High-resolution Elevation Data Research Articles

Detecting active sinkholes through combination of morphometric-cluster assessment and deformation precursors

Sinkhole hazards pose considerable challenges in the west-central region of Texas. This study integrates multisource datasets and innovative techniques to detect early sinkhole development and identify the processes governing their formation. The techniques employed encompass feature extraction, cluster analysis, and deformation process monitoring. Potential sinkholes were initially mapped using high-resolution elevation data, and a circularity index (CI) threshold value of 0.85 was applied to filter out depressions with noncircular shapes. Subsequently, Persistent Scatterer Interferometry (PSInSAR) followed by the Getis-Ord Gi* statistic methods were used to identify statistically significant subsidence clusters with rates of <−2 mm/yr, indicating active sinkhole formation. The findings reveal the following: (1) surface deformation analysis revealed significant subsidence hotspots, particularly in areas underlain by units containing significant sequences of evaporites, which nominally belong to the Clear Fork Group and the Blaine Formation, compared with those dominated by carbonates; (2) potential sinkholes are undergoing displacement up to a rate of −11.31 mm/yr, and (3) extreme groundwater pumping of karst aquifers and the subsequent decline in groundwater levels are found to be the leading causes of the susceptibility of the region to sinkhole hazards. In such cases, the decline of the water table deprives the overlying bedrock of buoyant support provided by the groundwater in the cavity. The bedrock will undergo sagging subsidence under the influence of gravity and eventually form a sinkhole. Extensive oil and gas activity in the study area, including extreme withdrawal rates and the injection of fluids associated with the activity, are other potential causes of the observed sinkhole susceptibility in several parts of the study area. This study underscores the importance of understanding the geologic, hydrologic, and anthropogenic factors driving sinkhole hazards for effective mitigation strategies to protect public safety, infrastructure, and the environment.

Hydrodynamic reconstruction of the paleoflood from the Early Holocene ice-dammed lake Nedre Glomsjø, Norway

Study regionNorway Study focusNumerous geological traces provide evidence for the existence, size and catastrophic drainage of the Early Holocene glacial lake Nedre Glomsjø in southern Norway. We present, for the first time, a hydraulic reconstruction that links the three domains of the glacial lake outburst flood process chain: lake hypsometry, tunneling under the remnant ice sheet and subsequent deluge downstream. This is done by first reconstructing the hypsometry of the lake using paleo shorelines observed from modern high-resolution elevation data. The development of an ice tunnel under the remnant ice sheet is then simulated using an ensemble approach to determine a suite of hydrographs for the range of possible parameter combinations. These hydrographs are applied as upstream input to a 2D hydrodynamic model of the flood propagation. The flood marker dataset includes abundant landforms and flood traces at multiple scales, such as erosive paleostage indicators, sediment records and large-scale flood bars. The results from each domain are constrained by the flood marker dataset, which adds to the robustness of our interpretation. New hydrological insights for the regionThe reconstruction shows that glacial lake Nedre Glomsjø covered roughly 1250 km2, and that ∼100 km3 of water was available for drainage. The peak discharge of the glacier lake outburst flood was at least 1.5 ×106 m3/s and probably closer to 2.0 ×106 m3/s. Our results demonstrate the benefit of assembling the entire process chain from glacial lake, tunnel expansion and flood propagation in a single modeling framework to give robust results that satisfy multiple geological constraints for each component of the glacial lake outburst flood (GLOF) system. The methodology described in this paper can be applied to contemporary and future glacial lakes to better predict GLOFs in a changing climate.

Characteristics and Simulation of Icing Thickness of Overhead Transmission Lines across Various Micro-Terrains

The hazard of ice accretion on overhead power circuits is significant, yet predicting it is very difficult. The key reason lies in the shortage of sufficient observational data on ice thickness, and previous studies have also rarely taken into account micro-terrain and micro-meteorological conditions. In response to the challenge of simulating overhead line icing, this study introduces a new icing simulation technique that fully considers the effects of micro-terrain and micro-meteorology. For this technique, typical micro-terrains of overhead line areas are first identified by using high-resolution elevation data, and the icing thickness characteristics in different micro-terrains are analyzed. Subsequently, icing thickness simulations for different micro-terrains are conducted. The results indicate that during the icing process, the icing thickness ranges from 5 mm to 8 mm under three types of micro-terrain, namely, “uplift type”, “alpine drainage divide type” and “canyon wind channel type”, whereas the icing thickness is less than 5 mm in the “flat type” of micro-terrain. This finding suggests that the first three micro-terrain types facilitate icing on overhead transmission lines due to the condensation and uplifting effects of water vapor caused by terrain. However, flat terrain lacks the conditions necessary for water vapor accumulation and thus is not easy to form icing. The results are advantageous for the deployment of overhead power lines in intricate terrain. It is advisable to steer clear of regions susceptible to icing, and endeavor to install circuits in level territories whenever feasible. In addition, the simulated icing thickness under different terrains is in good agreement with the observations. Specifically, the correlation coefficient between simulated and observed icing thickness is significant at the 0.99 confidence level, and the deviations between them are within 0.5 mm. This signifies that the forecasting methodologies employed are dependable and possess significant implications as a reference for disaster prevention and mitigation efforts.

Integrating Sentinel 2 Imagery with High-Resolution Elevation Data for Automated Inundation Monitoring in Vegetated Floodplain Wetlands

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.

A machine learning approach to the geomorphometric detection of ribbed moraines in Norway

Abstract. Machine learning is a powerful yet underutilised tool in geomorphology, commonly used for image-based pattern recognition. Analysing new high-resolution (1–10 m) elevation datasets, we investigate its usefulness for detecting discrete geomorphological features. This study develops a machine-learning-based method for identifying ribbed moraines in digital elevation data and progresses to test its performance versus time-consuming, manual methods. Ribbed moraines share geomorphometric characteristics with other glacial landforms, hence representing a valuable test of our new methodology in terms of differentiating between similar features, and for detecting landforms with similar characteristics. Furthermore, mapping ribbed moraines may provide valuable indications of their origin, a topic of debate within glacial geomorphology. To automatically detect ribbed moraines, we extract simple morphometrics from high-resolution digital elevation model data and mask regions where ribbed moraines are unlikely to form. We then test several machine learning algorithms before examining the best performer (K-means clustering) for three study areas of 15 km2 in Norway. Our results demonstrate a balanced accuracy of 65 %–75 % when validating versus ground-truthing. The performance depends on the availability of high-resolution elevation data in Norway that are needed to resolve the spatial scale of the target (10–100 m). We find the method effective at detecting both fields of ribbed moraines, as well as individual ribbed moraines. We propose pathways for the future implementation of this method on a large scale and for increasing the detail of information gained about detected landforms. In conclusion, we demonstrate K-means clustering as a promising method for detecting ribbed moraines, with great potential to reduce the time needed to produce landform maps.

Surface Solar Radiation Resource Evaluation of Xizang Region Based on Station Observation and High-Resolution Satellite Dataset

Xizang boasts a vast and geographically complex landscape with an average elevation surpassing 4000 m. Understanding the spatiotemporal distribution of surface solar radiation is indispensable for simulating surface processes, studying climate change, and designing photovoltaic power generation and solar heating systems. A multi-dimensional, long-term, spatial, and temporal investigation of solar radiation in Xizang was conducted using three unique datasets, including the difference in surface solar radiation (SSR) between high-resolution satellite and ground station data, the annual and monthly distribution of SSR, and the interannual–monthly–daily variation and the coefficient of hourly variability. Combined with high-resolution elevation data, a strong linear correlation was shown between the radiation and the elevation below 4000 m. Furthermore, analysis reveals greater differences in data between east and west compared to the center, as well as between summer and winter seasons. SSR levels vary in steps, reaching the highest from Ngari to Shigatse and the lowest in a U-shaped area formed by southeastern Shannan and southern Nyingchi. In June, high monthly SSR coverage was the highest of the year. Since 1960, the annual mean SSR has generally exhibited a declining trend, displaying distinctive trends across various seasons and datasets. Owing to intricate meteorological factors, some regions exhibited double peaks in monthly SSR. Finally, we have introduced a solar resource assessment standard, along with a multidimensional evaluation of the resources, and categorized all townships. We offer a thorough analysis of Xizang’s solar radiation to provide a comprehensive understanding, which will help to prioritize recommendations for PV construction in Xizang.

Sunny-Day Flooding and Mortality Risk in Coastal Florida

Sea-level rise is likely to worsen the impacts of hurricanes, storm surges, and tidal flooding on coastal access to basic services. We investigate the historical impact of tidal flooding on mortality rates of the elderly population in coastal Florida using administrative records of individual deaths, demographics, and residential location combined with tidal gauge and high-resolution elevation data. We incorporate data capturing storm and precipitation events into our empirical model to distinguish between disruptions from routine sunny-day flooding and less predictable tropical storm–induced flooding. We find that a 1-standard-deviation (20-millimeter) increase in tidal flooding depth increases mortality rates by 0.46% to 0.60% among those aged 65 or older. Our estimates suggest that future sea-level rises may contribute to an additional 130 elderly deaths per year in Florida relative to 2019, all else being equal. The enhanced risk is concentrated among residents living more than nine minutes away from the nearest hospital. Results suggest that tidal flooding may augment elderly mortality risk by delaying urgent medical care.

Precision Mapping of Coastal Wetlands: An Integrated Remote Sensing Approach Using Unoccupied Aerial Systems Light Detection and Ranging and Multispectral Data

Coastal wetlands, crucial for global biodiversity and climate adaptation, provide essential ecosystem services such as carbon storage and flood protection. These vital areas are increasingly threatened by both natural and human-induced changes, prompting the need for advanced monitoring techniques. This study employs unmanned aerial systems (UASs) equipped with light detection and ranging (LiDAR) and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina. Utilizing high-resolution elevation data and detailed vegetation analysis, coupled with sophisticated machine learning algorithms, we achieved differentiated and highly precise classifications of wetland types. Classification accuracies varied by type, with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures, especially when collections account for tidal influence. In contrast, palustrine forested and scrub–shrub wetlands presented lower accuracies, often due to the denser, mixed, and more complex vegetation structure and variable inundation levels, which complicate spectral differentiation and ground returns from LiDAR sensors. Overall, our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable, efficient, and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience. By demonstrating the usefulness of small-scale aerial data collection in ecological mapping, this study highlights the transformative potential of merging advanced technologies in environmental monitoring, underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies.

COMPARATIVE ANALYSIS BETWEEN GPS-BASED FIELD DIGITAL TERRAIN MODEL WITH TANDEM- X 90M, ASTER AND SRTM DIGITAL ELEVATION MODELS

This study compared the vertical accuracy of existing satellite’s Digital Elevation Model (STRM, ASTER) and the latest satellite remote sensing height data set TanDEM-90m. ASTER and STRM have vertical accuracies of ± 20m and ± 16m, respectively. TanDEMX-90m was processed before use, and the RMSE range of 19-20m was confirmed for TANDEM X 90m. The result of the study shows that ASTER DEM performed better than the rest of the global digital elevation models. The SRTM error of 4m between the first and second locations may be due to systematic error due to slightly different versions of SRTM used for processing. The TANDEM X 90m had the same resolution but performed poorer in positional fitness. The study found that digital elevation models have different resolutions and accuracy levels. Tandem-X90m has a resolution of up to 3 meters, while Aster and SRTM have 30 meters. GPS-based field digital terrain models provide higher accuracy, while Tandem-X90m has a 10 cm accuracy. The models cover a limited area, making them suitable for high-resolution elevation data. The data collection methods used by the models, such as SAR sensors, also affect their accuracy and resolution. Based on the above comparative analysis, it can be recommended that the GPS-based field digital terrain model is suitable for high-accuracy and high-resolution elevation data in a limited area. Tandem-X90m is suitable for high-resolution elevation data in a limited area but is expensive. Aster and SRTM are suitable for global-scale studies but have lower accuracy and resolution.

Not another hillshade: alternatives which improve visualizations of bathymetric data

Increasing awareness of the importance of effective communication of scientific results and concepts, and the need for more accurate mapping and increased feature visibility led to the development of novel approaches to visualization of high-resolution elevation data. While new approaches have routinely been adopted for land elevation data, this does not seem to be the case for the offshore and submerged terrestrial realms. We test the suitability of algorithms provided by the freely-available and user-friendly Relief Visualization Toolbox (RVT) software package for visualizing bathymetric data. We examine the algorithms optimal for visualizing the general bathymetry of a study area, as well as for highlighting specific morphological shapes that are common on the sea-, lake- and riverbed. We show that these algorithms surpass the more conventional analytical hillshading in providing visualizations of bathymetric data richer in details, and foremost, providing a better overview of the morphological features of the studied areas. We demonstrate that the algorithms are efficient regardless of the source data type, depth range, resolution, geographic, and geological setting. The summary of our results and observations can serve as a reference for future users of RVT for displaying bathymetric data.

Telescopic Megafans on the High Plains, USA Were Signal Buffers in a Major Source-To-Sink System

Sediment routing systems transport sediment and environmental signals inefficiently. The storage and recycling of sediment buffers the responses of sedimentary systems to tectonic, climatic, and geomorphic changes. Long-term ( &gt;106 a.) storage may occur in megafans—large, low-relief, hemiconical fluvial deposystems—but the behavior of these systems over such timescales is unclear. We examine late Neogene megafan deposits on the High Plains, USA that are stored in the catchment of the continental-scale Rocky Mountain-Gulf of Mexico source-to-sink system. Using high-resolution elevation data, we map numerous fluvial ridges (inverted channel relics) that can be differentiated into five, chronologically distinct groups. The oldest four groups comprise radial arrays with multiple channel divergences (avulsion nodes). The inferred fan apices and longitudinal intersection points are offset successively downgradient, indicating that fanhead entrenchment and fan-lobe deposition were approximately contemporaneous and strongly suggesting a telescopic morphology. The youngest group of channels, also the lowest in elevation, is confined to terraces along the modern valley of the South Platte River. We interpret this group as direct evidence for the abandonment of the megafan and the incision of the present valley. Uplift was the chief driving mechanism for early telescoping, but channel widening and uniform downcutting in the younger groups suggest that change in stream power was the primary driver of later telescoping and incision. The storage of sediment in the megafan effectively decoupled sources from downstream sinks. Some of this sediment was recycled during entrenchment, but much of it remains in storage as the Ogallala Group and Broadwater Formation. Emplacement of telescopic megafans should be considered as a long-term ( ≥106 years) buffer in other modern and ancient sediment routing systems.

Unveiling spatial variability within the Dotson Melt Channel through high-resolution basal melt rates from the Reference Elevation Model of Antarctica

Abstract. The intrusion of Circumpolar Deep Water in the Amundsen and Bellingshausen Sea embayments of Antarctica causes ice shelves in the region to melt from below, potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain ice shelf basal melt rates at a high spatial resolution. However, there has been limited availability of high-resolution elevation data, a gap the Reference Elevation Model of Antarctica (REMA) has filled. In this study we use a novel combination of REMA and CryoSat-2 elevation data to obtain high-resolution basal melt rates of the Dotson Ice Shelf in a Lagrangian framework, at a 50 m spatial posting on a 3-yearly temporal resolution. We present a novel method: Basal melt rates Using REMA and Google Earth Engine (BURGEE). The high resolution of BURGEE is supported through a sensitivity study of the Lagrangian displacement. The high-resolution basal melt rates show a good agreement with an earlier basal melt product based on CryoSat-2. Both products show a wide melt channel extending from the grounding line to the ice front, but our high-resolution product indicates that the pathway and spatial variability of this channel is influenced by a pinning point on the ice shelf. This result emphasizes the importance of high-resolution basal melt rates to expand our understanding of channel formation and melt patterns. BURGEE can be expanded to a pan-Antarctic study of high-resolution basal melt rates. This will provide a better picture of the (in)stability of Antarctic ice shelves.

The INOVMineral Project’s Contribution to Mineral Exploration—A WebGIS Integration and Visualization of Spectral and Geophysical Properties of the Aldeia LCT Pegmatite Spodumene Deposit

Due to the current energetic transition, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. A review of the literature indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated, namely, remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS platform for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provides valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets compared with Landsat 8. The user-friendly webGIS platform facilitates data integration, visualization, and sharing, supporting potential users in similar exploration approaches.

Automatically calculating the apparent depths of pits using the Pit Topography from Shadows (PITS) tool

AbstractPits, or pit craters, are near-circular depressions found in planetary surfaces, which are generally formed through gravitational collapse. Pits will be primary targets for future space exploration and habitability for their presence on most rocky Solar System surfaces and their potential to be entrances to sub-surface cavities. This is particularly true on Mars, where caves have been simulated to harbour stable reserves of ice water across much of the surface. Caves can also provide natural shelter from the high radiation dosages experienced at the surface. Since pits are rarely found to have corresponding high-resolution elevation data, tools are required for approximating their depths in order to find those which are the ideal candidates for follow-up remote investigation and future exploration. The Pit Topography from Shadows (PITS) tool has been developed to automatically calculate the apparent depth of a pit (h) by measuring the width of its shadow as it appears in satellite imagery. The tool requires just one cropped single- or multiband image of a pit to calculate a profile of h along the length of the shadow, thus allowing for depth calculation where altimetry or stereo image data is not available. We also present a method for correcting shadow width measurements made in non-nadir observations for all possible values of emission and solar/satellite azimuth angles. Shadows are extracted using image segmentation in the form of k-means clustering and silhouette analysis. Across 19 shadow-labelled Mars Reconnaissance Orbiter red-band HiRISE images of atypical pit craters (APCs) from the Mars global cave candidate catalogue (MGC3), PITS detected 99.6 per cent of all shadow pixels (with 94.8 per cent of all detections being true shadow pixels). Following this testing, PITS has been applied to 123 red-band HiRISE images containing 88 APCs, which revealed an improvement in the variation of the calculated h due to emission angle correction, and also found 10 APCs that could be good candidates for cave entrances on Mars due to their h profiles.

SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFSAR DATA IN ALASKA

Abstract. The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska.

Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index

Abstract. Increased interest in combining compound flood hazards and social vulnerability has driven recent advances in flood impact mapping. However, current methods to estimate event-specific compound flooding at the household level require high-performance computing resources frequently not available to local stakeholders. Government and non-governmental agencies currently lack the methods to repeatedly and rapidly create flood impact maps that incorporate the local variability in both hazards and social vulnerability. We address this gap by developing a methodology to estimate a flood impact index at the household level in near-real time, utilizing high-resolution elevation data to approximate event-specific inundation from both pluvial and fluvial sources in conjunction with a social vulnerability index. Our analysis uses the 2015 Memorial Day flood in Austin, Texas, as a case study and proof of concept for our methodology. We show that 37 % of the census block groups in the study area experience flooding from only pluvial sources and are not identified in local or national flood hazard maps as being at risk. Furthermore, averaging hazard estimates to cartographic boundaries masks household variability, with 60 % of the census block groups in the study area having a coefficient of variation around the mean flood depth exceeding 50 %. Comparing our pluvial flooding estimates to a 2D physics-based model, we classify household impact accurately for 92 % of households. Our methodology can be used as a tool to create household compound flood impact maps to provide computationally efficient information to local stakeholders.

Comparative analysis of freely available digital elevation models for applications in multi-criteria environmental modeling over data limited regions

Remotely sensed elevation data obtained from high-resolution Digital Elevation Models (DEMs) is one of the main sources of data required for environmental modeling and remediation especially when it has to do with identification of erosion sites and flood-prone areas. The focus of this study is to evaluate four recently improved and freely available high-resolution elevation data obtained from the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X 90), Multi-Error-Removed Improved-Terrain (MERIT) DEM and EarthEnv-DEM90 in order to determine the most suitable DEM that could be used as an input data in a multi-criteria environmental modeling to identify erosion sites and flood-prone areas in data limited regions such as Southeastern Nigeria. Intensive performance evaluation of these four DEMs was rigorously carried out using statistical and surface analyses tools (Root-Mean-Square Error (RMSE), Land Use/Land Cover, slope, and contour maps). The obtained RMSE range from 5.49 m to 19.79 m depending on the surface of contact. Slope values range from 0.00 to 89.49° depicting the complexity and ruggedness of the terrain. Analyses of the obtained RMSE showed that EarthEnv-DEM90 is more accurate (in terms of RMSE value) than the other three DEMs but further terrain analyses using slope and contour maps generated from each of the DEMs revealed that ALOS W3D30 represent the topography of the terrain (terrain variation) better than the other three DEMs. This study demonstrates the importance of carrying out rigorous and intensive performance evaluation of DEMs using many terrain parameters or attributes in order to avoid large errors that may be contributed by the use of a DEM in applications such as environmental modeling and remediation.

Resilience of U.S. coastal wetlands to accelerating sea level rise

Coastal wetlands provide a wide array of ecosystem services, valued at trillions of dollars per year globally. Although accelerating sea level rise (SLR) poses the long-term threat of inundation to coastal areas, wetlands may be sustained in two ways: by positive net surface-elevation change (SEC) from sediment and organic matter buildup and by accumulation, or horizontal migration into refugia—low-lying, undeveloped upland areas that become inundated. Using a simple model together with high-resolution elevation data, we provide, across the contiguous United States, analysis of the local effects of SLR, maximum SEC rates, and coastal development on the long-term resilience of coastal wetlands. We find that protecting current refugia is a critical factor for retaining wetlands under accelerating SLR. If refugia are conserved under an optimistic scenario (a high universal maximum SEC rate of 8 mm/yr and low greenhouse gas emissions), wetlands may increase by 25.0% (29.4%–21.5%; 50th, 5th–95th percentiles of SLR) by the end of the century. However, if refugia are developed under a more pessimistic scenario (a moderate universal maximum SEC rate of 3 mm/yr, high greenhouse gas emissions, and projections incorporating high ice-sheet contributions to SLR), wetlands may decrease by −97.0% (−82.3%–99.9%). These median changes in wetland area could result in an annual gain of ∼$222 billion compared to an annual loss of ∼$732 billion in ecosystem services in the US alone. Focusing on key management options for sustaining wetlands, we highlight areas at risk of losing wetlands and identify the benefits possible from conserving refugia or managing SEC rates.

Recent contrasting behaviour of mountain glaciers across the European High Arctic revealed by ArcticDEM data

Abstract. Small land-terminating mountain glaciers are a widespread and important element of Arctic ecosystems, influencing local hydrology, microclimate, and ecology. Due to their relatively small ice volumes, this class of ice mass is particularly sensitive to the significant ongoing climate warming in the European sector of the Arctic, i.e. in the Barents Sea area. Archipelagos surrounding the Barents Sea, i.e. Svalbard (SV), Novaya Zemlya (NZ), and Franz Josef Land (FJ), host numerous populations of mountain glaciers, but their response to recent strong warming remains understudied in most locations. This paper aims to obtain a snapshot of their state by utilizing high-resolution elevation data (ArcticDEM) to investigate the recent (ca. 2011–2017) elevation and volume changes of 382 small glaciers across SV, NZ, and FJ. The study concludes that many mountain glacier sites across the Barents Sea have been in a critical imbalance with the recent climate and might melt away within the coming several decades. However, deviations from the general trend exist; e.g. a cluster of small glaciers in north SV has been experiencing thickening. The findings reveal that near-stagnant glaciers might exhibit contrasting behaviours (fast thinning vs. thickening) over relatively short distances, which is a challenge for glacier mass balance models but also an opportunity to test their reliability.

Use of Cartosat-1 elevation data for local-scale terrain studies in India: a case study

ABSTRACT The Cartosat-1 satellite provides relatively high-resolution elevation data, which is publicly available for free, for most parts of India. Yet, published works applying such data in the local geographical context are few. This paper illustrates the application of CartoDEM, an elevation dataset based on Cartosat-1, to develop a coarse geographic narrative of the terrain at the tehsil level. Kotra tehsil in Udaipur district of Rajasthan, India, was used as a case study. It was found that the data, along with the calibration and analysis methods used here, allows a fairly well-resolved understanding of the terrain of the tehsil, including identification of major landforms and quantification of terrain metrics such as elevation and roughness. The free and easy availability of such data shows offers the potential for such studies to be performed for any local area for which CartoDEM or similar data exists.