show that even in monotonous contexts such as the foreland of the Rif chain (the Prerif), entities controlling the typological difference can be found and investigated, which improves our knowledge about the landslide hazard.

Core samples analysed through CT scans provide crucial reservoir information for reservoir development, encompassing porosity, permeability, pore size, grain distribution, grain sphericity and roundness, and distribution of their mineralogical assemblages. While 3D CT scans offer detailed insights, their availability is limited. This paper introduces a novel machine learning-based workflow for extracting reservoir properties from 2D CT scan images. The k-means clustering algorithm was applied to segregate and separate the constituent assemblages of the samples and consequently estimating effective porosity, clay volume, and individual pore area of the core samples. Subsequently, we employ a Monte-Carlo simulation on the Kozeny-Carman equation to predict low, mid, and high-case permeabilities of the core samples. The sphericity and roundness of the grains were estimated from the grain assemblage. Furthermore, grain sphericity and roundness were analysed using spectral clustering, another machine learning based algorithm in order to cluster the grains based on these attributes, which help in understanding the depositional transport mechanism of the grains. The feasibility of the proposed method was tested over the sandstone samples from the Lower Fulmar formation (Jurassic) and Leman formation (Permian) of the Anglo-Dutch and Central Graben basins in the North Sea. The results from the study show good correlation and inline with experimental results performed on the core samples. The methodology demonstrated is unique and has novel applications including simulating fluid flow behaviour in porous and permeable media of different constituent assemblages.

Abstract: Solar irradiance is a component of the climate system that plays a major role in the global climate change phenomenon. Top of the atmosphere (TOA) radiative flux affects the temperature of the Earth’s surface and the atmosphere. In this paper, the impact of CO2removal on radiative flux at the TOA and surface over West Africa is investigated. A decrease in TOA downwelling shortwave radiations across the periods is simulated, signifying a reduction in warming at the TOA within latitude 14°N and above. Reduction in warming is projected in Niger, Burkina Faso, and Mali in the period 2020–2049 as TOA upwelling longwave radiation increased in these locations. Significant changes are projected more under clear sky conditions than cloudy. Removal of 1.13 ppm/yr (8.81 Gigatonnes of ${\text{CO}}_2$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{CO}}_{2}$$\end{document}per year), 2.23 ppm/yr (17.39 Gigatonnes of ${\text{CO}}_2$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{CO}}_{2}$$\end{document}per year), and 3.50 ppm/yr (27.30 Gigatonnes of ${\text{CO}}_2$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{CO}}_{2}$$\end{document}per year) is projected for the periods 2020–2049, 2040–2069, and 2071–2100, respectively. Clear sky condition simulation shows a greater reduced level of warming before the end of the year 2100. Radiative flux reduction is projected far more at the surface than at TOA. Climate sensitivity and radiative forcing of −2.2 ± 0.1°CW/m2and −0.4 ± 0.1 W/m2are obtained, respectively. In contrast, a climate system’s response to forcing of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta T$$\end{document}= 0.88°C and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta T$$\end{document}= 3.76°C per unit decrease in radiative forcing are projected on global and West African scales, respectively. Research highlights:

Most radiative parameters were reduced in each period compared to the reference period under both cloudy and clear sky conditions at the TOA. Significant changes are projected more under clear sky conditions than cloudy.

The projection of decreased radiative flux over West Africa is more at the surface than at TOA.

Simulated impacts are from latitude 14°N and below as warming is reduced in this area, while moderate warming is expected above it.

Reduction in atmospheric CO2concentrations enhanced negative climate forcing.

The implementation of carbon dioxide removal is capable of reversing climate in the long run.

Urban sprawl usually expands in regularized and natural forms, and the character of an urban sprawl is found complex and unclear. Therefore, it becomes necessary to distinguish the character of an urban sprawl for sustainability in all aspects. In this paper, we have identified the proximity factors by which we could distinguish between both forms, i.e., regularized and natural expansion. We performed buffer analysis to calculate the areas covered by each land-use land-cover class in each buffer separately. Then, Shannon’s entropy index was calculated for each buffer zone and dataset. Geomatics tools were used to analyze the historical datasets, which included land-use land-cover maps, population density maps, and proximity maps.

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April–3 May 2008), Aila (23–26 May 2009) and Jal (4–8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.

This paper focuses on the Kotrupi landslide-triggered debris flow event in Himachal Pradesh, India, on March 13, 2017, leading to significant human and property losses. The study, situated in Kotrupi village of Mandi district, addresses three major landslide-triggered debris flows occurring between 1977 and 2017, each with a 20-yr return period. Ongoing erosion in the adjacent area underscores the imperative for a comprehensive investigation into debris flow events in the region. Employing the r.avaflow simulation tool, this study numerically models the slope, reconstructing the runout from the initial landslide to debris flow initiation. Calibration of friction parameters yielded optimized values of δ= 22° and ΦB= 9°. The debris flow, measuring 1044 m in total length, exhibited channelization with predetermined event volume and depth. Key characteristics included a peak velocity of 18.7 m/s, a maximum height of 7.34 m, and a kinetic energy of 375 MJ. Our findings underscore the significance of factors such as material availability, maximum velocity, release height, and proximity to streams in determining debris flow susceptibility, with peak velocity emerging as a primary factor. The r.avaflow simulation accurately reproduced debris flow metrics, indicating the potential for another event of similar or greater intensity due to the unstable slope condition. The research advocates for additional debris flow modelling to uncover novel parameters for predictive simulations. Furthermore, it sheds light on the limitations of debris flow investigations in the western Himalayan region.

In the current era, remote sensing is a powerful platform for detecting and predicting landslides. Moreover, the advancement in computing technologies has proven significant in artificial intelligence (AI) research. Researchers have made significant attempts in the existing literature by introducing landslide detection procedures from remote sensing images (RSIs) through deep learning (DpLr) algorithms. This research work aims to survey those methods. Our database consists of 204 published research articles. In addition, 50% (approximately) of the papers are directly related to landslide information extraction from satellite and unmanned aerial vehicles (UAV) images exploiting DpLr models. The suggested methods have been categorized into seven parts based on the applied model. Further, the evaluation methods have been discussed. The quantitative results are based on the following parameters: (1) contributing nations, (2) key study locations, (3) data set distribution, and (4) model utilization. Lastly, challenges in the studies of DpLr algorithms and the opportunities in landslide detection problems are discussed to motivate future research.

Biodiversity reserves are a crucial in-situmethod to conserve biodiversity hotspots as they are sensitive to climate change. The Nanda Devi Biosphere Reserve (NDBR) in the western Himalayas is enriched with diverse endemic flora and fauna and endorses the second-highest mountain peak in the world. However, in the recent decade, this region has potentially warmed at an alarming rate. With 36 temperature and precipitation indices from high-resolution 40-year data from ERA5 reanalysis and CHIRPS, this paper assesses the state of warming and extreme climatic events. Apart from the indices, Landsat (NASA/USGS, USA) and QuickSCAT (ISRO, India) were utilized to assess the region’s response to climate change. An increase of 0.73ºC in the last decade for minimum, 0.26°C for maximum temperatures was observed, with the highest anomaly of 1.7°C in 2016. The reserve’s vegetation pattern has changed with the vegetative region’s dispersal towards the north and higher elevations. In the year 2000, the area without any vegetation covered 79% of the total area, which declined to a mere 23.8% in the year 2020, equivalent to a 70% decline in the area. Similarly, the area with very dense region covered only 0.02% of the total area in the year 2000, and in the year 2020, it increased to 109%. Snow cover seems to be worst affected in the region with dense snow cover declining maximum by 2020. From coverage of 12.3% of the total area of the reserves, it was reduced to a mere 0.02%, showing a decline of nearly 100% in the region. Our findings show that although protected areas are meant to be resilient to external anthropogenic intrusions, they are highly susceptible to the intrinsic forces of induced climate change. We suggest that reserve managers enable robust measures to identify the distribution of vulnerable species and introduce new methods to preserve the pristine hotspot region.

Southward component of Interplanetary Magnetic Field (IMF) leads to the entry of energetic particles of solar origin into the magnetosphere. A part of this energy is subsequently released from the night-side as a geomagnetic substorm via the loading and unloading process in the magnetotail. However, other than this classical understanding of substorm, there exists an alternate particle entry mechanism, which mainly triggers weak to moderate substorms. So far, there is no clear understanding of how an intense substorm can be triggered without appreciable southward turning of IMF. In this paper, we have carried out a detailed multi-instrument analysis of a very strong substorm associated with the impingement of solar wind pressure impulse that took place on 21st January 2005 when IMF was not southward. It is observed that during the initial ~10 minutes of the substorm onset, the AL index reached ~–2000 nT, with particle precipitation being centred near dawn and not midnight. The usual substorm expansion phase with typical signatures of Near-Earth-Neutral-Line (NENL) magnetic reconnection, dipolarization and plasmoid formation started when IMF turned southward after ~10 minutes of the first onset on this day. Using the present case study, we have tried to explain what are the characteristics of shock-triggered substorms and where the energy comes from to power such strong events. The dominance of dawn centered precipitation during the initial phase can be attributed to increased particle interaction with the ULF waves enhanced due to prevalent K–H instability caused by strong velocity shear in the dawn sector.

Abstract: Hyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at each pixel. This data can be used in numerous remote sensing (RS) applications, along with mineral mapping. Mineral mapping is an important component of geological mapping, which helps in investigating the mineralization potential of an area. This work can be completed effectively by applying machine learning (ML) techniques to RS data. This paper proposes a stacked ensemble-based framework for mineral mapping using the dataset obtained by the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). The study area is situated in Jahazpur, Rajasthan, India. The purpose of this stacked ensemble-based model is to enhance the performance of ML-based mineral mapping. The proposed stacked ensemble model consists of two major elements: a base learner (Naïve Bayes, KNN, artificial neural network, decision tree, and support vector machine) and a stacked learner (random forest). The results of the experiments show that the stacked ensemble-based model has a lot of potential for accurately mapping the minerals talc, montmorillonite, kaolionite, and kaosmec. The proposed model has obtained an overall accuracy of 98.96%, an average accuracy of 98.21%, and a Kappa coefficient of 0.9628. Research highlights:

A stacked ensemble-based model for mineral mapping is proposed.

The well-known five conventional machine learning models (called base models) are investigated for mineral mapping.

The performance of the proposed model is evaluated on the AVIRIG–NG dataset. The study area is situated in Jahazpur, Rajasthan, India.

The proposed method outperformed all base models.

In machine learning, ensemble learning methods (ELM) consist of combining several machine learning algorithms to obtain better quality predictions compared to a single model. The basic idea of this theory is to learn a set of classifiers and allow them to vote. In this paper, to correctly apply the ELM for enhancing of an artificial neural network (ANN) performances, a strategy was devised which is to divide the data to be classified into two categories, ‘easy-to-classify’ category and ‘difficult-to-classify’ category using a main ANN. Hence, reliable ANN and unreliable ANN are created and applied for the classification of ‘easy-to-classify’ data and for the classification of ‘difficult-to-classify’ data, respectively. The AdaBoost algorithm and Bagging algorithm are implemented separately on the unreliable ANN. To increase performance, the AdaBoost results and Bagging results are merged. The developed scheme is applied to remote sensing images from Meteosat Second Generation (MSG). The final results show very interesting performances in the case of the fusion of the results from AdaBoost-ANN and the results from Bagging-ANN (Ada/Bag-ANN). Indeed, the POD, FAR, CSI and Bias pass from 87.2%, 17.4%, 80.8% and 1.3 (ANN) to 96.8%, 06.8%, 92.7% and 1.1 (Ada/Bag-ANN), respectively. The same trend was observed in the case of precipitation estimates. The estimates obtained from the developed model (Ada/Bag-ANN) largely surpass those obtained from the use of ANN without ELM. Compared to ECST (Enhanced Convective Stratiform Technique), EPSAT-SG (Second Generation Satellite Precipitation Estimation), TAMSAT (Tropical Applications of Meteorology using SATellite), and RFE-2.0 (Rain Fall Estimate) which showed correlation coefficients of 87%, 81%, 76% and 71%, respectively, the Ada/Bag-ANN method shows significantly better results with a correlation coefficient of 94%.

This paper describes the development of a new Near InfraRed Imaging Spectrograph (NIRIS) which is capable of simultaneous measurements of OH(6-2) Meinel and $$\hbox {O}_{2}$$ O2 (0-1) atmospheric band nightglow emission intensities. In this spectrographic technique, rotational line ratios are obtained to derive temperatures corresponding to the emission altitudes of 87 and 94 km. NIRIS has been commissioned for continuous operation from optical aeronomy observatory, Gurushikhar, Mount Abu ($$24.6^{\circ }\hbox {N}$$ 24.6∘N , $$72.8^{\circ }\hbox {E}$$ 72.8∘E ) since January 2013. NIRIS uses a diffraction grating of 1200 lines $$\hbox {mm}^{-1}$$ mm-1 and 1024$$\times $$ × 1024 pixels thermoelectrically cooled CCD camera and has a large field-of-view (FOV) of $$80^{\circ }$$ 80∘ along the slit orientation. The data analysis methodology adopted for the derivation of mesospheric temperatures is also described in detail. The observed NIRIS temperatures show good correspondence with satellite (SABER) derived temperatures and exhibit both tidal and gravity waves (GW) like features. From the time taken for phase propagation in the emission intensities between these two altitudes, vertical phase speed of gravity waves, $$c_{z}$$ cz , is calculated and along with the coherent GW time period ‘$$\tau $$ τ ’, the vertical wavelength, $$\lambda _{z}$$ λz , is obtained. Using large FOV observations from NIRIS, the meridional wavelengths, $$\lambda _{y}$$ λy , are also calculated. We have used one year of data to study the possible cause(s) for the occurrences of mesospheric temperature inversions (MTIs). From the statistics obtained for 234 nights, it appears that in situchemical heating is mainly responsible for the observed MTIs than the vertical propagation of the waves. Thus, this paper describes a novel near infrared imaging spectrograph, its working principle, data analysis method for deriving OH and $$\hbox {O}_{2}$$ O2 emission intensities and the corresponding rotational temperatures at these altitudes, derivation of gravity wave parameters ($$\tau $$ τ , $$c_{z}$$ cz , $$\lambda _{z}$$ λz , and $$\lambda _{y})$$ λy) , and results on the statistical study of MTIs that exist in the earth’s mesospheric altitudes.

Autonomous Underwater Profiling Drifter (AUPD) is developed by National Institute of Ocean Technology (NIOT), Chennai, India, for the vertical profiling of the ocean. AUPD is a free-drifting profiling platform for near real-time in-situobservation of the upper 2000 m of the ocean column. It measures conductivity and temperature profiles with reference to depth. The AUPD transmits measured data through ARGOS satellite Platform Terminal Transmitter (PTT) to ARGOS regional centre as per the requirement of the International ARGO program. The AUPD ascends or descends due change in buoyancy (±250 cm3). While on sea surface (to transmit the profile data), the position of the float is derived using the Doppler shift method with an accuracy of ~100 m. NIOT developed AUPD and deployed in the Arabian Sea in 2013–2015. The AUPD is ballasted for a pre-deployment depth of 2000 m and pre-programmed for a typical profiling cycle of 2 days. The AUPD is designed with 32 bit Atmel micro-controller and a lithium battery bank provides power. It has endurance for 150 profiles. AUPD float is suitable to fulfil the demand for profiling floats for International Argo Program. This paper is focused on the development of AUPD. The paper also discusses testing and field deployment of AUPD float.

The early detection of track and intensity of cyclones reduces the impacts of destruction, and hence this paper proposes a model to determine the intensity of tropical cyclones from satellite images. The proposed system employs radial features and angular features to depict the characteristics of the tropical cyclone. Here the convolutional neural network (CNN) is employed in investigating the similarities between the actual recorded image and the image of the target cyclone, thereby extracting the optimal features of a tropical cyclone. The cyclone image considered as a target is the query image and bi-directional long short-term memory-based nomadic people optimisation (Bi-LSTM based NPO) algorithm predicts the intensity of the tropical cyclone. The prediction accuracy is enhanced by employing the proposed model. This paper utilised tropical cyclones for an image to intensity regression dataset (TCIR dataset). The experimental analysis is conducted to deliberate the estimation of tropical cyclone intensity. Finally, the comparative analysis is carried out for the proposed approach and other existing approaches to determine the error value rate of the proposed model.

Abstract: In this paper, detailed petrology, mineral chemistry and geochemistry of lamprophyres from the Rongjeng–Nongchram area, Meghalaya, northeast India, is being reported. These dykes are intruded into the Proterozoic Assam Meghalaya Gneissic Complex and Neoproterozoic porphyritic granites with limited field exposures in two separate clusters controlled by lineaments. These lamprophyres are alkaline, potassic to ultra-potassic, and metaluminous and have been classified into camptonite and sannite from their petrography and geochemical affinity. Resorption texture of biotite in camptonite and compositional zoning in clinopyroxene of sannite indicate their crystallization under in-equilibrium conditions and subsequent interaction with changing composition of the magma(s). Detail geochemistry indicates fractionation as the imperative mechanism during the evolution of the magma(s) and shows heterogeneity at the mantle source and evidence for minimal crustal contamination. Various tectonic discrimination diagrams indicate the emplacement of the lamprophyres in a post-collisional and within plate setting, signifying a possible plume mechanism involved in their emplacement. The affinity of the lamprophyres towards an OIB setting was recorded from the chondrite-normalized REE and primitive mantle-normalized multielement patterns. The geochemistry of the lamprophyres points towards the presence of possible enriched metasomatic pods in the mantle, which might have melted due to the heat from the mantle plume and triggered the emplacement of the lamprophyres in the Rongjeng–Nongchram area. Research highlights:

New lamprophyre bodies from Garo Hills, Meghalaya Plateau, NE India, are being reported with detailed petrology, geochemistry, and mineral chemistry.

The lamprophyres are of alkaline affinity.

The Kerguelen plume played a vital role in the generation of these lamprophyres.

There are geochemical signals of the presence of enriched metasomatic pods in the mantle, which were melted due to plume interaction.

The study also indicates a distinctive mantle source heterogeneity across the Meghalaya Plateau.

The Himalayan Mountain range is prone to landslides and its influential factors vary from region to region. To identify these factors, it is essential to have comprehensive information about the study area. This study aims to assess Landslide Susceptibility Mapping in parts of the Aglar watershed in the Lesser Himalayas using the frequency ratio method in the GIS environment. An inventory map in the first place of landslide events was prepared using satellite data and field survey studies. In this study, pertinently, sixteen conditioning factors were considered as slope, aspect, profile curvature, plan curvature, elevation, relative relief, geology, distance to stream, distance to road, normalized difference vegetation index, stream power index, topographic wetness index, rainfall, distance to lineaments, geomorphology, and land use land cover based on the local topography and climate. To gain a comprehensive understanding of the study area, subsequently, the frequency ratio of each factor was analyzed. This determined the correlation between landslide classes and each class of conditioning factors. A thorough analysis was conducted to select the most significant factors using the higher and lower prediction ratio values. This study was eventually conducted by comparing three susceptibility maps that were created based on prediction ratio values. To confirm the comparison, the ROC–AUC curve was utilized. The accuracy of the results was evaluated through a prediction rate and success rate curve, resulting in 78.5 and 80.7%, respectively, demonstrating a commendable performance. In this paper, the significance of different conditioning factors is evident in the increase of the very high susceptible area from 2.44 to 5.99% and eventually to 8.44%.

This paper releases two regional precipitation products produced in the Himalayan Re-gridded and Observational Experiment (HiROX) conducted over the Central (Uttarakhand) Himalayas including the Upper Ganga Basin. The distributed precipitation products cover a period of about 70 yrs: HiROX-1 ranges from 1972 to 2018 (47 yrs) and is a regridded static daily precipitation product, and HiROX-2 ranges from 2000 to 2021 (22 yrs) and is a regridded dynamic daily precipitation product with continuous upgradation capacity. These datasets are capable of (a) precisely capturing and reproducing refined spatial distribution of precipitation and (b) preserving data accuracy along with other spatiotemporal characteristics over the study region, better than that observed in popular precipitation products covering the study region. The comparative investigation established HiROX-1 as better than some or all the tested datasets in terms of (a) spatial resolution, (b) accuracy, (c) temporal resolution, (d) temporal coverage, and (e) spatial distribution, and presents their application suitability. The second precipitation product, HiROX-2, closely followed HiROX-1, as reported in the ensuing investigation. We employed an open-source precipitation model, HiROX-M1, a flexible machine learning framework capable of processing precipitation information at fine spatial resolution. These regional precipitation products are provided at a spatial resolution of 5 × 5 km2on daily scale over projected and geographic coordinate systems. HiROX-1 is a station-dependent product and HiROX-2 is a near real-time station-independent product with an updated frequency equal to that of Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Precipitation L3 1 day 0.1° × 0.1° V06 (GPM_3IMERGDF V06) product. The released products are platform-independent in choice of operating system and geographic information system (GIS) interface. The released data shall assist in closing the spatial discontinuity of ground station information over the Central Himalayan region over Uttarakhand, including the Upper Ganga Basin.

Abstract: Volcaniclastic deposits are cynosures for reconstructing volcanic archives and evaluating the tectonic imprints of the study area. This paper presents an elaborate geological field litholog, petrography and geochemistry of ~1.2 km thick sequence of the quartz-feldspar porphyry (QFP) and rhyolites in the Kadiri–Hindupur section of the Kadiri Greenstone belt from Eastern Dharwar Craton (EDC). The QFP exhibits primary stratification; the thickness of the beds varies from very thin to thick (3–8 cm) and shows large-scale tabular cross-bedding in the lower half of the section. The bedding surfaces of QFPs are marked by current crescent-shaped marks (long axis: 5–25 cm, short axis: 2–8 cm), speculated as possible remnants of volcanic bombs and pyroclastic debris during the explosive volcanism. Petrographically, these QFPs are characterised by spectacular blue opalescent quartz grains with embayed grain boundaries, anhedral feldspar with sieved texture, fractured plagioclase and polycrystalline groundmass suggesting a volcaniclastic origin. They are also endowed with significant post-depositional textures such as domino-listric faulting and sigma-shaped porphyroblasts. Geochemically, QFPs are unaltered with trachyte-trachy dacite compositions showing high-potash, calc-alkaline nature displaying shoschonitic affinity. The primitive mantle normalised trace and rare earth elements are marked by an enriched LREE-LILE pattern coupled by negative zircon–hafnium, niobium–tantalum and titanium anomalies affirming their common genetic link to a prominent island arc–back-arc system where explosive volcanism produced the felsic crystal tuffs that were deposited and preserved as volcaniclastic QFP. The associated blocks and bombs produced the current crescent-shaped marks preserved on the bedding surfaces of QFPs. Research highlights: Research Highlights

Quartz-feldspar porphyry (~1.2 km thick) occurs in the Kadiri Greenstone Belt, Eastern Dharwar Craton.

Blue opalescent quartz and deformed plagioclase porphyry indicate volcaniclastic origin.

Enigmatic current crescent-shaped marks represent remnants of volcanic bombs and pyroclastic debris.

The primitive mantle normalised REE diagram is characterised by LREE-LILE enrichment.

They represent Neoarchean pyroclastic volcanism and sedimentation in an island arc–back-arc setup.

Self-potential (SP) method has been widely used to identify some geological structures related to electrical potential. Multiple sources of anomalies give some difficulty in SP data inversion or interpretation due to a high number of model parameters. This paper proposes enhanced Runge–Kutta optimization (ERUN) using a rank-based mechanism and the chaotic logistic map to balance exploration and exploitation ability for multiple anomalies of SP data inversion in determining SP model parameters. ERUN is compared to the RUN algorithm on SP inversion problem with multiple anomalies. Model parameters are estimated using a posterior distribution model with the applied threshold. The result shows that RUN and ERUN can provide uncertainty analysis using the posterior distribution model. Furthermore, ERUN and RUN are applied to solve synthetic and field data. ERUN and RUN yield good results and show good agreement with geologic information. Model parameters generated by ERUN and RUN are appropriate for validation with prior researchers. LUSI embankment assessment using SP method analyzed using RUN and ERUN algorithm shows that ERUN has more reasonable result than RUN. Overall, ERUN is relatively more reliable in solving SP inversion problems with a relatively high number of anomalies than other algorithms tested in this research. This advantage comes with a relatively high computational cost, which makes ERUN slower.

The Paleo-Asian Ocean (PAO) had multiple collage systems and closed along multiple sutures. The final timing of the closure of the PAO was closely related to the formation of Pangea. The final timing of closure of the eastern PAO is controversial and may have been from the Middle Permian to the Middle Triassic. This paper presents new zircon U–Pb ages and Hf isotope data for sedimentary and volcanic rocks layer in the Linxi Formation in the Xingmeng Orogenic Belt (XMOB). We also obtained geochemical data for the volcanic rocks layer to constrain the timing and mechanisms of the PAO closure. Detrital zircon U–Pb dating yielded the youngest ages peak of ca. 263 and 260 Ma for two sandstone samples. Combined with the crystallization age of the volcanic rocks layer (254.2 ± 1.3 Ma), this indicates the Linxi Formation was deposited during the Late Permian. Age spectra and Hf isotopic compositions of the detrital zircons from the Linxi Formation suggest that detrital zircons with ages of <1500 Ma were derived from the XMOB, whereas those with ages of 2100–1600 Ma were derived from both the XMOB and the North China Craton (NCC). The detrital zircons with ages of >2400 Ma were derived from the NCC. Geochemical features of the volcanic rocks layer are consistent with eruption in a compressional tectonic setting and derivation by partial melting of the lower crust. Based on our data and the Late Permian history of magmatism and sedimentation, the Linxi Formation was likely deposited in a tectonic setting that transitioned from convergence to collision. Finally, we suggest the PAO closed in a ‘scissor-like’ fashion in the eastern CAOB during the Late Permian, in a setting of tectonic compression.

This paper investigates the three-dimensional frequency-dependent attenuation structure of the Garhwal Himalaya in the Indian subcontinent. Based on the distribution of earthquakes and recording stations in the Garhwal Himalaya, the entire region of 152 × 94 km2is divided into 108 three-dimensional uniform rectangular blocks. These blocks are assumed to be of thickness 5 km that extends to 15 km depth. Each block represents the rock of different attenuation coefficients. The S-phase of strong motion records has been used to estimate the shear wave quality factor in each block by the inversion of spectral acceleration data. The inversion of spectral acceleration data is based on the modified technique of Joshi (2007) and Joshi et al.(2010) which was initially given by Hashida and Shimazaki (1984). The earthquake data of 19 events digitally recorded by 33 stations of the strong motion network between 2005 and 2017 have been used in this paper. The outcome of the inversion process is the shear wave quality factor at all frequencies present in the records. The three-dimensional attenuation structure at various frequencies is presented in this paper and is correlated with the regional tectonics of the Garhwal Himalaya. The correlation of attenuation structure at 10, 12 and 15 Hz with the tectonics of the region indicates that the shear wave quality factor has a strong relationship with the tectonics of the region. The values of the shear wave quality factor at different frequencies obtained from inversion have been used to obtain a relation of shear wave quality factor Qβf=107f0.82for the region of Garhwal Himalaya for frequencies 10–16 Hz. The comparison of obtained shear wave quality factor with other studied relations clearly indicates that the obtained relation is close to what has been obtained in earlier studies and thereby indicates the reliability of obtained three-dimensional shear wave attenuation structure from inversion of spectral data.

Site and topography effects are integral part of strong ground motion recorded during an earthquake. Site effects due to shallow subsurface velocity and topographic changes have been clearly seen in the Gorkha earthquake, 25th April, 2015 (Mw7.8) at Kapkot and Berinag stations, which lies at an epicentral distance of 507 and 485 km, respectively. The high peak ground acceleration was recorded at Kapkot station that is at valley, while comparatively low peak ground acceleration was recorded at Berinag station that is at hill. This paper investigates the effect of site topography and shallow velocity structure on ground acceleration generated due to propagation of SH wave generated by a finite far-field rupture. The propagation of SH wave in a shallow subsurface earth model with the vertical variation of velocity can be modelled by finite difference (FD) method based on staggered algorithm that can effectively model the propagation of the seismic wave in isotropic as well as heterogeneous elastic medium. This paper discusses the role of staggered algorithm in the generation of particle motion at the surface of modelled earth characterized by surface topography and vertical distribution of elastic constants. The developed software for FD modelling of the medium has been tested for SH wave propagation in a purely elastic medium in terms of numerical stability, dispersion and boundary conditions. Numerical experiments show that the method effectively models the topography and thin surface velocity layers in the model for varying cases. The obtained surface acceleration records from the propagation of SH wave at Kapkot and Berinag clearly show that both the site amplification and topographic effects have played a vital role in shaping the accelerograms at these stations.

Abstract: The Palaeo Archaean (3.5–3.2 Ga) volcano-sedimentary sequence of the Gorumahisani–Badampahar greenstone (GBG) belt occurs along the northeastern fringe of the Singhbhum Craton (SC) and is widely referred to as Iron Ore Group of rocks (IOG). Though sporadic occurrences of gold mineralisation have been reported from the central and southern arms of the GBG Belt, gold mineralisation in the northern part of the belt has hitherto gone unreported. Through this paper, the authors report, for the first time, a notable gold resource from the northern arm of the GBG Belt falling in the Bhitar Dari area, East Singhbhum district, Jharkhand. The authors also attempt to understand the nature and control of mineralisation in the area. Interfolial syn-D2quartz and quartz-carbonate veins act as a host for gold mineralisation, traversing along and across major litho units comprising of inter-banded sequence of phyllite, tuffaceous phyllite, carbonaceous phyllite, fuchsite quartzite, conglomerate, cherty quartzite, BIF, chlorite schist and ultramafic rocks. Sulphide minerals such as chalcopyrite, pyrite, pyrrhotite, and arsenopyrite constitute the major ore assemblage of the area. Research highlights: The Palaeo Archaean (3.5–3.3 Ga) volcano-sedimentary sequence of the Gorumahisani–Badampahar Greenstone (GBG) belt occurs along the northeastern fringe of the Singhbhum Craton (SC) and is widely referred to as Iron Ore Group of rocks (IOG). Though sporadic gold mineralisation has been reported from the central and southern arms of the GBG Belt, gold mineralisation in the northern part of the belt has hitherto gone unreported. Through this paper, the authors report, for the first time, a notable gold resource from the northern arm of the GBG Belt falling in the Bhitar Dari area, East Singhbhum district, Jharkhand. The authors also attempt to understand the nature and control of mineralisation in the area. Interfolial syn-D2 quartz and quartz-carbonate veins act as a host for gold mineralisation, traversing along and across major litho units comprising of inter-banded sequence of phyllite, tuffaceous phyllite, carbonaceous phyllite, fuchsite quartzite, conglomerate, cherty quartzite, BIF, chlorite schist and ultramafic rocks. Sulphide minerals such as chalcopyrite, pyrite, pyrrhotite, arsenopyrite constitute the major ore assemblage of the area. Gold values range from 60 ppb to 13.9 ppm from the explored block of Bhitar Dari area. Field work along with ground geophysics, drilling, laboratory studies, including SEM/EDX and EPMA, were used to give an account of the presence of gold resources in the area.

Flood risk assessment is an important component of risk management. Given this context, this paper aims to identify and map areas with high potential for flash-floods and flooding occurrence, at different spatial scales (from catchment to local scale), in order to estimate the flood/flooding vulnerability. The paper is based on three main methods, which were applied in the Slănic River catchment (427 km2), located in the external curvature region of the Romanian Carpathians: (i) statistical analyses; (ii) determination and mapping of some indices to assess the flash-flood and flooding potential (FFPI and respectively FPI) and (iii) hydraulic modelling. The data used mainly include hydrological statistics (maximum monthly and annual discharges, flood-related data) and spatial data on catchment geographical characteristics (hypsometry, geology, soils, land use) obtained or derived from various sources (maps, aerial images, digital databases, field measurements) which were integrated into the GIS environment. The aforementioned methods helped to (i) highlight specificities of floods in the Slănic catchment (magnitude, frequency, flood waves characteristics); (ii) identify areas with high potential for flash-floods and flooding at the catchment spatial scale; (iii) assess the structural vulnerability in the Cernăteşti village, by simulating flood-prone areas for flood peaks with exceedance probability of 1%, 5% and 10%. The results could lead to a better knowledge and understanding of flood characteristics in the study area, in order to mitigate the flood risk through a more effective management, both at the catchment scale, as well as local scale (in the Cernăteşti village).

Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic studies. Detection of dust storms is critical to accurately understand dust, their properties and distribution. Currently, remotely sensed data such as MODIS (Moderate Resolution Imaging Spectroradiometer) with appropriate temporal and spectral resolutions have been widely used for this purpose. This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface. Therefore, classification-based approaches could be deployed as an alternative. In this paper, the most relevant bands are chosen based on the physical effects of the major classes, particularly dust, cloud and snow, on both emissive infrared and reflective bands. In order to verify the capability of the methods, OMAERUV AAOD (aerosol absorption optical depth) product from OMI (Ozone Monitoring Instrument) sensor is exploited. In addition, some small regions are selected manually to be considered as ground truth for measuring the probability of false detection (POFD) and probability of missing detection (POMD). The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images. Quantitatively, the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7% and 6% of POFD and POMD respectively. Moreover, results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously. The performance of the physical-based approaches is found weaker than RF due to empirical thresholds that are not always true.

This paper looks at glacier area change in the Tomur Peak for the 1964/71–2000 and 2000–2011 periods. The results show that the total area of the selected 78 glaciers has decreased from 555.45 km2in 1964/71 to 530.39 km2in 2000, and further reduced to 521.89 km2in 2011. Overall, the selected glaciers have lost 4.50% of their surface area between 1964/71 and 2000, and have lost 1.60% more between 2000 and 2011. The area reduction rate of the 78 glaciers increased from 0.12%/a during 1964/71–2000 to 0.13%/a during 2000–2011. In addition, this paper describes a method for estimating the ice surface elevation change using the SRTM (2000) and elevation data generated from topographic maps (1964/71) to quantify the ice thickness change for the 1964/71–2000 period. The surface elevation for about 73.50% of grid area decreased, mostly in the glacier ablation zone. The overall average thickness loss is 22.35 m which yields an annual loss of 0.60 m/a.

The concept of collecting surface runoff on the slopes in canals cut across the western slopes of the Western Ghats, in order to divert part of a flow in the west flowing rivers to the east by gravity, is being promoted in Karnataka. This paper presents a study of the hydrological feasibility of such canals, termed ‘garland canals’ and claimed to be an environmental friendly alternative to the conventional technologies for inter-basin transfer of water, by their promoters. The paper presents a methodology for disaggregating normal annual rainfall into 15-min magnitudes and its application for simulating surface runoff, using a knowledge of infiltration rates on the slopes. The study has been carried out considering a part of a garland canal proposed for diverting the river Netravathi in the state. The results from the study reveal that significant amounts of flow can be collected in the proposed canal only if streams are also tapped and that because of the arrangements necessary for the purpose, garland canals cannot be considered a means superior to the conventional methods for diverting west flowing rivers.

Abstract: Atmospheric Boundary Layer (ABL) height variability, along with meteorological parameters and pollutant concentrations, are studied during different months in Mumbai region for the period of November 2020 to October 2021. ABL height is measured using a monostatic SODAR (SOnic Detection And Ranging) system. The capabilities, behaviour, and benefits of employing this ground-based remote sensing system in Mumbai are examined in this preliminary report after a year of SODAR monitoring. SODAR dataset is also compared with Radiosonde input ERA5 reanalysis data. The ventilation coefficient (VC) analysis for the same time period is also covered in the paper. Especially in the months of November, December, January, February, August, September, and October, the ABL height often approached 700 m. The ABL height is increased to 1000–1200 m in April because of the strong winds and warm weather. The month of April has the greatest monthly average height (418 m), while the month of February has the lowest monthly average height (179 m). In April, when the wind speed is 7.2 m/s, the convection duration is observed to be the longest, while in December, when the wind speed is 1.75 m/s, it is the shortest. Research highlights:

For the first time, real-time hourly atmospheric boundary layer height data measured from SODAR system are analysed over the Mumbai region.

The ABL height often reached 700 m, especially in the months of November, December, January, February, August, September, and October.

Due to the high wind and warmth in April, the ABL height is extended to 1000–1200 m.

The highest ventilation coefficient values around 7095 m2/s are recorded in the month of April, while the lowest values around 2912 m2/s are recorded in the months of November and December.

Cloudburst is a transient phenomenon, where intense rainfall occurs in a localised area in a very short period, resulting in catastrophic effects like landslides and floods. The Western Himalayas region is found to be more vulnerable to cloudburst events. In this paper, the robustness of the alerts provided by the nowcasting model NETRA (Nowcasting of Extreme Orographic Rain) has been evaluated for the Uttarakhand Region. These alerts are routinely provided over Indian Space Research Organisation (ISRO) web portal www.mosdac.gov.in. It was found that the alerts generated from NETRA compare very well with actual events with district-level accuracy. The results also indicate that May month is more vulnerable to cloudburst events. From the perspective of location, the districts of Chamoli, Rudra Prayag, and Uttarkashi are found to be the most vulnerable. Among all districts, NETRA gave the best location accuracy for the district of Uttarkashi. Moreover, Apriori-based analysis is used to find the co-occurrence patterns in a cloudburst. The co-occurrence analysis brings out the spatial linkage between districts concerning cloudburst. We find out that Pauri and Uttarkashi exhibit the maximum co-occurrence probability.

The ionosphere is a part of the Earth’s atmosphere with complex temporal and spatial distribution and variation. Radio occultation can obtain the vertical distribution of ionosphere, effectively making up for the deficiencies of ground-based Global Navigation Satellite System (GNSS) and other ionospheric sounding technologies; however, the inversion results are subject to the hypothesis model and observation accuracy, making it important to conduct quality assessment. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) project started in 2006 and has been renewed since 2019. So far, a large amount of observation data has been accumulated. In this paper, we used the electron density profile data obtained by COSMIC-1 and COSMIC-2 to evaluate the quality. Four evaluation parameters, which only consider the profile’s self-characteristics, were used, and the spatiotemporal distribution characteristics of the data quality were analyzed. The experimental results show that the number of occultation electron density profiles of COSMIC-2 is significantly greater, which is about seven times greater than COSMIC-1. In terms of spatial distribution, the unqualified ratio of COSMIC-1 profiles is less than 25% in most mid-latitude areas and about 15% near the equator, while the unqualified ratio rises to 25–50% in high-latitude areas and reaches 65% or even higher in part of the polar region. The unqualified ratio of COSMIC-2 profiles is about 25% near the equator and about 15% in middle and low-latitude areas. In terms of seasonal distribution, the quality of profiles is the worst in winter, followed by summer, and best in spring and autumn. In terms of day–night distribution, the unqualified ratio is higher at night than daytime and changes significantly at the turn of day and night. The nmF2 and hmF2 of the qualified profiles are significantly higher near the magnetic equator than in other regions, exhibiting a double-peaked phenomenon. The case of a large geomagnetic storm shows that the qualified ratio of inversion results can still maintain about 70% during extremely severe magnetic storms.