As Bangladesh’s domestic gas reserves decline and production falls, the country is becoming increasingly reliant on imported LNG, currently delivered through two offshore floating storage and regasification units. The government also plans additional floating capacity and a land-based terminal at Matarbari. This expanding mix faces coastal hazards, but floating and land terminals can lose service in different ways. We build separate loss models that estimate how extremes of wind, flood, surge and wave affect terminal components and operations, and then translate the resulting damage and downtime into lost operating margin. For onshore terminal at Matarbari, the model estimates a median conditional loss of US$97.9 million under the future 10- year flood-depth stress case, corresponding to 30-year financial stress of 2.21% against a standardised US$3.57-billion terminal-profit NPV baseline. Across 10-, 50- and 100-year cases, median future wave losses at the operating floating terminals remain near US$1.4 million and arise overwhelmingly from interrupted operations rather than hull repair. Incident-based scenarios show why recovery matters: applying a Mocha-like cyclone interruption duration gives one-off stress of 0.31% of terminal profit value, compared with 3.60% when we apply a Remal-like cyclone prolonged-recovery duration. These are separate conditional stress tests, not expected annual loss estimates, joint cyclone forecasts or vessel-certified engineering assessments. The framework can support resilience and continuity planning while showing where asset-specific engineering evidence would most improve decisions.

India’s growing reliance on imported liquefied natural gas (LNG) makes coastal terminals important to energy security, yet their exposure to extreme weather and financial disruption remain poorly understood. We develop an open, reproducible component-level framework that follows disruption through three core functions: unloading LNG from ships, storing it, and converting it back into gas for delivery. It captures how stored LNG buffers short interruptions and translates component damage and downtime into financial loss. To isolate location effects, we compare a 5-million-tonne-per-year terminal across nine sites. Risk varies sharply by hazard and location: flood produces high-loss cases on the west coast, while surge and wind create hotspots on both coasts. At the 10-year return level, aggregate financial stress is US$390 million for flood, US$175 million for surge and US$117 million for wind, equivalent to mean site-level stresses of 1.21%, 0.55% and 0.36% against a US$3.57-billion 30-year profit baseline. More frequent hazard levels create greater long-term pressure, while rarer events cause larger losses. Future conditions can increase severe losses locally, and tank and jetty assumptions can alter the leading hazard. The framework supports screening and resilience planning for comparable onshore LNG terminals, not engineering approval, insurance pricing, annual-loss estimation or joint-event forecasting.

Gneissic rock formations are most common formation covering around 80,000 km2 area of Chotanagpur. The laboratory study is required for physico-mechanical behaviour characterization of these gneissic rocks. In the present study, the strength and deformation behaviour of five variants of gneissic rocks, namely—(BG) Biotite Gniess, (HG) Horneblende Gneiss, (QBG) Quartz Biotite Gneiss, (GG) Granite Gniess and (QFG)—Quartzo Feldspathic Gneiss, obtained from Jharkhand and Purulia locations of Chotanagpur region were analysed. All these rocks were investigated in the laboratory, for uniaxial compressive strength (UCS) and tangent modulus (E), shear strength parameters (c, ϕ) and indirect tensile strength. The slake durability, density characteristics and petrographic analysis were also studied. The failure modes of gneissic rocks have discussed for deformation behaviour under compression. Qualitatively, BG has shown low density (2650–2720 kg/m3) whereas QBG and QFG have marginally higher porosity ( 1.0

The Ghaghara River originates from the Mapchachango glacier near Mansarovar lake and spreads over many parts of India and Nepal. It is the left bank and largest tributary of the Ganga River by volume. Its confluences with the Ganga River at Doriganj near Chhapra town in Bihar. Extensive studies have been carried out on flooding and lateral erosion, but no attention has been paid to sedimentary structures and facies along the Ghaghara River. The present study is focused on the facies analysis and sedimentary structures, which are exposed on a 196 cm high cliff section (Upland terrace surface T2), along the Ghaghara River, Ayodhya Ghat, Uttar Pradesh. The facies are the paleosol unit (Facies I), silty unit (Facies II), and sandy unit (Facies III), which show high energy conditions and a high degree of transport rate while the mud unit (Facies IV), show low energy conditions and low degree of transport rate. The sedimentary structures have been identified such as the massive bedding in silty unit, planar cross-bedding and trough cross-bedding in sandy unit, and parallel laminations in mud unit at different depths of litholog. It also indicates that fluvial hazards occur during low-discharge periods due to sandy, silty, and muddy facies in the river valley deposits, which contribute to increased lateral erosion in the low discharge period and flooding occurs during the high-discharge periods.

The Sunai River, a right-bank tributary of the Budhabalanga River in Odisha, originates near Siriapal village in Mayurbhanj district and confluences with the Budhabalanga near Dolagohira village in Baleshwar district, Odisha. The basin, categorized as a 7th order system, covers an area of 1,432.26 km2 and a basin length of 63.63 km. The mean stream length ratio (14.06) reflects an advanced stage of erosion and high runoff conditions, indicating active fluvial processes. A bifurcation ratio of 3.85 suggests a stable tectonic setting, while a drainage density of 2.27 km/km2 indicates moderate to long overland flow. The drainage texture (6.47) and stream frequency (2.85) point to low infiltration and high surface runoff, increasing susceptibility to erosion. Moreover, the low drainage intensity value (1.25) shows a higher vulnerability to soil erosion, likely exacerbated by sparse vegetation or unconsolidated surface material. Morphometric shape indices form factor (0.35), elongation ratio (0.67), and circulatory ratio (0.35) reveal the basin’s elongated form, which can influence flow concentration and flood risk. The low RHO coefficient (0.15) suggests limited water storage during heavy discharge periods, contributing to flash flood vulnerability. The region exhibits steep slopes and significant elevation variation on the basis of basin relief (1,151 m) and a high relief ratio (18.09). Despite this, the ruggedness number (2.61) reflects a relatively smooth and moderately dissected terrain. The analysis highlights the basin’s dynamic runoff and stream flow behavior, emphasizing the urgent need for integrated and sustainable watershed management strategies to counteract environmental degradation and hydro-geomorphological challenges.

Monsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

Thirteen Coupled Model Intercomparison Project phase 6 (CMIP6) models were employed to simulate mean, maximum, and minimum temperature across 7 homogenous temperature regions of India for both annual and summer season (June, July, and August (JJA)). The model fidelity was assessed by comparing them with observed Climate Research Unit temperature dataset. The JJA multi-model ensemble for the present (1981-2014) suggests large warm biases in the temperature. Although the models could simulate the spatial variability of the mean and maximum temperature over most of the homogeneous regions, they do not compare well for representing the temporal variability. We also found, that although different individual models have strengths and weaknesses in representing spatial and temporal temperature characteristics over India, a few of the models perform better than the others. For example, CNRM-CM6 could better represent the spatial temperature patterns however they struggle in capturing the temporal variability. HadGEM3-GC31-LL, KACE-1-0G, and UKESM1-0-LL are comparably the best-performing models for temporal temperature features over India. The annual maximum temperature during far future period is projected to increase by 1.5 degrees C, 2.3 degrees C, and 4.1 degrees C for Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 respectively. At regional scales, JJA mean temperature for SSP5-8.5 revealed significant increases in Interior Peninsula (3.8 degrees C), Western Himalaya (5.6 degrees C), Northwest (3.9 degrees C), West Coast (3.6 degrees C), East Coast (3.6 degrees C), Northeast (3.6 degrees C), and North Central (3.8 degrees C), highlighting the Western Himalaya's heightened sensitivity. Further, heat wave frequency is projected to rise, with the northern territories (NW, NC, NE, and part of IP) most affected, anticipating week-long heat waves affecting around 50% of India's population under stronger SSPs. Such unprecedented impacts seem to be less profound in case of abatement scenarios such as the SSP1-2.6. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to alleviate the public health impacts of climate change.

Extreme wind is the main driver of loss in North-West Europe, with flooding being the second-highest driver. These hazards are currently modelled independently, and it is unclear what the contribution of their co-occurrence is to loss. They are often associated with extra-tropical cyclones, with studies focusing on co-occurrence of extreme meteorological variables. However, there has not been a systematic assessment of the meteorological drivers of the co-occurring impacts of compound wind-flood events. This study quantifies this using an established storm severity index (SSI) and recently developed flood severity index (FSI), applied to the UKCP18 12 km regional climate simulations, and a Great Britain (GB) focused hydrological model. The meteorological drivers are assessed using 30 weather types, which are designed to capture a broad spectrum of GB weather. Daily extreme compound events (exceeding 99th percentile of both SSI and FSI) are generally associated with cyclonic weather patterns, often from the positive phase of the North Atlantic Oscillation (NAO+) and Northwesterly classifications. Extreme compound events happen in a larger variety of weather patterns in a future climate. The location of extreme precipitation events shifts southward towards regions of increased exposure. The risk of extreme compound events increases almost four-fold in the UKCP18 simulations (from 14 events in the historical period, to 55 events in the future period). It is also more likely for there to be multi-day compound events. At seasonal timescales years tend to be either flood-prone or wind-damage-prone. In a future climate there is a larger proportion of years experiencing extreme seasonal SSI and FSI totals. This could lead to increases in reinsurance losses if not factored into current modelling.

This present study investigates grain size, sediment dynamics and lateral erosion processes along the Ghaghara River from Faizabad to Deoria, Ganga Plain, India. The grain size analysis reflects that surface sediments have silt (24.50

Due to climate change, rapid warming and its further intensification over different parts of the globe have been recently reported. This has a direct impact on human health, agriculture, water availability, power generation, various ecosystems, and socioeconomic conditions of the exposed population. The current study thus investigates the frequency and duration of heatwaves, human discomfort, and exposure of the human population to these extremes using the high-resolution regional climate model experiments under two Representative Concentration Pathways (RCP2.6, RCP8.5) over India. We find that more than 90% of India will be exposed to uncomfortable warm nights by the end of the 21st century with the highest rise over western India, Madhya Pradesh (MP), Uttar Pradesh (UP), Punjab, and the Haryana region. States like Odisha, Chhattisgarh, eastern parts of MP and UP, and some parts of J&K will be the worst hit by the intense and frequent heatwaves and human discomfort followed by the densely populated Indo-Gangetic plains under RCP8.5. Strict enforcement of the stringent policies on stabilization of population growth, improvement of local adaptive capacities, and economic status of the vulnerable population along with enforcing effective measures to curb greenhouse gas emissions are important to reduce human exposure to future heat stress. We demonstrate that a proper mitigation-based development (RCP2.6) instead of a business-as-usual scenario (RCP8.5) may help to reduce 50–200 heatwave days, 3–10 heatwave spells, and 10–35% warm nights over the Indian region. Consequently, this can avoid the exposure of 135–143 million population to severe discomfort due to extreme heat conditions by the end of the 21st century.

It is evident that climate is changing however, there have been not many regional scale systematic efforts to quantify the climate extremes (e.g., heatwave) under various emission scenarios. South Asia has a population of over 1.4 billion, with most of this population located in India and is vulnerable to future changes in high temperature conditions e.g., heat waves and its duration. Here, we use 13 state-of-the-art coupled climate models from CMIP6 experiments along with their ensembles to estimate their fidelity and associated uncertainties in predicting heat waves over the Indian temperature homogenous regions that have resulted from human-induced warming, during the period 1984–2014. We applied various skill metrices for model performance evaluation and found that ensemble of all the 13 CMIP6 models is close to the observation whereas individual model performance varied geographically. This is because individual models have their own interannual variability which affects the overall performance. Further we computed the temperature changes for near future (2030-2060) and far future (2070-2100) at 95% significance level using SSP1, 2 and 5. The maximum temperature during the northern hemisphere summer is projected to increase by 1.3°C, 2.1°C and 3.7°C for SSP1, SSP2 and SSP5 respectively. The frequency of heat waves is also projected to increase, with the most affected areas showing 3+ more heat waves per summer season when compared to historical values. The Indo-Gangetic plain is found in the most affected regions, where weeklong heat wave duration is expected at higher emission scenario affecting larger portion of. population (~ 40% of India’s population). Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change.Keywords: Climate change, Extremes, CMIP

The risk posed by heavy rain and strong wind is now suspected to be exacerbated by the way they co-occur, yet this remains insufficiently understood to effectively plan and mitigate. This study systematically investigates the correlations between wintertime (Oct–Mar) extremes relating to wind and flooding at all timescales from daily to seasonal. Meteorological reanalysis and river flow datasets are used to explore the historical period, and climate projections at 12 km resolution are analysed to understand the possible effects of future climate change (2061–2080, RCP 8.5). A new flood severity index (FSI) is also developed to complement the existing storm severity index (SSI). Initially, Great Britain (GB) is taken as a comparatively simple yet informative study area, then analysis is extended to the full European domain.Aggregated across GB, wind gusts and precipitation correlate strongly (rs ∼0.6–0.8) at timescales from daily to seasonal, but peak around 10 days. A later peak is seen when considering correlations between wind gusts and river flows (40–60 days). This time is likely needed for catchments’ soils to saturate. A conceptual multi-temporal, multi-process model of GB wintertime flood-wind co-occurrence is proposed as a basis for future investigation. When historical analysis is extended across Europe we find the timescale of maximum correlation varies strongly between nations, likely as a result of different meteorological drivers.Impact focused correlation (FSI–SSI) is lower (rs∼0.2) but increases notably with climate change at timescales of ∼40 days (rs∼0.4). Tentatively, very severe episodes (i.e., both >99th percentile) appear heavily influenced by climate change, increasing roughly threefold by 2061–2080 (p < 0.05). The return period of such an event is 16 years historically (compared to 56 years if the two hazards were independent), reduces to 5 years in future. Such metrics provide actionable information for insurers and other stakeholders.

Gangotri glacier located in the Uttarkashi District of the Garhwal Himalaya, is one of the longest valley glacier. It exhibits Lateral Moraines (LM), Recessional Moraines (RM), Kame Terraces (KT) and Outwash Plains (OWP) as important landforms. The sediments coded in these landforms, provide the information about sedimentary characteristics, and the evolutionary history of the Gangotri Glacier Region (GGR). The Gangotri is a well studied glacier, however the sedimentological characteristics and evolution of many landforms are yet to be understood. Therefore, present paper aimed to explain the sedimentological characteristics and the evolutionary history of the outwash plain deposits. The OWP deposits were studied by making a trench near Bhujbas and collecting the samples from it. The granulometric analysis explain that the mean grain size of the OWP sediments varies from 0.258 φ to 2.006 φ indicate coarse to medium sand. The skewness, varies from 0.138 φ to 0.427 φ indicate dominance of fine grained sediments. The kurtosis varying from 0.839 φ to 1.067 φ explain the dominance of finer sediments. The standard deviation varies from 1.210 φ to 1.633 φ thus indicating poor sorting of the OWP deposits and fluctuation in the energy of the depositional environment. Five sedimentary facies identified are gravel sandy facies, ripple laminated silty sand facies, sandy facies, poorly sorted sandy facies and silty sand facies. The study describes that the OWP deposits are stratified, consolidated to semi-consolidated, coarse to fine grained silt, sand and gravels with primary sedimentary structures, which are evolved by glacio-fluvial environment under fluctuating energy conditions during the late Holocene period.

The Kali River is a sixth-order tributary of the Ganga River. It originates near Khatoli town in Muzaffarnagar and confluences with the Ganga River at Kanauj District, Uttar Pradesh. In this study, drainage morphometry, temporal groundwater level observations and precipitation data are used to examine surface and subsurface hydrological conditions. Survey of India topographic maps has been used to identify the basin characteristics based on their 1:50,000 scale. The groundwater and precipitation data were obtained from the Indian Meteorological Department and the Indian Water Resources Society. The basin area was calculated about 11,470 sq. km with an NW-SE sloping. In addition, it is elongated-shaped with a dendritic pattern. The bifurcation ratio (5.06), drainage density (0.65), stream frequency (0.24) and ruggedness number (0.09) show strongly permeable alluvium, low relief, high infiltration rate, least erodible and high permeability. The basin reflects a relationship between spatio-temporal groundwater level and rainfall (~21 years: 1996 to 2017). According to this study, the water level declined at a rate of 124.7 meters per year as a result of excessive groundwater use and climate changes such as a decrease in precipitation rates around the area. Therefore, it is recommended to conservation of groundwater resources in the future.

The Gangotri Glacier is one of the most prominent glacier of the Himalayas. Geologically the area comprises the Central Crystalline zone fringing the Tethyan sedimentary sequences, part of which can be seen at the top of Sudarshan peak and the ridge southwest of Gaumukh. Thick veins of coarse-grained biotite granite are intruded across the foliation of banded gneiss south of Jangla. The methodology includes the identification of various geomorphic features/land-forms in the field and marking the identified features on the satellite data. Lateral Moraines (LM) are the most common geomorphic feature which explains the dynamics of the glacier. It has been observed that some catastrophic events modify the landforms. The temperature also plays an important role in the modification process of geomorphic features in Gangotri Glacier. The Glacial sedimentary environment is dominantly active in the area during ice ages and evolves the landforms.

This study diagnoses the Satna flood event in the Tons River basin. The occurrence of this intense flood is attributed to the rainfall associated with the movement of the monsoon depression during the peak monsoon season. The study uses Weather Research Forecast (WRF) model to examine the origin, movement, and dissipation of the monsoon depression over India. The study also incorporates remote sensing techniques and field campaigns to better understand the impact on the areas. The model captures the origin of the monsoon depression and the highest precipitating days fairly well. However, it underestimates the rainfall magnitude with respect to different observations due to the limited ability of the model to capture the rainfall maxima spatially. Moreover, the conditional instability of the second kind (CISK) mechanism seems to drive the monsoon depression. A positive feedback mechanism is observed along the track of the depression between rainfall and convective activity leading to excess rainfall over the Tons basin. The remote-sensing-based analysis using Landsat 8 products shows that around 1309 km2 area of the Tons basin was inundated during the event. Based on the computed Flood Hazard Index values, the entire basin has been divided into the low, medium, high, and very high flood hazard zones with several affected hamlets 19, 178, 155, and 91, respectively. The flood hazard values are important for the planners to adopt appropriate adaptation and mitigation to minimize the impact of future flooding in the basin.

The present study aims to access the geomorphic analysis of the Baghain river basin and its implication on landscape evolution. Baghain river originates from the Panna hill, and flows through marginal and central Ganga plain. It empties itself in to Yamuna river near Chilla village, Banda district, Uttar Pradesh. The river basin comprises of three major geological units associated with this basin: Bundelkhand gneissic complex, Vindhyan Supergroup (hard rock terrain known as Panna hill) and Alluvial deposit (marginal Ganga plain and central Ganga plain). The current work focuses on study of morphometric parameters and geomorphic indices of Baghain river basin using digital elevation model (DEM) and topographic maps (1:50,000 scale). This basin exhibits four types of drainage patterns: dendritic, parallel, trellis and rectangular. Bifurcations ratio (4.82) of river basin suggest that the most part of the basin is not influenced by any geological structures. The elongation ratio (0.50) suggests that the basin is moderately elongated. The drainage density (0.62) and stream frequency (0.22) also suggest that the basin has moderate to high permeable and easily erodible alluvium terrain. The escarpment height is low in the proximal part (Panna Hill) and high in the middle and distal parts of the basin (marginal Ganga plain and central Ganga plain, respectively. Asymmetric factor (52) shows a leftward tilt of the basin. The region lies under marginal Ganga plain, and Bundelkhand plateau. The sharp bend present is probably due to the lithological variation. It reflects moderate gradient in the upper part (Panna hill), steep gradient in the lower part (Panna hill), and low gradient in the marginal to central Ganga plain.

Gangotri glacier, located in the Uttarkashi district, is one of the longest valley glaciers of the Garhwal Himalaya. It exhibits lateral moraines, recessional moraines, outwash plains, kame terraces, and debris cones. Kame terraces are significant landforms that preserve the climatic records and geological history in their deposits.The sedimentary and mineral magnetic analysis on 165 cm thick trenched sections sampled at 2.5 cm interval of the kame terraces together with the AMS (Accelerator Mass Spectrometer) age indicate its response to the major climatic events in this region and sedimentation history of the kame terraces. The granulometric analysis describes that the mean grain size of these sediments varies from coarse to fine sand, which are poorly sorted with an excess of finer sediments. This indicates, low to moderate energy over a longer duration with fluctuation of the depositional environment. The mineral magnetic analysis reflects unimodal detrital modes governed by the energy conditions from meltwaters that are in turn controlled by the climatic conditions from warmer to colder episodes. Therefore, the sedimentation in the kame terraces has taken place under the low energy lacustrine environment during warmer climates. The kame terraces sedimentation therefore has recorded the major climatic events, such as Last Glacial Maximum (LGM) 21-19.5 Ka BP, Older Dryas (OD) 16.5-14.5 Ka BP, Bolling-Allerod (BA) 14.5-13.5 Ka BP, Younger Dryas (YD) 13.5-12 Ka BP, Indus Valley Civilization Collapse (IVCC) 5.0-3.0 Ka BP, Medieval Warm Period (MWP) 1.25-0.7 Ka BP and Little Ice Age (LIA) 0.7-0.2 Ka BP.