Search

Abstract We present a new model called Bayesian Estimated Glacial Lake Volume (BE‐GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross‐section as trapezoidal, BE‐GLAV uses a Bayesian calibration approach to adjust the cross‐sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien‐Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root‐mean‐square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE‐GLAV performed well compared to 10 other models in a model inter‐comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.

Topographic data is a fundamental input to flood hazard models and controls the quality of the outputs. However, open-access global digital elevation models (DEMs) are dated and limited to 30 m resolution, which hinders modelling efforts in urban or topographically complex environments. We used the flood prone and expanding city of Kathmandu, Nepal, to evaluate the impact of topographic data source and resolution on flood model outputs. All DEMs evaluated featured spatially correlated topographic sinks with depths exceeding 20 m that required hydrological conditioning before being used in flood modelling. Incomplete hydrological conditioning appeared related to the overestimation of flood extent and therefore limited agreement when comparing a global 90 m resolution flood hazard model with a bespoke city-scale model at 10 m resolution (F1 score = 0.40). Instead, we found that the height above nearest drainage (HAND) metric was better able to replicate the higher resolution flood map as an indicator of flood susceptibility requiring only topographic information as an input. We also found that the computationally efficient FastFlood model was able to match the inundation extent (F1 score = 0.79) and flood depths (mean absolute error and root mean square error of 0.46 and 0.76 m respectively) of a published 10 m physics-based flood hazard model whilst requiring 212 times less computation time. Our analysis demonstrated that mapping city-scale flood inundation required hydrologically conditioned high-resolution topographic data but not physically complex flood models, highlighting the need for greater availability of high quality open access topographic data.

Rock debris covers ~30% of glacier ablation areas in the Central Himalaya and modifies the impact of atmospheric conditions on mass balance. The thermal properties of supraglacial debris are diurnally variable but remain poorly constrained for monsoon-influenced glaciers over the timescale of the ablation season. We measured vertical debris profile temperatures at 12 sites on four glaciers in the Everest region with debris thickness ranging from 0.08 to 2.8 m. Typically, the length of the ice ablation season beneath supraglacial debris was 160 days (15 May to 22 October)—a month longer than the monsoon season. Debris temperature gradients were approximately linear (r2 > 0.83), measured as −40°C m–1 where debris was up to 0.1 m thick, −20°C m–1 for debris 0.1–0.5 m thick, and −4°C m–1 for debris greater than 0.5 m thick. Our results demonstrate that the influence of supraglacial debris on the temperature of the underlying ice surface, and therefore melt, is stable at a seasonal timescale and can be estimated from near-surface temperature. These results have the potential to greatly improve the representation of ablation in calculations of debris-covered glacier mass balance and projections of their response to climate change.

Earth observation (EO) data can provide large scale, high-resolution, and transferable methodologies to quantify the sprawl and vertical development of cities and are required to inform disaster risk reduction strategies for current and future populations. We synthesize the evolution of Bishkek, Kyrgyzstan, which experiences high seismic hazard, and derive new datasets relevant for seismic risk modeling. First, the urban sprawl of Bishkek (1979–2021) was quantified using built-up area land cover classifications. Second, a change detection methodology was applied to a declassified KeyHole Hexagon (KH-9) and Sentinel-2 satellite image to detect areas of redevelopment within Bishkek. Finally, vertical development was quantified using multi-temporal high-resolution stereo and tri-stereo satellite imagery, which were used in a deep learning workflow to extract buildings footprints and assign building heights. Our results revealed urban growth of 139 km2 (92%) and redevelopment of ~26% (59 km2) of the city (1979–2021). The trends of urban growth were not reflected in all the open access global settlement footprint products that were evaluated. Building polygons that were extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61). Similarly, building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements obtained using ICESat-2 altimetry data and field-measurements (normalized absolute median deviation < 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 2000–10,700 buildings per year, which when combined with a trend of urban growth towards active faults highlights the importance of up-to-date building stock exposure data in areas of seismic hazard. Deep learning methodologies applied to high-resolution imagery are a valuable monitoring tool for building stock, especially where country-level or open-source datasets are lacking or incomplete.

Abstract Major faults of the Tien Shan, Central Asia, have long repeat times, but fail in large (Mw 7+) earthquakes. In addition, there may be smaller, buried faults off the major faults which are not properly characterized or even recognized as active. These all pose hazard to cities along the mountain range front such as Almaty, Kazakhstan. Here, we explore the seismic hazard and risk for Almaty from specific earthquake scenarios. We run three historical‐based earthquake scenarios (1887 Verny Mw 7.3, 1889 Chilik Mw 8.0 and 1911 Chon‐Kemin Mw 8.0) on the current population and four hypothetical scenarios for near‐field faulting. By making high‐resolution Digital Elevation Models (DEMs) from SPOT and Pleiades stereo optical satellite imagery, we identify fault splays near and under Almaty. We assess the feasibility of using DEMs to estimate city building heights, aiming to better constrain future exposure datasets. Both Pleiades and SPOT‐derived DEMs find accurate building heights of the majority of sampled buildings within error; Pleiades tri‐stereo estimates 80% of 15 building heights within one sigma and has the smallest average percentage difference to field‐measured heights (14%). A moderately sized Mw 6.5 earthquake rupture occurring on a blind thrust fault, under folding north of Almaty is the most damaging scenario explored here due to the modeled fault stretching under Almaty, with estimated 12,300±5,000 completely damaged buildings, 4,100 ± 3,500 fatalities and an economic cost of 4,700 ± 2,700 Million US dollars (one sigma uncertainty). This highlights the importance of characterizing location, extent, geometry, and activity of small faults beneath cities.

The formation and expansion of Himalayan glacial lakes has implications for glacier dynamics, mass balance and glacial lake outburst floods (GLOFs). Subaerial and subaqueous calving is an important component of glacier mass loss but they have been difficult to track due to spatiotemporal resolution limitations in remote sensing data and few field observations. In this study, we used near-daily 3 m resolution PlanetScope imagery in conjunction with an uncrewed aerial vehicle (UAV) survey to quantify calving events and derive an empirical area–volume relationship to estimate calved glacier volume from planimetric iceberg areas. A calving event at Thulagi Glacier in 2017 was observed by satellite from before and during the event to nearly complete melting of the icebergs, and was observed in situ midway through the melting period, thus giving insights into the melting processes. In situ measurements of Thulagi Lake’s surface and water column indicate that daytime sunlight absorption heats mainly just the top metre of water, but this heat is efficiently mixed downwards through the top tens of metres due to forced convection by wind-blown icebergs; this heat then is retained by the lake and is available to melt the icebergs. Using satellite data, we assess seasonal glacier velocities, lake thermal regime and glacier surface elevation change for Thulagi, Lower Barun and Lhotse Shar glaciers and their associated lakes. The data reveal widely varying trends, likely signifying divergent future evolution. Glacier velocities derived from 1960/70s declassified Corona satellite imagery revealed evidence of glacier deceleration for Thulagi and Lhotse Shar glaciers, but acceleration at Lower Barun Glacier following lake development. We used published modelled ice thickness data to show that upon reaching their maximum extents, Imja, Lower Barun and Thulagi lakes will contain, respectively, about 90 × 106, 62 × 106 and 5 × 106 m3 of additional water compared to their 2018 volumes. Understanding lake–glacier interactions is essential to predict future glacier mass loss, lake formation and associated hazards.

Measurements of glacier ice cliff evolution are sparse, but where they do exist, they indicate that such areas of exposed ice contribute a disproportionate amount of melt to the glacier ablation budget. We used Structure from Motion photogrammetry with Multi-View Stereo to derive 3-D point clouds for nine ice cliffs on Khumbu Glacier, Nepal (in November 2015, May 2016 and October 2016). By differencing these clouds, we could quantify the magnitude, seasonality and spatial variability of ice cliff retreat. Mean retreat rates of 0.30–1.49 cm d−1 were observed during the winter interval (November 2015–May 2016) and 0.74–5.18 cm d−1 were observed during the summer (May 2016–October 2016). Four ice cliffs, which all featured supraglacial ponds, persisted over the full study period. In contrast, ice cliffs without a pond or with a steep back-slope degraded over the same period. The rate of thermo-erosional undercutting was over double that of subaerial retreat. Overall, 3-D topographic differencing allowed an improved process-based understanding of cliff evolution and cliff-pond coupling, which will become increasingly important for monitoring and modelling the evolution of thinning debris-covered glaciers.

Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward toward improved characterization of evolving hazards and risk across HMA.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR), an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 coordinate system). The products are continuously updated at a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial damming and sedimentation is disputed in the literature due to the lack of a historical time series. In this paper, we present an analysis of the lake’s evolution from 1926 to 2018 and model the 50-year trajectory of shrinkage. The area of Phewa Lake expanded from 2.44 ± 1.02 km2 in 1926 to a maximum of 4.61 ± 0.07 km2 in 1961. However, the lake area change was poorly constrained prior to a 1957⁻1958 map. The contemporary lake area was 4.02 ± 0.07 km2 in April 2018, and expands seasonally by ~0.18 km2 due to the summer monsoon. We found no evidence to support a lake area of 10 km2 in 1956⁻1957, despite frequent reporting of this value in the literature. Based on the rate of areal decline and sediment influx, we estimate the lake will lose 80% of its storage capacity in the next 110⁻347 years, which will affect recreational use, agricultural irrigation, fishing, and a one-megawatt hydroelectric power facility. Mitigation of lake shrinkage will require addressing landslide activity and sediment transport in the watershed, as well as urban expansion along the shores.

Topography derived using human-portable unmanned aerial vehicles (UAVs) and structure from motion photogrammetry offers an order of magnitude improvement in spatial resolution and uncertainty over small survey extents, compared to global digital elevation model (DEM) products, which are often the only available choice of DEMs in the high-mountain Himalaya. Access to fine-resolution topography in the high mountain Himalaya is essential to assess where flood and landslide events present a risk to populations and infrastructure. In this study, we compare the topography of UAV-derived DEMs, three open-access global DEM products, and the 8 m High Mountain Asia (HMA) DEMs (released in December 2017) and assess their suitability for landslide- and flood-related hazard assessments. We observed close similarity between UAV and HMA DEMs when comparing terrain elevation, river channel delineation, landside volume, and landslide-dammed lake area and volume. We demonstrate the use of fine-resolution topography in a flood-modelling scenario relating to landslide-dammed lakes that formed on the Marsyangdi River following the 2015 Gorkha earthquake. We outline a workflow for using UAVs in hazard assessments and disaster situations to generate fine-resolution topography and facilitate real-time decision-making capabilities, such as assessing landslide-dammed lakes, mass movement volumes, and flood risk.

Glacier recession driven by climate change produces glacial lakes, some of which are hazardous. Our study assesses the evolution of three of the most hazardous moraine-dammed proglacial lakes in the Nepal Himalaya—Imja, Lower Barun, and Thulagi. Imja Lake (up to 150 m deep; 78.4 × 106 m3 volume; surveyed in October 2014) and Lower Barun Lake (205 m maximum observed depth; 112.3 × 106 m3 volume; surveyed in October 2015) are much deeper than previously measured, and their readily drainable volumes are slowly growing. Their surface areas have been increasing at an accelerating pace from a few small supraglacial lakes in the 1950s/1960s to 1.33 km2 and 1.79 km2 in 2017, respectively. In contrast, the surface area (0.89 km2) and volume of Thulagi lake (76 m maximum observed depth; 36.1 × 106 m3; surveyed in October 2017) has remained almost stable for about two decades. Analyses of changes in the moraine dams of the three lakes using digital elevation models (DEMs) quantifies the degradation of the dams due to the melting of their ice cores and hence their natural lowering rates as well as the potential for glacial lake outburst floods (GLOFs). We examined the likely future evolution of lake growth and hazard processes associated with lake instability, which suggests faster growth and increased hazard potential at Lower Barun lake.

Glacial lakes in the Nepal Himalaya can threaten downstream communities and have large socio-economic consequences if an outburst flood occurs. This study identified 131 glacial lakes in Nepal in 2015 that are greater than 0.1 km2 and performed a first-pass hazard and risk assessment for each lake. The hazard assessment included mass entering the lake, the moraine stability, and how lake expansion will alter the lake’s hazard in the next 15–30 years. A geometric flood model was used to quantify potential hydropower systems, buildings, agricultural land, and bridges that could be affected by a glacial lake outburst flood. The hazard and downstream impacts were combined to classify the risk associated with each lake. 11 lakes were classified as very high risk and 31 as high risk. The potential flood volume was also estimated and used to prioritize the glacial lakes that are the highest risk, which included Phoksundo Tal, Tsho Rolpa, Chamlang North Tsho, Chamlang South Tsho, and Lumding Tsho. These results are intended to assist stakeholders and decision makers in making well-informed decisions with respect to the glacial lakes that should be the focus of future field studies, modeling efforts, and risk-mitigation actions.