High-resolution Digital Elevation Models Research Articles

A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations

Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.

Channel morphological change monitoring using high-resolution LiDAR-derived DEM and multi-temporal imageries

Natural processes and human activities both cause morphological changes in channels. Remote sensing products are often used to assess planform changes, but they tend to overlook vertical changes. However, considering both planform and vertical changes is crucial for a comprehensive evaluation of morphological changes. Using spatiotemporal aerial imagery and topographic data, remote sensing plays a vital role in evaluating channel morphological changes and flood-carrying capacity. This study aimed to investigate the morphological changes of a creek in an urban catchment using very high-resolution remote sensing products. In this study, we developed a new framework for investigating overall channel morphology change by employing very high-resolution aerial imagery and a LiDAR-derived digital elevation model (DEM). By digitizing channel boundaries using ArcGIS Pro 3.0, and analyzing various morphological parameters, erosion, and deposition patterns, we examined the impact of urban expansion and infrastructure development on channel adjustments. Channel adjustments have been performed in the case study catchment (Dry Creek, South Australia, Australia) due to urban expansion and development of infrastructure in the downstream reaches. Our findings revealed a significant southwest shift in the planform of the channel, with a maximum shift of 478 m and an average shift of 217 m since 1998. This alteration resulted in an increase in the sinuosity index reaching 1.2. Over the period from 2018 to 2022, the channel experienced a net deposition depth of 3.4 cm to 3.6 cm in downstream reaches. The annual deposition volume in the downstream reaches was 1963 m3, necessitating regular desilting to prevent channel capacity loss and flooding in the surrounding environment. This study also highlights the incremental growth of riparian vegetation within the channel, which affects surface roughness, channel slope, and carrying capacity. These findings provide a valuable baseline for future investigations into stream channel morphology changes and emphasize the importance of implementing appropriate measures such as desilting and vegetation management to mitigate deposition levels, reduce flood risks, and enhance the overall health and functionality of Dry Creek. The framework used in this study can be applied to other case studies employing reliable and high-resolution remote sensing data products.

Prediction of debris flow initiation points using high resolution DEM and GIS analysis in South Korea

This study presents a GIS analysis model that utilizes high-resolution Digital Elevation Map data to predict the initiation points of debris flow through the overlap of slope stability analysis techniques and rainfall flow analysis. The debris flow that occurred on Mount Woomyun in Seoul, South Korea in 2011 was selected as the case study for comparing and analyzing the prediction model against the actual case. In South Korea, occurrences of debris flow mostly happen during the summer monsoon season. When a substantial amount of rainfall accumulates and heavy rainfall events occur, the pore water pressure within slopes reaches saturation. Consequently, flow is initiated along “temporary streams.” In this study, areas with a combination of low slope stability and abrupt changes in velocity vectors were assumed to have the highest probability of debris flow occurrence due to the overlapping of these factors. The research model was conducted under this assumption. To evaluate the performance of the model, 3D coordinates of the model’s result point, and actual occurrence points were obtained, and a Pearson correlation analysis was conducted to compare them. With these results, R2 values of X = 0.9147, Y = 0.8625, and H = 0.8942 were obtained. These high R2 indicate a strong correlation between the model’s predictions and actual occurrences.

Digital Elevation Models and Orthomosaics of the Dutch Noordwest Natuurkern Foredune Restoration Project

Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack the (open-access) monitoring observations needed to signal whether further actions are needed, and hence lack the opportunity to “learn by doing”. This submission presents an open-access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts (“notches”) in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems as well as developing guidelines to better design future restoration efforts with foredune notching.

Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

ABSTRACT Detecting building rooftops with very high-resolution (VHR) images is an important issue in many fields, including disaster management, urban planning, and climate change research. Buildings with varying geometrical features are challenging to detect accurately from VHR image due to complicated image scenes containing spectrally similar objects, illumination, occlusions, viewing angles, and shadows. This study aims to detect building rooftops with high accuracy using a new framework that includes VHR image, visible band difference vegetation index, digital surface and elevation models, the terrain ruggedness and the topographic position index. Five distinct feature sets were generated in order of importance by exposing the ten related stacking features to a feature selection procedure using the maximum relevance minimum redundancy method. Then, Auto-Encoder, k-NN, decision tree, RUSBoost, and random forest machine learning algorithms were utilized for binary classification. Random forest yielded the highest accuracy (97.2% F-score, 98.72% accuracy) when all features (F10) were used, while decision tree was the least successful (59.16% F-score, 83.56% accuracy) for RGB feature set (FRGB). It was revealed that classification of F10 with random forest increased F-score by about 23% compared to classification with FRGB. Additionally, McNemar’s tests showed no statistically significant difference between random forest vs k-NN and decision tree vs RUSBoost.

Technical Note: Resolution enhancement of flood inundation grids

Abstract. High-resolution flood maps are needed for more effective flood risk assessment and management. Producing these directly with hydrodynamic models is slow and computationally prohibitive at large scales. Here we demonstrate a new algorithm for post-processing low-resolution inundation layers by using high-resolution terrain models to disaggregate or downscale. The new algorithm is roughly 8 times faster than state-of-the-art algorithms and shows a slight improvement in accuracy when evaluated against observations of a recent flood using standard performance metrics. Qualitatively, the algorithm generates more physically coherent flood maps in some hydraulically challenging regions compared to the state of the art. The algorithm developed here is open source and can be applied in conjunction with a low-resolution hydrodynamic model and a high-resolution DEM to rapidly produce high-resolution inundation maps. For example, in our case study with a river reach of 20 km, the proposed algorithm generated a 4 m resolution inundation map from 32 m hydrodynamic model outputs in 33 s compared to a 4 m hydrodynamic model runtime of 34 min. This 60-fold improvement in runtime is associated with a 25 % increase in RMSE when compared against the 4 m hydrodynamic model results and observations of a recent flood. Substituting downscaling into flood risk model chains for high-resolution modelling has the potential to drastically improve the efficiency of inundation map production and increase the lead time of impact-based forecasts, helping more at-risk communities prepare for and mitigate flood damages.

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

Providing solutions for data scarcity in urban flood modeling through sensitivity analysis and DEM modifications

Abstract Developing countries face significant challenges in accessing sufficient and reliable hydro-meteorological data, hindering the implementation of effective disaster management strategies. This research proposes solutions for limitations on performing flood simulations through parameter sensitivity analysis and digital elevation model (DEM) modifications. The methodology provides alternatives to account for insufficient rainfall, roughness coefficient, infiltration data in simulating large-scale rainfall-runoff, and high-resolution DEMs incorporating road and building networks for urban flood modeling. By applying the method to an extreme flood event in the Marikina Basin, Philippines, a combination of ground-based and remotely retrieved rainfall data, roughness (n = 0.3861–0.5005), and infiltration parameters (Δθ = 0.326–0.505 and ψ = 0.4547–1.565) set at the maximum range were found to replicate the increase in the upstream water level. Simulations were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area compared to untreated digital terrain and surface model (DTM and DSM) data, with root-mean-square error range improvements from 0–7.13 (DTM) and 0.29–4.20 (DSM) to 0–0.63 (modified DEM). The proposed methodology significantly improved the accuracy of the simulations, which is crucial for proposing adequate flood action plans, despite the lack of high-resolution data available for under-developed nations.

Investigating paleoseismicity using high-resolution airborne LiDAR data - A case study of the Huangxianggou fault in the northeastern Tibetan plateau

Fault-offset landforms have long been recognized as holding important information about paleoseismic slip. Constructing an along-strike fault slip distribution could help reveal a fault's long-term rupture patterns and facilitate a more precise assessment of its future behavior. In this study, we documented the paleoseismic history of the Huangxianggou Fault, an oblique sinistral-thrust segment of the West Qingling Fault in the northeastern region of the Tibetan Plateau, using LiDAR-derived high-resolution DEM (0.1 m/pixel) and measurement of offset landforms. A total of 46 well-preserved and well-shaped landforms were chosen for horizontal and vertical displacement measurements, and the dense measurement data allow us to generate the cumulative offset probability distribution curves of horizontal and vertical displacements. Statistical analysis revealed at least seven surface-rupturing events, each with similar coseismic horizontal displacements of ∼7.0 m and vertical displacements of ∼0.7 m, in agreement with previous paleoseismic trench results. Using empirical relationships between moment magnitude and horizontal displacement, a plausible moment magnitude range of Mw7.3–7.7 was determined for these paleoearthquakes. Furthermore, by evaluating rupture parameters and fault slip rate, we determined a recurrence interval of 2.7–3.4 kyr for significant surface-rupturing events on this fault. The elapsed time since the last earthquake closely approaches this interval, suggesting a potential seismic risk for the Huangxianggou Fault. Our results emphasizes the importance of high-resolution topographic data in paleoseismic investigations, enabling the identification of numerous offset landforms and precise displacement measurements across faults. Paleoearthquake counts derived from horizontal and vertical displacement analyses were found to be consistent, highlighting the significance of vertical displacements in quantifying paleoearthquakes for strike-slip faults with a thrust component.

Human-centered avalanche susceptibility mapping (H-CASM): shifting the cartographic emphasis of backcountry avalanche susceptibility maps

ABSTRACT With backcountry winter travel increasing in popularity, the importance of accessible avalanche safety information is crucial. Widely accessible avalanche backcountry maps use slope shading to show where avalanches are likely to start. This paper presents a new methodology called Human-Centered Avalanche Susceptibility Mapping (H-CASM), which shifts the cartographic emphasis of backcountry avalanche maps from the hazard of an avalanche starting in a specific location to risk to a human traveling in the terrain. Utilizing ArcGIS, RAMMS avalanche runout model, and Python, we developed H-CASM as an open-source code to incorporate possible avalanche runouts, terrain traps, and connected slopes while trying to reduce instances of false certainty in visualizations. To provide a more comprehensive representation of susceptible terrain, we integrated aspect, distance to ridges, and vector ruggedness in addition to slope for start zone susceptibility using high-resolution one-meter Digital Elevation Models (DEMs). Avoiding the use of discrete susceptibility categories, we applied continuous gradient shading reducing false certainty in the final visualization. This proof of concept sets a new precedent for the future of backcountry avalanche susceptibility maps.

Reconstructing glacial lake outburst floods in the Poiqu River basin, central Himalaya

The Poiqu River basin in central Himalaya is a transboundary basin between China and Nepal that has experienced rapid glacier ice loss and glacial lake growth, some of which have drained in catastrophic glacial lake outburst floods (GLOFs). Here, we focus on the outbursts of two glacial lakes in this basin, Gongbatongsha Co in 2016, and Poiqu No.1 in 2002, and integrate multiple data sources and approaches to improve our understanding of past GLOFs and project the magnitude of potential future GLOFs. We identify their potential triggers and assess downstream damage by combining remote sensing observations and field surveys. We find that after the outbursts, Gongbatongsha Co shrank by 0.14 ± 0.003 km2 (∼91 %) in 2016 and almost disappeared, and Poiqu No.1 shrank by 0.05 ± 0.001 km2 (∼80 %) in 2002 but then expanded by 0.10 ± 0.002 km2 (∼670 %) by 2022. Our study identifies ice avalanches from the parent glaciers as the dominant trigger of the two GLOF events, and clarifies that the 2002 GLOF event stemmed from Poiqu No.1 instead of the reported Jialong Co. We also reconstructed the flow depths, velocities, and inundation areas using the hydrodynamic models HEC-RAS and FLO-2D. For Gongbatongsha Co, the models suggest that the discharge peaked at ∼550 m3s−1 about 15 min after the outburst, and the flood reached the settlement of Zhangmu with a peak flow of ∼1600 m3s−1 about 8.5 h later. The peak discharge during the GLOF from Poiqu No.1 was ∼2700 m3s−1. The flood reached the town of Nyalam 1.3 h later, with a peak discharge of ∼1100 m3s−1. The simulation of a future GLOF from Poiqu No.1 shows significant potential impacts to Nyalam town and other downstream communities due to the high flow depth. A large GLOF scenario for Poiqu No.1 would result in a peak discharge of more than 2500 m3s−1, with the flood wave reaching Nyalam in less than 40 min. We show that very high-resolution digital elevation models such as obtained from uncrewed aerial vehicles perform better in flood simulation. Our approach can be applied to potentially dangerous glacial lakes in other high mountain areas.

Optimizing Terrain Classification Methods for the Determination of Bedrock Depth and the Average Shear Wave Velocity of Soil

The advancement of remote sensing has enabled the creation of high-resolution Digital Elevation Models (DEMs). Topographic features such as slope gradient (SG), local convexity (LC), and surface texture (ST), derived from DEMs, are related to subsurface geological conditions. In South Korea, bedrock depth (Dbedrock) and the average shear wave velocity of soil (VSsoil) serve as metrics for determining the site class, which represents the degree of site amplification in seismic design criteria. These metrics, typically measured through geotechnical and geophysical investigations, require predictive methods for preliminary estimation over large areas. Previous studies developed an automatic terrain classification (AC) scheme using SG, LC, and ST, and subsequent research revealed that terrain classification effectively represents subsurface conditions such as Dbedrcok and average shear wave velocity down to 30 m depth. However, AC intrinsically depends on the regional features of DEMs, dividing regions based on nested means of topographic features (SG, LC, and ST). In this study, we developed two terrain classification methods to determine the thresholds of class divisions, aiming to optimize Dbedrock and VSsoil predictions: Sequentially Optimized Classification (SOC) and Non-Sequentially Optimized Classification (NOC). Through the study of the sensitivity of terrain classification methods, smoothing levels, and threshold levels for terrain class generation, we identified the best classification method by comparing it with the geological and mountainous region distribution. Subsequently, we developed DEM-dependent regression models for each class to enhance the accuracy of predicting Dbedrock and VSsoil. The main findings of this study are: (1) the terrain class map suggested in this study represents the distribution of alluvial plane and mountainous regions well, and (2) the DEM calibration for each class provides increased accuracy of Dbedrock and VSsoil predictions in South Korea. We anticipate that the terrain class map, along with Dbedrock and VSsoil maps, will be effectively utilized in geological interpretations and land-use planning for seismic design.

D-SRCAGAN : DEM Super-resolution Generative Adversarial Network

High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.

An Infinite Slope Model Considering Unloading Joints for Spatial Evaluation of Coseismic Landslide Hazards Triggered by a Reverse Seismogenic Fault: A Case Study of the 2013 Lushan Earthquake

Coseismic landslides pose a significant threat to the sustainability of both the natural environment and the socioeconomic fabric of society. This escalation in earthquake frequency has driven a growing interest in regional-scale assessment techniques for these landslides. The widely adopted infinite slope model, introduced by Newmark, is commonly utilized to assess coseismic landslide hazards. However, this conventional model falls short of capturing the influence of rock mass structure on slope stability. A novel methodology was previously introduced, considering the roughness of potential slide surfaces on the inner slope, offering a fresh perspective on coseismic landslide hazard mapping. In this paper, the proposed method is recalibrated using new datasets from the 2013 Lushan earthquake. The datasets encompass geological units, peak ground acceleration (PGA), and a high-resolution digital elevation model (DEM), rasterized at a grid spacing of 30 m. They are integrated within an infinite slope model, employing Newmark’s permanent deformation analysis. This integration enables the estimation of coseismic displacement in each grid area resulting from the 2013 Lushan earthquake. To validate the model, the simulated displacements are compared with the inventory of landslides triggered by the Lushan earthquake, allowing the derivation of a confidence level function that correlates predicted displacement with the spatial variation of coseismic landslides. Ultimately, a hazard map of coseismic landslides is generated based on the values of the certainty factor. The analysis of the area under the curve is utilized to illustrate the improved effectiveness of the proposed method. Comparative studies with the 2014 Ludian earthquake reveal that the coseismic landslides triggered by the 2013 Lushan earthquake predominantly manifest as shallow rock falls and slides. Brittle coseismic fractures are often associated with reverse seismogenic faults, while complaint coseismic fractures are more prevalent in strike–slip seismogenic faults. The mapping procedure stands as a valuable tool for predicting seismic hazard zones, providing essential insights for decision-making in infrastructure development and post-earthquake construction endeavors.

A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling

Urban floods can cause severe loss of economic and social assets, and remote sensing has been an effective tool for flood mapping during disaster response. Due to the complexity of high-density urban structures, high-resolution (HR) optical images can only extract visible floods in open spaces, and floods in shadows and under the canopy are challenging to map. Accurate digital elevation models (DEMs) are essential for inundation estimation towards urban flood mapping, but HR DEMs are often unavailable due to the high acquisition costs. Through DEM upscaling, HR DEMs could be obtained from existing low-resolution (LR) DEMs using deep learning. To this end, a novel multi-sensory HR urban flood mapping framework is proposed in this research. The framework consists of three components: 1) a new DEM upscaling network to infer HR DEMs from existing LR DEMs with a fusion approach, 2) a rapid flood segmentation network to extract visible flood from very-high-resolution (VHR) optical images with limited human labelling, and 3) an accurate Geographical Information System (GIS)-based tool for floodwater extent and depth estimation from the visible flood information along with HR DEMs. The proposed framework was validated on a fluvial flood that occurred in Calgary, Canada, in 2013, where the proposed DEM upscaling network produced an upscaled HR DEM at 2 m resolution from an existing LR DEM at 18 m resolution. In addition, the proposed flood segmentation network has shown accurate visible flood extraction from VHR RGB aerial imagery with over 80% intersection-over-union (IoU) using 10% of human labelling as training samples. Finally, the floodwater extent and floodwater depth estimation using the proposed GIS tool showed significant improvement over conventional flood mapping methods.

Seismotectonic aspects of the Ms 7.3 1948 October 5 Aşgabat (Ashgabat) earthquake, Türkmenistan: right-lateral rupture across multiple fault segments, and continuing urban hazard

SUMMARY The Ms 7.3 1948 Aşgabat earthquake was one of the most devastating earthquakes of the 20th century, yet little is known about its location, style and causative fault. In this study, we bring together new seismic and geomorphic observations with previously published descriptions of surface rupture and damage distributions to determine the likely source of the earthquake. We determine the epicentre and focal mechanism of this earthquake from digitized historical seismograms and the relocation of regional seismicity to show that the earthquake most likely nucleated close to the city of Aşgabat. The earthquake ruptured a right-lateral strike-slip fault to the southeast of the city, which has a clear long-term expression in the landscape, and also likely reactivated a subparallel concealed thrust along the Gyaursdag anticline east of the city. The earthquake potentially also ruptured a right-lateral segment northwest of Aşgabat, which does not have an identifiable expression in the landscape. Using high-resolution satellite imagery and digital elevation models we investigate the geomorphology of active faulting around Aşgabat and adjacent parts of the Köpetdag (Kopeh Dagh) mountain range front, showing that there are significant strike-slip and oblique strike-slip segments adjacent to the city that apparently did not rupture in 1948, and yet show clear geomorphic expression and potential right-lateral displacement of Parthian-era (∼2000 yr) and post-Sassanian era (∼1500 yr) archaeological remains. Luminescence dating of displaced fluvial terraces west of Aşgabat yields a vertical displacement rate of 0.6 mm yr−1, though the strike-slip rate remains undetermined.

Mapping inundated bathymetry for estimating lake water storage changes from SRTM DEM: A global investigation

Lake water budgets are sensitive to climate change and human activities and have been undergoing rapid changes worldwide over the past few decades. Satellite altimeters greatly promote the large-scale monitoring of lake level and storage variations. However, limited spatiotemporal coverage of the existing individual altimeters prevents long-term and spatially seamless observations on global lakes. These limitations are partially surmounted by the gridded digital elevation model (DEM) data, which can facilitate spatially contiguous measurements of lake bathymetry exposed at the low-level period. The high-resolution SRTM DEM has been widely used for estimating lake level variations due to its early collection time and reliable data quality, while how many lakes worldwide and to what extent of the newly inundated areas can be monitored based on SRTM DEM remains unexplored. Here, we conduct a quasi-global investigation of SRTM DEM regarding its capability of mapping inundated-area bathymetry and estimating lake storage variations. After generating a global-scale lake inventory (122,444 lakes below 60°N, >1 km2) representing the maximum water inundation extents between 1984 and 2020, the feasibility of mapping lake inundated-area bathymetry was evaluated by comparing the maximum inundated area with the waterbody extent derived from SRTM DEM and analyzing the characteristics of interannual and seasonal variability of each lake. Results show that the maximum exposed lake bathymetries from the SRTM DEM data cover an area of 372,986 km2, accounting for 15.95% of the maximum water area of our investigated lakes. Climate-driven lake expansions or human-induced reservoir impoundments after 2000 (the SRTM mission period) are the main contributors to the higher availability of SRTM DEM-based bathymetry mapping on the Tibetan Plateau, India, eastern China, and Amazon Plains. As the newly launched Surface Water and Ocean Topography (SWOT) mission is anticipated to monitor lake water budget variation at an unprecedented resolution, this study provides a base map for supplementing inundated bathymetry information. The joint use of SRTM and SWOT-based observations will extend the time and space extents of lake-level measurements, thereby enhancing our understanding of lake dynamics at a longer timescale.

From landform inventories to landscape evolution? - Karst development in the Central Styrian Karst (Austria)

In the Central Styrian Karst (CSK, Styria, Austria) 1158 karst landforms were used to obtain a better understanding of the landscape evolution in this region. An automatically delineated doline inventory was combined with an existing cave inventory to investigate surface and subsurface karst landforms in relation to their elevation above the local base level at the transition zone between the Eastern Alps and the Pannonian Basin. The landform entities analysed consist of 51 % dolines and 49 % caves and are related to various stages of landform evolution and thus ages.The CSK was ice-free during glacial times and therefore allow for a much longer preservation of surface landforms compared to landforms that were reshaped during glaciations. Nevertheless, weathering and downwasting do re-shape and even may lead to the disappearance of surface landforms (i.e., dolines); however, subsurface landforms (i.e., caves), if accessible, might preserve information about older geomorphological stages.An old, a mature, and a young stage of landform evolution can be distinguished in the CSK and be related to planation surfaces preserved in the area, presumably older than five Ma. The chosen approach is likely applicable for other areas in the world too by implying karstification over million years' time scale.Besides those regional implications, there are local (neo-) tectonic inferences derived from the analyses that might explain some discrepancies in correlations of levels and planation surfaces, but requiring further more detailed investigations. The automatic doline delineation in the forested karst of the CSK made an area not yet mentioned in the literature become apparent.With an increasing availability of high-resolution digital elevation models, remote-sensed inventories of dolines (surface landforms) renew the question of dolines as diagnostic landforms for karstification. However, in the CSK it is revealed that an absence of dolines due to e.g., surface erosion does not indicate the absence of karstification. Therefore, landscape evolution should consider both surface (i.e., doline) and subsurface (i.e., cave) landforms.

High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia

Abstract. The natural resources of Ethiopian high-altitude ecosystems are commonly perceived as increasingly threatened by devastating land-use practices owing to decreasing lowland resources. Quantified time-series data of the course of land-use cover changes are still needed. Very-high-resolution digital data on the historical landscape over recent decades are needed to determine the impacts of changes in afro-alpine ecosystems. However, digital elevation models (DEMs) and orthomosaics do not exist for most afro-alpine ecosystems of Africa. We processed the only available and oldest historical aerial photographs for Ethiopia and any afro-alpine ecosystem. Here, we provide a DEM and an orthomosaic image for the years 1967 and 1984 for the Bale Mountains in Ethiopia, which comprise the largest afro-alpine ecosystem in Africa. We used 298 historical aerial photographs captured in 1967 and 1984 for generating DEMs and orthomosaics with a structure-from-motion multi-view stereo photogrammetry workflow along an elevation gradient from 977 to 4377 m above sea level (a.s.l.) at very high spatial resolutions of 0.84 m and 0.98 m for the years 1967 and 1984, respectively. The structure-from-motion multi-view stereo photogrammetry workflow, employed with Agisoft Metashape, represents a modern approach that combines computer vision and photogrammetry. This method proves useful for reconstructing DEMs and orthomosaics from historical aerial photographs, with a focus on high spatial resolution. To validate the accuracy of the reconstructed DEMs, ground control points gathered through GPS measurements were used, resulting in root mean square error (RMSE) values of 3.55 m for the year 1967 and 3.44 m for the year 1984. Our datasets can be used by researchers and policymakers for watershed management, as the area provides water for more than 30 million people, landscape management, detailed mapping, and analysis of geological and archaeological features as well as natural resources, analyses of geomorphological processes, and biodiversity research. All the datasets are available online at https://doi.org/10.5281/zenodo.7271617 (Muhammed et al., 2022a) for all the inputs used and at https://doi.org/10.5281/zenodo.7269999 (Muhammed et al., 2022b) for the results obtained (very-high-resolution DEMs and orthomosaics) for both the years 1967 and 1984.

Coastal Vulnerability Assessment of Thrace Peninsula: Implications for Climate Change and Sea Level Rise

This study evaluates the susceptibility of the coastal regions on the Thrace Peninsula to sea-level rise (SLR) and the corresponding vulnerability to climate change. To achieve this, a high-resolution digital elevation model with a 5 m granularity was used to apply the Coastal Vulnerability Index, adjusted for region-specific coastal sensitivity factors. Various global mean sea-level rise scenarios were examined for the near-term (2020–2050), mid-term (2050–2100), and long-term (2100–2300) to assess the impact of SLR. The examination of the immediate consequences of SLR on coastal areas included the analysis of land cover characteristics in the near-term. Results indicate that the Thrace Peninsula is highly susceptible to natural and socio-economic hazards caused by SLR. The concentration of population and socio-economic activities in coastal regions is a primary contributing factor to this vulnerability. In addition, hydrodynamic models are used to enhance understanding of the effects of SLR. The study reveals limited preparedness for planned adaptations to SLR in the region. The data highlight the crucial necessity for policymakers, researchers, and stakeholders to collaborate in executing strategic interventions and proactive initiatives. Upholding the ecological, economic, and societal welfare of the Thrace Peninsula, as well as comparable areas, necessitates addressing both the vulnerability and resilience of immediate coastal regions.