ABSTRACT We present a homogeneous and spatially explicit dataset at county and tract levels that supports climate change risk assessment across the contiguous United States, designed to enable user‐defined construction of hazard, exposure, vulnerability, resilience, and composite risk indices through application‐specific weighting of variables and selection of hazards or future hazard projections. The dataset follows the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) risk framework, which defines risk through four key components: hazard, exposure, vulnerability, and resilience. It includes 130 variables compiled from multiple national sources for 3198 counties and 83,481 tracts. Major data sources include the Baseline Resilience Indicators for Communities (BRIC) from the University of South Carolina, the National Risk Index (NRI) from the Federal Emergency Management Agency (FEMA), and the Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention (CDC). Additional datasets were integrated to provide a more comprehensive set of exposure variables. To facilitate integration of variables with different units and scales, all data were winsorised at the 5th and 95th percentiles and normalized using fuzzy membership functions. The resulting normalized values represent relative positions within the distribution of each indicator and provide a consistent numerical framework for visualization, spatial analysis, and user‐defined risk modelling. This process reduces the influence of outliers while preserving data variability. The resulting dataset provides a consistent foundation for constructing composite risk indices, replicating or extending IPCC AR6‐aligned analyses, and supporting climate risk and adaptation research. It is particularly suited for applications in systemic risk assessment, spatial modelling, and policy evaluation.
ABSTRACT Climate warming is intensifying soil moisture droughts worldwide, with serious consequences for agriculture and terrestrial ecosystems. Robust assessment of their severity, extent, evolution and impacts on natural and managed systems requires globally consistent, harmonized and event‐based datasets with sub‐monthly temporal resolution. Here, we introduce a global catalogue of 755 Global Land soil moisture Drought Events (GLDEs) over the past four decades (1980–2024) derived from the gridded root‐zone soil moisture estimates produced by two structurally different models: the hydrological model mHM and the soil water balance model SoilClim. Drought events are identified using a combination of 10th‐ and 20th‐percentile soil moisture anomaly thresholds and clustered with a machine‐learning‐based OPTICS algorithm applied to 10‐day averaged soil moisture fields. Each GLDE is characterized using seven severity categories (from extremely weak to extremely severe) and seven dynamic categories (from extremely static to extremely dynamic) based on four spatiotemporal and three motion‐related characteristics. Extremely severe (7s) and extremely dynamic (7d) GLDEs occur most frequently in South America, followed by North America, whereas Eurasia experiences the longest and most extensive events. The dataset of all 755 GLDEs with their statistical characteristics is publicly available. Overall, the GLDE catalogue provides a unique, globally consistent basis for future investigations into the drivers and impacts of dynamically evolving drought events.
ABSTRACT Estuaries are on the frontline of sea‐level rise impacts around Australia but remain poorly represented in existing public water level research datasets in Australia. Here we present the Estuary Water Elevation Research (EWER) dataset, which comprises data equivalent to 12,072 years of continuous non‐flagged water level observations from 724 unique gauges across tidal environments, principally estuaries, but also bays and open‐coast sites. The dataset brings together disparate national and state agency records to improve coverage of estuarine systems, including upstream tidal rivers that are often excluded from ocean‐focused products. We describe the origins of the data and a consistent quality control framework used to assign diagnostic flags identifying potential spikes, outliers, datum shifts and periods of riverine influence. We provide information on the organisation of the dataset and possible use cases. Together, the EWER dataset provides a consistent, national‐scale foundation to support research and decision‐making on estuarine water‐level change and its associated hazards under sea‐level rise.
ABSTRACT This review provides a data‐driven synthesis of the evolution of geoscience research from 2004 to 2023. A field‐level bibliometric overview of 439,680 records indexed in the Web of Science Core Collection (Geosciences, Multidisciplinary) characterises broad publication patterns, while the in‐depth structural and semantic analysis focuses on the 200 most‐cited papers per year (4000 articles in total), to which keyword co‐occurrence network analysis and SciBERT‐based BERTopic modelling were applied. This two‐tier design situates an analysis of the field's most influential literature within its full publication landscape, enabling systematic examination of both the structural and semantic dynamics of geoscience. The keyword‐network analysis indicates that model, climate and water have consistently constituted the structural backbone of geoscience research, whereas the centrality of evolution, initially a dominant hub, declined steadily over the study period, while data‐ and technology‐oriented themes, such as remote sensing, GIS and machine learning, have gained increasing prominence since the 2010s. Complementary semantic patterns identified through the SciBERT‐based topic model reveal a parallel progression: early research emphasised geological and geotechnical processes; the 2010s were characterised by expanding attention to climate change, paleoenvironmental reconstruction and ecosystem dynamics; and the late 2010s onward show a growing focus on disaster response, resource management and advanced technological applications. In the most recent period, AI‐ and data‐intensive approaches emerge as a dominant thematic direction. Together, this review identifies three major paradigm transitions in geoscience research—from geology‐centred foundations, through climate‐ and environment‐oriented expansion, to AI‐driven and data‐intensive Earth science. Trend and change‐point analysis of annual topic prevalence in a single 2004–2023 topic model dates the statistically significant shift towards AI‐ and data‐intensive research to 2013–2014, whereas the climate‐ and environment‐oriented expansion proceeded gradually, without an abrupt transition. By offering a systematic and reproducible synthesis of long‐term disciplinary change, this review complements conventional narrative reviews and provides a reference framework for future cross‐domain research in the era of foundation models and scientific large language model applications.
Physicochemical data acquisition was carried out within the northeastern Bay of Bengal (BoB), adjacent to the newly declared Naf Marine Protected Area (Naf MPA) of Bangladesh. A total of 21 sampling locations were covered over shallow coastal water for the study during the winter monsoon and pre-monsoon seasons, comprising 12 in situ physicochemical parameters. A multiparameter was used to measure surface water temperature, salinity, pH, dissolved oxygen (DO), and total dissolved solids (TDS). Turbidity was measured using a 2100Q portable turbidimeter (EPA). Surface water samples were taken to measure total suspended solids (TSS), chlorophyll-a (Chl-a), nitrate, phosphate, ammonia, and silica concentration following the standard procedure. A fishing trawler was used as a data collection platform whereas the surface water samples were collected using the HYDRO-BIOS Niskin sampler, a non-metallic interior. As shallow water influences coastal phenomena and its coastal process, physicochemical parameters can play a significant role in knowing the impacts and influence on associated biotic resources around Naf MPA. However, Bangladesh has just started its exploration in view of oceanographic data collection. With the vast area and lack of oceanographic research vessels, there is still a deficiency of oceanographic data. Seasonal datasets are important to know the influence that changes with season in the coastal bay, as well as there is a lack of seasonal in situ data. Thus, these inter-seasonal oceanographic datasets can help the future study and management strategy of the newly declared Naf MPA, as well as to compare satellite-derived datasets with in situ data for sensor validation and algorithm development.
