Seismic observations, numerical modeling, and geomorphic analysis of a glacier lake outburst flood in the Himalayas

A comprehensive analysis of Holocene extraordinary flood events in the Langxian gorge of the Yarlung Tsangpo River valley

Increasing extreme temperature, precipitation and rapid meltwater events have added stress to the Himalaya's hydrological sensitivity and major flood risks, however, current extreme hydrological dataset and their genesis are insufficient to assess future flood discharge extremes in High Asian' rivers. Here, Holocene extreme floods in the Yarlung Tsangpo River valley were reconstructed by using physic-chemical analysis, optically stimulated luminescence dating and palaeohydraulic techniques. Palaeoflood slackwater deposits (SWDs) were identified by means of palaeohydrological criteria and comparison with SWDs from large flood that occurred in 2018. Palaeoflood SWDs consist of well-sorted silt and sand with a consistent geochemical composition, implying a similar sedimentary source. Such results suggest that these SWDs were transported in suspension over long distances during flood events. The chronological analysis indicates that there are three palaeoflood events, dated to 5.7, 3.9 and 2.9-1.2 ka, during the mid-late Holocene. Palaeoflood peak discharges in the bedrock reach and meandering channel were estimated to be 27,600-35,000 m³/s using one-dimensional and two-dimensional hydrodynamic modelling. The simulation results clearly show the potential palaeoflood depositional range in the lower-velocity and eddy backwater environments between Jiacha and Langxian gorge. The palaeoflood magnitudes redefine the regional largest flood discharge, and fit well with global maximum flood curves. And mid-late Holocene extreme flood magnitudes were generally 2.5-3.5 times larger than the current maximum gauged flood, but lower than the Jiedexiu glacial lake outburst floods. Comprehensive analysis highlights the three extraordinary floods were possibly induced by monsoon rains and glacial meltwater. Site-specific palaeoflood information advances our knowledge of rare and extraordinary floods in the highest and largest river in the southern Tibetan Plateau.

Glacial lake outburst floods threaten millions globally

Glacial lake outburst floods (GLOFs) represent a major hazard and can result in significant loss of life. Globally, since 1990, the number and size of glacial lakes has grown rapidly along with downstream population, while socio-economic vulnerability has decreased. Nevertheless, contemporary exposure and vulnerability to GLOFs at the global scale has never been quantified. Here we show that 15 million people globally are exposed to impacts from potential GLOFs. Populations in High Mountains Asia (HMA) are the most exposed and on average live closest to glacial lakes with ~1 million people living within 10 km of a glacial lake. More than half of the globally exposed population are found in just four countries: India, Pakistan, Peru, and China. While HMA has the highest potential for GLOF impacts, we highlight the Andes as a region of concern, with similar potential for GLOF impacts to HMA but comparatively few published research studies.

Geomorphic response of outburst floods: Insight from numerical simulations and observations--The 2018 Baige outburst flood in the upper Yangtze River

Outburst floods related to glacial or landslide damming are a major agent of geomorphic change in mountain rivers. Although the evidence between outburst flooding and riverine landscapes has been gradually recognized, the lack of hydraulics to the extent that there has still not been quantified on the relationship of how the amount and spatial distribution of these changes relate quantitatively to the hydraulic conditions and durations of these catastrophic events. This study combined remote and field observations of the 2018 Baige outburst flood with two-dimensional numerical simulation using the diffusive wave equation. By feeding the measured dam-breach hydrograph and comparing three different Manning coefficients in numerical experiments, the simulation results show that when n = 0.055, the time of peak flow was only 0.5 h different from that indicated by measured data in Yebatan, 54 km downstream of the Baige landslide dam. Under high shear stress over several hours at sustained ~20 m water depth, lateral erosion caused by these outburst floods contributed to the adjacent landslide, which was activated in association with intermittent water velocity waves of approximately 17 m/s. Sustained high stream power (>50 kW m²) from the outburst flood eroded slope toes and accelerated slippage of six slopes. Combining simulation and observations, we also developed a physical model related to hillslope instability caused by high hydrodynamic erosion of riverbanks generated by flow waves lasting several hours, which explained the hydrodynamic response of the outburst flood to the canyon geomorphology. Furthermore, we suggest that the pattern of channel widening erosion and deposition is governed by the variation in shear stress and Froude number as the high-energy flood flows from a wide channel into a narrow river valley. Our findings highlight that the hydraulics of high-magnitude outburst floods and sediment transport play crucial roles in reshaping canyon geomorphology.

Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km2 and 1710 km2 of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.

An integrative method for identifying potentially dangerous glacial lakes in the Himalayas

Glacial lakes in the Himalayas are widely distributed. Since 1900, more than 100 glacial lake outburst floods (GLOFs) have originated in the region, causing approximately 7000 deaths and considerable economic losses. Identifying potentially dangerous glacial lakes (PDGLs) is considered the first step in assessing GLOF risks. In this study, a more thorough inventory of PDGLs was presented that included numerous small-sized glacial lakes (<0.1 km²) that were generally neglected in the Himalayas for decades. Moreover, the PDGL evaluation system was improved in response to several deficiencies, such as the selection of assessment factors, which are sometimes arbitrary without a solid scientific basis. We designed an optimality experiment to select the best combination of assessment factors from 57 factors to identify PDGLs. Based on the experiments on both drained and non-drained glacial lakes in the Sunkoshi Basin, eastern Himalayas, five assessment factors were determined to be the best combination: the mean slope of the parent glacier, the potential for mass movement into the lake, the mean slope of moraine dams, the watershed area, and the lake perimeter, corresponding to the GLOF triggers for ice avalanches, rockfalls and landslides, dam instability, heavy precipitation or other liquid inflows, and lake characteristics, respectively. We then applied the best combination of assessment factors to the 1650 glacial lakes with an area greater than 0.02 km² in the Himalayas. We identified 207 glacial lakes as very high-hazard and 345 as high-hazard. It is noteworthy that in various GLOF susceptibility evaluation scenarios with different assessment factors, weighting schemes, and classification approaches, similar results for glacial lakes with high outburst potential have been obtained. The results provided here can be used as benchmark data to assess the GLOF risks for local communities.

Assessing the potential impact of glacial lake outburst floods on individual objects using a high-performance hydrodynamic model and open-source data

Glacial lake outburst floods (GLOFs) are one of the major natural hazards in certain populated mountainous areas, e.g., the Himalayan region, which may lead to catastrophic consequences including substantial loss of lives. Evaluating the potential socio-economic impact of GLOFs is essential for risk mitigation and enhancing community resilience. Yet in most of the cases, this is confronted with the challenges of limited availability of data and inaccessibility to most of the glacial lakes in the high-altitude areas. This study aims to exploit open data from different sources and high-performance hydrodynamic modelling to develop a new framework for GLOF exposure and impact assessment. In the new framework, different GLOF scenarios are created using a simple dam breach model. A high-performance hydrodynamic model is then adopted to simulate the resulting flood hydrodynamics. Necessary socio-economic information is collected and processed from multiple sources including OpenStreetMap, Google Earth, and global data products to support exposure analysis. Established depth-damage curves are used to assess the GLOF damage extents to different exposed objects and an existing fatality estimating procedure is adopted to assess the potential loss of lives. The evaluation framework is applied to the Tsho Rolpa glacial lake in Nepal. From the results, the worst GLOF scenario as considered can potentially inundate 1647 buildings, impact 5038 people and hit 123 key facilities including schools, hospitals, airports, hydropower plants, etc. It may substantially damage 900 buildings, 10.63 km² of agricultural land and 50.9 km roads and may potentially lead to 45 deaths even if warning is available.

Detection and potential early warning of catastrophic flow events with regional seismic networks

[Figure: see text].

Reason Analysis of the Jiwenco Glacial Lake Outburst Flood (GLOF) and Potential Hazard on the Qinghai-Tibetan Plateau

Glacial lake outburst flood (GLOF) is one of the major natural disasters in the Qinghai-Tibetan Plateau (QTP). On 25 June 2020, the outburst of the Jiwenco Glacial Lake (JGL) in the upper reaches of Nidu river in Jiari County of the QTP reached the downstream Niwu Township on 26 June, causing damage to many bridges, roads, houses, and other infrastructure, and disrupting telecommunications for several days. Based on radar and optical image data, the evolution of the JGL before and after the outburst was analyzed. The results showed that the area and storage capacity of the JGL were 0.58 square kilometers and 0.071 cubic kilometers, respectively, before the outburst (29 May), and only 0.26 square kilometers and 0.017 cubic kilometers remained after the outburst (27 July). The outburst reservoir capacity was as high as 5.4 million cubic meters. The main cause of the JGL outburst was the heavy precipitation process before outburst and the ice/snow/landslides entering the lake was the direct inducement. The outburst flood/debris flow disaster also led to many sections of the river and buildings in Niwu Township at high risk. Therefore, it is urgent to pay more attention to glacial lake outburst floods and other low-probability disasters, and early real-time engineering measures should be taken to minimize their potential impacts.

Simulation and Assessment of Future Glacial Lake Outburst Floods in the Poiqu River Basin, Central Himalayas

A glacial lake outburst flood (GLOF) is a typical glacier-related hazard in high mountain regions. In recent decades, glacial lakes in the Himalayas have expanded rapidly due to climate warming and glacial retreat. Some of these lakes are unstable, and may suddenly burst under different triggering factors, thus draining large amounts of water and impacting downstream social and economic development. Glacial lakes in the Poiqu River basin, Central Himalayas, have attracted great attention since GLOFs originating there could have a transboundary impact on both China and Nepal, as occurred during the Cirenmaco GLOF in 1981 and the Gongbatongshaco GLOF in 2016. Based on previous studies of this basin, we selected seven very high-risk moraine-dammed lakes (Gangxico, Galongco, Jialongco, Cirenmaco, Taraco, Beihu, and Cawuqudenco) to simulate GLOF propagation at different drainage percentage scenarios (i.e., 25%, 50%, 75%, and 100%), and to conduct hazard assessment. The results show that, when any glacial lake is drained completely or partly, most of the floods will enter Nepal after raging in China, and will continue to cause damage. In summary, 57.5 km of roads, 754 buildings, 3.3 km2 of farmland, and 25 bridges are at risk of damage due to GLOFs. The potentially inundated area within the Chinese part of the Poiqu River basin exceeds 45 km2. Due to the destructive impacts of GLOFs on downstream areas, appropriate and effective measures should be implemented to adapt to GLOF risk. We finally present a paradigm for conducting hazard assessment and risk management. It uses only freely available data and thus is easy to apply.