The isotopic composition of atmospheric methane (δ13C-CH4) provides critical constraints for attributing methane emissions to specific sources. In this study, we present updated global maps of δ13C-CH4δ13C-CH4δ13C-CH41°×1°δ13C-CH4δ13C-CH4Tapin et al., 2025, https://doi.org/10.57780/ESA-6D202E9).
Coastal ecosystems are highly dynamic and vulnerable to both climate changes and anthropogenic pressures. The Sept-Îles region, located in the northwestern Gulf of St. Lawrence, is a high-use subarctic coastal system with diverse urban, industrial and maritime activities. This study presents analyses of monthly water column profiles at 35 sites focusing on temperature, salinity and chlorophyll fluorescence, used as a proxy of phytoplankton biomass, during the ice-free season. Using a conductivity, temperature and depth (CTD) probe, water column profiles were collected from May to October 2022 along the coastline, at sites between 2 and 52 m depth. Results revealed a thermocline developing in spring, intensifying in summer and disappearing in autumn. Chlorophyll aa) concentrations peaked below the thermocline in July, while secondary increases were recorded at the surface in September, consistent with observations of an autumn bloom in similar environments. These findings highlight the complex dynamic of physical and biological variables in the coastal water column, and the importance of the timing of sampling to fully capture seasonal variability. To improve future research in the area, measuring nutrient concentrations would be essential for detecting potential upwelling events and better explaining phytoplankton variation during summer. This study provides a valuable baseline for future investigations and justifies the continuation of measurements of water column variability in the region, in the context of rapid climate change. The complete dataset is available via https://doi.org/10.5683/SP3/ALRWON
Comprehensive, large-sample hydrological datasets, such as CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), have provided the basis for advances in many aspects of hydrological research in recent years. They can be utilised for several purposes, such as training or calibrating hydrological models, comparisons between regions dominated by different types of hydrological processes and testing of general validity of hydrological theories. The value of these datasets is in combining a multitude of data sources into one easily accessible and usable, harmonised high-quality package. We present CAMELS-FI, an extensive dataset for 320 catchments in Finland. It combines hydrological and meteorological time series with biophysical and human influence catchment attributes in a format that enables comparisons between catchments within the dataset but also between earlier CAMELS datasets. CAMELS-FI includes a diverse set of catchments with human influence varying from near natural to heavily regulated. CAMELS-FI is available at https://doi.org/10.5281/zenodo.15853357
Ground-based microwave radiometers (MWRs) are indispensable instruments for the continuous observation of atmospheric temperature and humidity profiles. The reliability of brightness temperature (TB) measurements and the accuracy of retrieved atmospheric profiles are fundamental to their effective use in both research and operational applications. In this study, we present a long-term dataset of multi-channel microwave brightness temperature observations and corresponding retrieved atmospheric profiles derived from the RPG-HATPRO MWR deployed at the Xianghe Integrated Observatory (XH) in Hebei Province, China, covering the period 2013–2022. Minute-level TB observations over the 10-year period were integrated with collocated infrared cloud detection data to establish a comprehensive dataset featuring detailed weather-related information. The quality of the observed TBs was carefully evaluated using a radiative transfer model. The results demonstrate excellent agreement between simulated and observed multi-channel TBs, with correlation coefficients typically exceeding 0.96 and mean biases within 1 K, confirming the stable and reliable performance of the XH MWR throughout the entire observation period. Based on the quality-controlled TBs, two retrieval schemes for atmospheric temperature and humidity profiles were developed using collocated radiosonde observations and ERA5 reanalysis data. For clear-sky conditions, an optimal estimation (OE)-based retrieval model was employed, whereas a deep neural network (DNN)-based model was designed for cloudy-sky retrievals. Validation against radiosonde measurements shows that both retrieval schemes achieved substantially improved accuracy up to 35 % for temperature and 25 % for humidity profiles compared with the retrieval approach provided by the manufacturer. Combining the two retrieval models with the 10-year quality-controlled TB dataset, we constructed a comprehensive data record characterized by decadal-scale coverage, high temporal resolution (1–10 min), and integrated MWR observations and retrieved profiles (https://doi.org/10.5281/zenodo.20178914, Gong et al., 2025). The dataset captures changes in the frequency of surface-based inversion (SBI), from 6 % in summer to 68 % in winter. More frequent SBIs are associated with higher PM2.5>60 % of the most severe pollution events (>250 µg m−3). These applications demonstrate the dataset's value for boundary layer studies, climate trend analysis, and air quality forecasting.
Landslides are a serious threat to all communities due to their potential for property damage and loss of life. Triggered by different natural, climatic and anthropogenic factors, landslides are complex phenomena and difficult to identify, monitor, and manage (https://doi.org/10.1016/j.geomorph.2015.03.016, Kirschbaum et al., 2015). Accurate and comprehensive data are essential in the mitigation of landslide risk, where both the likelihood and impact of landslides on communities must be quantified. Robust datasets allow for the development of dependable prevention strategies such as land use planning and early warning systems. These proactive measures play a crucial role in landslide risk mitigation (https://doi.org/10.1007/s11069-023-05848-8, Gomez et al., 2020).This study presents a single global scale standardised landslide catalogue, the Unified Global Landslide Catalogue (UGLC), which is intended as an harmonised landslide information framework designed to support data discovery, comparative analyses, and large-scale descriptive investigations relevant to risk-related studies. UGLC integrates multiple open data landslide datasets and reports spatiotemporal data with trigger factors for landslides. Landslide occurrence data are collected from extensive field surveys, GPS data, GIS techniques, satellite imagery, and historical records sourced from government agencies, universities, and researchers.UGLC contains more than 1 million landslide events as point and polygonal data, from the period spanning circa 1700 to 2023. The catalogue is standardised across 18 field attributes, and systematically grouped into seven main categories: (1) UGLC Reference – a unique event identifier; (2) Source Reference that enables back-tracing to the original data source; (3) and (4) Spatial Accuracy and Temporal Accuracy – precisely describe the geographic location and temporal resolution of recorded events, respectively; (5) Geological Information, including triggering factors; (6) Reliability, which assigns a trustworthiness value to the data; and (7) Notes and Information containing supplementary details such as source links, authorship, scientific publications, and other relevant metadata.UGLC is intended as a robust catalogue of standardised landslide information worldwide. The aim is to provide a reliable and user-friendly source for the characterisation of landslide occurrence. Uniquely, it presents a comprehensive range of data for global analysis and thus compensates for the shortcomings of small-scale heterogeneous datasets. UGLC will facilitate a deeper understanding of landslide phenomena inhttps://doi.org/10.1016/j.geomorph.2015.03.016, Kirschbaum et al., 2015). UGLC is publicly available at Zenodo (https://doi.org/10.5281/zenodo.18643456, Mancino et al., 2025a).
The Global Heat Flow Database is a comprehensive data compilation on published heat-flow measurements dating back to the 1950s. The International Heat Flow Commission first released the database in 1963. Recent activities within the World Heat Flow Database Project (funded by the DFG German Research Association) and the Task Force VIII of the International Lithosphere Program (ILP) have focused on (1) developing a new, modern digital data infrastructure with integrated quality control of the data, (2) creating a new dedicated metadata scheme for reporting heat-flow data, (3) conducting a comprehensive review of the original literature to supplement the original metadata according to the new scheme, and (4) thoroughly adding new measurements from the literature. As a result, the 2024 release presents a substantial update, with the number of heat-flow observations increasing from 58 302 data points in 2012 to 91 182 in 2024, while the number of literature sources simultaneously increased from 572 to 1586 documents. A key part of this process was the introduction of a new, comprehensive metadata scheme and the development of the GHFDB Data Template (Global Heat Flow Data Assessment Group, 2024, https://doi.org/10.5880/fidgeo.2024.014), which facilitates the structured and detailed reporting of heat flow observations in accordance with the new scheme (Fuchs et al., 2025a, https://doi.org/10.5880/fidgeo.2025.042). The GHFDB Data Template captures methodological details, uncertainty estimates, and contextual information, forming the basis for a newly implemented, multi-dimensional quality-assessment system. The improved data submission workflow, now supported by the option of obtaining digital object identifier (DOI), making the newly submitted data citable in literature, as is increasingly required by journals. This service encourages direct contributions from researchers and ensures transparency, attribution, and long-term data stewardship by the partner repository GFZ Data Services. The new heat flow database release marks a significant step towards establishing a global, quality- assured data infrastructure and lays the foundation for more reliable, reusable, and interoperable heat-flow datasets across scientific disciplines.
In this work, we present a new, high-resolution earthquake catalog for the Kefalonia region, Greece, together with the distributed acoustic sensing (DAS) waveform dataset used for its construction (https://doi.org/10.60517/cv43p1601, Bocchini et al., 2025; https://doi.org/10.5281/zenodo.20558686, Bocchini, 2026). We build the catalog from DAS data recorded between 1 August 2024, 23:00 UTC and 15 August 2024, 23:00 UTC, combined with open-access seismic-station recordings from the Hellenic Unified Seismic Network (HUSN). The DAS dataset consists of continuous strain measurements acquired along a 15 km long telecommunications fiber-optic cable connecting northern Kefalonia and Ithaki. We use a semblance-based detector on the DAS waveforms to identify 5734 earthquakes within ∼50 km of the cable origin. We jointly locate 356 high-SNR (SNR >12 dB) events with DAS and seismic stations and calculate their local magnitudes from seismic records. We then apply waveform cross-correlation to match unlocated detections with the most similar template events and estimate relative magnitudes from amplitude ratios to enhance the newly constructed catalog. Enhancement adds 2515 earthquakes, resulting in 2871 events with assigned locations and magnitudes and represents a ∼32-fold increase in the number of earthquakes with respect to the official National Observatory of Athens (NOA) catalog. Most events (2790) cluster within a ∼5 km radius offshore northwest of Kefalonia, where seismicity rates reach >100∼38-fold increase in the number of earthquakes with respect to the official catalog from NOA in the region encompassing the earthquake cluster northwest of Kefalonia. Our dataset provides a detailed spatio-temporal view of seismicity in a region with limited station coverage and demonstrates the value of integrating DAS with conventional seismic networks to monitor intense earthquake sequences. The combination of high seismicity and open-access data from the HUSN makes this DAS dataset particularly valuable for the seismological community. We provide a 2-week-long catalog, the full detection list (local and distant events and false detections), and two weeks of continuous DAS recordings. Possible applications of the datasets include testing and benchmarking DAS processing algorithms for tectonic earthquakes, as well as studies of physical processes associated with complex seismic sequences.
Real-time and accurate three-dimensional ocean temperature–salinity (T–S) field are of great significance for a deeper understanding of ocean dynamics and prediction skill improvement of numerical models. However, current ocean observations, especially those below the sea surface, still suffer from significant limitations in temporal and spatial resolution. Several neural network methods using multi-source satellite data for underwater temperature and salinity reconstruction have been proposed, achieving real-time temperature and salinity reconstruction, but their biases relative to in-situ observations are still significant. This study focuses on the northwestern Pacific region (0–40° N, 120–160° E) and proposes an attention-enhanced three dimensional U-Net
A set of 11 aerosol turbidity profiles (ATP) and 2 aerosol extinction profiles (AEP) at λ=0.55 µm, observed with searchlight in New Mexico at 32° N, has been digitized from plots in scientific articles. They cover the period February to June 1963 and September 1965 to May 1975, complementing the already rescued and previously published 105 individual AEP, corresponding to 36 days, between December 1963 and December 1964. Eleven AEP are calculated (AEPc) from the ATP, and the corresponding stratospheric aerosol optical depth (sAOD) between 12 and 25 km is also derived. Estimates of the digitization errors for the AEPc and the sAOD are also calculated using information available in the literature. The combined set of rescued AEP reported here and the earlier rescued set of AEP from searchlight observations, are the only AEP dataset covering the period between the 1963 Mt Agung and the 1974 Fuego eruptions at northern midlatitudes. In this regard two relevant features identified in the AEP and the sAOD are described here. The first, using AEPc from March and April 1963 identified what could be the date of arrival of the stratospheric aerosols from the Mt. Agung first eruption on 17 March 1963. This would challenge the accepted criteria that the stratospheric aerosols from Mt Agung arrived at the northern hemisphere midlatitudes in the second half of 1963. The second feature evidences two anomalous increases of the sAOD during a period supposed to be the decay of the sAOD from Mt. Agung eruption. They show our limited knowledge and understanding of the 1963 Mt Agung volcanic stratospheric aerosol transport. Finally, we describe evidences found in the literature pointing to the possible existence of the original searchlight raw signals and its processing software. The dataset described in this work is available at: https://doi.org/10.1594/PANGAEA.992616
Studying the pathways of atmospheric moisture and heat is crucial for understanding global water and energy cycles, and their response to climate change. Here, we present a new global dataset of atmospheric parcel trajectories generated with the FLEXible PARTicle dispersion model (FLEXPART v11) and forced by ERA5 reanalysis. The dataset spans 1979–2024 and provides a consistent and physically grounded record for studying Lagrangian moisture and heat transport. The dataset includes 20 million global, domain-filling air-parcel trajectories together with their (thermo)dynamic properties, enabling detailed investigation of long-range atmospheric transport processes. By providing the complete trajectory archive openly, the dataset enables quantitative analyses of moisture and heat pathways without the need to perform computationally expensive Lagrangian simulations. While the trajectory dataset itself can be used with any moisture and heat tracking attribution methodology, here it is explored using the new version of the Heat And MoiSture Tracking framEwoRk (HAMSTER v2). The dataset's usability is demonstrated by (i) global analyses of moisture source–sink patterns and recycling over multiple decades, (ii) global attribution of diabatic temperature increments to upwind surface sensible heat fluxes for a representative year (2021), and (iii) two local-scale case studies which showcase how the dataset and associated tools can be applied to hydrological and temperature extremes across a range of spatial and temporal scales. Overall, this resource lowers computational barriers and supports reproducible research across the atmospheric science community. The dataset is available at https://doi.org/10.5281/zenodo.17952362(Deman et al., 2025).
Wind power serves as a vital zero-carbon alternative to fossil fuels for climate change mitigation. Nevertheless, the vast expansion of wind turbine installation requires extensive terrestrial resources, raising wide concerns regarding land use competition and ecological impacts. Quantifying these effects necessitates near real-time geospatial data on wind turbine placement and density. However, current methods remain inadequate for monitoring the fast-growing wind turbine deployment. Here, we developed an integrated framework that combines OpenStreetMap (OSM) data with multi-source remote sensing images (Google Earth and Sentinel-1/2), and deep learning and traditional machine learning models (ResNet-18 and Random Forest) to map global onshore wind turbines. Our models achieve validation accuracy > 97 % while enabling cost-effective, timely updates of global onshore wind turbines. Eventually, we established a geographical dataset (GonshoreWT2024) covering a total of 416 532 wind turbines globally by 2024. This dataset represents a tenfold expansion over global wind turbine inventories as of 2020, and updates 42 955 more onshore wind turbines compared to the Global Renewables Watch based on lower computational requirements. In addition, we found that 87 % of wind turbines are situated on cropland and grassland, followed by forest and bare ground. This dataset facilitates essential studies on renewable energy land management, ecological impact analysis, and data-driven energy transition policies. The codes and dataset of the global onshore wind turbines are available at the Zenodo link: https://doi.org/10.5281/zenodo.18984175
Beaches provide essential ecological functions and support socio-economic resilience, yet accurate mapping is hindered by systematic limitations in global Digital Elevation Models (DEMs). A critical challenge remains in the intertidal zone, where frequent tidal inundation creates extensive data voids, disrupting the continuity of coastal topography. To bridge this fundamental data gap, we present NZ-BeachTopo30 which is a national-scale and full-coverage 30 m beach topography dataset for New Zealand constructed by fusing ICESat-2 photon-counting altimetry with Sentinel-2 multispectral time series. The dataset is available at https://doi.org/10.5281/zenodo.17785546R22. It delivers the first spatially continuous and full-coverage beach topography dataset for New Zealand. Given the global availability of ICESat-2 and Sentinel-2, NZ-BeachTopo30 offers a scalable solution for worldwide applications and provides a robust foundation for inundation modeling and coastal management.
Publicly available global high-resolution terrestrial water storage anomaly (TWSA) datasets derived from satellite gravimetry remain scarce. Many existing global downscaling products rely heavily on hydrological models. Consequently, their performance can degrade in regions where key mass variations observed by the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GFO) are poorly represented in the models, notably those associated with mountain glaciers and large lakes. Here we provide SYSU TWSA, a global monthly 0.5° TWSA dataset spanning April 2002 to December 2022, generated using a joint-inversion spatial downscaling framework that integrates large-scale constraints from GRACE/GFO, high-resolution spatial patterns from the WaterGAP Global Hydrological Model (WGHM), and additional mascon groups that explicitly represent mountain glaciers and selected large or rapidly changing lakes. The dataset helps alleviate the current shortage of global high-resolution products and explicitly strengthens the representation of glacier- and lake-related signals. We assess SYSU TWSA through four complementary evaluations: (1) basin-wise consistency with raw GRACE/GFO estimates, (2) a basin water-balance consistency check, (3) an independent evaluation against in situ groundwater well observations, and (4) comparisons with representative downscaled products in both the spectral and spatial domains. SYSU TWSA shows generally good agreement with GRACE/GFO at the basin scale, with coefficients of determination (R2) exceeding 0.85 across basin-size classes. In small basins, consistency with terrestrial water fluxes derived from the basin water-balance equation improves substantially, with NSE increasing by 17.1 % relative to raw GRACE/GFO across 1200 basins. Agreement with groundwater wells also improves, with correlations increasing at 67.7 % of 28 248 wells. Comparisons with representative assimilation-based and deep-learning downscaled products further indicate that SYSU TWSA demonstrates competitive overall accuracy while strengthening the representation of glacier- and lake-related signals. The SYSU TWSA dataset is openly available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Terre.tpdc.303322, Xiong et al., 2026).