Geoscience Data Journal最新文献

{"title":"A Method for Landslide Deformation Detection Based on Projection Surface Element Matching of 3D Models","authors":"Mengxi Sun,&nbsp;Hui Cao,&nbsp;Yansong Duan","doi":"10.1002/gdj3.290","DOIUrl":"https://doi.org/10.1002/gdj3.290","url":null,"abstract":"<p>Landslides represent one of the most prevalent natural disasters worldwide, exerting significant adverse effects on social stability and economic development. Timely and accurate monitoring of landslide changes is crucial for disaster prevention and mitigation. Unlike traditional change detection, which often focus on broad environmental changes, landslide monitoring specifically aims to capture critical parameters such as the precise location of deformation, the direction of movement, and the rate of displacement associated with landslide events. Conventional monitoring techniques are typically constrained to fixed-point observations or are limited to the collection of deformation location data, which may not provide a comprehensive understanding of the landslide's behaviour. To address these limitations, this study proposes an innovative approach for detecting landslide deformation utilising multi-temporal imagery acquired through Unmanned Aerial Vehicles (UAVs). Initially, UAVs are deployed to perform multi-temporal photogrammetric surveys of the landslide-affected area, enabling the construction of high-resolution 3D models. These models facilitate the extraction of the exposed surface by employing advanced vegetation segmentation techniques. Following this, the generated 3D models undergo surface segmentation and normal direction projection, resulting in the creation of orthoimages that accurately represent the slope surface. Subsequently, feature matching is conducted between the orthoimages of the slope surface to identify corresponding points across different temporal datasets. Utilising the forward and inverse transformation relationships of these orthoimages, the deformation direction and velocity of the identified deformation points are calculated. This methodology ultimately enables precise and comprehensive monitoring of landslide deformation. To validate the efficacy of the proposed method, a longitudinal study spanning 4 years was conducted at the Che Yiping landslide site located in western Yunnan Province, China. The findings from this extensive experiment indicate that the proposed approach effectively captures the deformation characteristics of the entire landslide, with point displacement accuracy at specific locations comparable to Global Navigation Satellite System (GNSS) measurements. Furthermore, a detailed analysis of the deformation characteristics within the landslide area revealed significant displacement variations at multiple deformation sites, thereby elucidating the overarching deformation trends present at the landslide location. Through this research, we aim to provide critical data support and a scientific foundation for the prevention of landslide disasters and the management of geological hazards. The insights gained from this study are intended to inform relevant decision-making processes, thereby contributing to enhanced safety and resilience in landslide-prone regions.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ClimUAd: Observation-Based Gridded Daily Climate Data for Ukraine, 1946–2020","authors":"Volodymyr Osadchyi,&nbsp;Oleg Skrynyk,&nbsp;Vladyslav Sidenko,&nbsp;Enric Aguilar,&nbsp;Jose Guijarro,&nbsp;Tamás Szentimrey,&nbsp;Olesya Skrynyk,&nbsp;Zita Bihari,&nbsp;Liudmyla Palamarchuk,&nbsp;Dmytro Oshurok,&nbsp;Igor Kravchenko,&nbsp;Dmytro Pinchuk","doi":"10.1002/gdj3.70000","DOIUrl":"https://doi.org/10.1002/gdj3.70000","url":null,"abstract":"<p>In this work, we present results of the development of an observation-based gridded climate dataset (ClimUAd), which covers the territory of Ukraine for the period of 1946–2020. The spatial resolution of the developed data is 0.1° × 0.1° (approximately 10 km in both longitude and latitude directions), with a 1-day time step. Four essential climate variables are included in the dataset, namely daily sums of atmospheric precipitation and daily minimum, mean, and maximum air temperature. The created gridded product is based on the complete collection of station measurements performed at 178 weather stations in Ukraine. Quality control, homogenisation, and gridding of the station time series were performed by means of the widely used software, INQC, Climatol, and MISH, respectively. The created gridded time series were statistically compared with several existing datasets that have the same spatial resolution (i.e., previously developed gridded monthly data of Ukraine, ERA5-Land and E-OBS) on monthly and daily scales. The comparison showed good accordance with the Ukrainian monthly data (partly obtained from other paper sources than the daily data and homogenised with HOMER software) and acceptable agreement with ERA5-Land and E-OBS data. The developed data are of great importance as they were built with the involvement of as many real weather measurements as possible, representing a denser network than those included in continental/global gridded products. They can be used for regional climate monitoring and as the reference for a wide variety of climatological applications for the territory of Ukraine. The dataset is freely available for research purposes and can be downloaded from the data repository of the Ukrainian Hydrometeorological Institute.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data on Physical Properties of Sea Ice in the Northern Barents Sea and Adjacent Arctic Basin From the Nansen Legacy Project","authors":"Dmitry V. Divine,&nbsp;Adam Steer,&nbsp;Mats A. Granskog,&nbsp;Sebastian Gerland,&nbsp;Øyvind Foss,&nbsp;Anca Cristea,&nbsp;Polona Itkin,&nbsp;Malin Johansson,&nbsp;Emily Down,&nbsp;Agneta Fransson","doi":"10.1002/gdj3.70001","DOIUrl":"https://doi.org/10.1002/gdj3.70001","url":null,"abstract":"<p>Recent decline of sea ice in the Barents Sea represents a clear manifestation of the ongoing Arctic warming. This study presents a compilation of data sets on sea ice physics acquired during 2018–2022 during Nansen Legacy—a Norwegian multidisciplinary national research programme that focused on the northern Barents Sea. The data were acquired using a variety of methods such as sea ice coring, thickness drillings, snow pits, snow depth surveys, drone flights, on-ice and helicopter-borne electromagnetic measurements of sea ice thickness, and ice draft measurements by bottom-anchored moorings. The collected data sets cover several key physical parameters describing the sea ice cover and encompass a range of spatial (local to regional) and temporal (daily to annual) scales. These data sets aid in filling a substantial knowledge gap of recent sea ice conditions in the rapidly changing northern Barents Sea region.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rainy Ottoman Days: Rescuing and Analysing Rainfall Data (1846–1917) in Constantinople (Istanbul, Türkiye)","authors":"Ferhat Yilmaz,&nbsp;Michel Tsamados,&nbsp;Dan Osborn","doi":"10.1002/gdj3.70002","DOIUrl":"https://doi.org/10.1002/gdj3.70002","url":null,"abstract":"<p>This study focuses on rescuing and analysing historical monthly rainfall data in Istanbul from 1846 to 1917 for the first time. Rainfall records from various stations, collected by foreign scientists, engineers and officials during the last century of the Ottoman Empire, were digitised in accordance with the Guidelines on Best Practices for Climate Data Rescue by the World Meteorological Organisation and assessed for homogeneity. The Pettitt test was employed to identify and address inhomogeneities and detect any potential change points at each station. Monthly and annual rainfall time series (1846–2023) were reconstructed, and long-term trends were analysed by using the Hamed and Rao Modified Mann–Kendall test to evaluate changes in Istanbul's rainfall patterns over time. Comparisons between historical data (1846–1923) and recent data (1946–2023) reveal a shift towards increased early-year rainfall and decreased late-year rainfall in recent decades. The study also identifies significant variability in observation data prior to 1937 compared to 20th CRv3 reanalysis data, attributed to the limited early records, with improved consistency in recent years. An analysis of reconstructed rainfall data from 1846 to 2023 revealed a significant annual decrease, with decreasing trends in August, September and November. In contrast, the 20thCRv3 reanalysis data indicates a significant annual increase with increasing trends in October over the same period.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of Hydrogeological Units for 3D Modelling Using Open-Source Tools in the Mexico Basin","authors":"Juan Camilo Montaño-Caro,&nbsp;Oscar Escolero-Fuentes,&nbsp;Eric Morales-Casique","doi":"10.1002/gdj3.292","DOIUrl":"https://doi.org/10.1002/gdj3.292","url":null,"abstract":"<p>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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Hydrologic Data Estimates From ERA5 Reanalyses in Benin, West Africa","authors":"René Bodjrènou,&nbsp;Luc Ollivier Sintondji,&nbsp;Yekambessoun M' Po N'Tcha,&nbsp;Diane Germain,&nbsp;Francis Esse Azonwade,&nbsp;Fernand Sohindji,&nbsp;Gilbert Hounnou,&nbsp;Edid Amouzouvi,&nbsp;Arthur Freud Segnon Kpognin,&nbsp;Françoise Comandan","doi":"10.1002/gdj3.288","DOIUrl":"https://doi.org/10.1002/gdj3.288","url":null,"abstract":"<p>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 &gt; 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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Update to the Central England Temperature Series—HadCET v2.1","authors":"Tim Legg,&nbsp;Stephen Packman,&nbsp;Thomas Caton Harrison,&nbsp;Mark McCarthy","doi":"10.1002/gdj3.284","DOIUrl":"https://doi.org/10.1002/gdj3.284","url":null,"abstract":"<p>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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Climate Simulation Dataset From 11 Overriding Experiments for Analysing Cloud and Air–Sea Feedbacks","authors":"Xiao Guo,&nbsp;Biao Feng,&nbsp;Zhiying Zhao,&nbsp;Jian Ma","doi":"10.1002/gdj3.286","DOIUrl":"https://doi.org/10.1002/gdj3.286","url":null,"abstract":"<p>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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of tracked mesoscale precipitation systems in the tropics","authors":"James Russell,&nbsp;Manikandan Rajagopal,&nbsp;Peter Veals,&nbsp;Gregor Skok,&nbsp;Edward Zipser,&nbsp;Michell Tinoco-Morales","doi":"10.1002/gdj3.275","DOIUrl":"https://doi.org/10.1002/gdj3.275","url":null,"abstract":"<p>Mesoscale Convective Systems (MCSs) are often quantified via surface-based radar network, geostationary satellite, or low earth orbit satellite observations. However, each of these has drawbacks for detecting cloud systems such as a lack of global coverage, a lack of variables to quantify deep convective cloud and precipitation properties, and a lack of continuous observations of individual MCSs, respectively. To generate a dataset of tropical Tracked IMERG Mesoscale Precipitation Systems (TIMPS), we use the Forward in Time tracking algorithm to track precipitation systems in the Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG). IMERG is a global gridded precipitation product that incorporates observations from a constellation of satellites with passive microwave sensors and other sources, allowing the TIMPS dataset to have continuous temporal precipitation information for MCSs in a global tropical strip with data every 30 min in time and 0.1° in space. TIMPS are provided in a publicly available data base with a variety of variables including MCS size, motion, and precipitation properties, estimations of MCS life cycle stages, and their proximity to the nearest tropical cyclone. By combining the TIMPS dataset with the University of Washington Convective Features database, we also provide snapshots of information from more spatially detailed space-borne radar coverage. The TIMPS dataset provides the means for detailed long-term and large-scale study of MCSs in all regions of the tropics with applications such as composite studies of MCS life cycles and the evaluation of model performance.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 2","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Global Lakes/Reservoirs Surface Extent Dataset (GLRSED): An Integration of Multi-Source Data","authors":"Bingxin Bai,&nbsp;Lixia Mu,&nbsp;Yumin Tan","doi":"10.1002/gdj3.285","DOIUrl":"https://doi.org/10.1002/gdj3.285","url":null,"abstract":"<p>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.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}