Geoscience Data Journal最新文献

The damage status of 44 locations was investigated, incorporating ground condition parameters such as Vs30, engineering bedrock depth (EBd), and predominant frequency (f0), as well as strong-motion parameters including PGA, Repi, and Rrup (epicentre and rupture distance, respectively). Various machine learning methods—logistic regression (LR), classification and regression trees (CART), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and artificial neural networks (ANN)—were employed to evaluate the dataset through three approaches: the complete parameter set, solely ground-based parameters, and strong-motion parameters alone. Model performance, measured by Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), ranged from 0.466 to 0.989, with KNN achieving the highest performance (0.989) when using the complete dataset and 0.988 with ground-based parameters alone. The analysis highlighted EBd and f0 as the most significant contributors to damage outcomes (normalised variable importance of 100% and 85%, respectively), demonstrating strong correlations with structural vulnerability. Among earthquake-related parameters, PGA was identified as the most influential factor in models established through strong-motion parameters, whereas Repi and Rrup demonstrated a considerably lower influence. On the other hand, specificity values (determining no-damage status) consistently exceeded sensitivity (determining damage status) in models based solely on earthquake parameters. Overall, the outputs demonstrate that traditional seismic hazard approaches based on earthquake parameters could provide a broad framework for risk mitigation; local site conditions, particularly EBd and f0, are the primary drivers of seismic risk. Integrating these detailed ground parameters into seismic assessments is critical for improving predictive accuracy and advancing earthquake engineering practices.

The accurate estimation of terrestrial water and vegetation is a grand challenge in hydrometeorology. Many previous studies developed land data assimilation systems (LDASs) and provided global-scale land surface data sets by integrating numerical simulation and satellite data. However, vegetation dynamics have not been explicitly solved in these land reanalysis data sets. Here we present the newly developed land reanalysis data set, ECoHydrological Land reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X-band microwave brightness temperature satellite observations into a land surface model which can explicitly simulate the dynamic evolution of vegetation biomass. The ECHLA data set provides semiglobal soil moisture from surface to 1.95 m depth, Leaf Area Index (LAI), and vegetation water content. The ECHLA data set is publicly available in the Japan Aerospace eXploration Agency's repository and is expected to contribute to understanding terrestrial ecohydrological cycles and water-related natural disasters such as drought.

To accurately determine multi-centennial trends in climate data records of the Earth's surface temperature, measurements are commonly analysed in the form of anomalies relative to a climatological reference period such as the World Meteorological Organization (WMO) 1961–1990 baseline. One of many climate-monitoring challenges is that weather records of land surface temperature can be short, typically of the order of several years or decades, and often do not sufficiently overlap the reference period to allow calculation of the climatological normals needed to convert the observations to anomalies. Moreover, the volume of records of this type is increasing due to the rescue of early (pre-baseline) instrumental paper-based records and the growing prevalence of newer (post-baseline) weather stations. To address this, we apply a method to estimate the climatological normal for each calendar month of temperature time series that do not have sufficient data during the baseline period, using an approximation to local expectation kriging with station holdout (LEK). This exploits the information in neighbouring time series to estimate the expected mean level of short series of observations. We apply the method to a global database of monthly land air temperature at 11865 stations based on CRUTEM5 but with the acquisition of an additional 1233 station series including some that extend back to 1781, and with mid-latitude stations adjusted for exposure bias arising from the transition to Stevenson screens. We evaluate the LEK-based normals using climatological normals calculated directly from the station observations. Using this method, we obtain estimated normals for 2699 stations that did not previously have normals and we improve the estimated normals for a further 2611 which had previously been estimated from incomplete data. Finally, we demonstrate how incorporating these thousands of previously unused station observation fragments affects hemispheric temperature averages. Pre-1850 data—primarily from Europe—show a modest warming trend but pronounced multidecadal variability that is greater than after 1850. The additional stations improve spatial coverage by a few percent in recent decades and raise pre-1860 Northern Hemisphere temperature estimates by approximately 0.1°C.

This paper details the compilation of data and application of quality assurance procedures for constructing a 157-year snow and sleet series for the Greater Dublin Area, Ireland. Snowfall is particularly sensitive to climate variability in temperate regions, and long-term records are essential for understanding changes in winter weather extremes over time. The dataset integrates observations from six sites and provides a regional snow and sleet frequency dataset at monthly, seasonal (October–May) and annual resolutions. Data sources include archived meteorological records, digitised station logs and synoptic weather reports. A brief analysis offers insights into long-term snowfall climatology in the Greater Dublin region from 1867 to 2024, revealing substantial interannual and decadal variability, as well as notable reductions in snow frequency in recent decades. This dataset provides a valuable baseline for assessing historical trends in snowfall and contributes to broader efforts in climate reconstruction and climate change impact studies in Ireland and beyond.

This dataset comprises vertical arrays of air temperature measurements collected on Livingston and Deception Islands, Antarctica, between 2006 and early 2024. The arrays, part of the PERMATHERMAL network integrated into the Global Terrestrial Network for Permafrost (GTN-P) database, were designed to support studies on permafrost thermal regimes and snow cover dynamics. Standard configurations included temperature sensors placed at heights of 2.5, 5, 10, 20, 40, 80, and 160 cm above the ground, mounted on wooden masts to minimise thermal interference. Higher-resolution configuration with up to 15 vertical measurements (between 2.5 and 160 cm above the ground surface) and miniature configuration with 8 sensors (between 2.5 and 40 cm above the ground surface) were also occasionally deployed for spatial-specific studies. Data were mainly recorded using iButton DS1921G (Miniature configuration) and DS1922L (standard and high-resolution configurations) temperature loggers (Maxim Integrated). Despite occasional gaps due to energy depletion or device damage, the dataset provides reliable long-term monitoring in a region where such measurements are logistically challenging. Originally acquired to estimate seasonal snow thickness through the analysis of vertical thermal gradients, the dataset has broader applications. These include investigating snowpack thermophysical properties, ground-atmosphere energy exchanges, snow hydrology, ecological processes, and remote sensing calibration. Raw data in American Standard Code for Information Interchange (ASCII) format, without filtering or preprocessing, are made available to ensure flexibility for diverse research needs, allowing users to apply tailored cleaning and analysis protocols. The dataset is particularly valuable for addressing the scarcity of observational air temperature data in Antarctica. It provides a ground-based complement to satellite measurements and supports studies on snow-atmosphere interactions, soil thermal regimes, and the micrometeorology of polar environments. This unique resource facilitates multidisciplinary research across cryospheric science, hydrology, ecology, and remote sensing, offering insights into processes in extreme environments. The contribution of these long-term measurements highlights the importance of accessible, high-resolution datasets to advance understanding of Antarctica's complex environmental systems.

This manuscript presents a comprehensive presentation of ground temperature data collected at 16 nodes of the 121 of the Crater Lake Circumpolar Active Layer Monitoring (CALM) site on Deception Island, Antarctica, from 2008 to early 2022. Each one of the 16 shallow boreholes has been equipped with miniature temperature loggers, providing valuable insights into the thermal regime of the ground at a depth of 50 cm, which corresponds to the mean depth of the top of the permafrost table as observed by annual mechanical probing in the CALM site. Despite a 9-month long gap in data collection during 2017 due to persistent snow cover, the time series remains largely intact, with annual measurements taken every 3 h. The manuscript details the methodologies employed for data collection, including the use of iButton loggers, and outlines the challenges faced in retrieving and processing the data in the harsh Antarctic environment. The cleaned dataset, which consolidates data from various nodes while removing erroneous records, is made freely accessible to the scientific community without any additional processing of the data such as offset corrections or gaps interpolation. This resource is expected to facilitate further research into the thermal dynamics of the active layer and permafrost and its implications for climate change since both are influenced by external factors such as snow cover, air temperature and others. Overall, the presented dataset contributes to the limited body of knowledge regarding Antarctic permafrost and provides a foundation for future investigations into the effects of climate change on frozen ground dynamics. The dataset serves as a vital tool for researchers aiming to model ground thermal behaviour and assess the impacts of environmental changes in polar regions.

The nitrogen distribution in Weifang was examined using measured data from August 2022 to investigate the distribution features of nitrogen in the Weifang Sea region and its relationship with land-based rivers. The findings indicate that nitrogen levels in Weifang are higher during the August flood season, and water quality varies by location. Land-based pollution usually affects the water quality, with lower amounts of nitrogen seen in the open sea and rather steady levels in other places. The distribution of nitrogen across different coastal regions is influenced by nearby rivers, with higher concentrations observed in the Mi River and Xiaoqing River. This leads to the most severe nitrogen exceedances in the sea area near Weifang Port. Based on these findings, strategies for targeted actions, improved land and sea management, and heightened environmental awareness are recommended. The research results enhance the understanding of water quality distribution in Weifang waters and provide valuable data for controlling nitrogen pollution and improving environmental management in the region.

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.