Sustainable groundwater management in semi-arid regions requires a precise understanding of the complex interactions between climatic variables and subsurface storage. To address this, the primary objective of this study is to develop a robust machine learning framework for the spatio-temporal zonation of groundwater depletion across Purulia, India, by integrating 20 years (2005 to 2024) of multi-parametric hydro-climatic trends. In terms of methodology, the non-parametric Mann-Kendall test and Sen’s Slope estimator were utilized to quantify the velocity of change in Ground Water Levels (GWL), Actual Annual Rainfall (AAR), Evapo-Transpiration (ET), and Annual Soil Moisture (ASM). These dynamic trend signatures were subsequently clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to delineate three distinct hydrological regimes: high depletion, moderate depletion, and potential zones, with model validity confirmed via one-way ANOVA (F = 17,796, p < 0.001) and silhouette analysis. The key results reveal that 14.71
Earth’s climate oscillations between interglacial and glacial periods have driven significant sea level changes. Sri Lanka, located in the Indian Ocean, provides an ideal natural setting for studying paleo sea level changes. This study compiles 92 Holocene sea level records and identifies last interglacial stratigraphic evidence from Point Kudrimalai to reconstruct paleo sea level fluctuations in Sri Lanka. In addition, this study evaluates underutilized sea level indicators such as beachrock, coastal barrier sands, and mangrove swamps to address methodological challenges in reconstructing past sea levels. Results show that diverse sea level indicators, including beachrock, coastal barrier sands, raised marine deposits, and marine shell beds, primarily reflect Holocene sea level changes and their geological impacts. Using standardized sea level indicators such as coral and marine shell beds, this study reveals Holocene sea levels approximately 1.0–2.0 m above present between 6000 and 2000 cal BP. The 6–9 m elevation associated with Point Kudrimalai can possibly be correlated with regional Pleistocene sea level highstands. In contrast, the Palk Strait land bridge can be identified as a potential paleogeographic connection during periods of substantially lower sea level. Accordingly, these findings elucidate that the impact of sea level changes in Sri Lanka has likely influenced coastal geomorphology. Therefore, this reconstruction enhances understanding of regional sea level variability and its role in shaping coastal landscapes in the Indian Ocean.
Subsurface cultivation and gasification (SCG) seeks to enhance biogenic methane production from lignite by cultivating methanogens in situ, and its rate-limiting step is the supply of a bioavailable substrate. Hydrogen peroxide (H2O2) is injected into a fractured lignite seam to solubilize part of the coal macromolecule into dissolved organic carbon (DOC), which the methanogens then convert to methane. The same oxidation, however, also alters the lignite skeleton, so the hydraulic conductivity that governs oxidant delivery and the subsequent transport of methanogen-bearing groundwater may change during treatment; this response has not previously been resolved. A fractured lignite specimen from the Tempoku coalfield, Hokkaido, Japan, was immersed in 1 wt
Flash floods present a recurrent and severe hydrometeorological threat to the Sylhet Basin of Bangladesh, severely compromising rural livelihoods, ecosystem integrity, and physical infrastructure. This study presents a diagnostic evaluation of community resilience across four representative unions (Sukhair, Sachna Bazar, Raniganj, and Jagadal) using an integrated Rural Resilience Index (RRI) framework to assess baseline conditions (2025) and prospective trajectories (2030). Utilizing a mixed-methods empirical approach combining 383 household surveys, 10 Key Informant Interviews (KIIs), and spatial analysis, resilience was systematically quantified across five dimensions: physical, social, economic, institutional, and environmental. One-way ANOVA demonstrated statistically significant baseline spatial disparities across the unions (F (3, 379) = 14.82, p < 0.001). While Jagadal exhibited relatively higher baseline resilience (Observed RRI = 1.93) due to superior physical infrastructure and social capital, deep-haor unions like Sukhair (RRI = 1.36) and Sachna Bazar (RRI = 1.31) suffered from severe structural vulnerabilities, driven by seasonal submergence, mono-crop Boro dependence, and weak institutional governance (scores ranging from 1.07 to 1.13). Prospective 2030 projections highlight diverging pathways: Sachna Bazar transitions into moderate resilience (RRI = 2.09) through non-farm economic diversification, whereas Sukhair remains locked in low resilience (RRI = 1.61) due to persistent infrastructural and governance deficits. Qualitative findings from KIIs confirm that households heavily depend on informal social capital and local coping strategies to buffer formal disaster management shortfalls. The study aligns its empirical insights with strategic policy mandates, including the Bangladesh Delta Plan 2100, Standing Orders on Disaster (SOD 2019), and the Sendai Framework. Ultimately, this research offers a robust, scalable diagnostic framework for disaster risk governance across flood-prone deltaic and riverine regions globally.
Land Surface Temperature (LST) and vegetation dynamics are widely recognized as reliable indicators of environmental change, urbanization, and climate variability. Understanding their spatial and temporal interactions is essential for sustainable environmental planning, particularly in agriculturally intensive regions experiencing rapid land-use transformation. This study investigates the spatio-temporal variations of LST and the Normalized Difference Vegetation Index (NDVI) in Thanjavur District, Tamil Nadu, India, using multi-temporal Landsat imagery acquired during 2011 and 2021. Satellite images were processed using standard remote sensing techniques to derive LST and NDVI, while Geographic Information System (GIS)-based spatial analysis was employed to evaluate block-level variations. A simple linear trend analysis was employed to project potential environmental conditions for 2031 based on observed temporal changes. The results indicate considerable spatial heterogeneity in both thermal conditions and vegetation cover across the district. During 2011, the highest LST (46 °C) was recorded in Ammapettai Block, whereas Thiruvonam exhibited the highest NDVI (0.7128), reflecting dense vegetation supported by the Cauvery Delta irrigation system. In 2021, Thanjavur Block emerged as the principal thermal hotspot (43 °C), while vegetation cover declined in several urban and peri-urban areas. The projected 2031 scenario suggests a continued increase in thermal intensity in rapidly developing regions together with a gradual reduction in vegetation density. Regression analysis showed a weak and statistically insignificant relationship between LST and NDVI (R² = 0.043; p = 0.477), indicating that surface temperature is influenced not only by vegetation but also by urban expansion, irrigation practices, land-use characteristics, and local environmental conditions. The study demonstrates the usefulness of integrating remote sensing and GIS techniques for long-term environmental monitoring and provides baseline information for sustainable land-use planning, urban greening, climate adaptation, and environmental management in the Cauvery Delta region.
