166 research outputs found
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GeoTopo-Net: A GIS-Based Framework for Automated Topology Recognition and Equipment Grouping in Power Distribution Networks
Full text linkPower distribution networks are critical infrastructure, yet their effective management is limited by the lack of standardized, topologically coherent equipment representations. Existing research focuses on isolated tasks such as load profiling or failure detection without offering integrated frameworks for automated network structuring. This work introduces GeoTopo-Net, a novel multi-stage pipeline that automatically groups power distribution equipment into topologically meaningful components. The pipeline employs three key stages: Data Standardization converts diverse raw equipment into consistent geographic formats, distinguishing between linear network components and discrete devices; AI-Assisted Spatial Sampling uses density-based clustering to identify switch groups and generates localized analysis regions with accompanying spatial data, separating simple from complex equipment configurations; and Heuristic Grouping applies specialized algorithms tailored to equipment complexity. For simple busbar arrangements, the algorithm uses network traversal and geometric proximity to identify main sections and their connectors. For complex multi-switch configurations, a refined multi-phase approach systematically segments equipment at critical boundaries, builds connectivity relationships, and applies classification rules based on connection patterns. Simultaneously, a dedicated algorithm processes network’s transmission lines by merging continuous segments while respecting critical equipment locations and identifying logical connection points. The framework transforms raw, heterogeneous network data into fully organized, spatially-referenced datasets—optimally structured for network optimization, outage simulation, and asset monitoring—while enabling topological correctness checks of network equipment, with particular focus on busbar architectures, to support integration with real-time SCADA systems. By systematically addressing power network topology complexities through automated analysis, GeoTopo-Net advances beyond existing approaches, providing a comprehensive foundation for intelligent grid management with modular design facilitating integration with existing geographic information systems. 
A Digital Layer of Memory: 3D Documentation, Art Historical Interpretation, and Web-Based Presentation of the Saint Michael Church
Full text linkThis paper presents the three-dimensional documentation and web-based dissemination of Saint Michael Church in Akçaabat, Trabzon, Türkiye. A high-resolution UAV photogrammetry survey consisting of 178 aerial images produced a detailed 3D model that was optimized for online delivery and exported in glTF format. The model is rendered in a browser through a Three.js viewer. An art-historical assessment accompanies the geometric record. The analysis identifies two construction phases: the medieval core dating to the 13th–14th centuries and the 1846 westward addition. Key formal features are described, including the nave dome on a high octagonal drum, the heptagonal exterior apse with arched niches, and the north and south entrances with their masonry treatments. The workflow allows close inspection of stonework and ornament while maintaining a faithful representation of the building fabric. The resulting dataset and viewer serve conservation documentation, research use, and broader access to a protected monument, and they align with international aims for heritage safeguarding, including Sustainable Development Goal 11.4. Potential extensions include semantic enrichment to link model elements with interpretive notes and scalable deployment using multi-LOD CityJSON for comparative studies. 
Assessment of Industrial Areas in Terms of Plan Sustainability through GIS and AHP Integration: The Case of Trabzon
Full text linkThis study aims to identify the most suitable locations for large-scale industrial facilities in the districts of Yomra, Arsin, Araklı, Sürmene, and Köprübaşı in Trabzon Province. Eight physical, infrastructural, and environmental criteria, determined through a literature review and consultations with local experts, were weighted using the Analytical Hierarchy Process (AHP) method. The criteria were selected based on their frequency of use in previous studies and the availability of data. The obtained weights were integrated into a raster analysis process within the Geographic Information Systems (GIS) environment. The data were converted into raster format with a 10-meter resolution in the TUREF/TM33 coordinate system, reclassified, and analyzed through a weighted overlay method. As a result of the analysis, areas classified as “high” and “very high” suitability were identified as potential industrial zones, and these areas were compared with existing zoning plan decisions to evaluate the sustainability of the plans. The resulting suitability map provides an original output that can serve as a decision-support tool in the industrial area planning of Trabzon
Age Matters: Demographic-based Perceptions of Included Location Information
Full text linkUnderstanding how individuals perceive the importance of location information is critical for improving the communication of spatial data. This study investigates how demographic factors, particularly age, affect the perceived significance of location in different contexts. Using survey data from 101 UK-based participants, we analysed responses to questions assessing the informativeness of location relative to other contextual data (e.g., time, source, quantity) in scenarios involving pollution and sexual crime statistics. The results indicate that age is a key determinant in evaluating location information, with older participants placing greater emphasis on location for pollution data, while gender emerged as more influential in the context of sexual crime. Education showed minimal impact. These findings suggest that location perceptions vary between different age groups and contexts, with implications for tailoring location-based information presentation to diverse audiences. Future research should explore adaptive strategies for communicating spatial data across demographic groups to enhance comprehension and decision-making
Influence of Geological and Geomorphological Factors on Vertical Co-Seismic Deformation Induced by the Mw 7.2 Al Haouz Earthquake (Morocco, 2023)
Full text linkThis study analyzes the influence of structural, lithological, and geomorphological factors on vertical co-seismic deformation caused by the Mw 7.2 Al Haouz earthquake of 8 September 2023 in Morocco. Vertical deformation, derived from Differential InSAR analysis of Sentinel-1A imagery, was compared with lithological units, fault structures, and terrain attributes extracted from the FABDEM digital elevation model, including slope, aspect, and geomorphon-based landform classes. Results show that deformation was strongly controlled by geology. Cambrian shaly formations, known for their plasticity and instability, exhibited the highest vertical displacements. Proximity to faults also enhanced deformation, with a 1000 m buffer around mapped faults yielding mean uplift of 5.3 cm (SD = 5.4 cm) and deformation gradients averaging 1.02% (SD = 1.8%). Topography played a significant role. Flat areas (<5°) generally subsided (–2.6 cm), while steeper slopes showed uplift, peaking at 5.4 cm near 45°. Slope orientation influenced deformation distribution: northeast-, east-, southeast-, and south-facing slopes recorded uplift (1.5–3.2 cm), whereas north- and northwest-facing slopes and flats showed subsidence (–0.9 to –1.6 cm). Landform analysis revealed systematic variations. Subsidence dominated flat, shoulder, and footslope forms, while uplift prevailed in pits, hollows, spurs, ridges, and peaks. Spurs in particular displayed strong deformation (mean uplift: 2.9 cm, SD = 10.6 cm), with ~11% of deformation boundaries coinciding with spur boundaries, suggesting they act as structural controls on rupture propagation. Overall, the findings demonstrate that lithology, faulting, and terrain morphology jointly modulate co-seismic deformation. Integrating DInSAR with terrain analysis provides valuable insights into earthquake surface processes and spatial hazard assessment
Climate Hazard Assessment for Dairy Farming in Aydin, Türkiye: Preliminary Results from the CliResDairy Project
Full text linkThis paper presents the preliminary findings of a climate hazard assessment for the dairy farming sector in Aydin, Türkiye, conducted under the Climate Resilience Enhancement in Dairy Farming (CliResDairy) Project. Aydin is a vital agricultural province, ranking fourth nationally for its cultural breed cattle population and holding a significant position in regional milk production. However, the sustainability of the sector is increasingly threatened by impacts of climate change such as prolonged droughts and extreme temperatures. Using the standardized Climate Risk Assessment (CRA) framework of the European Union's CLIMAAX project, this study assesses four key hazards: heatwaves, agricultural drought, heavy rainfall, and river flooding. The analysis, based on regional climate model projections, indicates that heatwaves pose the most severe and urgent threat, with their frequency projected to increase significantly in future climate scenarios, potentially reaching 6-8 events per year. Such events, where temperatures exceed critical thresholds for livestock health (32°C), directly reduce milk production and strain animal welfare. Currently, agricultural drought is projected to cause substantial yield losses for essential forage crops such as maize and wheat, leading to increased feed costs and threatening the financial viability of farms. Furthermore, an increase in the intensity of extreme precipitation and persistent risks of river flooding present additional threats to farm infrastructure, pastures, and general operations. These findings underscore the multi-faceted climate vulnerability of the Aydin dairy sector and highlight the urgent need for data-driven, targeted adaptation strategies to ensure its long-term resilience and sustainability
Zooming in: SCREAM at 100 m using regional refinement over the San Francisco Bay Area
No full textPushing global climate models to large-eddy simulation (LES) scales over complex terrain has remained a major challenge. This study presents the first known implementation of a global model – SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) – at 100 m horizontal resolution using a regionally refined mesh (RRM) over the San Francisco Bay Area. Two hindcast simulations were conducted to test performance under both strong synoptic forcing and weak, boundary-layer-driven conditions. We demonstrate that SCREAM can stably run at LES scales while realistically capturing topography, surface heterogeneity, and coastal processes. The 100 m SCREAM-RRM substantially improves near-surface wind speed, temperature, humidity, and pressure biases compared to the baseline 3.25 km simulation, and better reproduces fine-scale wind oscillations and boundary-layer structures. These advances leverage SCREAM's scale-aware SHOC turbulence parameterization, which transitions smoothly across scales without tuning. Performance tests show that while CPU-only simulations remain costly, GPU acceleration with SCREAMv1 on NERSC's Perlmutter system enables two-day hindcasts to complete in under two wall-clock days. Our results open the door to LES-scale studies of orographic flows, boundary-layer turbulence, and coastal clouds within a fully comprehensive global modeling framework.</p
Variability of greenhouse gas (CH4 and CO2) emissions in a subtropical hydroelectric reservoir: Nam Theun 2 (Lao PDR)
Full text linkHydroelectric reservoirs, though fundamental to renewable energy generation, are increasingly recognized as important sources of greenhouse gases (GHGs) in tropical and subtropical regions. However, substantial uncertainty persists regarding their contributions to the GHG cycle and their overall climate impact. Here, we present a 14 year dataset (2009–2022) of CH4 and CO2 emissions from the Nam Theun 2 reservoir in Lao PDR. We report emissions through diffusion, ebullition, and downstream degassing below turbines and spillways. This work represents one of the longest continuous records of reservoir GHG emissions in a subtropical system. Complementary eddy covariance approach was also employed, the results showed that CO2 emissions were consistently higher than those estimated from discrete sampling, likely due its ability to capture real-time turbulence and hot moments, and to the location of the EC system in shallow, high-emission areas for the last two campaigns. In contrast, CH4 emissions upscaled from EC measurements were often lower than those derived from discrete sampling, particularly during later campaigns. This difference was attributed to spatial coverage limitations, meteorological influences, wind filtering, and the lower sensitivity of the EC system to episodic ebullition events. CH4 showed a clear diurnal pattern; while CO2 fluxes differed between day and night only during periods of strong stratification. In this study, discrete sampling provided broader spatial coverage and higher data availability; therefore, it was used for emission calculations. CH4 emissions peaked during the warm dry period due to lower water levels and intensified stratification that favored methanogenesis and ebullition, whereas CO2 emissions peaked during cold dry season overturn events that released accumulated hypolimnetic carbon. Across the study period, ebullition accounted for 77 % of total CH4 emissions and remained relatively stable, supported by substantial flooded organic matter reserves. In contrast, during that same period, diffusive CH4 fluxes declined by 97 %, and CO2 emissions – dominated by diffusive fluxes (96 %) – declined by 87 %, indicating reservoir aging and pro gressive depletion of labile organic matter. Over 14 years, cumulative gross emissions totaled 10 736 Gg CO2 eq., with CH4 (51 %) slightly exceeding CO2 (49 %). Annual emissions were greatest in 2010 (1276 Gg CO2 eq.), declining by ∼70 % by 2021. These findings provide new insight into long-term GHG budgets in subtropical reservoirs, refine global carbon budget estimates, and inform climate-sensitive hydropower planning.</p
EuroCrops v2.0: Multi-annual harmonized parcel level crop type data linked to European Union-wide survey, statistical and Earth Observation products
Full text linkAs part of the Common Agricultural Policy (CAP) of the European Union (EU), farmers make annual declarations of the agricultural activities for which they receive subsidies. The declarations include the crops they grow at parcel level, referred to as Geo-Spatial Application (GSA) data. Paying Agencies (PA) of every EU Member State (MS) use specific crop classifications in their native language, and not all provide access to the GSA data. In the past, the EuroCrops initiative harmonized openly available GSA data for a single year (2021) using the Hierarchical Crop and Agriculture Taxonomy (HCAT), but multiple years are available depending on the country. Harmonizing a time series of farmers' crop declarations at parcel level would allow for comparative spatiotemporal analysis across the EU, the development of indicators that can be used for CAP and other policy monitoring purposes, and would provide data for training and validation of remotely sensed products. Here we have collected the GSA crop type declarations and parcel geometries that are publicly available from 18 PAs, the administrative bodies managing GSA data, for a minimum of three years. We have then harmonized the GSA data using HCAT v.4, a new version developed as part of this work. The data set includes nearly 47 million parcels covering 21 Mha. To facilitate integration and interoperability of the GSA data with other EU data sets containing spatial information on crops, we harmonized the crop classes used in the following data sets with HCAT v.4: 1) LUCAS, 2) the Integrated Farm Statistics/Farm Structure Survey, 3) the Farm Accountancy Data Network (FADN), and 4) the classification systems of the Copernicus High Resolution Layer on Crop Types. To demonstrate the potential of the multiannual, harmonised dataset presented in this paper, the GSA data were aggregated to NUTS 2 regions and compared with statistics on crop areas from Eurostat, showing good correspondence for many crops but also highlighting those crops and countries where the agreement is less good, providing possible reasons why. The data can also be used for mapping crop rotations, and a map showing maize monoculture illustrates this application. Farmers' declarations will increasingly become available as MS are required to publish these under the High-Value Dataset regulation. The EuroCrops v2.0 data set is registered and publicly available under the DOI https://doi.org/10.2905/b9fb9e67-78a9-4327-9d59-39a928d812d3
Operational numerical weather prediction with ICON on GPUs (version 2024.10)
Full text linkNumerical weather prediction and climate models require continuous adaptation to take advantage of advances in high-performance computing hardware. This paper presents the port of the ICON model to GPUs using OpenACC compiler directives for numerical weather prediction applications. In the context of an end-to-end operational forecast application, we adopted a full-port strategy: the entire workflow, from physical parameterizations to data assimilation, was analyzed and ported to GPUs as needed. Performance tuning and mixed-precision optimization yield a 5.5× speed-up compared to the CPU baseline in a socket-to-socket comparison. The ported ICON model meets strict requirements for time-to-solution and meteorological quality, in order for MeteoSwiss to be the first national weather service to run ICON operationally on GPUs with its ICON-CH1-EPS and ICON-CH2-EPS ensemble forecasting systems. We discuss key performance strategies, operational challenges, and the broader implications of transitioning community models to GPU-based platforms.</p