Geoscience Data Journal最新文献

{"title":"Catalogue of Global Land Soil Moisture Drought Events","authors":"Vojtěch Moravec,&nbsp;Jan Řehoř,&nbsp;Vít Šťovíček,&nbsp;Jan Balek,&nbsp;Rudolf Brázdil,&nbsp;Monika Hlavsová,&nbsp;Milan Fischer,&nbsp;Martin Hanel,&nbsp;Rohini Kumar,&nbsp;Oldřich Rakovec,&nbsp;Miroslav Trnka","doi":"10.1002/gdj3.70108","DOIUrl":"https://doi.org/10.1002/gdj3.70108","url":null,"abstract":"<p>Climate warming is intensifying soil moisture droughts worldwide, with serious consequences for agriculture and terrestrial ecosystems. Robust assessment of their severity, extent, evolution and impacts on natural and managed systems requires globally consistent, harmonized and event-based datasets with sub-monthly temporal resolution. Here, we introduce a global catalogue of 755 Global Land soil moisture Drought Events (GLDEs) over the past four decades (1980–2024) derived from the gridded root-zone soil moisture estimates produced by two structurally different models: the hydrological model mHM and the soil water balance model SoilClim. Drought events are identified using a combination of 10th- and 20th-percentile soil moisture anomaly thresholds and clustered with a machine-learning-based OPTICS algorithm applied to 10-day averaged soil moisture fields. Each GLDE is characterized using seven severity categories (from extremely weak to extremely severe) and seven dynamic categories (from extremely static to extremely dynamic) based on four spatiotemporal and three motion-related characteristics. Extremely severe (7s) and extremely dynamic (7d) GLDEs occur most frequently in South America, followed by North America, whereas Eurasia experiences the longest and most extensive events. The dataset of all 755 GLDEs with their statistical characteristics is publicly available. Overall, the GLDE catalogue provides a unique, globally consistent basis for future investigations into the drivers and impacts of dynamically evolving drought events.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205856","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":"LacusPyr, a Catalogue of Lakes and Ponds of the Pyrenees","authors":"Jordi Catalan,&nbsp;Meritxell Batalla,&nbsp;Teresa Buchaca,&nbsp;Lluís Camarero,&nbsp;Marisol Felip,&nbsp;Alexandre Miró,&nbsp;Miquel Ninyerola,&nbsp;Ibor Sabás,&nbsp;Marc Ventura","doi":"10.1002/gdj3.70109","DOIUrl":"https://doi.org/10.1002/gdj3.70109","url":null,"abstract":"<p>Lake districts—regions characterised by a high density of lakes—provide exceptional case studies for investigating how landscape geomorphology shapes both individual lake characteristics and broader ecosystem dynamics. High-mountain regions are particularly rich in lakes and ponds formed by past glaciation. In these landscapes, elevation adds a third spatial dimension beyond the predominantly two-dimensional organization of lowland lake districts, making them especially well-suited for studies that leverage large numbers of lakes, including research on species metacommunity dynamics, comparative biogeochemistry, and other spatially explicit ecological and environmental topics. The Pyrenees, extending from the Mediterranean to the Atlantic along the boundary between the Iberian Peninsula and continental Europe, contain thousands of lakes and ponds isolated from surrounding lowland water bodies, where standing water is less abundant and the climate is drier. Here we present LacusPyr, a comprehensive georeferenced inventory of 6361 Pyrenean lakes and ponds covering the entire mountain range. For each water body, the dataset provides surface and watershed polygons, along with key geomorphological attributes relevant to limnological research and conservation, including the lake centre coordinates, elevation, surface area, watershed area, and shoreline perimeter. The dataset also includes currently available information on maximum and mean depth, with a structure that accommodates future updates as new measurements become available. By providing a standardised, range-wide inventory of Pyrenean lakes, LacusPyr offers a valuable resource for environmental research, biodiversity conservation, and long-term monitoring of high-mountain lake ecosystems.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145881","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":"Supporting Digital Agriculture Through GeoAI: A Crop Type Dataset of Four Food Basket Crops in Rwanda","authors":"Benson Kenduiywo,&nbsp;Stephen Sande,&nbsp;Anastasia Wahome,&nbsp;Joseph Chemutt,&nbsp;Ange Marie Tesire,&nbsp;Samuel Ishimwe,&nbsp;Yves Hategekimana,&nbsp;Lilian Ndungu,&nbsp;Golo Rademacher,&nbsp;Aniruddha Ghosh","doi":"10.1002/gdj3.70097","DOIUrl":"https://doi.org/10.1002/gdj3.70097","url":null,"abstract":"<p>Agriculture is a major economic activity, contributing approximately 25% of Rwanda's gross domestic product and employing nearly 65% of its population. Recognising its critical role, the Government of Rwanda has prioritised digital agriculture to manage the sector sustainably. Decision support systems based on remote sensing and Geospatial Artificial Intelligence (GeoAI) offer a transformative opportunity to enhance regional food security by providing real-time data, improving agricultural forecasting and mitigating the impacts of climate variability. However, GeoAI approaches require labelled, ground-referenced crop type data for model calibration and validation, which is scarce in sub-Saharan Africa. To address this gap, we collected crop type data across Musanze, Nyabihu, Nyagatare and Ruhango districts of Rwanda between May and June 2025 to support the development of GeoAI crop monitoring approaches. The dataset, collected using KoboToolbox, integrates the Joint Experiment for Crop Assessment and Monitoring guidelines for field data collection and the Food and Agriculture Organisation's indicative crop classification. We targeted four food-basket crops from Rwanda's Crop Intensification Programme: bean, maize, Irish potato and rice. In total, 2739 GPS-located points were collected and used to generate 914 crop-labelled polygons: 112 for beans, 107 for Irish potato, 417 for maize, 34 for rice and 244 for mixed-crop fields. Polygons were digitised using four cardinal-direction photographs taken during fieldwork and Planet imagery acquired during the season. Besides crop labels, the point data include detailed agronomic information such as farm management practices, anti-erosion measures, cropping systems, agroforestry practices (including fruit-tree presence), administrative units and GPS coordinates. This rich dataset provides robust, nuanced information for crop monitoring, yield estimation and digital agriculture innovation in Rwanda and beyond.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145540","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 7-Year Eddy Covariance Dataset of the Energy, Water Vapour and Carbon Dioxide Fluxes Observed in a Siberian Dwarf Pine Ecosystem","authors":"Hiroki Iwata,&nbsp;Ryo Sato,&nbsp;Naoki Makita,&nbsp;Hajime Kobayashi,&nbsp;Hiroyuki Oguma","doi":"10.1002/gdj3.70101","DOIUrl":"https://doi.org/10.1002/gdj3.70101","url":null,"abstract":"<p>Alpine ecosystems provide a variety of valuable services, such as freshwater provision, timber supply, and climate regulation. Quantifying atmosphere–ecosystem exchanges of water and carbon is essential to understanding the mechanisms underlying these services and predicting how climate change may alter them. To clarify the role of alpine ecosystems in water and carbon cycling, we measured fluxes of energy, water vapour, and carbon dioxide, alongside micrometeorological data, in a Siberian dwarf pine (<i>Pinus pumila</i>) ecosystem in the Japanese Alps from 2018 to 2024. The fluxes were observed using the eddy covariance technique. The quality of the flux data was assessed using micrometeorological criteria, and data gaps were filled using a random forest regression approach. Despite being conducted on a non-ideal steep slope, the measurements adhered to surface-layer similarity scaling relationships, and the surface energy balance showed no significant deviation from observations over flat terrain, confirming high data quality. This 7 year dataset constitutes an important addition to the available databases on alpine ecosystems and will facilitate a better understanding of the roles of alpine ecosystems in water and carbon exchanges under climate change. It will also provide a valuable resource for parameterising and validating terrestrial ecosystem models and remote-sensing products for alpine regions in East Asia.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048141","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":"QG-Python: A Reproducible 1.5-Layer Quasi-Geostrophic Modelling Service With Direct Sparse Solvers, Spectral Stability Diagnostics and Filter Sensitivity Data","authors":"Elias D. Nino-Ruiz","doi":"10.1002/gdj3.70107","DOIUrl":"https://doi.org/10.1002/gdj3.70107","url":null,"abstract":"<p>In this paper, we describe <i>QG-Python</i>, an open, reproducible Python implementation of the 1.5-layer quasi-geostrophic (QG) model originally distributed as QG-C, and the accompanying <i>spectral stability and filter sensitivity dataset</i> produced with it. The software service replaces the legacy Fortran multi-grid Helmholtz solver of QG-C with a direct sparse LU factorization (SuperLU through SciPy) that yields machine-precision streamfunction inversions at a deterministic, reproducible cost. Three time integrators are exposed under a single API: the classical fourth-order Runge–Kutta scheme (RK4), the Leapfrog scheme with the Robert–Asselin (RA) filter and the Leapfrog scheme with the Robert–Asselin–Williams (RAW) filter, together with diagnostic modules that compute the linearized operator, its leading eigenspectrum and the maximum Lyapunov exponent. The published dataset consists of (i) the analytic and numerical eigenspectrum of the linearized operator at two grid resolutions, (ii) the maximum Lyapunov exponent and the associated Benettin convergence trace, and (iii) a sensitivity sweep of the RA filter strength <i>α</i> ∈ {0.05, 0.10, 0.20, 0.40}. The data support a clean operational message: the classical von Neumann bound underestimates the true Leapfrog stability limit by a factor of 13.7 at the higher resolution, the maximum Lyapunov exponent is <i>σ</i>₁ = 0.03178 (<i>τ</i>_<i>L</i> = 31.5 time units), and the RAW filter reduces filter-induced biases in kinetic energy and enstrophy from ∼21% and ∼46% (LF + RA) to under <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>4</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 4% $$</annotation>\u0000 </semantics></math> at identical computational cost. The software, the run configurations and the diagnostic outputs are archived under a permanent identifier and are intended as a community benchmark for time-integration studies in geophysical fluid dynamics and ensemble data assimilation.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048245","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":"Deep Learning–Based Fusion of Multi-Source Precipitation in Complex Terrain: A Case Study Over Chongqing, China","authors":"Rong Mu,&nbsp;Xinyue Li,&nbsp;Jun Yu,&nbsp;Hongping Gu,&nbsp;Chengzhi Deng","doi":"10.1002/gdj3.70104","DOIUrl":"https://doi.org/10.1002/gdj3.70104","url":null,"abstract":"<p>Accurately capturing the spatial distribution of precipitation over complex terrain remains a major challenge in meteorology. Satellite observations struggle to simultaneously achieve both high spatiotemporal resolution and retrieval accuracy, while radar observations are easily affected by terrain blockage in mountainous areas. As a result, a single data source is insufficient for high-resolution precipitation monitoring. In this study, we developed a U-Net–based deep learning fusion framework incorporating digital elevation model (DEM) information as a terrain constraint. Using hourly GPM IMERG satellite precipitation, SWAN QPE radar precipitation, and rain-gauge observations collected over Chongqing, China, during the rainy seasons (April–October) of 2016–2021, three multi-source fusion schemes were constructed: GModel (satellite + gauges), QModel (radar + gauges), and GQModel (satellite + radar + gauges). The performance of the two original precipitation products and the three fused products was comprehensively evaluated. The results show that GPM IMERG tends to overestimate precipitation, and the peak time of precipitation is delayed by approximately 1 h. Although its overall performance is inferior to that of SWAN QPE, the radar product generally underestimates precipitation because of terrain blockage, with substantially better performance over the relatively flat western hills than over the mountainous eastern region in Chongqing. All three fusion products exhibit markedly better agreement with rain-gauge observations than the original satellite and radar products in both temporal evolution and precipitation magnitude, effectively reducing the systematic biases associated with complex terrain. The generalisation capability of the proposed framework was further evaluated using independent observations from April to October 2023 together with two representative heavy rainfall events that occurred over the western hilly region and the northeastern mountainous region, respectively. The fused precipitation products successfully reproduced both the temporal evolution and rainfall magnitude observed by rain gauges. Overall, GQModel achieved the best comprehensive statistical performance, whereas QModel showed superior skill in estimating extreme precipitation events (&gt; 40 mm h⁻¹ and representative heavy rainfall cases).</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047860","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":"Machine Learning Approaches for Predicting Total Organic Carbon From Petrophysical Well Logs in the Wufeng–Longmaxi Shale, Southeast Sichuan Basin, China","authors":"Haseeb Ahmad,&nbsp;Jiang Shu,&nbsp;Naveed Rehman,&nbsp;Muhammad Asad,&nbsp;Muhammad Tariq,&nbsp;Aqib Saeed,&nbsp;Attiqullah Ayaz","doi":"10.1002/gdj3.70096","DOIUrl":"https://doi.org/10.1002/gdj3.70096","url":null,"abstract":"<p>Total Organic Carbon (TOC) is a fundamental parameter for evaluating shale oil and gas potential, as it influences hydrocarbon generation and a wide range of petrophysical properties, including porosity, density, brittleness, and reservoir quality. Traditional TOC determination using laboratory geochemical analysis, though accurate, is slow, expensive, and unable to provide continuous depth profiles. Empirical log-based estimation methods such as the Schmoker density model, gamma-ray correlations, multiple linear regression, and the ∆logR technique offer faster alternatives but suffer from limited generalizability and poor performance across formations with variable mineralogy and depositional settings. This study develops and compares three machine learning (ML) models, Random Forest (RF), Multiple Linear Regression (MLR) and XGBoost, for predicting TOC from four common well logs (GR, NPHI, RHOB, DT). Data from four shale-rich wells are preprocessed, scaled, analysed, and used to train, test, and validate each model. The models are further benchmarked against traditional log-based empirical methods. Across all wells, the RF model consistently outperforms other approaches, achieving the lowest RMSE values and highest <i>R</i>² scores and demonstrating strong generalization capabilities. XGBoost shows moderate performance, while MLR performs the weakest due to its inability to model nonlinear relationships. The results confirm that ML-based workflows, especially ensemble algorithms, substantially improve TOC prediction accuracy and reliability across diverse geological settings.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047861","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":"Dataset of Coastline Positions for Sandy Beaches of England and Wales Derived Using Remote Sensing","authors":"Pavitra Kumar,&nbsp;Nicoletta Leonardi","doi":"10.1002/gdj3.70102","DOIUrl":"https://doi.org/10.1002/gdj3.70102","url":null,"abstract":"<p>This article presents a dataset of coastline positions, comprising annual sandy coastline detections at approximately 10-m resolution for England and Wales from 1999 to 2020. Coastlines are inherently dynamic, and monitoring these changes over time enables the quantification of the extent and severity of coastal change at specific locations. This study provides observations of the coastline of England and Wales from 1999 to 2020, identified from satellite imagery using the modified CoastSat toolkit. The CoastSat toolkit was modified to export coastline positions in geographic coordinate formats (GeoJSON and tabular files) rather than its default transect-based distance output. To meet the toolkit's requirement for regions of interest (ROIs) under 100 km², the entire coastline was divided into 276 ROIs. Extracted coastline points were corrected for tidal influence. Analysis of the dataset revealed significant changes between 1999 and 2020 in different regions across the United Kingdom. The average rate of coastline change across several sandy ROIs has been observed to range from −2 m/year of erosion to +2 m/year of accretion.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994060","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":"Mapping Oil Spills and Offshore Industrial Activities in the Northwest Atlantic for Marine Conservation and Management","authors":"David Beauchesne,&nbsp;Kevin Cazelles,&nbsp;Stephanie Avery-Gomm,&nbsp;April Hedd,&nbsp;Philippe Muise,&nbsp;Robert A. Ronconi,&nbsp;Katharine R. Studholme,&nbsp;Sarah N. P. Wong","doi":"10.1002/gdj3.70100","DOIUrl":"https://doi.org/10.1002/gdj3.70100","url":null,"abstract":"<p>Anthropogenic activities such as oil pollution, vessel traffic and artificial light pose growing threats to marine ecosystems across Canada's Exclusive Economic Zone (EEZ) and adjacent Atlantic waters. Despite their importance, data describing the spatial and temporal distribution of these threats have remained fragmented or difficult to access. To address this gap, we developed a harmonized, high-resolution geospatial dataset covering Canada's Exclusive Economic Zone (EEZ) and adjacent Atlantic waters. The dataset integrates multiple data sources—including the Integrated Satellite Tracking of Pollution (ISTOP), National Aerial Surveillance Program (NASP), National Environmental Emergency Centre (NEEC), Automatic Identification System (AIS) vessel tracking and VIIRS satellite observations—into a suite of threat layers representing petroleum pollution, offshore infrastructure, vessel activity and artificial light exposure. All layers were processed using reproducible workflows and standardized to a geographic grid in EPSG:4326 with an approximate 1-km spatial resolution, and provided as monthly or annual products depending on the source data and threat type. The resulting products, delivered as Cloud Optimized GeoTIFFs (COGs), are openly available via Zenodo and accompanied by comprehensive metadata and processing documentation. The dataset is designed to support cumulative effects assessments, marine spatial planning, conservation prioritization and environmental response strategies. By providing consistent and reusable data on key marine stressors, this dataset supports cumulative risk assessment for at-risk species, such as the globally threatened Leach's Storm-Petrel (Hydrobates leucorhous), and broader marine management efforts under national and international policy frameworks.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942638","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":"Estuary Water Elevation Research (EWER) Dataset: Observational Data for Studying and Quantifying Water Level-Related Hazards in Australia's Tidal Environments","authors":"Ben S. Hague,&nbsp;Mandi C. Thran,&nbsp;Karen Palmer,&nbsp;Danial Khojasteh,&nbsp;Isabela S. Cabral,&nbsp;Danielle G. Udy,&nbsp;David A. Jones","doi":"10.1002/gdj3.70099","DOIUrl":"https://doi.org/10.1002/gdj3.70099","url":null,"abstract":"<p>Estuaries are on the frontline of sea-level rise impacts around Australia but remain poorly represented in existing public water level research datasets in Australia. Here we present the Estuary Water Elevation Research (EWER) dataset, which comprises data equivalent to 12,072 years of continuous non-flagged water level observations from 724 unique gauges across tidal environments, principally estuaries, but also bays and open-coast sites. The dataset brings together disparate national and state agency records to improve coverage of estuarine systems, including upstream tidal rivers that are often excluded from ocean-focused products. We describe the origins of the data and a consistent quality control framework used to assign diagnostic flags identifying potential spikes, outliers, datum shifts and periods of riverine influence. We provide information on the organisation of the dataset and possible use cases. Together, the EWER dataset provides a consistent, national-scale foundation to support research and decision-making on estuarine water-level change and its associated hazards under sea-level rise.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942081","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}