Geoscience Data Journal最新文献

This dataset contains hydrogeological cross-sections for 3D modelling in the Mexico Basin, developed using Python scripts and GIS tools. The cross-sections are based on existing geological studies and integrate a variety of lithologies and structural features, including volcanic and sedimentary units. While the dataset provides comprehensive coverage, it does acknowledge limitations in geological and structural resolution due to the availability of data. The dataset includes shapefiles representing hydrogeological units in both line and polygon formats, alongside topographic sections, surface hydrogeological distribution and regional fault systems. Although modifications may be required for specific applications, it serves as a strong foundation for multidisciplinary studies in groundwater and geological modelling. Hosted on open-source repositories, the data can be easily adapted for use in 3D modelling frameworks like GemPy and FloPy. This dataset is a valuable resource for understanding groundwater dynamics in the Mexico Basin and offers flexibility for future updates as new data become available or project needs evolve.

In West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in situ. ERA5 is a climate reanalysis product produced by the European Centre for Medium-range Weather Forecasts (ECMWF) and is suggested to overcome this constraint. This study assessed and compared the quality of ERA5 and its variant ERA5-Land (namely, LAND) over Benin at spatial and monthly time scales. ERA5 relies on a single-level version with a 0.25° × 0.25° resolution, while LAND is a land surface version with a 0.1° × 0.1° resolution. Four variables were collected, namely, surface runoff (SRO), evapotranspiration (PET), water table depth (WTD) and soil water content (SWC). Single nearest pixel (SNP) and inverse distance weighting (IDW) selection methods were used to compare the reanalyse data to point station data based on the correlation (c), mean absolute error (MAE) and relative mean absolute error (RMAE). With the SNP method, both reanalyses showed a best peak simulation in mean SRO. Their performance in terms of correlation ranged from 0.26 to 0.65 for ERA5 vs. 0.34 to 0.60 for LAND. The reanalyses showed high correlations (generally > 0.80) for SWC and for the PET (sometime greater than 0.90). The correlations were below 0.5 in both reanalyses for the WTD, with slight overestimations (4.73 m for ERA5 vs. 3.13 m for LAND). Similar results were reported with the IDW selection method. One or the other of the two reanalyses can be recommended for model calibration/validation, but care must be taken in the choice because the one chosen may be better in terms of correlation even though it has significant biases and vice versa. Correcting the variables of these reanalysis datasets could also improve their performance.

The Central England Temperature (CET) series is one of the longest instrumental climate records in the world. The CET record from 1659 represents a roughly triangular area of England extending from the Lancashire plain in the north, to London in the south-east and south-west of the Midlands of England. HadCET is a composite series produced by the Met Office Hadley Centre, using data from a succession of observing sites for which the data have been adjusted to remove inhomogeneities to be consistent with the original long running series and be updated in near real time. This paper documents a technical update to the HadCET which is referred to as HadCET version 2 (v2), and at time of publication v2.1.0.0 is the latest available version.

Under global warming, cloud change and its radiative feedback have often been considered to evolve from thermodynamic processes; however, cloud feedback may also force sea surface temperature to trigger such air–sea interactions. Due to complex cloud physics in air–sea coupling, this contributes to the surface warming pattern formation with significant uncertainty. Here we develop a novel overriding technique for climate projections that substitutes specific variables in control runs to isolate such feedback mechanisms, decoupling thermodynamic, dynamical and radiative responses of the surface ocean to the atmosphere. We apply this to the Community Earth System Model version 2 (CESM2) and perform a series of 150-year simulations with 1% CO₂ increase per year (1pctCO₂). In real time, the key variables under 1pctCO₂ are replaced with those from the current climate, such as downwelling shortwave radiation, wind speed in latent and sensible heat and wind stress. These experiments provide monthly output of global distributions including surface temperatures, winds and precipitation, with a spatial resolution of 1.9° × 2.5° in latitude and longitude and 32 levels for the atmosphere and of ~1° and 60 layers designated as gx1v7 for the ocean. This open access dataset for partial air–sea coupling under climate change can help understand the tropical and polar warming patterns and quantify the relative contributions of forcing and triggering mechanisms.

The surface water extent of global lakes/reservoirs is a fundamental input data for many studies. Although some datasets are currently available, issues such as incomplete data or spatial inconsistencies persist. In this study, a new Global Lakes/Reservoirs Surface Extent Dataset (GLRSED), which provides a more comprehensive spatial extent and basic attributes (e.g., name, area, source, depth and type) of 2.17 million individual features, was developed based on HydroLAKES and OpenStreetMap (OSM). In addition, by spatially overlaying with mountainous polygon, lakes/reservoirs in mountainous areas were identified. The Global Reservoir and Dam database (GRanD), GlObal geOreferenced Database of Dams (GOODD), Georeferenced global Dams and Reserves (GeoDAR) dataset, and OSM were used to distinguish reservoirs from natural lakes. The lakes/reservoirs in the rivers were identified by overlaying them with the Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD). Similarly, endorheic, glacier-fed and permafrost-fed lakes/reservoirs were identified using the same method. Furthermore, the coverage of the SWOT ground track for each lake/reservoir in the GLRSED was calculated to explore the potential of SWOT in monitoring water resources. Although preliminary and with some limitations, this dataset is promising. It can provide essential data for monitoring global lakes/reservoirs, support refined water resource management, and facilitate comprehensive studies on the impacts of human activities and climate change on these water bodies.

The dataset presented in the paper contains meteorological data from four automatic weather stations (AWS) located in the central and western parts of King George Island (near Arctowski Station and Cape Lions Rump). The dataset includes daily mean, maximum and minimum values of air temperature, relative air humidity, air pressure, wind speed and daily sum of solar radiation. The measurement period ran from 2018.01.01 to 2023.12.31, but it is shorter for two of the stations. Mean values were calculated from measurements taken every 10 min. Direct measurements were used to identify extreme values. The described dataset consists offour files, each for one AWS. It is available in the PANGEA online repository under a non-restrictive CC BY 4.0 licence for anyone after registration. Despite a strong correlation between the daily mean values of the parameters measured at certain stations, some differences between them were also noticeable. These were due to their location at different altitudes, in a place open to the sea or in a shaded place. Generally, values of wind speed, air humidity, solar radiation and pressure are similar to Arctowski during 2013–2017. The only notable distinction is that the mean annual air temperature and the mean air temperature in the winter months were higher than during 1977–1999 and 2013–2017. The data presented can be used as background for other research projects on King George Island, as well as for analysis of the meteorological conditions themselves. They may also be useful for the evaluation of the management plans of the eight Antarctic Specially Protected Areas or Antarctic Specially Managed Area no. 1 that are located on King George Island.

A dataset of multi-tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel-1 Wave Mode TenGeoP-SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel-1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel-1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP-SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG-formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand-labelled ocean SAR image datasets and offers the potential for new deep-learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

PCOT is a Python program and library which allows users to manipulate multispectral images and associated data. It is in active development in support of the ExoMars mission and intended to be used on data from the Rosalind Franklin rover, but it has much greater potential for use beyond this specific context. PCOT operates on a graph model – the data are processed through a set of nodes which manipulate it in various ways (e.g. add regions of interest, perform maths, splice images together, merge image channels, plot spectra). A PCOT document describes this graph, and we intend that documents are distributed along with the data they generate to help reproducibility. PCOT is open-source, and contributions can be made to the core software, as plugins, or by using PCOT as a library in your own code.

The internal defects in rock masses can significantly impact the quality and safety of geotechnical projects. Mechanical waves, as a common nondestructive testing (NDT) method, can reflect the external and internal structures of rock or rock masses. Analyses on the reflected and transmitted waves enable nondestructive identification and assessment of potential defects within rocks. Previous studies mainly focused on the variation of single or limited wave features like main frequency, amplitude and energy between the intact and non-intact samples. In fact, most information contained in the waveforms is neglected. Techniques of data mining can provide a powerful tool to reveal this information and therefore a more accurate determination of the internal structures. In this study, 995,412 NDT data from 14 types of granite and gypsum samples with different cross-section shapes and different types of defects are recorded by an ultrasonic wave generation and collection system. This dataset can be used not only as the training data for defect classification in NDT but also as a good reference for conventional NDT analyses. Besides, time-series data analysis is an opportunity and challenging issue, this dataset holds great potential for broader application in general time-series classification analysis.

The study evaluated the use of Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) data for monitoring rainfall data in Eswatini. Various statistical metrics such as Bias, correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the CHIRPS 2.0 data against 14 rain gauge observations acquired during 1981–2020. CHIRPS 2.0 rainfall agrees well with rain gauge precipitation at monthly (r = 0.73, Bias = 1.02, RMSE = 50.44 and MAD = 31.44), seasonal (r = 0.77, Bias = 1.01, RMSE = 36.99 and MAD = 24.15) and annual scales (r = 0.65, Bias = 2.46, RMSE = 500.78 and MAD = 468.06). Moreover, areas characterized by complex topography and land use, and areas in transition zones (to a different agroecological zone) had generally poor correlations. Nonetheless, CHIRPS 2.0 captures well the spatial distribution of rainfall in the different agroecological zones of Eswatini, even in areas with no rain gauge data. In conclusion, CHIRPS 2.0 can be a very valuable tool in filling gaps created by poor spatial coverage of ground-based rain gauges, especially in the developing world where this is often the case.