Document: s41748-021-00230-9
Section: 5 Concluding Remarks
Type: text

is an urgent need to monitor and assess the expanding glacial lakes and potentially hazardous lakes in the Himalayan region with immediate impact on the mountain communities of countries like India, China, Pakistan, Bhutan and Nepal. Although various field investigations have been conducted in the area, they are usually limited by lack of in situ glaciological and meteorological observation sites and unfavorable environmental conditions. Integration of Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) has been observed to be the best tool for mapping and monitoring dynamics of glacial lakes and the associated outburst scenarios in the Himalayan region. However, the updated and detailed glacial lake inventories for the region using high-resolution satellite data will be a resource-based for future studies to monitor the behavior of glacial lakes in the region to save many lives and reduce damages to infrastructures, such as houses, bridges, hydro-power projects, roads, etc. With the advancement in geospatial technology for polar and oceanic research, new technologies will open ways for effective management for GLOFs in the region; Geoinformatics is that specialized area where we need invest more to further the scope for cryospheric and climate research. The need has been felt to link the scientific knowledge on GLOF hazards into policy and planning at the local level as well as involve indigenous mountain communities in the GLOF risk prevention and mitigation programs.


Document: s41748-021-00230-9
Section: Declarations
Type: text

**Conflict of Interest** The authors have no conflicts of interest to declare that are relevant to the content of this article.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Type: text

by Sarah Fecht and Ben Orlove September 16, 2020

Vibrations in the ground may help to improve advanced warnings about sudden floods that result from glacial melting, according to a study published today in *Science Advances*.

On October 7, 1994, a natural dam that had been holding back a glacial lake burst, sending floodwaters crashing downstream into the Bhutanese village of Punakha. The sudden flood killed 21 people, destroyed 816 acres of crops and 6 tons of stored food, and washed away homes and other infrastructure. The new study, led by researchers at Columbia University's Lamont-Doherty Earth Observatory, discovered that local seismic devices unknowingly recorded this glacial lake outburst flood five hours before it reached the village.

data/('Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods  State of the Planet', '.pdf')_extraction.md


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Type: figure
Figure

Image /page/1/Picture/2 description: A figure containing two satellite maps, labeled A and B, illustrating a river system in Asia. The main map, A, shows a wide view of the Brahmaputra River, highlighted in blue, flowing from the Pho Chhu river in the Himalayas down to the Bay of Bengal. Several yellow dots in the mountains are labeled as "Seismic stations". A scale bar indicates 200 km. The inset map, B, is a zoomed-in view of the Pho Chhu river area, showing the locations of Punakha and Wangdue along the river. A scale bar on this map indicates 25 km.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Type: text

The larger map (A) shows the Pho Chhu river as it flows from the Himalayas into the Bay of Bengal. Seismometer locations are marked with yellow dots. The inset (B) zooms in on the area inside the red box in A, indicating the area where the glacial lake outburst flood began and the location of the village of Punakha 90 kilometers downstream. Image: Maurer et al./Science Advances 2020

Glacial lake outburst floods are becoming more frequent and more destructive in mountainous areas. As glaciers melt, the water pools into lakes trapped behind dams made of rocky glacial debris and ice jams. When the dam shifts or too much pressure builds behind it, the lake water rushes out in a catastrophic burst, posing a danger to downstream communities. As the planet warms, glacial lakes are becoming larger and more common, thus increasing the potential for glacial lake outburst floods (GLOFs).

In the study, led by Lamont-Doherty graduate student Josh Maurer, researchers discovered that a seismometer array located about 100 kilometers from the glacial lake had recorded a clear high-frequency signal at approximately 1:45am, around the time that the dam would have burst. They hypothesize that as the dam ruptured, the powerful and sudden outflow of water and/or sediments struck the riverbed, causing the vibrations that were picked up by the seismometers. The team was able to use the seismic data to reconstruct the flood as it made its way 90 kilometers downstream, reaching the village of Punakha at around 7am.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Type: figure
Figure

Image /page/2/Figure/2 description: A two-panel scientific figure, labeled 'A', displaying seismic data over a 12-hour period on 1994-10-07. The top panel is a seismogram showing velocity (in m s⁻¹) on the y-axis versus the hour of the day on the x-axis. The velocity scale ranges from -1 to 1, multiplied by 10⁻⁸. The bottom panel is a spectrogram showing frequency (in Hz) from 0 to 4 on the y-axis versus the same time axis. A color bar on the right indicates the Power Spectral Density (PSD), with yellow representing higher power (-180) and blue representing lower power (-200). The seismogram shows several distinct seismic events annotated with arrows: 'Kuril Islands – mb 5.0' and 'Chile – mb 4.8' before hour 2; a 'Local earthquake?' around hour 4; 'Kuril Islands – Mw 5.6' and a 'Nuclear Explosion – mb 6.0 Southern Xinjiang, China' between hours 9 and 10; and another 'Local earthquake?' around hour 11. A prolonged period of high-amplitude seismic noise is visible from approximately hour 2 to hour 8.5. The spectrogram below shows this prolonged event as a bright yellow and green band labeled 'Primary GLOF signal'. Two time intervals, '1:45 - 3:15 AM' and '5:45 - 7:15 AM', are highlighted in red text and marked with dashed red vertical lines within this GLOF signal.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Type: text

The tremors triggered by the GLOF and detected by far away seismometers: the initial outburst at 1:45 a.m., flood getting stronger at 2:15 a.m., and slowly tapering off after 7:15 a.m. Image: Maurer et al./Science Advances 2020

Currently, instruments monitor local water level in some glacial lakes and alert local communities if the lake level suddenly drops, indicating a GLOF. However, such systems are known to be somewhat unreliable and have issued false alarms in the past. The study authors suggest that with some refinement, real-time seismic monitoring could be combined with water level monitoring systems to minimize false alarms and maximize warning times. In addition, a few strategically placed seismic sensors could potentially monitor for GLOFs over a large area, whereas water level monitors must be installed lake by lake.

The authors note that more research is needed before seismic GLOF monitors would be ready for deployment. The team hopes to find and explore other instances where seismometers have captured GLOF events, to better understand how to read and analyze the signals in real time. They also caution that the Punakha flood was very large, so the signal stood out clearly in the data; in the future, they hope to better understand whether the technique can reliably detect smaller glacial lake outburst floods, which can still cause severe damage.

By reconstructing the Punakha flood, the researchers were also able to test various models of how flood waters would be expected to flow through the area, showing that seismic data could help to improve flood modeling. In addition, the paper used satellite imagery before and after the GLOF to assess its impacts on the area.

Experts who were not involved in the study, including geographer Simon Allen and glaciologist Holger Frey (both from the University of Zurich), said the study represents a

promising first step toward a seismology-based early warning system. Allen said that more research is needed, since the technique has only been tested on one lake so far, and cautioned that maintaining a real-time seismic monitoring network in the Himalayas or elsewhere would present financial and technical challenges.

"The algorithms need to be extremely reliable," said Frey. "All events must be detected, but at the same time false alarms need to be avoided by all means." He also emphasized that including people from the affected communities in the design and implementation of such systems is critical in determining whether or not they are ultimately successful.

"This study is a great demonstration of the potential for long-range seismic detection of large outburst floods," said Kristen Cook, a geologist at the GFZ German Research Centre for Geosciences who was not involved in the study. "This seismic detection could have important implications looking both back in time to validate flood models and better understand the processes of outburst floods, and potentially forwards in time if a seismic early warning system can be developed. Outburst floods are a big concern in the Himalaya, especially as development along river corridors increases and lakes are growing, so both more robust early warning and better modeling would have significant societal benefits."

Other authors of the study include: Joerg Schaefer, Joshua Russell, and Nicolas Young from Columbia University; Summer Burton Rupper from the University of Utah; Norbu Wangdi from the Center for Water, Climate, and Environmental Policy in Bhutan; and Aaron Putnam from the University of Maine.

Learn more about the study in a brief Q&A with study co-author <u>Joerg Schaefer</u>, below.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How did the idea for this study first develop?
Type: text

This all started when we were working on the well-preserved and nearly complete moraine sequences in front of the GLOF lakes. They were in the pathway of the 1994 GLOF, and beryllium dating shows that they are old, like 4,000 years old. I was puzzled as to how such a devastating GLOF could pass these old glacial landforms without destroying them, washing them out. I asked graduate student Josh Maurer to check the spy satellite imagery and the subsequent remote sensing images for pictures of the lakes and moraines just before and just after the flood. He did that, and we documented the outburst and early phase of the 1994 GLOF. We learned that the flood was just not super dramatic right at the start, and only took out a small part of the terminal moraine section. This is a striking and scary reminder that GLOFs starting at these high altitudes pick up their devastating energy by gravity on their way downhill.

Josh realized the potential, and we started to wonder if the GLOF signal should not be visible in the seismometer record. Josh got in touch with Josh Russell, a PhD student in seismology at Lamont, and together they went to work and applied a technique called 'cross-correlation based seismic analyses,' with which they could track the evolution of the GLOF with seismometers as far as 100 km away from the actual flood. They found the flood signal in stunning clarity and synthesized the seismic data with eyewitness reports and a downstream gauge station within a numerical flood model.

We also used the remote imagery before and after the flood to estimate the sediment deposition in the valley downstream to assess the damage, and traced the speed of vegetation recovery.

This is probably the most innovative earth science paper I have had the pleasure to be part of. My main role in it has been to support the work of these brilliant grad students.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Did you encounter any obstacles in the development of this project? If so, what were they? How did you overcome them?
Type: text

Josh and Josh encountered a variety of problems during their cross-correlation analyses, but they worked brilliantly and effectively as a team. Once all the results were on the table, it took us a while to organize the pieces from many different disciplines to form a coherent earth science manuscript, and to realize and formulate the potential of this technique for a new generation of GLOF early warning systems.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > How do you think other glacial lakes could be prioritized for future research along these lines?
Type: text

One of the biggest strengths of this approach is the regional applicability. We can use this toolkit, for example, to ask the seismometer record whether or not there are similar 'GLOF-type signals' in the system. And, using Josh's satellite image processing techniques, we can search the region for the source of similar floods that might have occurred in the area over the last 40 years.

Being able to track the formation, growth and in particular increase in lake level over time is the key to evaluate and identify the most hazardous lakes in the region. Topography and sediment availability are probably similar across different GLOF-prone valleys in the region, but we should absolutely produce a map highlighting human settlements and areas that are key to their livelihoods in relation to the GLOF hazard from higher up in the Himalayas.

Tags: climate change, floods, glacial lake outburst floods, GlacierHub, Lamont-Doherty
Earth Observatory, seismology


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: figure
Figure

Image /page/5/Picture/4 description: A wide shot of a flooded residential area at dusk or dawn, with several cars submerged in murky water. In the foreground, the roof of a silver car is visible above the water. The water reflects the dark silhouettes of tall evergreen trees. In the background, more cars are partially submerged, and houses or cabins can be seen amongst the trees, with a mountain visible in the far distance against a pale sky.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: text

Glacier Flooding in Alaskan Capital Sets New Record


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: figure
Figure

Image /page/5/Picture/6 description: A scenic, eye-level, full shot of a majestic mountain range on a sunny day. The foreground consists of a rocky, barren terrain with patches of snow. In the background, towering snow-capped mountains are partially obscured by thick, white clouds. The sky is a deep blue with some wispy clouds, and the bright sun in the upper right corner creates a lens flare effect across the image.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: text

Glacial Melting in High Mountain Asia Has the Potential to Overwhelm Hydropower Systems


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: figure
Figure

Image /page/5/Picture/8 description: A wide shot of a rugged mountain landscape featuring a large glacier at its peak. The glacier has a jagged, icy front and sits atop a steep, rocky slope made of gray scree. To the right, a waterfall cascades down a dark rock face, likely from glacial melt. Another patch of snow is visible on the upper right side of the mountain. The sky above is a vibrant blue with scattered, wispy white clouds.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: text

International
Framework Aims to
Protect and Manage
Lands Recently
Exposed by Glacier
Retreat


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: figure
Figure

Image /page/5/Picture/10 description: A promotional banner for the Columbia Climate School's UN Climate Change Conference COP30. The banner has a dark green background in the center with text. On the left is an aerial photo of a winding river through a lush green forest. On the right is a nighttime photo of an illuminated cityscape featuring a large institutional building. The text in the center reads: 'COLUMBIA CLIMATE SCHOOL' in light green, followed by 'UN CLIMATE CHANGE CONFERENCE COP30' in white and bright yellow. Below this, it says 'BELÉM, BRAZIL | NOV 10-21, 2025' in light green.


Document: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods
Section: Seismic Monitoring May Improve Early Warnings for Glacial Lake Outburst Floods > Related Posts
Type: text

During COP30—the 2025 UN Climate Change Conference taking place November 10–21 in Belém, Brazil—experts from Columbia Climate School and Columbia University will be contributing to key events, sharing insights, and helping shape the dialogue toward ambitious, science-based solutions. <u>Learn More</u>


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Type: text

Glacial lake outburst floods (GLOFs) are a substantial hazard for downstream communities in vulnerable regions, yet unpredictable triggers and remote source locations make GLOF dynamics difficult to measure and quantify. Here, we revisit a destructive GLOF that occurred in Bhutan in 1994 and apply cross-correlation-based seismic analyses to track the evolution of the GLOF remotely (~100 kilometers from the source region). We use the seismic observations along with eyewitness reports and a downstream gauge station to constrain a numerical flood model and then assess geomorphic change and current state of the unstable lakes via satellite imagery. Coherent seismic energy is evident from 1 to 5 hertz beginning approximately 5 hours before the flood impacted Punakha village, which originated at the source lake and advanced down the valley during the GLOF duration. Our analysis highlights potential benefits of using real-time seismic monitoring to improve early warning systems.

Copyright © 2020
The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

data/('Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas', '.pdf')_extraction.md


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: INTRODUCTION
Type: text

Glacial lakes in the Himalayas are rapidly growing due to climate change and acceleration of glacier melt in recent decades (1, 2). Many of these lakes are dammed by unstable, often ice-cored moraines (3), surrounded by steep topography prone to landslides, and frequently subjected to seismic events and intense monsoonal precipitation. Risks associated with glacial lake outburst floods (GLOFs) are substantial for downstream inhabitants in these regions, and thousands of fatalities have already occurred as a direct result of sudden catastrophic releases of water (4). Societies and economies of GLOF-prone regions are severely impacted, including destruction of infrastructure, disruption to communities, and loss of life (2-5). The nation of Bhutan, in particular, has been classified as having some of the greatest national-level economic consequences of glacier flood impacts, as hydropower dominates the nation's gross domestic product and socioeconomic development potential (4, 6). The electric power demands of Himalayan nations are on a steep rise with rapid economic growth, and hydropower development continues to expand into higher sites closer to glaciers (2).

Numerous GLOFs have been documented in the Himalayas by firsthand observation and satellite imagery after their occurrence (7–9). The most comprehensive analysis to date of the Landsat imagery archive finds an average GLOF frequency in High-Mountain Asia (HMA) of 1.3 GLOFs per year since the 1980s (9). Despite this steady rate, quantitative in situ observations of GLOFs are scarce (3) and limited to rare situations where preinstalled instruments are coincidentally located in the same valley as the GLOF (10). Efficient strategies to mitigate outbursts, design reliable early warning systems, and minimize destructive impacts would benefit from continuous observation of flood evolution through time. While satellite observations can help identify regions of high GLOF risk and quan-

tify geomorphic impacts after occurrence, they cannot capture GLOF events in real time. Numerical flood models can be used to simulate dam outbursts and flood waves (7, 11–13) yet require many physical parameters as input, which are often unknown or poorly constrained (12). For example, the shape of a breach hydrograph can strongly influence flood characteristics downstream (14). These essential upstream boundary conditions in flood routing simulations are usually unknown and must be estimated using physical or parametric dam breach models with large uncertainties (15). Here, we demonstrate how flood models can be tested using continuous time-stamped seismic observations of GLOF mass movement, thus helping to determine whether estimates of downstream flood arrival times are realistic.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: INTRODUCTION
Type: text

be used to simulate dam outbursts and flood waves ( 7 , 11 – 13 ) yet require many physical parameters as input , which are often unknown or poorly constrained ( 12 ) . For example , the shape of a breach hydrograph can strongly influence flood characteristics downstream ( 14 ) . These essential upstream boundary conditions in flood routing simulations are usually unknown and must be estimated using physical or parametric dam breach models with large uncertainties ( 15 ) . Here , we demonstrate how flood models can be tested using continuous time - stamped seismic observations of GLOF mass movement , thus helping to determine whether estimates of downstream flood arrival times are realistic .

The Pho Chhu (river) flows from the pristine mountain region of Lunana [4500 meters above sea level (masl)], southward through the Bhutan foothills where it eventually joins the Brahmaputra River in northern India (Fig. 1). The valley has a typical step-like elevation profile, alternating between sections of very steep and relatively flat terrain separated by river knickpoints. Fed by seasonal snow melt, glacier melt, and summer monsoon rains, the river plays a vital role in the welfare and livelihood of the people of Bhutan. In the Pho Chhu valley, residents of small villages live and farm along the banks of the river, with rice paddies being a primary seasonal crop. In the river source region, several large proglacial lakes are continually expanding in size as a result of accelerated glacier melting in recent decades (1, 2). Approximately 90 km downstream from the proglacial lakes is the village of Punakha (1200 masl), where a large 17th century Buddhist temple (the Punakha Dzong) is situated along the riverbank at the confluence of the Mo Chhu and Pho Chhu. This temple and surrounding region are historically and culturally important, and there is a great deal of concern about catastrophic outburst floods from the unstable lakes above.

Such an event occurred on the night of 7 October 1994, when the moraine dam of Lugge Tsho breached and debris-laden flood waters surged down the Pho Chhu valley. Twenty-one lives were lost; and the flood destroyed an estimated 12 houses, 5 water mills, 816 acres of crops, 965 acres of pasture land, 16 yaks, 6 tons of stored food grains, 4 bridges, 2 stupas, and damaged part of the Punakha Dzong (16).

<sup>1Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA. 2Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA. 3Department of Geography, University of Utah, Salt Lake City, UT 84112, USA. 4Center for Water, Climate, and Environmental Policy, Bumthang, Bhutan. 5School of Earth and Climate Sciences and Climate Change Institute, University of Maine, Orono, ME 04469, USA.

\*Corresponding author. Email: jmaurer@ldeo.columbia.edu


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: INTRODUCTION
Type: figure
Figure

Image /page/1/Figure/2 description: A figure composed of five satellite images labeled A, B, C, D, and E, showing a region of study at progressively finer scales. The caption identifies Lugge Tsho as the source of a 1994 GLOF event and Raphstreng Tsho and Thorthormi Tsho as high-risk for future events.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: INTRODUCTION
Type: text

Panel A is a wide-scale satellite map showing the Pho Chhu river flowing from the Himalayas, joining the Brahmaputra River, and emptying into the Bay of Bengal. Several seismic stations are marked with yellow dots. The scale bar is 200 km.

Panel B is a zoomed-in view of the Pho Chhu river's upper reaches, showing the towns of Punakha and Wangdue. The scale bar is 25 km.

Panel C is a detailed view of a glacial valley with longitude and latitude markers (90.18°E, 90.28°E, 28.08°N). It shows several glacial lakes, including Raphstreng, Thorthormi, and Lugge, as well as the settlement of Thanza. The scale bar is 2 km.

Panel D is a close-up showing the Tshojo plain and features labeled 'Mid-Holocene moraines'. The scale bar is 0.75 km.

Panel E is a close-up of the Lugge Tsho lake, showing the Thorthormi glacier and the location of a 'GLOF breach'. The scale bar is 0.5 km.

Fig. 1. Region of study. Lugge Tsho was the source of the 1994 GLOF event, while Raphstreng Tsho and Thorthormi Tsho are also considered high risk for future GLOFs. Locations of the five seismometers used in the study are denoted by the yellow circles in (A). (B) Location of the Punakha and Wangdue villages downstream of the GLOF source area. (C) Unstable glacial lakes in the Lunana region. (D) Mid-Holocene glacier moraines that were left largely intact by the GLOF. (E) Lugge Tsho and moraine breach zone.

While the exact trigger of the Lugge Tsho breach is unknown, various causes of moraine failure have been hypothesized such as the melting of its ice core (8), a gradual increase in hydrostatic pressure as the lake depth increased due to melting (5), or collapse of part of the right lateral hillside into the lake, causing a sudden increase in hydrostatic pressure (17). After the tragic incident, much effort was put forth by the government of Bhutan to assess the risk of future GLOFs in the region, establish an emergency warning system, artificially lower lake water levels, and study Lugge Tsho in more detail. Post-GLOF field investigations found a mean lake depth of approximately 50 m, a lake volume of 58.3 million m3, and a typical discharge at

the lake outlet varying from 2.5 to 5 m3 s-1 during September and October 2002. The total volume of water released during the GLOF was estimated by Yamada *et al.* (18) as  $17.2 \pm 5.3$  million m3 based on a differential Global Positioning System (GPS) survey of the lowering of the lake level by  $16.9 \pm 3.2$  m, while an integration of the Wangdue station hydrograph (fig. S1) during the GLOF yields approximately 25 million m3 (19). In addition, dam breach, flood propagation, and debris flow models (sediment-water mixtures) have also been used to simulate GLOF scenarios in this region, including sequential and simultaneous (as may happen during a large earthquake) breaches occurring below high-risk lakes in Bhutan. These

models suggest that downstream villages including Punakha and the major portion of Wangdue Phodrang are at risk for severe inundation if another large GLOF occurs (11-13, 19, 20).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: INTRODUCTION
Type: text

of the Wangdue station hydrograph ( fig . S1 ) during the GLOF yields approximately 25 million m  3  ( 19 ) . In addition , dam breach , flood propagation , and debris flow models ( sediment - water mixtures ) have also been used to simulate GLOF scenarios in this region , including sequential and simultaneous ( as may happen during a large earthquake ) breaches occurring below high - risk lakes in Bhutan . These models suggest that downstream villages including Punakha and the major portion of Wangdue Phodrang are at risk for severe inundation if another large GLOF occurs ( 11 - 13 , 19 , 20 ) .

Across the Himalayan region as a whole, the destructive potential of GLOFs is increasing. Yet the scarcity of observational measurements deters robust validation of numerical simulations, hinders quantification of GLOF dynamics, limits real-time warning, and increases uncertainties regarding societal impacts. Continuous observational coverage offered by seismic monitoring is one potential avenue for addressing this problem. Displacement of mass at Earth's surface generates elastic seismic waves, which carry information about the source and can be recorded by seismometers at high temporal resolution across large spatial scales (21). Proof-of-concept studies have already shown the potential of seismic monitoring for diverse types of surface activities including river bedload transport and debris flows (21-24) and further demonstrate the ability of seismic records to specifically provide insight into flood mechanics (10, 23). Here, we extend the application of seismic data to a Himalayan GLOF using data from the International Deep Profiling of Tibet and the Himalaya (INDEPTH) II experiment (25, 26). These data were collected by a passive broadband seismic array situated on the Tibetan Plateau, which was coincidentally recording when the GLOF occurred in Bhutan in 1994. Insofar as the authors are aware, INDEPTH II is the only seismic data available from 1994 in the region (within a 150-km radius). We perform a time-frequency analysis of the seismic signal produced by the GLOF and use cross-correlation functions (CCFs) between seismic stations to locate and track the source of coherent seismic energy through time. With the seismic data, firsthand accounts, and gauge station measurements, we constrain the progression of the flood from initial outburst to arrival in populated villages using a numerical flood model. To further quantify geomorphic impacts of the GLOF, we apply historical spy satellite images, Landsat, and modern high-resolution imagery to analyze lake area changes over time, the extent of flood-deposited sediments, the rate of vegetation regrowth post-GLOF, and pre- and post-flood morphology of the source area dated by 10Be.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Analysis of seismic data
Type: text

Seismic energy generated during the GLOF was recorded by five seismometers (with locations ranging from approximately 75- to 130-km distance from the breach point) as a clear high-frequency (1 to 4 Hz) signal lasting several hours (Fig. 2 and fig. S2). Seismic energy at these frequencies was most likely excited by energy from turbulent flow and bedload transport processes, as observed in previous studies of seismic signals generated during high-flow regimes (10, 21–23, 27). The GLOF signal strength was 5 to 15 dB above typical background noise levels (fig. S10) and occurred during the night and early morning when local anthropogenic noise was at a minimum. We also note several earthquakes (with epicenters in the Kuril Islands and Chile) and a nuclear explosion (Southern Xinjiang, China) that were recorded by the seismic array during these hours (Fig. 2A). Spectrograms show a similar pattern on vertical and horizontal components across all five stations (fig. S2). Some interstation variability in peak frequency content is observed during the two GLOF phases, likely due to lateral heterogeneity in attenuation structure and differences in distance from the source. The first detectable high-frequency signal arrived at approximately 1:45 a.m., beginning with relatively weak amplitude and limited primarily to frequencies between 1 and 3 Hz. Over the next several hours, the seismic energy varied somewhat through time, as the flood wave passed through sections of the valley with different slope and river channel characteristics. Around 25 min after this first arrival, an increase in spectral amplitude occurred across a wider range of frequencies (1 to 4 Hz) (fig. S2) and then subsequently tapered off over the next 1.5 hours. At approximately 3:50 a.m., the signal power began increasing again and reached a maximum at 6:00 a.m. with frequency content ranging from approximately 1 to 5 Hz. During this interval, the flood wave passed through the main branch of the Pho Chhu and impacted Punakha at approximately 7:00 a.m. based on eyewitness accounts.

We further examined the signal correlation across stations to constrain the origin of the seismic energy in space and time (21, 28, 29). Computing CCFs for every station pair across a series of frequencies ranging from 1 to 5 Hz, we find strong correlation of waveforms during two distinct intervals (Fig. 2B). The first spans from approximately 1:45 to 3:15 a.m., during which a strong peak in CCF amplitude is apparent (fig. S3). Migrating the CCFs during this interval and subsequently summing them together, a region of high coherence emerges, focused directly on the GLOF breach location at Lugge Tsho (Fig. 2), indicating that during this time, the outburst event was the dominant source of seismic energy at these frequencies. Approximately 4 hours later, a second prominent interval of high coherence spans from around 5:45 to 7:15 a.m. Migration of the CCFs during this later interval indicates that the GLOF-induced seismic energy originated from a lower (downstream) section of river, indicating that the flood wave had reached this point in the valley (approximately 70 km from the breach and 20 km above Punakha) by around 5:45 a.m.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Seismic signal generation
Type: text

Generation of seismic waves from fluvial processes is understood to occur by two main processes: (i) transport of sedimentary grains that stochastically impact the riverbed (30) and (ii) turbulent fluid flow that interacts with the riverbed (31). Previous observations of seismic energy associated with bedload transport and turbulent flow processes made at much smaller distances from the seismic source (typically only hundreds of meters to a few kilometers away) show peak frequencies ranging from 1 to 100 Hz depending on distance from the source and local seismic attenuation structure (10, 21-23, 27). Here, we demonstrate that coherent 1- to 5-Hz seismic energy is generated and can propagate to distances of ~100 km from the source region during large GLOF events.

Numerical models that predict seismic energy excitation due to turbulent flow and bedload transport processes suggest that >1-Hz seismic energy is difficult to produce at such large distances because of seismic attenuation of the high frequencies (30-32). However, the high river flow rates (~2500 m3 s-1) and thick water flow depths associated with the GLOF represent extreme conditions that have not been explored in detail using numerical models and may well violate their underlying physical assumptions. A GLOF scenario of this magnitude may contain physics not captured by bedload transport and turbulent flow processes alone, such as flexure of the riverbed due to the initial flood wave. Additional work is needed to fully understand the physical processes responsible for the observed seismic signal generation during the GLOF event and how these observations compare with predictions from recent numerical models. In this study, we attribute the primary seismic signal to the initial flood wave as it proceeded down the valley.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: text

Hydropower viability, disaster preparedness, and paleoseismic investigations have previously simulated flood events in this region using numerical models (11–13, 19, 20). Here, we build on results

from these earlier studies by calibrating a flood model using the new set of independent observations: (i) an estimated start time and location based on the beginning of detectable seismic energy and migrated CCFs, (ii) the second interval of correlated seismic energy


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: figure
Figure

Image /page/3/Figure/5 description: A multi-part figure titled "Observations on 7 October 1994" showing seismic data related to a Glacial Lake Outburst Flood (GLOF).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: text

Part A consists of two plots. The top plot is a seismic trace from station BB20 BHZ, filtered between 1 and 5 Hz. The x-axis represents the "Hour of day (1994-10-07)" from 0 to 12, and the y-axis is "Velocity (m s⁻¹)" from -1 to 1 (x10⁻⁸). The trace shows a significant increase in amplitude from approximately hour 2 to hour 8. Several seismic events are annotated, including earthquakes in the Kuril Islands and Chile, a nuclear explosion in China, and local earthquakes. The bottom plot is a spectrogram with the same time axis and a y-axis of "Frequency (Hz)" from 0 to 4. A color scale indicates power spectral density (PSD), with yellow being the highest. A strong signal, labeled "Primary GLOF signal," is visible between approximately hour 2 and hour 8, primarily in the 1-4 Hz frequency range. Two time intervals are highlighted: 1:45–3:15 a.m. and 5:45–7:15 a.m.

Part B is a line graph showing "Average coherence" versus "Hour of day (1994-10-07)". It compares the coherence on the day of the GLOF (red line) with a background level from the previous day (black line). The GLOF day shows a sharp peak in coherence to about 0.28 around hour 2, corresponding to the 1:45–3:15 a.m. interval, while the background level remains around 0.125.

Part C displays two maps showing the spatial distribution of average coherence. The axes are "North (km from breach)" and "East (km from breach)". The left map, for 1:45–3:15 a.m., shows the highest coherence (up to 0.7) originating from the "Breach" location (marked with a red X). The right map, for 5:45–7:15 a.m., shows the area of high coherence (up to 0.55) has moved southwest from the breach, indicating propagation. Several station locations (BB18, BB20, BB23, SP25, SP27) and the town of Punakha are marked on the maps.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: text

) " and " East ( km from breach ) " . The left map , for 1 : 45 – 3 : 15 a . m . , shows the highest coherence ( up to 0 . 7 ) originating from the " Breach " location ( marked with a red X ) . The right map , for 5 : 45 – 7 : 15 a . m . , shows the area of high coherence ( up to 0 . 55 ) has moved southwest from the breach , indicating propagation . Several station locations ( BB18 , BB20 , BB23 , SP25 , SP27 ) and the town of Punakha are marked on the maps .

**Fig. 2. Key seismic observations on 7 October 1994.** (**A**) Example of a seismic trace (station BB20 BHZ) band-pass filtered between 1 and 5 Hz and spectrogram from 1 to 5 Hz during the GLOF duration. Black arrows mark seismic events that correlate in time with events in the USGS catalog, and gray arrows mark potential local earthquakes that appear on all five stations (fig. S2) but are not in the catalog.  $m_b$ , body wave magnitude;  $M_w$ , moment magnitude. (**B**) Twelve-hour time series of signal coherence for the day containing the GLOF event (red; 1994-10-07) and the day before the GLOF event (gray; 1994-10-06). The top and bottom bounds of each shaded region represent the 95th and 75th percentile of coherence values across all lag times, respectively. The blue shaded regions mark 90 min of most significant GLOF signal relative to background levels. (**C**) Migrations of the CCFs during the two intervals using a velocity of 3.0 km s<sup>-1</sup> (likely indicating regional short-period Rayleigh waves; fig. S3) for several station pairs is summed together to form these final coherence maps (see Materials and Methods and fig. S3). These two images illustrate the downstream progression of seismic energy generated by the GLOF. The coherence map on the left corresponds to seismic energy generated approximately 4 hours later and ~70 km downstream. Times are Asia/Thimphu local time [universal time coordinated (UTC) +6].


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: text

* ) Migrations of the CCFs during the two intervals using a velocity of 3 . 0 km s < sup > - 1 < / sup > ( likely indicating regional short - period Rayleigh waves ; fig . S3 ) for several station pairs is summed together to form these final coherence maps ( see Materials and Methods and fig . S3 ) . These two images illustrate the downstream progression of seismic energy generated by the GLOF . The coherence map on the left corresponds to seismic energy generated approximately 4 hours later and ~ 70 km downstream . Times are Asia / Thimphu local time [ universal time coordinated ( UTC ) + 6 ] .

that occurred several hours later and ~70 km downstream of the initial breach, (iii) the approximate arrival time of the flood at Punakha from firsthand observations, and (iv) the flood hydrograph from Wangdue station (around 110 km downstream from the breach). Together, these provide key constraints on the location of the flood wave through the duration of the GLOF and allow us to parameterize a model with a higher degree of confidence than previously possible. The start time of the GLOF, in particular, is a key aspect that was previously unknown. Because of the sensitivity of flood models to input parameters (fig. S1), these independent observations are especially useful for validating and selecting model runs that are most realistic (see Materials and Methods). We use the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC-RAS) software to perform a series of two-dimensional (2D) unsteady flow simulations; select model runs that agree with the independent observations within a ±30-min threshold (see Materials and Methods and Fig. 3); and report ranges of simulated breach-to-arrival times for the main populated villages along the river valley for Thanza (0.4 to 0.6 hours), Tshojo (1.0 to 1.3 hours), Lhedi (1.4 to 1.8 hours), Samdingkha (3.9 to 4.6 hours), Punakha (4.4 to 5.2 hours), and Wangdue (5.7 to 6.5 hours) (Table 1). We note that around 6:30 a.m., the peak flows in the best-fit model runs precede (in time) the peak in seismic coherence, approximately 70 km downstream from the breach. While this may reflect the actual order


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Constraining a flood model
Type: text

to 0 . 6 hours ) , Tshojo ( 1 . 0 to 1 . 3 hours ) , Lhedi ( 1 . 4 to 1 . 8 hours ) , Samdingkha ( 3 . 9 to 4 . 6 hours ) , Punakha ( 4 . 4 to 5 . 2 hours ) , and Wangdue ( 5 . 7 to 6 . 5 hours ) ( Table 1 ) . We note that around 6 : 30 a . m . , the peak flows in the best - fit model runs precede ( in time ) the peak in seismic coherence , approximately 70 km downstream from the breach . While this may reflect the actual order

of events, the model may also be slightly overestimating the flood wave velocity within this part of the valley. In all model runs, the duration between arrival and peak flow gradually decreases as the flood travels down the section of valley above Punakha. This consistent aspect of the flood illustrates the manner in which topography can shape a flood wave, in this case, causing the wave to become steeper and more prominent while traveling down a steep valley gorge. Furthermore, a reduction in hillslope angle of the arriving flood wave correlates in time with the second peak in coherent seismic energy at approximately 5:45 to 7:15 (fig. S13), perhaps indicating that changes in topographic slope influence the degree to which flood wave energy is transferred into the solid earth as seismic waves. Results from model runs with different breach hydrographs all converge to a similar shape before reaching the larger villages downstream. In the village of Samdingkha, for example (~8 km above Punakha), the duration between first arrival and peak flow ranges from 10 to 30 min. Without an external early warning system, this sudden rise in water level permits only a short time for inhabitants to move to safe ground, particularly if the early stages of the rise go unnoticed for several minutes.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Geomorphic change caused by the GLOF
Type: text

Satellite imagery reveals prominent changes in the Lunana region both before and after the GLOF. The most consistent change is a

RESULTS
Geomorphic change caused by the GLOF


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Geomorphic change caused by the GLOF
Type: figure
Figure

Image /page/4/Figure/6 description: The image displays three related graphs, labeled A, B, and C, that model a flood event.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Geomorphic change caused by the GLOF
Type: text

Graph A, titled "Hourly location of flood arrival along valley profile (modeled)", shows an elevation profile. The y-axis represents Elevation in meters (m), from 0 to over 4000, and the x-axis represents Distance from breach in kilometers (km), from 0 to over 140. A black line depicts the decreasing elevation of the valley. A breach is marked at 1:45 at 0 km and an elevation of approximately 4200 m. Orange dots along the profile mark the hourly arrival of the flood: 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, 8:00, and 9:00. Locations such as Thanza, Tshoijo, Lhedi, Samdingkha, Punakha Dzong, and Wangdue are labeled along the profile.

Graph B is a line chart plotting Distance from breach (km) on the y-axis against the Hour of day (1994-10-07) on the x-axis. It shows a flood model with three curves: a red line for 'Arrival', a blue line for 'Peak flow', and a yellow line for '37% of peak'. The red and blue lines have shaded uncertainty bands. The model is compared with observational data, including 'Onset of high coherence (seismic)' at two points, 'Punakha (eyewitnesses)', and 'Wangdue (hydrograph)' with start and max points.

Graph C is a grayscale topographic map showing the path of the flood. A red 'X' marks the '1:45 breach'. A white line traces the flood's path through the valley, with hourly locations from 3:00 to 8:00 marked with orange dots. Labeled locations include Thanza, Tshoijo, Lhedi, Samdingkha, Punakha Dzong, and Wangdue. A scale bar indicates a distance of 20 km.

Fig. 3. Results from the HEC-RAS 2D unsteady flood model. (A) Elevation profile of the river valley, with hourly locations of flood arrival time from the best-fit model run. (B) Distance from the moraine breach versus time. The gray square symbols and brackets are independent observations from seismic, eyewitness, and gauge station sources. The color-shaded regions represent the range of model outputs that match observations within ±30 min, and the colored curves represent the single best-fit model run. The orange curve is the simulated arrival of the flood wave, the blue curve is the peak flow, and the yellow curve indicates when the flow has subsided and reached 1/e (~37%) of the peak. The horizontal separation of the orange and blue curves indicates the duration between flood arrival and peak flow for a given location. The gray (dashed) boxes indicate the intervals in Fig. 2 during which the peaks in coherent seismic energy were detected. (C) Map view of region also with modeled flood arrival times. Times are Asia/Thimphu local time (UTC+6).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Geomorphic change caused by the GLOF
Type: table
Table

| Location         | Latitude | Longitude | Elevation (m) | Distance from breach (km) | Flood arrival |          |       |                   |          |       | Peak flow |          |       |                   |          |       |
|------------------|----------|-----------|---------------|------------------------------------|---------------|----------|-------|-------------------|----------|-------|-----------|----------|-------|-------------------|----------|-------|
|                  |          |           |               |                                    | Time          |          |       | Hours from breach |          |       | Time      |          |       | Hours from breach |          |       |
|                  |          |           |               |                                    | Lower         | Best fit | Upper | Lower             | Best fit | Upper | Lower     | Best fit | Upper | Lower             | Best fit | Upper |
| Thanza           | 28.089   | 90.213    | 4150          | 7                                  | 01:43         | 02:08    | 02:19 | 0.4               | 0.4      | 0.6   | 02:22     | 03:20    | 03:35 | 0.9               | 1.8      | 2.1   |
| Tshojo           | 28.062   | 90.164    | 4060          | 14                                 | 02:19         | 02:41    | 03:00 | 1.0               | 1.0      | 1.3   | 03:20     | 04:10    | 04:25 | 1.8               | 2.7      | 3.0   |
| Lhedi            | 28.034   | 90.092    | 3690          | 23                                 | 02:47         | 03:06    | 03:26 | 1.4               | 1.4      | 1.8   | 03:33     | 04:21    | 04:36 | 2.0               | 2.9      | 3.2   |
| Samdingkha       | 27.641   | 89.865    | 1270          | 90                                 | 05:29         | 05:39    | 06:08 | 3.9               | 3.9      | 4.6   | 05:43     | 06:09    | 06:24 | 4.1               | 4.6      | 4.9   |
| Punakha Dzong | 27.582   | 89.863    | 1210          | 98                                 | 06:01         | 06:12    | 06:38 | 4.4               | 4.5      | 5.2   | 06:37     | 06:58    | 07:13 | 5.0               | 5.5      | 5.8   |
| Wangdue          | 27.462   | 89.901    | 1190          | 114                                | 07:19         | 07:35    | 07:54 | 5.7               | 5.9      | 6.5   | 08:47     | 09:12    | 09:27 | 7.1               | 7.7      | 8.1   |

Location
Latitude
Longitude
Elevation (m)
Distance from breach (km)
Flood arrival
Peak flow


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: RESULTS > Geomorphic change caused by the GLOF
Type: text

steady increase in the area of Lugge Tsho over the past 45 years, starting at approximately 0.42 km2 in January 1976. The lake area increased at a rate of 0.038 km2 year-1 as the glacier receded and melted, reached 1.1 km2 in September 1994, and then underwent a sudden decrease to 0.87 km2 during the GLOF in October 1994. Subsequently, the lake area continued increasing at a rate of 0.025 km2 year-1 and has since reached a size of 1.42 km2 as of September 2018 (fig. S4). We note that the lake expansion is primarily due to the receding glacier rather than rising lake level (see Discussion). The location of the 1994 breach is evident in both visible imagery and digital elevation model (DEM) on the lower left lateral moraine, where a cross section along the moraine crest shows a channel approximately 180 m wide and 40 m deep. Declassified satellite imagery from 1976 clearly shows that this location was a preexisting outlet for Lugge Tsho (fig. S5). Below the breach, changes in spectral reflectance are visible in post-GLOF Landsat imagery where the flood deposited debris and sediment along large regions of the valley floor. Upon first breaching the lake-fringing moraine, the flood waters flowed into another small lake approximately 500 m downstream. This seasonal lake was full at the time the GLOF occurred due to accumulated snowmelt and monsoonal precipitation from the prior summer months. The flood washed out the natural dam of this small lake basin; thus, now it no longer accumulates water as it did in years prior.

Around 10 km from the breach, a prominent set of glacier moraines is situated below Thanza (Fig. 4 and table S2). Here, we analyzed three boulders on the well-preserved outermost moraine ridge using 10Be surface exposure dating, following procedures in Schaefer *et al.* (33). Results yielded three consistent ages ranging from 4.4 to 4.7 thousand years (ka), indicating that the moraines were deposited during the mid-Holocene and left largely intact by the 1994 flood (fig. S5). After passing through the moraines, the flood wave spilled over the Tshojo plain, where it subsequently deposited a 2.2-km2 swath of sediment. In total, we estimate approximately 4.8 km2 of valley floor that was covered by sediment as a result of the GLOF. This occurred primarily along the upper 25 km of the river (fig. S6), as below this upper region, the channel is highly confined, and sediment deposition was minimal. In the decades following, the impacted vegetation has slowly recovered (fig. S7). In 1994, the mean enhanced vegetation index (EVI) of the Tshojo plain dropped to around 25% of the pre-GLOF value as a result of the flood. From 1994 to 2005, the EVI steadily increased as the vegetation began reclaiming the area, attaining around 75% of the pre-GLOF value in 2005 and remaining steady for several

years afterward. Another increase occurred from 2011 to 2013, during which time an EVI approaching that of the pre-GLOF conditions was attained.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Arrival time of flood in Punakha
Type: text

Accurate estimates of GLOF arrival times are vital for disaster preparedness. Existing model estimates for Punakha include 4.75 hours (19), 5.75 hours (20), and 7 hours or more (5). Given the onset of seismic energy at 1:45 a.m. at Lugge Tsho and eyewitness accounts of rising waters around 7:00 a.m. in the village, we estimate that the flood took approximately 5 hours to reach Punakha. Assuming that the delay between the initial breach and generation of detectable seismic energy was short (see Materials and Methods), our results suggest that some previously published simulations give reasonably accurate arrival times (within ±30 min) and that the shorter (4.75 to 5.75 hours, 17 km hour-1 average) times are more accurate than the 7-hour (12 km hour-1 average) estimate (5). These results show how a simple estimate of the GLOF start time based on the onset of seismic energy can be highly useful for validation of numerical models, by providing an estimate of the average velocity of the flood wave. However, a future breach may have different hydrograph characteristics depending on the trigger mechanism and nature of the moraine dam failure. The 1994 GLOF also cleared out a substantial amount of blocking debris, which would allow a future GLOF to travel more quickly down the valley. Further research toward constraining probable breach hydrograph characteristics and valley roughness parameters will be crucial for refinement of GLOF models. With the vast amount of existing seismic data [in databases such as the IRIS DMC (Incorporated Research Institutions for Seismology Data Management Center), for example] and increasing number of seismic networks, it is likely that other GLOF events have been recorded by seismic instruments but have yet to be investigated. A comprehensive search and deeper analysis of any available seismic records in GLOF-prone locations may reveal new insights into quantifying and modeling GLOF trigger events and flood waves in these regions.

DISCUSSION
Arrival time of flood in Punakha


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Sediment deposition and vegetation recovery
Type: text

The area covered by new sediment from the GLOF (4.8 km2) combined with existing sediment-covered areas (3.0 km2) amount to 7.8 km2. This approximately agrees with previous damage assessments,


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Sediment deposition and vegetation recovery
Type: figure
Figure

Image /page/6/Picture/2 description: A geological map is overlaid on a satellite image of a mountainous, glaciated region. The background is a Google Earth view showing snow-covered peaks and a wide river valley. The map overlay in the upper section shows large glacial features colored in solid blue, light blue with black dots, and various shades of purple. In the lower section, along the valley, there are numerous elongated formations, likely moraines, colored in red and pink. A scale bar in the bottom left indicates a distance of 2.5 km. In the lower right, three red formations are linked by white lines to a data box which lists the following sample data: '4700 ± 170 LUNA-14-07', '4590 ± 180 LUNA-14-04', and '4380 ± 170 LUNA-14-03'. The map is marked with coordinates N28.085° and E90.265°.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Sediment deposition and vegetation recovery
Type: text

**Fig. 4. Geomorphic map of the Lunana area.** The lower (red) moraines were deposited by the glacier more than 4000 years ago during the mid-Holocene, as indicated by the  $^{10}$ Be ages of three sampled boulders given with  $1-\sigma$  analytical error. The younger lake-fringing (purple) moraines are late-Holocene in age.

which estimated a total of 7.2 km2 of crops and pastures affected by the flood. The 2.2-km2 region of the most prominent GLOF sediment deposition is located in a scrub alpine vegetation zone, which receives around 1 m year-1 of precipitation primarily during the summer monsoon months (34). The flora in this area are composed of sedges, mosses, accessorial herbs, and some patches of woody vegetation (rhododendrons, junipers, and spireas) (35). The nonlinear vegetation recovery rates (fig. S7) are likely due to factors such as soil moisture, nutrient availability, competition between species, and seasonal precipitation in this high-elevation ecosystem. The observed recovery patterns may inform future studies regarding resilience of aromatic medicinal plants to changing climate, as these flora play key roles in the lives of local inhabitants (36).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Comparison with other GLOFs in the Himalayas
Type: text

Compared to other GLOF occurrences in HMA during the past three decades, this 1994 event is a top contender for the largest volume of water released. Yet in terms of the percentage of lake volume released, it is on the lower end (fig. S8). Yamada et al. (18) surveyed the lake bathymetry and estimated the total volume of Lugge Tsho to be 58.3 million m3 in September 2002. Accounting for the lake growth between 1994 and 2002 (3 million m3) and volume of water released during the GLOF (17 to 25 million m3), we estimate that the lake volume was 73 to 80 million m3 in 1994. On the basis of this approximation, around 24 to 30% of the volume of Lugge Tsho was released. Because of the large size of the lake, substantial rate of outflow during the breach, and considerable vertical relief, this smaller-than-average percentage of draining resulted in a major destructive GLOF downstream. This is consistent with the flood simulations that suggest very low dampening and low deceleration of the flood wave peak due to high relief energy and the gorge character of the Pho River (20).

Regarding the seismic signal, we note that the GLOF event was exceptionally large, resulting in a high signal-to-noise ratio. Smaller

GLOF events that produce less seismic energy would require seismometers to be located closer to the GLOF source to observe the weaker signal. This trade-off between geographical coverage of a seismic array versus the capability of detecting smaller events is an optimization problem, which future studies could address. There is no detectable coherent seismic energy generated from the river valley on the day before the GLOF event, suggesting that background-level river transport processes are not strong enough to be detected at these distances (fig. S12). A search for smaller GLOF events, which have occurred in other locations, along with any associated seismic signals (observed by nearby stations) may help further constrain signal strength versus distance from the source. Such observations could also be used to constrain numerical models that simulate seismic energy generated by various flood magnitudes.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Current state of high-risk lakes in Lunana
Type: text

We find that Lugge Tsho surpassed the pre-GLOF size of 1.1 km2 in 2005 and reached an area of 1.4 km2 in 2018 due to retreat of the glacier terminus (fig. S4). If a future event were to cause the same 16 to 23 m of lake surface lowering as occurred during the 1994 GLOF, then this would translate to 22 to 32 million m3 of water released or approximately 30% more than the 1994 flood volume. While satellite imagery indicates that the lake boundaries (excluding the retreating glacier terminus) are nearly unchanged since the GLOF, the ongoing glacier retreat has also exposed unstable steep valley walls and lateral moraines above the lake (37, 38). A large mass movement into the water could result in a sudden increase in hydrostatic pressure and subsequent overtopping or structural failure of the Lugge Tsho moraine dam. The adjacent Thorthormi and Raphstreng lakes are also vulnerable to the same problem. Thorthormi sits topographically above Raphstreng by approximately 80 m, and a breach in its moraine could result in a cascading combined GLOF from both lakes (3). Efforts to artificially lower the level of Raphstreng by enlarging the

outlet channel were undertaken during 2009-2012 in an attempt to reduce the risk, but this dangerous manual work by the local people was extremely difficult with uncertain effectiveness. On 20 June 2019, a minor breach occurred below Thorthormi lake, during which residents of Lunana were evacuated and no deaths or serious injuries occurred. While the increased flow was relatively minimal (water level increased by approximately 1 m during ~6:00 to 7:20 p.m.), it was large enough to wash away two bridges in Thanza and Tenchey. A field investigation after the event found that enhanced melting and basal sliding of the Thorthormi glacier had caused it to surge this displaced water from Thorthormi lake, which overtopped the primary moraine, spilled into the subsidiary lakes, and breached the lower subsidiary lake that drained completely. Satellite imagery before and after the event confirmed the draining of the subsidiary lake, but the stability of the primary Thorthormi moraine dam is uncertain (39).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: DISCUSSION > Potential for early warning systems
Type: text

In Bhutan and elsewhere across the Himalayas, numerous glacial lakes pose immediate GLOF threats (2). Existing warning systems usually consist of automatic water level (AWL) stations installed in priority locations to monitor lake levels and river flows and transmit data in real time using Global System for Mobile Communications (GSM) or Iridium satellite technologies. If lake levels are detected to suddenly drop and/or stream levels rise, emergency warnings are issued (via mobile text messages), and a network of warning sirens is sounded. However, AWL sensors are known to be somewhat unreliable and susceptible to false alarms (40). Our results demonstrate the feasibility of seismic monitoring as another way to remotely detect GLOFs, which could potentially improve the next generation of early warning systems. The CCF methodology would need to be automated and tested more robustly to ensure reliable distinction between GLOF seismic signatures and other tectonic, meteorological, anthropogenic, or geomorphic sources (21), but with further refinement, a network of seismometers strategically deployed across a region could hypothetically monitor for sustained signals originating from probable GLOF source locations. To detect smaller GLOF events, a priori information should be used to focus detection efforts on known hot spots. Implementation of probability density functions in coherence map calculations may help remedy artifacts due to asymmetric distribution of seismic stations. During the CCF peak at the onset of the GLOF (around 1:45 a.m.), we find that only a few minutes of seismic data are sufficient to detect the anomalous high-frequency signal originating from the upper Lunana valley (fig. S9). Unlike AWL sensors, seismometers can be installed in safer and more accessible sites with the capability to monitor large regions across multiple valleys, although further research is needed to determine the optimal trade-off between array density and event detection capabilities. The use of seismometers and AWL sensors jointly could substantially improve existing early warning systems, with cross-validation of the two independent detection methods helping to minimize occurrence of false alarms and maximize warning time.

DISCUSSION
Potential for early warning systems


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: Conclusion
Type: text

The risk of larger and potentially more destructive floods is rapidly increasing across the Himalayas, because of growing glacial lakes and ongoing construction of hydroelectric dams and other infrastructure in vulnerable regions (2). We have demonstrated that a GLOF can be detected remotely from seismometers located many kilome-

ters away from the source, which has the potential to improve efficiency and maximize warning time. Our robust spatiotemporal constraints indicate at least 5 hours between source and main impact areas in Punakha, which affords sufficient warning time for many downstream inhabitants if a GLOF is detected in its earliest phase. We find that the 1994 flood event eroded, transported, and deposited large quantities of sediment in the Lunana valley but left the local moraine ridges largely intact near the source region. We also estimate a post-GLOF recovery of two to three decades for the affected alpine scrub vegetation, which may help to quantify resilience of the local ecosystem in future studies. Given the current situation and ongoing GLOF risks in the Himalayas, future research could focus on (i) deeper analysis and characterization of tectonic, meteorological, anthropogenic, and geomorphic seismic signatures to ensure clear distinction of natural hazard signals and prevention of false alarms; (ii) continued development of efficient algorithms for automated real-time processing of environmental seismic data; and (iii) deployment of optimized seismic arrays in vulnerable regions to detect GLOF events and provide efficient early warning systems.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Seismic data
Type: text

Recent proliferation of high-quality broadband seismic data in addition to developments in the analysis of the ambient seismic wavefield and other seismic signals have forged new avenues in studying characteristics of seismic energy generated by environmental processes (10, 21-24, 27). For example, time-frequency analyses of passive broadband seismic data have been used to quantify increases in high-frequency energy associated with high-flow regimes in rivers and cross-correlation between multiple stations used to isolate coherent seismic phases and provide estimates of their origin (28, 41). Here, we use seismic data from the INDEPTH II experiment as a tool to investigate the 1994 GLOF in Bhutan, which coincidentally occurred while this temporary seismic network was actively recording. INDEPTH II was a collaborative geoscience project between the Chinese Academy of Geological Sciences and investigators from U.S., German, and Canadian Geoscience institutions to investigate the deep structure and mechanics of the Himalaya-Tibet region (25). In 1994, the second phase of the project acquired passive seismic data in southern Tibet and the Himalayas, continuously recording threecomponent broadband and short-period 24-bit data along a ~350-km linear array at a sample rate of 50 Hz (42). For our analysis, we used data from a total of five broadband and short-period INDEPTH stations ranging from 75 to 135 km in distance (99 km average) to the northwest of the GLOF source area (Lugge Tsho). We downloaded the data from the IRIS DMC using a window for the approximate time of the GLOF occurrence (7 October 1994, from 00:00 to 12:00 hours, Asia/Thimphu time zone) for stations BB18, BB20, BB23, SP25, and SP27 (network code XR). The corresponding seismic traces were detrended, and instrument responses were removed to obtain units of velocity (m s<sup>-1</sup>) using the opensource Python framework ObsPy (43) and band-pass filtered between 1 and 5 Hz. This frequency range corresponded to the coherent high-frequency signal observed across all five stations (fig. S2) during the GLOF duration and also excluded lower frequency bands associated with noise sources such as ocean-generated microseisms (44, 45) as well as higher-frequency anthropogenic noise. Previous studies also observed a similar increase in seismic energy in these same frequency bands originating from turbulence and sediment

transport by rivers and flood events (10, 22, 23, 27). For the purposes of this study, we assume that the beginning of detectable seismic energy marked the initiation of the GLOF event. We note that the actual outflow may have begun slightly earlier, but the seismic energy was below the threshold of detection at first due to a gradual increase in outflow through the moraine breach. This remains difficult to constrain as the exact shape of the breach hydrograph is unknown; thus, our evaluation of the time between the breach and downstream arrival of the flood wave is a minimum estimate.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Time-frequency analysis and CCFs
Type: text

To explore the spectral characteristics of the event and quantify the temporal variation of the seismic signal generated by the GLOF, we estimated the power spectral density of the time series for each station using Welch's averaging method. We first divided each seismic trace into 2-min segments each with 50% overlap. Then, for each 2-min segment, we used Welch's method to average modified periodograms computed using 10-s windows, also overlapping by 50%. To approximate source locations of the GLOF energy, we followed an approach similar to those outlined by previous studies for locating coherent seismic noise sources (28, 41, 46). We first applied a 1-bit normalization to reduce the influence of punctual sources of seismic energy, such as earthquakes, anthropogenic noise, or instrument issues. This simply means keeping the sign of the time series (-1 if less than 0 and +1 if greater than 0) and discarding the magnitude (46). We then calculated the normalized cross-correlation of 20-min segments (overlapping by 50%) in the time series and computed their envelopes (hereafter referred to as the CCFs) for every station pair along the seismic array for time lags ranging from -40 to +40 s for a series of frequencies ranging from 1 to 5 Hz, using a window size of 0.5 Hz. Time information from each CCF envelope was migrated to positions in space as follows: We defined a regular grid of potential source locations in the region, and for each station pair, we calculated the theoretical time delays between the two stations for every grid point. The CCF amplitude at each corresponding lag time was then mapped to positions in space. The resulting coherence map  $A_{ii}$  for stations i and j is given by

$$A_{ij}(x,y) = CCF_{ij}\left(\frac{d_i - d_j}{v}\right)$$
 (1)

MATERIALS AND METHODS
Time-frequency analysis and CCFs


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Time-frequency analysis and CCFs
Type: text

Time information from each CCF envelope was migrated to positions in space as follows : We defined a regular grid of potential source locations in the region , and for each station pair , we calculated the theoretical time delays between the two stations for every grid point . The CCF amplitude at each corresponding lag time was then mapped to positions in space . The resulting coherence map $ A_ { ii } $ for stations i and j is given by $ $ A_ { ij } ( x , y ) = CCF_ { ij } \ left ( \ frac { d_i - d_j } { v } \ right ) $ $ ( 1 )

where *d* is the distance between the hypothetical source and corresponding station and  $\nu$  is the assumed seismic velocity (see below). Thus, each CCF delay time (along the vertical axes in fig. S3) maps to a hyperbola, where the amplitude of the hyperbola is simply the CCF amplitude. This was repeated for each 20-min segment and station pair. The resulting maps for hand-selected station pairs and frequencies (those with distinct correlation peaks; see fig. S3 and table S1) were summed together to form a final coherence map for a given interval, where high coherence values indicate the most probable source locations (Fig. 2). Similar results are also obtained if all station pairs are included in the stack (fig. S11). To determine an appropriate velocity during migration, we calculated coherence maps for a range of velocities between 1 and 5 km s-1. We found that a velocity of 3.0 km s-1 resulted in the highest coherence (fig. S3) for these frequencies, similar to previous studies (28), and likely indicates short-period Rayleigh wave energy. We found that two distinct peaks in coherence occurred at approximately 2:00 and 6:30 a.m. and chose to focus on these peaks for further analysis. We defined time windows of 90-min duration spanning each respective peak, which we found to be a sufficient length for capturing the intervals of strongest GLOF signal relative to background levels. We also note that quasilinear placement of the seismometers parallel to the valley northwest of the GLOF means that the source location was well constrained along the valley but poorly constrained perpendicular to the valley, leading to blurring in that direction. This artifact may be remedied by invoking a probability density function centered on the river channel to better localize the signal, although the GLOF event studied here was large enough such that this was not necessary. While this and other sources of error such as lateral velocity heterogeneities and varying surface topography caused some blurring of the coherence maps, we found this basic methodology precise enough to clearly track the start and a subsequent down-valley shift in the location of seismic energy during the GLOF.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Flood model
Type: text

We implemented the U.S. Army Corps of Engineers HEC-RAS software to perform a 2D unsteady flow simulation using the diffusionwave equation (47). This equation is a simplified version of a full dynamic wave model (neglects inertial force and advective accelerations) and has been found to be a satisfactory approximation in many situations (48). Simulations were run at a nominal mesh resolution of 30 m using a time step of 1 s, solved using an implicit finitevolume approach (47). We used the 30-m Advanced Land Observing Satellite (ALOS) DEM as terrain input, preprocessed using standard carve and fill operations to remove any spurious elevation artifacts that may cause unrealistic damming and pooling in localized sections where the river channel may be narrower than the DEM resolution (12, 49). To approximate the normal (pre-GLOF) river flow conditions, we specified inflows for 15 major tributaries between Lugge Tsho and Wangdue station and allowed the model to come to equilibrium. The (relative) contributions of each tributary were estimated by performing a flow accumulation analysis for the region based on upstream watershed areas (49). We then multiplied all the relative inflow values with a single scale factor to estimate (absolute) contributions, so that the river flow at Wangdue station (located downstream of all tributaries) matched the observed pre-GLOF conditions of approximately 290 m3 s-1 (fig. S1). In HEC-RAS, we expressed the tributaries as inflow boundary conditions and allowed the model to run for 24 hours to establish initial conditions before simulating the flood. We then performed multiple model runs using a range of Manning roughness coefficient values (n) and various breach hydrograph shapes (fig. S1). We tested values of *n* spanning from 0.05 to 0.07 (in increments of 0.01), which is the typical range for mountain streams with cobbles and large boulders (47). For the breach hydrographs, we used a simple triangular approximation scaled to have ramp-up times  $(t_{ru})$ ranging from 15 to 120 min (in increments of 15 min). On the basis of previously published differential GPS survey of the lowering of the lake level (18) and the Wangdue station GLOF hydrograph (19), we assumed 17 to 25 million m3 as a probable range for the total volume of water released during the GLOF and constrained the simulated breach hydrographs to 25 million m3. As a conservative threshold, we discarded any model runs, which did not agree with all independent constraints within ±30 min, and report the corresponding range of input parameters and model outputs of the remaining ones. We note that the breach may have initiated before the seismic signal was detectable (see "Seismic data" section); thus, we also include simulated breach times of 1:15, 1:30, and 1:45 a.m. in our analysis. Out of a total of 72 model runs (3 values of n, 8 values of  $t_{ru}$ , and 3 breach


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Flood model
Type: text

. As a conservative threshold , we discarded any model runs , which did not agree with all independent constraints within ± 30 min , and report the corresponding range of input parameters and model outputs of the remaining ones . We note that the breach may have initiated before the seismic signal was detectable ( see " Seismic data " section ) ; thus , we also include simulated breach times of 1 : 15 , 1 : 30 , and 1 : 45 a . m . in our analysis . Out of a total of 72 model runs ( 3 values of n , 8 values of $ t_ { ru } $ , and 3 breach

times), 14 runs produced output that satisfied the specified  $\pm 30$ -min threshold. Last, we quantified the model sensitivity of arrival time estimates in Punakha (fig. S1), and found that a more gradual release of water and greater channel roughness both resulted in a slower-moving flood wave. Increasing  $t_{\rm ru}$  from 45 to 60 min delayed arrival in Punakha by 20 to 25 min (fig. S1E), and increasing n from 0.05 to 0.06 delayed arrival time in Punakha by 25 to 35 min (fig. S1F). In general, we found that the HEC-RAS model performed well in satisfying the independent observations within the specified range of model parameters. However, we stress that the overall sensitivity to breach hydrograph characteristics requires caution if external constraints are not available.


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Satellite imagery
Type: text

For analyzing GLOF-induced changes in land cover, we used the U.S. Geological Survey (USGS) Landsat 5 through 8 Thematic Mapper (TM) Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance product in Google Earth Engine (50). We quantified Lugge Tsho area changes over time by manual delineation of the lake boundaries and glacier front between 1976 and 2018 using declassified spy satellite imagery (KH-9 Hexagon) and Landsat. The Hexagon images were downloaded from the USGS Earth Explorer website (https://earthexplorer. usgs.gov/) after digital scanning of the images by the USGS and have a ground resolution of approximately 15 m. To measure the extent of sediments deposited by the GLOF, we selected two Landsat 5 scenes acquired in August 1994 (pre-GLOF) and September 1995 (post-GLOF) and used supervised classification with manually defined training samples of vegetation, sediment, water, ice, and clouds from the pre-GLOF scene. We classified both images using the maximum likelihood algorithm and retained the pixels classified as sediment along the valley bottom before and after the GLOF. To analyze the post-GLOF vegetation recovery trend, we focused on the largest swath of sediment deposition over the Tshojo plain. For this region, we computed the EVI for all Landsat 5 and 7 scenes, excluding those acquired during monsoon season months (May to October). We evaluated the topography of the Lunana region using the High Mountain Asia 8-m DEM data (version 1) distributed by the National Snow and Ice Data Center (NSIDC) and obtained the valley profile from the 30-m ALOS global digital surface model dataset distributed by the Japan Aerospace Exploration Agency (JAXA).


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: MATERIALS AND METHODS > Cosmogenic 10Be surface exposure dating
Type: text

We applied cosmogenic 10Be dating (*51*, *52*) to three boulders sampled in 2014 from the prominent and well-preserved lateral-terminal moraines near Thanza, about 10 km downstream from the GLOF breach location (Fig. 4). Geochemical processing was performed at the Cosmogenic Nuclide Laboratory at Lamont-Doherty Earth Observatory (LDEO) following standard protocols given in Schaefer *et al.* (*33*), and 10Be/9Be measurements were completed at the Center for Accelerator Mass Spectrometry at the Lawrence Livermore National Laboratory. The background correction for these measurements was below 1%. We used version 3 of the online cosmogenic nuclide calculator (*53*) with the default production rate and time-dependent Stone/Lal scaling scheme for exposure age calculations (*52*, *54*). Geographic and analytical data are given in table S2, and the geomorphic map with the 10Be ages is shown in Fig. 4. This glacier chronology will be discussed in detail in forthcoming papers.

MATERIALS AND METHODS
Cosmogenic 10Be surface exposure dating


Document: Seismic observations numerical modelingand geomorphic analysis of a glacier lake outburstflood in the Himalayas
Section: SUPPLEMENTARY MATERIALS
Type: text

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/38/eaba3645/DC1


Document: The causes and mechanisms of moraine-dammed lake failures
Section: ABSTRACT
Type: text

Glacial lake outburst floods (GLOFs) from moraine-dammed lake failures represent a significant threat to inhabitants of high mountain areas across the globe. The first part of this paper summarises the causes and mechanisms of moraine-dammed lake failures through a review of the scientific literature and unpublished reports. There are eight main causes, of which five are characterised as dynamic and three as long-term, and these are associated with around twenty failure mechanisms. The dynamic causes are slope movements into the lake, earthquakes, flood waves from a lake situated upstream, blocking of underground outflow channels, and intensive rainfall or snowmelt. The long-term causes are the melting of buried ice, the impact of hydrostatic pressure, and the effect of time. These causes (triggers) and the consequent mechanisms of dam failure are described in detail. The second part compares the historical moraine-dammed lake failures within three regions between 1900 and 2009: the Cordillera Blanca of Peru, the North American Cordillera, and the Himalayas. It has been found that dynamic causes are around four times more common than long-term causes although significant regional differences have been observed. The most frequent causes in these regions were found to be slope movements in which the displaced material was dominated by solid-state water (ice falls, ice avalanches, and snow avalanches). The other causes tended to show distinct regional patterns while the temporal distribution of events also differs according to region. In the North American Cordillera and Himalayas moraine dam failures occur exclusively during the summer season while in the Cordillera Blanca they are more evenly distributed with the exception of the dry season. This reflects the general climatic setting of each of the study regions.

Keywords: moraine-dammed lakes, natural dam failures, GLOFs, natural hazards, high mountain areas

data/('The causes and mechanisms of moraine-dammed lake failures', '.pdf')_extraction.md


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 1. Introduction
Type: text

In this study moraine-dammed lakes are defined as natural freshwater reservoirs dammed by material accumulated from a glacier (the moraine). This type of lake is not considered to be particularly stable because the morainic material forming the dam is usually unconsolidated and, therefore, even a relatively weak trigger can cause the dam to fail, leading to a glacial lake outburst flood (GLOF) (e.g. Costa & Schuster 1988; Richardson & Reynolds 2000a; Shrestha 2010). This form of flood describes the sudden water release from any form of glacial lake (e.g. bedrock-dammed, moraine-dammed, or ice-dammed) irrespective of its cause (Benn & Evans 1998). These floods are characterised by peak discharges that are many times higher than those of a hydrometeorological flood (Clauge & Evans 2000). They, therefore, have the potential to be highly catastrophic. GLOFs may occur as a result of dam failure (moraine-dammed or icedammed) or dam overflow (all types of glacial lake).

The study of moraine-dammed lake failure is complex and requires interdisciplinary cooperation. It is appreciated that two main groups of parameters are decisive in contributing to a moraine dam failure, the first is that of dam stability and the second is the possibility of a triggering event (Richardson & Reynolds 2000a; Hegglin & Huggel 2008). These are difficult to assess and quantify which leads to problems in estimating the probability

of a moraine dam failure (hazard assessment) (Emmer & Vilímek 2013). It is possible for a moraine dam failure to be total or partial and, therefore, a given lake can be subjected to repeated GLOFs (O'Connor et al. 2001; Bajracharya et al. 2007a). GLOFs represent an increasingly significant threat in high-mountain areas because the number of potentially dangerous glacial lakes is increasing as a result of global climate change and concurrent glacier retreat (e.g. Evans & Clauge 1994; Bolch et al. 2008; Ives et al. 2010).

GLOFs from moraine-dammed lake failures have been studied in high-mountain regions all over the world including the Himalaya (Vuichard & Zimmerman 1987; Kattelmann & Watanabe 1997; Bajracharya et al. 2007a,b), Hindu-Kush (Iturrizaga 2005; Ives et al. 2010), Karakoram (Hewitt 1982), Tian-Shan (Janský et al. 2009; Narama et al. 2010; Bolch et al. 2011), Caucasus Mts. (Petrakov et al. 2007), Cascade Range (O'Connor et al. 2001), British Columbia (Clauge & Evans 2000; Kershaw et al. 2005), Peruvian Andes (Zapata 1984; Vilímek et al. 2005a; Carey et al. 2011; Klimeš 2012), Patagonia (Harrison et al. 2006; Dussaillant et al. 2009) as well as in the European Alps (Haeberli et al. 2001; Huggel et al. 2004) and Scandinavia (Breien et al. 2008). The objectives of this paper are to provide an overview of moraine-dammed lake failures, to investigate their causes and mechanisms, and to compare the temporal aspect of these events within three


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 1. Introduction
Type: text

. 2001 ) , British Columbia ( Clauge & Evans 2000 ; Kershaw et al . 2005 ) , Peruvian Andes ( Zapata 1984 ; Vilímek et al . 2005a ; Carey et al . 2011 ; Klimeš 2012 ) , Patagonia ( Harrison et al . 2006 ; Dussaillant et al . 2009 ) as well as in the European Alps ( Haeberli et al . 2001 ; Huggel et al . 2004 ) and Scandinavia ( Breien et al . 2008 ) . The objectives of this paper are to provide an overview of moraine - dammed lake failures , to investigate their causes and mechanisms , and to compare the temporal aspect of these events within three

regions: the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. These areas were chosen because they have a long history of moraine-dammed lake research and there is, therefore, a greater chance of acquiring information about GLOFs. The flooding that occurs as a result of moraine-dammed lake failures poses a significant threat in high mountain regions and by comparing events it is possible to recognise the regional specifics that are necessary in order to build an optimal regionally focused hazard assessment.


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 2. Materials and methods
Type: text

The causes (triggers) and mechanisms of morainedammed lake failures are summarised following an extensive search of the published scientific literature and unpublished reports from the archive of the Autoridad Nacional de Agua in Huaraz, Peru. These causes and consequent mechanisms of dam failure are described in detail with examples given for each. The similarities and disparities that exist between the particular causes have been investigated as well as the temporal characteristics of events in the Cordillera Blanca of Peru, the North American Cordillera, and Himalaya. The GLOF database (GLOFs database 2012) has been used as the basis for this comparative analysis: the details of around ninety-five moraine dam failures within the three study areas have been compiled for the period 1900 to 2009. The causes of the GLOFs are known in sixty of these cases (twenty in Cordillera Blanca of Peru, eleven in the North American Cordillera, and twenty-nine in Himalaya) but unknown in the other instances. The precise dates of the GLOFs are known in sixty-six of the cases and can be attributed to a year in seventy-five instances. The temporal characteristics of the GLOFs are considered both in terms of their monthly and annual distributions.


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 3. The causes and mechanisms of moraine-dammed lake failures
Type: text

There is a close relationship between various types of natural hazards and moraine-dammed lake failures that result in GLOFs. The former, the various natural hazards, often represent the cause of the latter, the lake failures. These relationships are summarised in Figure 1. Yamada (1998) divided the process of moraine dam failure into two groups: the first consisted of dam failure caused by a dynamic initiating event while the second consisted of spontaneous "dam self-destruction". The latter group are caused by long-term degradation of the dam without a dynamic initiating event. This grouping is followed here and the causes of moraine dam failure are classifies as either dynamic causes or long-term.


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures
Type: text

The dynamic causes are slope movements into the lake, earthquakes, a flood wave from a lake situated upstream, blocking of underground outflow channels, and intensive rainfall or snowmelt. In this group the time interval between the trigger and the moraine dam failure ranges from minutes in the case of, for example, a slope movement into the lake to hours or days in the case of, for example, intensive rainfall or snowmelt. These dynamic causes were termed "quasicoincidental" by O'Connor et al. (2001) as it is not possible to accurately predict the time or place in which they will occur.

The slope movement into the lake includes various types of mass-movement such as icefalls, avalanches, rockfalls, landslides, debris flows, and mudflows. The mass of material entering the lake causes water displacement in the form of surge (displacement) waves (Richardson & Reynolds 2000a) and these can reach heights of tens of metres (Plafker & Eyzaguirre 1979 in Costa & Schuster


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures
Type: figure
Figure

Image /page/2/Figure/10 description: A flowchart illustrating the causes and mechanisms of dam failure. The chart is divided into a vertical axis labeled "CAUSE OF FAILURE" and a horizontal axis labeled "MECHANISM". The flowchart uses ovals of different shades and connecting arrows to show the progression from initial causes to final failure.


Document: The causes and mechanisms of moraine-dammed lake failures
Section: 3. The causes and mechanisms of moraine-dammed lake failures > 3.1 The dynamic causes of moraine-dammed lake failures
Type: text

On the left, under "CAUSE OF FAILURE", are several light gray ovals representing dynamic causes: "EARTHQUAKE", "DYNAMIC SLOPE MOVEMENT INTO THE LAKE", "INTENSIVE RAINFALL OR SNOWMELT", "FLOOD WAVE FROM LAKE SITUATED UPSTREAM", and "BLOCKING OF OUTFLOW CANALS".

The "MECHANISM" section includes several stages. Medium gray ovals represent the "interphase": "DISPLACEMENT (SURGE) WAVE", "DAM OVERFLOW", "RAPID RISE OF WATER LEVEL", and "DAM DEGRADATION". Dark gray ovals represent the "final phase": "WATER EROSION" (subdivided into "INTENSIVE SHORT-TERM", "INTERNAL (PIPING)", and "LONG-TERM"), "RUPTURE", "HYDROSTATIC PRESSURE", "BURIED ICE MELTING", and "TIME EFFECT".

Arrows indicate the causal relationships. For example, an earthquake can lead to dynamic slope movement, which causes a displacement wave. This wave can lead to dam overflow and a rapid rise in water level. Both dam overflow and other factors like dam degradation contribute to water erosion, which, along with hydrostatic pressure, can lead to the final rupture of the dam.

A legend at the bottom clarifies the symbols: a white box with a gray outline is a "long-term cause"; a light gray box is a "dynamic cause"; a medium gray box is an "interphase"; a dark gray box is a "final phase". It also distinguishes between line types: a dashed line for "lakes without surface outflow" and a solid line for "lakes with surface outflow".

Fig. 1 The causes and mechanisms that underpin the process of moraine dam failure.