Geoscience Data Journal最新文献

{"title":"An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021","authors":"Caihong Ma,&nbsp;Xin Sui,&nbsp;Linlin Guan,&nbsp;Yanmei Xie,&nbsp;Tianzhu Li,&nbsp;Pengyu Zhang,&nbsp;Yubao Qiu,&nbsp;Weimin Huang","doi":"10.1002/gdj3.259","DOIUrl":"10.1002/gdj3.259","url":null,"abstract":"<p>The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large-scale IHSs. First, a density segmentation method based on an improved K-means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high-resolution remote-sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform-related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"833-845"},"PeriodicalIF":3.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340048","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":"Geochemistry of forty-one eclogitic and pyroxenitic mantle xenoliths from the Central Slave Craton, Canada (Ekati Diamond Mine)","authors":"D. E. Jacob,&nbsp;A. Fung","doi":"10.1002/gdj3.258","DOIUrl":"10.1002/gdj3.258","url":null,"abstract":"<p>This article describes a novel dataset on non-diamondiferous eclogite and garnet pyroxenite xenoliths from four kimberlite pipes of the Ekati Diamond Mine (Central Slave Craton, Canada). Xenoliths brought to the surface by kimberlite eruptions are direct sources of information on the composition and evolution of the Earth's mantle. Eclogite and garnet pyroxenite xenoliths, specifically, are testimony of subduction into, and metasomatism of, the mantle beneath cratons. Furthermore, these rocks are major hosts for diamond and thus an important part of the deep carbon cycle. The sample suite consists of 41 small xenoliths (2–5 cm) recovered from drill cores. The dataset includes major and trace element concentrations for garnet, clinopyroxene and ilmenite, as well as stable oxygen isotope compositions of garnets. Strontium and neodymium isotopic compositions are reported for garnet and clinopyroxene for four samples which were large enough to allow for analysis. Overall, this dataset significantly expands and complements existing datasets on diamondiferous and non-diamondiferous xenoliths from the Slave Craton in Canada, furthering our understanding of the composition of the Slave subcratonic lithosphere. The dataset includes several samples with rare mineral assemblages, including an olivine-bearing eclogite as well as ilmenite and apatite-bearing garnet-pyroxenites, and thus provides data shedding light on rarely reported compositional nuances in xenolith suites found in kimberlites.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"825-832"},"PeriodicalIF":3.3,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356599","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 four-dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles","authors":"Cunjin Xue,&nbsp;Zhenguo Wang,&nbsp;Linfeng Yue,&nbsp;Chaoran Niu","doi":"10.1002/gdj3.251","DOIUrl":"10.1002/gdj3.251","url":null,"abstract":"<p>Lack of a long-term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC-Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC-Argo has shorter time series coverage and fewer profiles compared to Core-Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core-Argo and BGC-Argo profiles and then developing a spatial, temporal and depth-specific gridded DOC dataset, named G4D-DOC. Validation results demonstrate that G4D-DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D-DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D-DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D-DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D-DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"775-789"},"PeriodicalIF":3.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387821","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":"Corrigendum to “Millions of digitized historical sea-level pressure observations rediscovered”","authors":"Ed Hawkins,&nbsp;Lisa V. Alexander,&nbsp;Rob J. Allan","doi":"10.1002/gdj3.250","DOIUrl":"10.1002/gdj3.250","url":null,"abstract":"&lt;p&gt;We have revised the dataset associated with the paper “Millions of digitized historical sea-level pressure observations rediscovered” by E. Hawkins et al. (Geoscience Data Journal, 10, 385, doi: 10.1002/gdj3.163, 2023). The dataset includes more than 5 million observations of sea level pressure every 3 hours from April 1919 to December 1960 over the UK &amp; Ireland which were contained in the Daily Weather Reports (DWRs) published by the Met Office.&lt;/p&gt;&lt;p&gt;A dataset user brought a small footnote to our attention which stated that in the original DWR documents for April 1919 to February 1930, the column giving the pressure change over the previous 3 hours was in units of half-millibars rather than whole millibars as we had previously assumed. This means that all pressure observations during this period derived using the ‘Change in last 3 hours’ column required small revisions – around 10% of the total dataset.&lt;/p&gt;&lt;p&gt;The ‘change over last 3 hours’ column was first introduced in the DWRs in May 1911 when the units of both pressure observations and the change in 3 hours were in/Hg using two decimal places. From May 1914 onwards, the pressure units were changed to mb, with half-millibars used for the change in pressure. After February 1930, the change in pressure was given in tenths of mb, and this was correctly used. The pressure observations from the DWRs for January 1911 to March 1919 remain unrescued.&lt;/p&gt;&lt;p&gt;The discussion of Figure 1 should read:&lt;/p&gt;&lt;p&gt;&lt;i&gt;Figure 1 shows an example DWR page from 5th April 1919, showing the stations from which eight sea-level pressure observations per day can be derived. Each station has a listing for 01Z, 07Z, 13Z and 18Z, with a pressure observation converted to sea-level (given to a precision of 0.1 mb) and a change in pressure over the previous 3 hr in units of half-millibars. This allows the pressures for 22Z, 04Z, 10Z and 15Z to be calculated, but with a small uncertainty as the change is only given with a precision of 0.5mb. Note that the rows are not always complete, highlighting missing data, especially for 01Z, and therefore also for 22Z the day before&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;The dataset revision means there are small visual differences in updated versions of Figures 6, 9 &amp; 10, but these are not shown here. A revised version of Figure 7 is shown, and the discussion around Figures 7 and 8 should now read:&lt;/p&gt;&lt;p&gt;&lt;i&gt;For example, the missing observation at Eskdalemuir in southern Scotland at 15Z on 23rd November is 956 mb, with other missing observations in Ireland from Malin Head at 972 mb and Blacksod Point at 984 mb. Recovering such individual missing observations may be worthwhile if analysing case studies of particular severe storms&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Note one almost certainly erroneous observation in the middle panel of the top row of&lt;/i&gt; Figure 7&lt;i&gt;. The 991 mb observation for Birmingham (south-east of the lowest pressure values) at 15Z on 16th November 1928 has no correction listed in the DWRs and is correctly tr","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 3","pages":"351-353"},"PeriodicalIF":3.3,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925163","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":"Investigating the potential for students to contribute to climate data rescue: Introducing the Climate Data Rescue Africa project (CliDaR-Africa)","authors":"S. Noone,&nbsp;C. D'Arcy,&nbsp;S. Donegan,&nbsp;W. Durkan,&nbsp;B. Essel,&nbsp;K. Healion,&nbsp;H. Hersbach,&nbsp;S. Madden,&nbsp;J. Marshall,&nbsp;L. McConnell,&nbsp;I. Mensah,&nbsp;N. Scroxton,&nbsp;S. Thiesen,&nbsp;P. Thorne","doi":"10.1002/gdj3.248","DOIUrl":"10.1002/gdj3.248","url":null,"abstract":"<p>The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard-copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good-quality historical observational data. The aim of this innovative project was to use classroom-based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second-year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data-sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll-out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"758-774"},"PeriodicalIF":3.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.248","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925134","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":"Open-source stand-alone version of atmospheric model Aeolus 2.0 software","authors":"Masoud Rostami,&nbsp;Stefan Petri,&nbsp;Sullyandro Oliveira Guimaräes,&nbsp;Bijan Fallah","doi":"10.1002/gdj3.249","DOIUrl":"10.1002/gdj3.249","url":null,"abstract":"<p>In this discourse, we present the unveiling of an open-source software package designed to facilitate engagement with the atmospheric model, Aeolus 2.0. This particular iteration stands as a self-contained model of intermediate complexity. The model's dynamical core is underpinned by a multi-layer pseudo-spectral moist-convective Thermal Rotating Shallow Water (mcTRSW) model. The pseudo-spectral problem-solving tasks are handled by the Dedalus algorithm, acknowledged for its spin-weighted spherical harmonics. The model captures the temporal and spatial evolution of vertically integrated potential temperature, thickness, water vapour, precipitation, and the intricate influence of bottom topography. It comprehensively characterizes velocity fields in both the lower and upper troposphere, employing resolutions spanning a spectrum from the smooth to the coarse, enabling the exploration of a wide range of dynamic phenomena with varying levels of detail and precision.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"1086-1093"},"PeriodicalIF":3.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925135","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":"Full-scale measurements of thunderstorm outflows in the Northern Mediterranean","authors":"F. Canepa,&nbsp;M. P. Repetto,&nbsp;M. Burlando","doi":"10.1002/gdj3.247","DOIUrl":"10.1002/gdj3.247","url":null,"abstract":"<p>Downbursts are severe wind systems originating from thunderstorm clouds, and their strong horizontal outflows can pose serious hazards to natural and built environments. In the context of the activities of the European project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures—a comprehensive database of full-scale downburst measurements was built. All records were acquired by bi- or tri-axial ultrasonic anemometers installed in the main ports of the High Tyrrhenian Sea, namely Genova, Livorno and La Spezia, within the European projects ‘Wind and Ports’ and ‘Wind, Ports and Sea’. The very limited space and time structure of downburst outflows makes the available records in nature inadequate for developing models that could be used in the atmospheric science and engineering communities. The database described herein represents a step forward in attempting to fill this gap. The downburst nature of all events contained in the dataset was verified through detailed meteorological analyses, including comparisons with radar and satellite images and lightning recordings. The wind speed records associated with the events detected by the anemometric network are made publicly available through the online repository Zenodo and can be reused for multiple purposes. The dataset is expected to convey an important impulse towards the physical characterization and modelling of downburst winds and their codification into design tools for the assessment of wind loading and its effects on structures and infrastructure. Furthermore, it could serve as a promising, essential tool for researchers and risk-related insurance companies.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"742-757"},"PeriodicalIF":3.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841717","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":"Landslides of China's Qinling","authors":"Liye Feng,&nbsp;Chong Xu,&nbsp;Yingying Tian,&nbsp;Lei Li,&nbsp;Jingjing Sun,&nbsp;Yuandong Huang,&nbsp;Peng Wang,&nbsp;Xuewei Zhang,&nbsp;Tao Li,&nbsp;Wentao Yang,&nbsp;Siyuan Ma,&nbsp;Xiaoyi Shao,&nbsp;Jixiang Xu,&nbsp;Jingyu Chen","doi":"10.1002/gdj3.246","DOIUrl":"10.1002/gdj3.246","url":null,"abstract":"<p>The Qinling Mountains in China frequently experience geological disasters, with large-scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km². By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large-scale landslides, covering a combined area of approximately 1575 km². The average size of these landslides is approximately 92,734 m². The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m² and the largest reaching 12.9 km². Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large-scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"725-741"},"PeriodicalIF":3.3,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677067","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":"DDE KG Editor: A data service system for knowledge graph construction in geoscience","authors":"Chengbin Hou,&nbsp;Kaichuang Liu,&nbsp;Tianheng Wang,&nbsp;Shunzhong Shi,&nbsp;Yan Li,&nbsp;Yunqiang Zhu,&nbsp;Xiumian Hu,&nbsp;Chengshan Wang,&nbsp;Chenghu Zhou,&nbsp;Hairong Lv","doi":"10.1002/gdj3.245","DOIUrl":"10.1002/gdj3.245","url":null,"abstract":"<p>Deep-time Digital Earth (DDE) is an innovative international big science program, focusing on scientific propositions of earth evolution, changing Earth Science by coordinating global geoscience data, and sharing global geoscience knowledge. To facilitate the DDE program with recent advances in computer science, the geoscience knowledge graph plays a key role in organizing the data and knowledge of multiple geoscience subjects into Knowledge Graphs (KGs), which enables the calculation and inference over geoscience KGs for data mining and knowledge discovery. However, the construction of geoscience KGs is challenging. Though there have been some construction tools, they commonly lack collaborative editing and peer review for building high-quality large-scale geoscience professional KGs. To this end, a data service system or tool, DDE KG Editor, is developed to construct geoscience KGs. Specifically, it comes with several distinctive features such as collaborative editing, peer review, contribution records, intelligent assistance, and discussion forums. Currently, global geoscientists have contributed over 60,000 ontologies for 22 subjects. The stability, scalability, and intelligence of the system are regularly improving as a public online platform to better serve the DDE program.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"1073-1085"},"PeriodicalIF":3.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577766","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":"Sediment particle tracking data for the Carpathian reservoir under climate and land use change scenarios","authors":"Ewa Szalińska,&nbsp;Paweł S. Hachaj,&nbsp;Monika Szlapa,&nbsp;Paulina Orlińska-Woźniak,&nbsp;Paweł Wilk","doi":"10.1002/gdj3.242","DOIUrl":"10.1002/gdj3.242","url":null,"abstract":"<p>Although the Carpathian Mts. area is considered as extremely prone to surface erosion which results in capacity loss of the dammed reservoirs, a lack of data to follow details of this process is perceivable. The research of the selected sediment fractions transport tracking was conducted using the capabilities of the digital platform—Macromodel DNS (Discharge-Nutrient-Sea) for the catchment with drinking water reservoir in the Polish part of Western Carpathian. The continuity of sediment transport simulation in two hydrologically different elements of the catchment—the river and the reservoir—was possible due to consolidation of two models in the platform—SWAT (Soil &amp; Water Assessment Tool) and AdH/PTM (Adaptive Hydraulics Model/Particle Tracking Model). The result of those modules' integration was a database for tracking the individual sediment fractions delivered to the reservoir and deposited in specific reservoir zones. The implementation of climate and land use change scenarios allowed additionally to analyse the estimation of those processes in the future. The simulation outcomes consist of daily flows and monthly sediment loads at the reservoir inflow and the individual sediment particle fractions deposition location inside of the reservoir.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 4","pages":"699-717"},"PeriodicalIF":3.3,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150456","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}