Dams, reservoirs, waterpower facilities, and other water-control structures can modify streamflow and propagate their influence downstream through connected river networks, complicating hydrologic modelling and aquatic ecosystem assessment. Large-domain assessments often rely on binary agency classifications at hydrometric gauges (e.g., “natural” and “regulated”) or on storage- and discharge-based metrics that are difficult to apply consistently where operational data are incomplete. We introduce the Streamflow Alteration Index (SAI), a parsimonious, network-based screening metric that estimates the percentage of upstream drainage area whose routed flow passes through mapped alteration signal sources. Under a first-order drainage-area scaling approximation, SAI provides a proxy for the fraction of streamflow potentially exposed to upstream alteration sources, rather than a measure of the exact magnitude of hydrologic alteration. The framework comprises two paired indices, SAI_I (0 %–100 %) and SAI_II (0 %–100 %), independently representing confirmed Level I and potential Level II sources; values are propagated downstream by drainage-area-weighted mixing and reset at new sources of the same level. We compiled 2528 infrastructure records, of which 2476 were retained after geospatial alignment and screening. Following source classification and consolidation of multiple records within the same subbasin, 643 unique subbasins were identified as Level I and/or Level II alteration signal locations. The resulting SAI v1.0 database provides province-wide subbasin and river-reach layers for screening potential instream alteration at gauged and ungauged locations. We evaluated SAI-derived natural, conditional, and altered classes against 148 Reference Hydrometric Basin Network (RHBN) gauges, 125 additional Water Survey of Canada (WSC) gauges labelled natural, and 215 WSC-regulated gauges. SAI classified 86 % of both natural reference groups as natural, with no RHBN gauge classified as altered. Among WSC-regulated gauges, 26 % were classified as altered, 40 % as conditional, and 34 % as natural, highlighting differences between binary agency labels and the SAI screening framework and identifying sites requiring follow-up assessment. SAI v1.0 therefore provides a practical, transparent, and reproducible screening baseline for large-domain hydrologic and ecological applications without requiring detailed operating rules and offers a scalable framework for broader network-based assessments of potential streamflow alteration. The SAI v1.0 dataset is available from Zenodo (Shen et al., 2026; https://doi.org/10.5281/zenodo.19547932).
High-frequency observations of tropospheric nitrogen dioxide (NO2) support regional air quality assessment and analysis of its photochemical evolution. Although the Geostationary Environment Monitoring Spectrometer (GEMS) and other new-generation geostationary satellites provide hourly observations during daytime, their spatial coverage is often severely compromised by cloud obstruction and inherent retrieval limitations. Furthermore, their short observation history hinders long-term, cross-regional environmental assessments. To address this, this study presents a gapless, 0.05° daytime-hourly tropospheric NO2 vertical column density (VCD) dataset for representative hotspots in Asia spanning 2019 to 2024, reconstructed using a physics-aware deep learning framework. The framework integrates partial convolutions to handle irregularly missing satellite observations and proposes a Physics-aware Normalization (PhysNorm) module. Specifically, the model inputs comprise the 0.05° TROPOMI tropospheric NO2 VCD and its validity mask, together with 0.25° predictors including ERA5 meteorological variables, EAC4 total-column NO2, land cover, elevation, and the cosine of the solar zenith angle. The supervised training target is the hourly GEMS tropospheric NO2 VCD from 2023. PhysNorm dynamically modulates 0.05° high-resolution feature maps using 0.25° low-resolution physical features, thereby ensuring rigorous physical continuity while filling data gaps. Validation results show that the model performs exceptionally on the test set (R2= 0.889) and maintains high generalization stability in an independent validation on 2024 data, which was not used for training. Cross-validation against an independent polar-orbiting satellite instrument (GOME-2C) confirms the reliability of the dataset in reconstructing pollution hotspots and its applicability to periods without GEMS observations (2019–2022). Comparison with independent ground-based Pandora observations showed an overall correlation of R=0.644 for 2019–2024. Using this reconstructed dataset, this study finely delineates the periodic diurnal characteristics of NO2 in typical Asian regions and characterizes inter-annual variations in NO2 concentration patterns over the past six years. This newly generated dataset provides a spatiotemporally continuous daytime record, overcoming the limitations of cloud cover and short observational histories, thereby facilitating long-term, high-resolution air quality assessments and epidemiological studies. This dataset is available at https://doi.org/10.5281/zenodo.20427767 (Gao et al., 2026).
Reanalysis datasets based on coupled ocean circulation and ecosystem models are essential tools for understanding coastal environmental variability and supporting water-quality management. However, the development of such datasets for semi-enclosed coastal systems remains limited, particularly for those incorporating subsurface biogeochemical observations such as dissolved oxygen (DO). This study presents the Ise Bay Reanalysis (IBRA), a high-resolution, 12-year (2011–2022) reanalysis dataset of hydrodynamic and biogeochemical variables for Ise Bay, Japan. The dataset was generated using the Ecological hydrodynamics simulation system of the Port and Airport Research Institute Simulator (EcoPARI-Simulator), coupled with an ensemble Kalman filter data assimilation system that assimilated in situ observations of water temperature, salinity, and bottom-layer DO at 6 h intervals. The resulting dataset has an hourly temporal resolution and resolves the entire water column. IBRA has three key distinguishing features. First, unlike many previous reanalysis datasets that rely primarily on satellite observations, IBRA assimilates continuous in situ measurements throughout the water column, including high-frequency DO observations, thereby improving the representation of subsurface dynamics. Second, the 6 h assimilation cycle enables the dataset to resolve short-term variability driven by tidal processes, which are a dominant control of coastal transport and mixing. Third, spatially coherent corrections are applied across the entire bay using an ensemble Kalman filter, ensuring spatial consistency in both physical and biogeochemical fields while avoiding artificial discontinuities often associated with simpler data assimilation approaches. The quality of IBRA was evaluated using statistical metrics and detailed comparisons against in situ observations. Data assimilation improved overall agreement with observations across multiple stations and depths, particularly for bottom-layer DO concentrations. The dataset successfully reproduced seasonal variability, vertical stratification, and the formation and persistence of hypoxic conditions in the inner bay, while also capturing well-oxygenated conditions near the bay mouth. Furthermore, the spatial distributions of temperature, salinity, and DO were realistically represented, including the intrusion of offshore water masses and associated horizontal gradients. The IBRA dataset therefore provides a dynamically consistent and comprehensive representation of physical and biogeochemical variability in a semi-enclosed coastal system. It is expected to serve as a valuable resource for studies of coastal hypoxia, water-quality assessment, and environmental management. The IBRA dataset is publicly available via Zenodo at https://doi.org/10.5281/zenodo.19808667 (Matsuzaki et al., 2026).
Mesoscale eddies are ubiquitous rotating water masses with horizontal scales of tens to hundreds of kilometers that play a key role in redistributing heat and salt throughout the global ocean. However, the lack of long-term observational records has limited systematic assessments of their thermohaline effects from regional to global scales. Here, we present a global eddy-collocated temperature and salinity profile dataset spanning 29 years (1993–2021), constructed by integrating multiplatform in situ hydrographic observations with satellite-derived mesoscale eddy tracking products. The dataset contains 5.46 million quality-controlled temperature–salinity profiles, each collocated with its nearest mesoscale eddy and is publicly available at https://doi.org/10.5281/zenodo.20607854 (He, 2026). The observations provide broad global coverage, with most 2° × 2° grid boxes containing more than 200 profiles, enabling statistically robust analyses of eddy-induced temperature and salinity anomalies. Validation against previously documented regional eddy signatures demonstrates that the dataset reliably captures characteristic temperature and salinity anomaly structures associated with eddies. Example applications demonstrate its utility for characterizing regional three-dimensional eddy thermohaline patterns, assessing global eddy effects on upper ocean stratification and subsurface temperature extremes, and providing an observational basis for estimating eddy-induced heat, salt, and water-mass transports. As a long-term observation-based eddy-collocated profile product, this dataset provides a valuable resource for studies of eddy impacts on ocean circulation, tracer redistribution, climate variability, and model evaluation.
BorFIT is a novel northern boreal forest training dataset designed to assist in the segmentation of individual trees and the detection of species from uncrewed aerial vehicle (UAV)-LiDAR point clouds, thus contributing to deep learning-based forestry applications. Recent advancements in AI-supported individual tree detection have shown significant progress; however, satisfactory results remain elusive in dense and structurally-complex boreal forests. We compiled a training dataset designed to remedy this issue. BorFIT comprises 384 UAV-LiDAR point clouds, each with an area of 20 m × 20 m, in the form of reference plots, with up to 200 manually segmented and species classified trees per point cloud. The BorFIT training dataset is derived from UAV-LiDAR surveys at 146 sites carried out between 2021 and 2024 in East Siberia (Yakutia), northwest Canada, and Alaska (USA). The sites were selected along bioclimatic gradients to represent the circumboreal region. From each UAV-LiDAR transect derived point cloud, we extracted a minimum of four reference plots (each 20 m × 20 m) based on maximum tree heights within the plots to systematically sample the apparent tree density gradient. We manually segmented identifiable trees within each reference plot point cloud leading to 16 530 individual trees in total. Following individual tree segmentation, we trained four random forest classifiers for different regions to predict the species of every segmented tree. The predicted tree species include: Picea mariana Britton, Sterns & Poggenb., Picea sitchensis Bong. Carrière, Picea glauca Moench, Voss, Pinus contorta Douglas ex Loudon, Abies lasiocarpa Hook., Nutt., Larix laricina Du Roi, K. Koch, Betula papyrifera Marshall, Betula neoalaskana Regel, Ashburner McAll., Populus balsamifera L., Populus tremuloides Michx., Pinus sylvestris Thunb. and Alnus spp. The data offer the means for 3D space analysis of species distribution and stand structure around the circumboreal region. Furthermore, it can be used as a training dataset for artificial intelligence (AI) applications, and thereby improve our understanding of the boreal forest's vegetation reorganization in response to significant global warming (https://doi.org/10.1594/PANGAEA.980505, Kruse et al., 2026).
