Heatwaves across India have intensified substantially over the past few decades. Most existing assessments focus primarily on air temperature and often neglect the influence of atmospheric moisture on human thermal stress. High humidity makes people feel much hotter, especially in tropical regions, with direct effects on their health and work productivity. Therefore, this study investigates the evolution of extreme heat conditions across India from 1975 to 2024 using India Meteorological Department (IMD) observations and ERA5 reanalysis data. Extreme-heat days were identified independently in both datasets using a maximum temperature (Tmax) threshold of ≥45 °C and a two-day persistence criterion, and dates common to both datasets were retained for subsequent heat-stress analysis. Long-term trends are assessed with the Mann–Kendall test and Sen’s slope estimator. To better reflect physiological heat stress, the Heat Index (HI) combines air temperature and relative humidity to represent apparent temperature. The corresponding impacts on labour productivity are estimated using the Decline in Work Performance (DWP) metric. The results indicate an overall increase in heat days over the study period, with substantial inter-decadal variability and the highest decadal average during 2015–2024. The HI analysis shows that atmospheric moisture makes heat events much worse by raising the apparent temperature above the dry-bulb temperature. This intensification is associated with substantial reductions in work performance, leading to a decline in work capacity. Regionally, northwest and central India consistently experience the highest levels of heat stress and associated productivity loss. The results demonstrate that heatwaves in India cannot be characterised solely by temperature, as atmospheric moisture is a critical factor in intensifying thermal stress. The associated productivity losses indicate an increasing socio-economic burden of heat stress and workforce exposure across India. These results highlight the need for integrated climate adaptation and heat-risk management strategies that account for both climatic and workforce vulnerability.
In coastal hot dry rock (HDR) geothermal development, utilizing seawater as a heat-exchange fluid provides an important pathway for efficient and sustainable geothermal extraction. To clarify the thermochemical coupling mechanisms between seawater and reservoir rock under cyclic thermal shock, this study systematically investigated changes in the mass, density, P-wave velocity, surface chromaticity, surface roughness, and mineral composition of HDR subjected to high temperatures (100–500 °C) and cyclic seawater thermal shocks (0–20 cycles). Dynamic variations in seawater pH were monitored concurrently, while surface roughness and microstructural damage were characterized using three-dimensional optical profilometry, scanning electron microscopy, and thin-section analysis. The results show that the macroscopic physical properties of HDR deteriorate significantly with increasing temperature and number of cycles. After 20 cycles at 500 °C, the P-wave velocity decreased by 48.27
Geothermal energy is a sustainable resource for meeting global energy demands, offering an eco-friendly alternative to conventional fossil fuels. The Puga Valley, recognized for its significant geothermal potential, has become a focal point for understanding geothermal systems. This region’s geothermal richness is attributed to Upper Cretaceous and late Tertiary thermal activity. This study presents a novel approach to precisely map the geothermal resistivity structure using joint inversion strategy utilizing nature-inspired algorithms: Particle Swarm Optimization, Grey Wolf Optimization, and a hybrid vPSOGWO. Initially, the algorithms were applied to various synthetic datasets, allowing for a comprehensive assessment of their reliability and efficacy. The study utilized Gaussian probability density functions and correlation matrix analysis for stability testing. The superior performance of vPSOGWO in inverting synthetic Magnetotelluric and Vertical Electrical Sounding dataset led to its application in delineating the geothermal resistivity structure of the Puga Valley. Revealing two anomalously conductive geothermal layers, with resistivity between 3 and 12 Ωm. A shallow conductive layer of thickness 27–55 m, overlies a deep conductive layer at depths of 6.5–7.0 km, at approximately 250 °C. These layers are separated by a highly resistive metamorphic basement (200–500Ωm), showing a strong correlation with drill samples and existing data, demonstrating the effectiveness of hybrid-joint inversion technique. Unlike other inversions, this approach significantly reduces model uncertainty and enhances resolution, providing a significant characterization of geothermal properties.
Flood susceptibility prioritization is a critical component of integrated watershed management, particularly under increasing climate variability and ongoing socio-economic development. This study presents a modified D-numbers framework as a novel multi-criteria decision-making (MCDM) approach for prioritizing flood susceptibility across 31 sub-watersheds of the Fakhrabad watershed in central Iran. To evaluate its performance, the proposed framework was compared with the Analytic Hierarchy Process (AHP), one of the most widely used conventional MCDM methods. Seven morphometric and hydrological criteria, including slope, elevation, Topographic Control Index (TCI), Stream Power Index (SPI), Topographic Wetness Index (TWI), fractal dimension of the drainage network (FD), and Index of Connectivity (IC), were selected as flood-conditioning factors. Both MCDM approaches were applied to derive criterion weights and prioritize flood susceptibility among sub-watersheds. Subsequently, the calibrated Soil and Water Assessment Tool (SWAT) model was used as a hydrological reference to assess the consistency of the sub-watershed rankings produced by the two MCDM approaches. The results indicate that both methods were capable of reproducing the general spatial pattern of flood susceptibility; however, the modified D-numbers framework showed substantially stronger agreement with the SWAT-derived rankings. Spearman’s rank correlation coefficients were 0.98 and 0.79 for the modified D-numbers and AHP methods, respectively. The improved performance of the modified D-numbers approach can be attributed to its ability to explicitly incorporate uncertainty, decision-maker consistency, and confidence levels into the weighting process. These features enhance the robustness of flood susceptibility prioritization and provide a more reliable basis for flood risk management and sustainable watershed planning in data-limited and hydrologically complex environments.
Mafic and ultramafic rocks are sources of critical minerals and also provide a medium to securely store CO2. The objective of this study is to delineate the geometry and assess the volumes of ultramafic rocks in the Livengood Terrane in central Alaska using high-resolution aeromagnetic, petrophysical, and borehole datasets. We analyzed magnetic anomalies using 3D inversion using constraints from previous outcrop and petrophysical data. The high-resolution aeromagnetic anomaly map reveals elongated high-magnetic signatures extending from northeast corner to southwest corner of the study area, which are associated with the Cambrian Cascade ophiolite assemblage of the Livengood Terrane. 3D magnetic inversion results show three high-susceptibility bodies (HS-1 to HS-3) with magnetic susceptibility values ranging from 0.03 to 0.055 SI, and a combined estimated volume of 12 km3. Estimates of in situ carbon mineralization based on laboratory experiments and stoichiometric calculations indicate a plausible CO2 storage capacity of approximately 250 Mt to 1 Gt CO2. These findings highlight first-order target areas in the Livengood Terrane for initial assessment of carbon mineralization potential. Furthermore, this study demonstrates that geophysical inversion of magnetic data can help guide site selection and development for carbon mineralization.
