Geoscience Data Journal最新文献

Snow cover is a crucial component of the global climate system, with cloud cover significantly affecting the accuracy of remote sensing snow products. This dataset, leveraging the MODIS daily snow cover product, was crafted through combining Terra and Aqua, temporal Filter, spatial correlation synthesis, combining MODIS and IMS. It encompasses a detailed snow cover dataset for Central Eurasia (0°–160° E, 40°–80° N) for the autumn months (September to November) from 2004 to 2021. Accuracy validation was conducted using ground monitoring station data, indicating an overall accuracy of 89.48%, with snow cover and terrestrial accuracies at 89.52% and 89.47%, respectively. Overestimation and underestimation errors were 9.65% and 0.87%, with 69.75% of stations reporting overestimation errors below 10% and 85.03% reporting underestimation errors below 5%. The dataset exhibits high accuracy in forests, grassland, croplands and urban construction land, while accuracy is relatively lower in shrubland and barren due to fewer samples and low snow cover. This dataset significantly enhances snow and climate variability research, offering a robust foundation for climate change projections.

Contrary to the widespread discussion of pesticide fate in the environment, there are surprisingly few publicly available datasets for the development and testing of pesticide fate models. Here, we present a comprehensive dataset that is designed to examine the environmental exposure of surface water pollution by herbicides in an intensively agricultural headwater catchment (catchment area 1032 ha) in Flanders, Belgium. From May 2010 through December 2013, stream discharge was measured, and water samples were taken at two sampling locations, one at the outlet and one within the catchment. During the 1325 days, the temporal resolution of sampling was at least daily, with sub-daily sampling of two or four samples on 61% of the days. In total, 4350 water samples were analysed for 11 herbicides and one metabolite. Additional meta-information on application practice was collected beginning in autumn of 2009 from all farmers working in the study area. In addition to analytical and meta-data, we also present links to publicly available spatial data on land use, soils and topography. The full dataset (including streamflow, precipitation and application data) is available at https://doi.org/10.5281/zenodo.10189609.

Mountain regions face unique challenges in managing water resources due to their complex topography, diverse climates and their role as water towers for the surrounding lowlands. Here, we present a spatially explicit, annual water balance dataset for the Upper Adige catchment in South Tyrol (Italy), covering the period from 1993 to 2022. The dataset is based on a distributed modelling approach and includes very high-resolution precipitation, evapotranspiration and land use data to compute the annual water balance. It captures both long-term trends and extreme conditions, taking into account gradients in terrain, slope and elevation using local correction factors. Modelled results are validated using stream gauge measurements from nine watersheds, achieving a correlation of over 0.9. This dataset provides a valuable resource for eco-hydrological studies and water resource management in alpine regions, offering detailed insights into the spatial variability and distribution of water availability.

Mining has a lasting impact on the environment, not only during the active mining process but also long after operations cease. The anthropogenic landforms that remain after mining require monitoring due to the instability of the land, which may pose a threat to the local environment. This paper presents bathymetric data acquired as part of the monitoring process for postmining lakes, collected using an unmanned surface vehicle (USV). The data were gathered with a Satlab SLD-200 single-beam echosounder and a Trimble R6 GNSS receiver to measure the lakebed elevation. By comparing data from two different periods, a high-accuracy 3D representation of the lakebed was produced, enabling the detection of changes in lake depth over time. The observed elevation change suggests that the area may still be affected by mining activities that occurred decades ago. This open dataset provides valuable insights for further research on the impact of underground and open-pit mining on the environment and its degradation.

We produced maps of sediment fractions for the US Gulf of America (formerly Gulf of Mexico) and South Atlantic Bight at 1 km² spatial resolution using compositional kriging. Quantitative tools were used to identify the optimal pixel size of the output map, which was produced using compositional kriging of log-ratio transformed variables. Input data were extracted from the databases usSEABED and dbSEABED, and were in the form of 167,854 sediment samples with the percentage composition of sand, mud and gravel. Sediments for the Gulf of America were mostly muddy (35% median, while sand and gravel took 20% and 0%) and for the South Atlantic Bight were mostly sandy (86%, with sand and gravel fractions having 0% of the median). Gravel was always the least common fraction. Anisotropy (variable spatial continuity in different directions) was negligible in the Gulf of America but relevant in the South Atlantic Bight. Sediment data were uncorrelated with bathymetry in both regions. Spatial resolution for the output maps was identified as 1 km² based on quantitative analyses. Interpolated maps were computed using compositional kriging on log-ratio transformed variables. The standard deviation of the estimator based on the kriging variance was 0.12 for gravel, 0.18 for sand and 0.06 for mud in the Gulf and 0.14 for gravel, 0.17 for sand and 0.001 for mud in the Atlantic. Compositional kriging is the method that provides the best accuracy in terms of mean absolute error. Interpolation of raw variables provides the best accuracy according to root mean square error, but handling of fractions individually is statistically inappropriate for this type of data.

The Northeast region of China, serving as a crucial hub for grain production and an ecological security barrier, confronts significant challenges such as soil degradation and nutrient imbalance. Addressing the need for dynamic soil quality monitoring in the major grain-producing areas of Northeast China, this study innovatively develops a spatiotemporal sparse grid modelling framework and produces high-precision soil spatiotemporal datasets, based on soil testing and fertiliser recommendation data collected from various locations between 2009 and 2020. By integrating a spatiotemporal covariance function with the Kriging interpolation algorithm, the study systematically resolves the challenge of spatiotemporal collaborative modelling for multi-year discontinuous observational data. Consequently, continuous spatiotemporal datasets for soil pH, soil organic matter (SOM), total nitrogen (TN) and available potassium (AK) at a 500-m resolution in Yian County were successfully reconstructed. Various error metrics, including RMSE, MAE, MAXE, MINE and SE were employed to verify the high accuracy and reliability of the spatiotemporal Kriging interpolation method, with the relative error controlled at a minimum of 0.04. Geodetector analysis revealed significant spatial variability in soil properties (q > 0.8, p < 0.001). A spatiotemporal trend analysis framework, coupling Theil-Sen Median with Mann-Kendall, quantitatively demonstrated significant decreasing trends in pH, SOM and TN during the study period (with decreasing area proportions of 49.02%, 47.32% and 43.17%, respectively), while AK exhibited a significant increase of 41.96%. The spatial variability patterns were highly coupled with the spatial gradient characteristics of agricultural management measures. This dataset transcends the limitations of traditional static soil databases in spatiotemporal representation. Through a high-precision spatiotemporal continuous modelling technique system, it provides multi-scale spatiotemporal benchmark data support for precision agriculture, optimising conservation tillage of black soil, and simulation of agricultural carbon neutrality pathways. It holds significant scientific value for the sustainable management of farmland ecosystems in the context of global change. This dataset can be downloaded from https://doi.org/10.5281/zenodo.13978751.

A dataset of the atmospheric Potential Gradient (PG) from Lerwick observatory in Shetland is now available, which provides hourly-averaged PG for each month, from January 1964 to July 1984. The measurements were made consistently, with calibrated and well-maintained instrumentation. Co-located meteorological observations are also available from the same site, where disturbing effects of air pollution are small. Other sources of atmospheric data such as satellite observations became increasingly abundant during the era of the measurements, making broader comparisons possible. On average, the Lerwick PG measurements contain a diurnal cycle characteristic of the global circuit and show relationships with the El Niño-Southern Oscillation (ENSO), especially in December. The value of the data is in the information it contains about the global atmospheric electric circuit, which is embedded in the climate system.

The Sand Atlas is a publicly accessible repository dedicated to the collection, processing and sharing of high-resolution 3D models of sand-sized particles. This dataset offers valuable insights into the morphology of a wide variety of natural and synthetic sand-sized particles from different regions, with varying mineralogy and history. The primary goal of The Sand Atlas is to support researchers, educators and industry professionals by providing detailed, easily accessible and uniformly produced surface meshes and level-set data. The underlying code that converts volumetric data to meshes is also available via the sand-atlas python package. This platform encourages community participation, inviting contributors to share their own data and enrich the collective understanding of granular materials.

The NICAM–LETKF JAXA Research Analysis (NEXRA) version 2.0 has been released on the JAXA-NEXRA Analysis website. This dataset is produced using the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF)-based atmospheric data assimilation system. The system assimilates in situ atmospheric observations, satellite data, and satellite-derived precipitation data into global atmospheric simulations. NEXRA provides a 128-member ensemble surface output (2D) and an analysis of ensemble mean and spread (3D) covering the period from January 2019 to June 2024. This dataset supports ensemble studies in geoscience research and serves practical applications, including initial conditions for hindcast simulations with atmospheric models, boundary conditions for ocean and land models, and investigations into spatiotemporal variations in atmospheric variability.

The eastern monsoon region of China, characterised by its high population density and rapid economic development, is particularly sensitive to global climate change. Issues such as water scarcity, droughts and floods, as well as ecological and environmental degradation, are particularly pronounced in this region. Consequently, there is a significant scientific imperative to investigate the land, atmosphere, carbon and hydrology interactions within this region. It is imperative for enhancing the efficiency of water usage, elucidating the evolutionary mechanisms of the carbon and hydrological cycles and evaluating ecological and environmental impacts. In this context, the present study introduces an integrated observation platform of the land–atmosphere–carbon–hydrology interactions in the eastern monsoon region—Ningxiang Station of the Institute of Atmospheric Physics, Chinese Academy of Sciences, and provides a long-term (2012–2024) integrated observation data set of the land–atmosphere–carbon–hydrology interactions. The integrated observation data set comprises hourly basic meteorological elements, half-hourly flux data and half-hourly groundwater depth data. These continuous, long-term, and high-resolution data sets have been employed to investigate land–atmosphere–carbon–hydrology interactions in the eastern monsoon region. Furthermore, the data set provides a critical foundation for the development of a new generation high-resolution earth system models and for advancing understanding of the impacts and feedback mechanisms of natural processes and anthropogenic activities on water, energy, material exchanges and climate. The complete data set is publicly accessible via the Science Data Bank (https://doi.org/10.57760/sciencedb.20182).