This study examines the hydraulic performance and operational vulnerabilities of the Sunkoshi Hydropower Plant in Nepal through two-dimensional (2D) hydraulic modeling. The objectives include analyzing flow dynamics under various operational conditions, optimizing gate management for flood resilience, and maintaining the minimum ecological flow required to ensure environmental needs. Employing historical hydrological data (1965-2012) from the Department of Hydrology and Meteorology, Nepal, and a high-resolution Digital Elevation Model (DEM), the study simulates flood scenarios (10 to 100–year return periods) and operational configuration of the barrage and intake gate with 2D HEC-RAS Modeling. The findings show that during extreme floods, water depths and velocities significantly surpass the design limits, increasing the risks of structural damage and environmental disruption. The findings highlight the necessity of adaptive gate operations, structural reinforcements, and ecological flow considerations to enhance resilience and sustainability. The methodology provides a replicable framework for such hydropower projects in mountainous regions, contributing to sustainable hydropower development.

Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning–based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: 1.the visual occlusion of low-lying dry-stone walls by dense vegetation and 2.the scarcity of labeled training data. This study presents DINO-CV , a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate and data-efficient mapping of dry-stone walls using Digital Elevation Models (DEMs) derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the Budj Bim Cultural Landscape at Victoria, Australia, a UNESCO World Heritage site, the approach achieves a mean Intersection over Union ( mIoU ) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions. • DINO-CV enables self-supervised learning on LiDAR DEM derivatives. • Cross-view pre-training learns robust geometric terrain features. • Automated mapping of hidden stone walls in high-resolution LiDAR. • Intra-site generalization and label efficiency for heritage mapping. • Scalable archaeological detection in complex vegetated landscapes.

Coastal regions in Indonesia are currently facing unprecedented risks from the convergence of global climatic shifts and localized geological instability. This study investigates the intensifying vulnerability of the Jakarta-Bekasi coastal corridor, highlighting it as a critical zone within the broader context of regional climate adaptation. The objective is to evaluate the synergistic impact of eustatic sea-level rise and aggressive land subsidence on permanent inundation projections through 2030. Utilizing a quantitative geospatial design, the research integrates satellite altimetry from the Sentinel-6 mission with terrestrial geodetic data from 12 Continuous Operating Reference Stations (CORS) across a 12,500-hectare study area. Key variables include vertical land motion rates and sea surface height anomalies, processed through high-resolution Digital Elevation Models (DEMNAS). Results indicate that localized land subsidence, peaking at 11.2 cm per year, is the primary driver of flood risk, rendering Relative Sea Level Rise () significantly more destructive than global eustatic averages. Statistical analysis confirms that subsidence accounts for 82% of the variance in coastal inundation expansion, with critical hotspots in the Penjaringan and Muara Gembong sectors. These findings imply that current coastal defense structures are nearing functional failure due to the rapid erosion of operational freeboards. Consequently, the study concludes that regional resilience necessitates a shift from static engineering to adaptive water management and the implementation of Nature-based Solutions. Future research should prioritize AI-driven predictive modeling and volumetric building load analysis to enhance long-term mitigation strategies.

Abstract High-resolution digital elevation models (DEMs) are an important resource both for lunar science and for human and robotic exploration. Photoclinometry, or Shape-from-Shading (SfS), is particularly well adapted to producing highly resolved DEMs even under the extreme illumination conditions typical of the lunar polar regions. The resulting DEMs can achieve pixel scales up to the native resolution of the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images, which is 0.7–1.5 m pix −1 at south polar latitudes. Here we present 5 m pix −1 SfS DEMs (SDEMs) for the list of 13 Artemis III candidate landing regions released in 2022. While our SDEMs match the resolution of the recently updated polar DEMs (or LDEMs) from the Lunar Orbiter Laser Altimeter, we use SfS to enhance them by introducing details from the LROC NAC images. The result is a complementary product which leverages the geodetic control of the LDEMs while also providing a more uniform effective resolution. To deal with the cumbersome preparation of properly selecting and coregistering NAC images, necessary to produce the SDEMs of such large regions, we developed a semi-automated SfS pipeline leveraging widely used open-source tools. We show that our accurate models are a powerful resource to quantify surface characteristics (e.g., slope, roughness, Sun and Earth illumination), to assess the viability of potential landing sites, and to plan surface activities.

Refined late-Pleistocene evolutionary and sea-level history for the Delmarva Peninsula, US Mid-Atlantic Coast

ABSTRACT Accurate alignment between overlapping lidar swaths and ground checkpoints is essential for generating seamless digital elevation models (DEMs), particularly in dynamic coastal environments. This study assessed the geometric integrity of airborne lidar datasets acquired by researchers from the Bureau of Economic Geology (BEG) along the Texas Gulf Coast in 2024 through combined vertical and horizontal accuracy evaluations. The airborne lidar system calibration was validated using 789 GNSS-derived ground control points (GCPs) along the coast, yielding an absolute mean elevation difference of 0.012 m and root-mean-square error (RMSE) of 0.025 m. High-resolution DEMs were validated against the United States Geological Survey (USGS) 3-Dimensional Elevation Program (3DEP) products and legacy datasets along the coast. The results revealed minor systematic differences, primarily influenced by surface-type variability, ground modelling approaches, and variations in GNSS base station configuration and post-processing methods. Additional analyses and practices demonstrated that lidar swath misalignments and lever arm offset inconsistencies were effectively reduced through algorithm corrections, while geoid model differences introduced minor but measurable offsets. To isolate geomorphic influences, validation transects were divided into inland and beach segments. Inland sites showed strong agreement (mean R2 = 0.96), with mean elevation differences <0.01 m and RMSE < 0.08 m. In contrast, beach segments exhibited greater variability (R2 = 0.87), with absolute mean differences of 0.12 m and RMSE up to 0.44 m, demonstrating variability outside natural error thresholds, attributable to detections of geomorphic change which is characteristic of highly morphodynamic near-shore environments like those surveyed along the Texas Gulf Coast. Overall, the findings confirm that BEG and current USGS DEMs exhibit high geometric fidelity and are suitable for coastal applications, including shoreline change detection, flood risk assessment, and habitat monitoring. In addition, the study emphasizes the importance of achieving sub-decimetre accuracy in beach and dune environments, highlighting the significance of standardized calibration, alignment, and processing workflows to ensure reliable long-term coastal and environmental monitoring.

Abstract In May 2024, Porto Alegre, in southern Brazil, experienced the most devastating flood event ever recorded, affecting over 160,000 people and causing extensive damage to buildings and essential services. The city was not adequately prepared to respond to an event of this magnitude, and many residents are still recovering from its impacts. This study proposes a hydrological hazard map for Porto Alegre as a reliable and accessible tool to support government planning and risk management. The proposed hazard map was developed from three precursor maps: (i) the Flood Susceptibility Map (FSM), (ii) the Flood Frequency Map (FFM), and (iii) the Historic Flood Depth Map (FDM). Both FSM and FFM were generated using artificial neural network models trained on historical records of urban flooding and seven thematic maps representing key terrain characteristics. The FDM was created by integrating pre- and post-disaster optical satellite imagery, a high-resolution Digital Elevation Model, and field-surveyed flood marks collected during or near the flood peak. The final Hydrological Hazard Map was obtained by combining these three components using different weighting schemes, selecting the configuration that best represented observed flood patterns. Model performance was satisfactory: the FSM validation yielded an AUC of 0.9484, the FFM showed strong predictive agreement with observed values, and the FDM achieved an AUC above 90% with an average spatial displacement of less than 40 m. The final hazard map aligns with previous studies and consistently identifies the highest hazard levels in densely populated and highly urbanized areas with intense human activity.

• Repeated Pléiades Neo images can be used to effectively monitor RGV in remote areas. • Multitemporal block adjustment provides robust co-registration of images at a sub-pixel level. • Surface velocity errors can achieve one-third of the native pixel level. • Tristereo Pléiades Neo images provide accurate topography reconstruction from a single acquisition. The Pléiades Neo (PNEO) constellation, deployed in 2021 and conceived for both civilian and military applications, can be utilised to monitor ground deformation in remote periglacial environments due to its exceptionally high spatial resolution and tasking capabilities. In this study, we demonstrate the potential of utilising PNEO to derive Rock Glacier Velocity (RGV) with exceptionally high detail from repeated image acquisitions. We processed tristereo images acquired in February 2023 and March 2024 over an area of 50 km 2 in the Dry Andes (Chile), resulting in one high-resolution digital elevation model and three orthorectified images for each acquisition. By using a feature tracking algorithm, we were able to gather the uncertainty on different combinations of the tristereo images over a stable area. Our results indicate a high consistency in the velocity vectors with mean RGV values between 0.90 to 0.25 m/year. The high granularity of the derived flow field means that yearly RGV monitoring in remote regions using high resolution optical images from the PNEO sensor is now a viable option.

This study examines whether the spatial distribution of major geomorphological and archaeological features in the Bosnian Valley of the Pyramids exhibits measurable geometric structure and statistical coherence beyond natural terrain variability. High-resolution LiDAR-derived digital elevation models, precise geodetic summit coordinates, and GIS-based spatial analysis were used to evaluate large-scale landscape geometry through distance-matrix comparison, angular alignment testing, and rotation-invariant similarity measures. A Monte Carlo simulation framework with 100,000 randomized iterations was applied as a null model to assess the probability of non-random spatial organization. The results indicate that several inter-feature distances, angular relationships, and triangular configurations fall within narrow tolerance ranges exceeding random expectation thresholds. The study does not propose cultural causation, but presents a reproducible, data-driven methodology for identifying geometric coherence in complex landscapes. The proposed approach is applicable to geophysical and geomorphological investigations of large-scale terrain organization.

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments.

Abstract. Subglacial lakes influence glacier hydrology, dynamics, and mass balance; however, they are poorly documented outside the polar ice sheets. Here we use high-resolution digital elevation models during 2011–2021 and regression analysis to characterize subglacial lakes. We identified 37 subglacial lakes across the Canadian Arctic, 35 of which are newly identified. These lakes have an area of 0.3–48.5 km2 and can change surface elevation by 10–150 m, corresponding to a water volume of 0.003–4.5 km3. We classify these subglacial lakes into three types: (1) classic subglacial lakes, (2) terminal subglacial lakes at places where two glacier termini converge and coalesce, and (3) partial subglacial lakes with an area of open water at the ice margin. Types 2 and 3 are newly introduced in this study, there are 11 and 15 lakes classified as these two types, respectively. Lake activities negatively correlate with regional mass balance (r=-0.69, p-value =0.039), implying a need for fine-scale monitoring in the era of increased glacier loss.

ABSTRACT During the regional Last Glacial Maximum, the Cordilleran Ice Sheet (CIS) enveloped coastal British Columbia, Canada spreading across parts of the adjacent continental shelf influencing subsequent postglacial ecosystem development and the dispersal of early humans and other biota along the Northwest coast of North America. The current understanding of these ice sheet dynamics is primarily derived from aerial photo mapping and field campaigns between 1970–1990; however, the glacial geomorphology of large portions of the region remains undescribed. We present a detailed map of glacial landforms in the terrestrial and surrounding marine regions of northern Vancouver Island. Using high-resolution lidar-derived digital elevation models (DEMs), a 10–100 m topo-bathymetry DEM, high resolution 3 m satellite imagery, and targeted ground truthing, we map 13,386 newly identified geomorphic and glacial geomorphic features and include 136 previously mapped glacial features. Findings indicate that the CIS was undergoing complex multiphase ice retreat along its coastal margins.

Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km2 agricultural watershed in China’s Jianghuai region using unmanned aerial vehicle (UAV) -derived 0.1 m digital elevation models (DEMs) and coupled hydrological–erosion modeling. The Soil Conservation Service Curve Number (SCS-CN) and Modified Universal Soil Loss Equation (MUSLE) models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km2·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km2·a) and forested areas showing minimal export (&lt;6.1 kg/km2·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution.

Abstract The severity and frequency of tundra fires in Arctic permafrost landscapes is expected to increase with ongoing climate change. By burning the insulating organic layer of soils, tundra fires impact the soil thermal regime for underlying permafrost and can accelerate thaw in the years following the burn. In this paper, we address the scarcity of long-term studies on post-fire permafrost degradation in ice-wedge landscapes by using a space-for-time substitution analysis spanning a chronosequence (pseudo-time series) of up to 67 years of remote sensing data from Alaskan tundra fire scars. We use computer vision and graph analysis on high-resolution digital elevation models derived from airborne lidar of fire-affected areas in Western Alaska to investigate the effects of tundra fires on the post-fire development of microtopography and surface hydrology in polygonal ice-wedge landscapes. Our analysis indicates a modest overall trend toward recovery of polygonal surface structure over timescales of 70+ years, though considerable variability among fire scars highlights that post-fire trajectories are not uniform.

The Lake Chambon area, located between the Col de la Croix-Morand and Murol (Massif Central, France), consists of a Hercynian crystalline basement partially overlain by Cenozoic formations, largely composed of volcanic products related to the Mont-Dore stratovolcano. The present-day topography, sedimentation patterns, and drainage network are strongly controlled by a complex fault system. A detailed morphostructural analysis identified more than 500 lineaments from a high-resolution digital elevation model (DEM), which were digitized and analyzed in a GIS environment using QGIS. A directional classification combining expert-based interpretation with a semi-supervised machine-learning approach (k-means clustering) revealed seven major fault families, grouped into clusters consistent with a regional dextral shear regime. An interpretive tectonic model is proposed, consistent with the current stress field ( σ 1 trending between N160°E and N170°E). Faults of the F1 family are interpreted as dextral shear zones related to the South Armorican Shear Zone–Cholet–Poitiers Fault–Southern Border Fault of the Limagne graben system, associated with secondary Riedel-type structures. The influence of the sinistral Sillon Houiller Fault is expressed by the F6′′ family (N20°E) and by the F2′ family, whose orientation is comparable to that of the Tauves–Aigueperse fault system (N50°E). The F4 family corresponds to extensional faults, locally reactivated within this broader strike-slip tectonic framework. The proposed neotectonic framework allows for the interpretation of several key geomorphological features. The Lake Chambon Basin may correspond to a transtensional pull-apart structure. In contrast, the slow-moving landslide at Chambon-sur-Lac, located between the transtensional zones of the Rochers de Pousseterre to the west and Lake Chambon to the east, appears to be controlled by the structural inheritance and kinematics of faults F4, F6′′ and F2′, which locally accommodate oblique deformation within a transpressive regime. Finally, the study suggests that deep hydrothermal activity at Chambon-sur-Lac may be linked to regional seismicity associated with the F1 fault system.

Geomorphometric soil erosion modelling in Sub-Saharan Africa: A systematic review of model applicability, validation gaps, and policy integration

Revisiting slope-area thresholds for gully initiation: a systematic review and meta-analysis

In the arid Red Sea margin setting, episodic Wadi runoff is a primary mechanism for groundwater recharge; its effectiveness depends on surface morphology, structural fabric, and subsurface architecture. This study assessed groundwater potential and recharge dynamics in Wadi Ramliya, Wadi Umm Alda, and Wadi Hamad in Northeastern Egypt by integrating morphometric, geophysical, hydrochemical, and meteorological datasets to prioritize sites for managed aquifer recharge and reconnaissance drilling. High-resolution Digital Elevation models (DEMs) and multi-azimuth hillshades were used to map topographic zoning and structural trends, while morphometric analysis quantified drainage metrics. Flood-event hazard mapping was developed by integrating the Suez rain gauge station as a reference, satellite-based rainfall from the CHIRPS dataset, and data on DEM, slope, drainage density, land-use/land-cover, and road distance. Twenty-eight Vertical Electrical Soundings (VES) were used to constrain a six-layer geoelectric model, and Archie-based transforms (ρw = 2.85 Ω.m) were applied to the estimated formation factors and porosities. Water quality was classified using hydrochemical data from the JICA-5 borehole, while land magnetic surveys, utilizing Total magnetic intensity (TMI), Reduced to the Pole (RTP), and analytic signal filters, delineated basement structural highs and depocenters to guide structural targeting. DEM/hillshade analysis delineated four topographic zones (17.5-1,292m) and showed dominant NW-SE and NE-SW trends that control drainage orientation. Morphometric indices highlight contrasting basin behavior; Wadi Hamad (A = 46.8km²) exhibits the highest drainage density (Dd = 1.79km km⁻²), relief ratio (Rr=31.3), and bifurcation ratio (Rb=3.0), consistent with steep, structurally guided, flash-prone headwaters. By contrast, Wadi Ramliya (A = 452.6km²) and Wadi Umm Alda (A = 377.4km²) act primarily as transit and depositional basins. The VES interpretation yielded three principal curve types (QH, HK, and QQ), with QH dominating the dataset. It resolves a hydraulically significant Middle Miocene calcareous-sandstone aquifer at 77-122m (with resistivity of 12-23 Ω.m). Archie-derived formation factors (F = 4.2-8.2) imply porosities of 35-49% (mean ≈ 40.6%); resistivity-based saturation remained low (illustrative Sw= 5.6%). JICA-5 chemistry classified the deeper layer (layer-6) as slightly brackish (TDS = 2,447mg L⁻¹). Magnetic depth estimates and analytic-signal mapping identified shallow structural highs and deeper depocenters that correlated with depositional fans and VES targets. Flood-hazard mapping identified low-slope fan toes and coastal plain cells as high-priority recharge locations, which coincide with favorable geophysical signatures. Integrated data indicate that managed recharge pilots at alluvial fan toes and targeted reconnaissance drilling at lineament intersections are the highest-priority field actions; these should be accompanied by downhole logging, pumping tests, water-quality monitoring, and sediment control measures.

Soil erosion poses a significant challenge to environmental sustainability, especially in regions with varying land-use patterns and topography. Soil erosion is a major environmental threat affecting soil quality, reservoir sedimentation, agricultural land, and watershed hydrology. This study aims to identify and classify homogeneous sub-watersheds in a mountainous watershed in Iran using GIS. Forty years of climate data, a high-resolution DEM, land-use maps, soil texture, and NDVI were applied to derive the main factors, while the P factor was determined based on slope classes and land-use types. The RUSLE results showed that annual soil erosion in the watershed had an average of about 7-ton ha⁻¹ year⁻¹, with more than 65% of the watershed area falling into the moderate to very high erosion classes. Average key factors were R = 78.08 MJ·mm/ha·hr·year, K = 0.28 t·ha·h/MJ·mm·ha, LS = 1.62, and C = 0.39. The highest erosion occurred in areas with heavy rainfall, steep and long slopes, fine-textured soils, and sparse vegetation. Spatial autocorrelation analysis using Moran’s I and the Getis–Ord Gi* statistic showed a clustered spatial pattern of erosion. High–high (HH) clusters, indicating severe erosion hotspots, were found in the southwest, while low–low (LL) clusters, representing minimal erosion coldspots, occurred in the north and northeast. These results support sub-watershed prioritization and indicate the need for targeted erosion control in high-rate zones. These results contribute to the development of more targeted and sustainable land management practices to mitigate soil erosion rates and improve watershed conservation efforts.

Imbricated gravel beds have a distinctive surface morphology, which strongly affects riverbed stability and near-bed hydrodynamic processes. High-resolution digital elevation models combined with image processing techniques were used to quantitatively characterize imbricated surface features across three representative subregions of a gravel bar in the upper Yangtze River. The results indicate that (1) the long axes of imbricated pebbles are predominantly perpendicular to the flow direction; (2) greater bed-surface coarsening corresponds to steeper slope angles, more pronounced imbrication structures, and higher surface roughness; and (3) the self-similarity of the bed surface is anisotropic, being the strongest along the flow direction, and the Hurst index exhibits a quadratic relationship with the inclination index. A field-based method for identifying imbricated clusters was proposed. The results show that where imbrication is more pronounced, clusters tend to form more linear arrangements and increasingly align with the flow direction. These findings provide a new perspective for understanding the microtopographic features of gravel bed river surfaces.