Himalayas: An Avalanche-Triggered GLOF Could Be 15 Times Larger Than the Worst on Record

⏱️ Lectura: 13 min

A rock or ice avalanche in the Himalayas could overflow a glacial lake and trigger a flood up to 15 times larger than any case recorded or anticipated so far, according to a study published in Nature Hazards and Earth System Sciences (NHESS). The Tibetan town of Nyalam would have just 5 to 11 minutes of warning before impact, and the border with Nepal, about 30 minutes.

📑 En este artículo

1. TL;DR
2. What happened: Nyalam’s glacial lakes under the worst-case scenario
3. Context and history: why the Himalayas concentrate this risk
4. Technical details and performance: how a worst-case GLOF is simulated
5. Getting started: reproducing this type of analysis with open tools
6. Impact and analysis: why the Jialongco mitigation project isn’t enough
7. What’s next: modeling the hazard before the lake exists
8. Frequently Asked Questions
   1. What is a GLOF, or glacial lake outburst flood?
   2. Why do the Himalayas concentrate so much GLOF risk?
   3. What’s the difference between this study and a conventional early warning system?
   4. How is a worst-case scenario defined in this type of simulation?
   5. Does this approach apply to the Andes, or only to the Himalayas?
   6. What open source tools exist for modeling this risk?
9. References

The study, published on November 23, 2022 and led by Simon K. Allen (University of Zurich) together with researchers from the Institute of Tibetan Plateau Research and the University of St Andrews, modeled worst-case scenarios in a transboundary basin between Tibet (China) and Nepal. The team didn’t limit itself to lakes that already exist: it also simulated a future glacial lake, one that hasn’t formed yet but that the retreat of the ice could create upstream of Nyalam.

TL;DR

• A study in NHESS (November 23, 2022) modeled extreme scenarios of glacial lake outburst flooding (GLOF) in a transboundary basin in the Himalayas.
• They analyzed two lakes already flagged as dangerous: Galongco and Jialongco, near the Tibetan town of Nyalam.
• Rock and/or ice avalanches larger than 20 million m3 would generate flows at the border with Nepal more than 15 times higher than anything observed so far.
• The simulated warning time is 5 to 11 minutes in Nyalam and 30 minutes at the border with Nepal.
• Lowering the water level in Jialongco (a mitigation project already carried out) would have little effect in a worst-case event, especially in Nyalam.
• The study also simulates a future glacial lake, still nonexistent, that could form upstream as the ice retreats.
• The authors call for combining early warning systems with land-use zoning and local response training.

What happened: Nyalam’s glacial lakes under the worst-case scenario

The basin studied drains from Tibet into Nepal and holds two glacial lakes already on danger lists: Galongco and Jialongco. Both formed as nearby glaciers retreated, a process documented across the Himalayan region (GLOF is the technical name for this type of flood).

The researchers compared the magnitude and arrival time of a simulated flood from each lake, both at the town of Nyalam and at the border with Nepal. The three scenarios (Galongco, Jialongco, and the future lake) were compared at those two points, and that comparison is what shows that none of the current lakes, not even at their most severe known record, comes close to the magnitude of a worst-case avalanche: the difference is more than 15 times the peak flow recorded at the border.

The central finding: when the trigger is a large avalanche (rock, ice, or both combined, above 20 million m3), the resulting flow at the border exceeds by more than 15 times any figure previously observed or estimated with gradual-outburst simulations, the slower type of dam breach usually used as a reference in earlier literature.

The third scenario evaluated is prospective: a glacial lake that doesn’t exist yet, avalanche-generated, forming upstream of Nyalam as the glacier retreats. This forward-looking analysis is the most methodologically novel part of the study: instead of waiting for the lake to appear before assessing it, the team modeled its potential hazard in advance.

Context and history: why the Himalayas concentrate this risk

Glacial retreat has accelerated over the last few decades in the Himalayas, as in most mountain ranges on the planet, as a consequence of global warming. The direct result is the expansion and formation of new glacial lakes, bodies of water that solid ice used to occupy and that are now held back, often, by an unstable moraine instead of solid rock.

That kind of natural dam (loose sediment, residual ice blocks, poorly consolidated material) is exactly what makes a glacial lake dangerous: it’s enough to hold the water under normal conditions, but it can break suddenly when triggered by a rockslide, an ice collapse, or an earthquake. When that happens, the lake’s volume is released within minutes and rushes down the valley as a flash flood capable of carrying sediment, rocks, and everything else in its path.

The physics behind the worst-case scenario is intuitive: when a mass of rock or ice of tens of millions of cubic meters falls onto the lake’s surface, it generates an impact wave that can top the height of the natural dam and sweep it away within seconds, releasing the entire stored volume at once. That’s a different mechanism, and a considerably more violent one, than a slow seepage that erodes the dam until it breaks: that’s the gradual-outburst scenario used as a reference in earlier studies.

The transboundary basin analyzed in this study is a textbook case: it crosses an international border, feeds a town with relatively recent infrastructure (Nyalam), and flows into Nepali territory, where warning coordination depends on two different countries. That combination, growing physical risk plus binational management, is what led the authors to push for an anticipatory rather than reactive approach to disaster risk management.

Technical details and performance: how a worst-case GLOF is simulated

Modeling a worst-case GLOF means chaining together several layers of simulation, not just one. First, the terrain has to be characterized with a digital elevation model (DEM) with enough resolution to reproduce the valley’s channel. On top of that DEM, the trigger is defined: in this study, rock and/or ice avalanches of different volumes, including the critical threshold of more than 20 million m3 that sets off the worst-case scenario.

With the trigger defined, the impact on the lake and the possible failure of the natural dam are simulated. The resulting flow of water and sediment is then propagated with a hydrodynamic model that calculates flow rate, flow depth, and advance velocity at every point along the channel, until it reaches Nyalam and, further down, the border with Nepal.

Tool	When to use it	Advantage	Limitation
r.avaflow (GRASS GIS)	Rock or ice avalanches and debris flows that trigger a GLOF	Open source, models two phases (solid and fluid) in 2D on the same DEM	Steep learning curve, requires a well-configured GRASS GIS setup
RAMMS::debrisflow	Debris flows and avalanches in complex alpine terrain	Extensively validated in the Alps and in Himalayan basins	Commercial license, not open source
BASEMENT	2D hydrodynamic propagation of the flood wave downstream	Free, developed by ETH Zurich, robust for mountain rivers	Doesn’t simulate the avalanche trigger, only the water propagation
HEC-RAS	General hydraulic modeling of rivers and floodplains	Free, widely documented, well adopted in Latin America	Less specialized for hyperconcentrated debris flows

flowchart TD
    A["Glacial lake identified"] --> B["Digital elevation model (DEM)"]
    B --> C["Trigger scenario: rock or ice avalanche"]
    C --> D["Hydrodynamic dam-break simulation"]
    D --> E["Peak flow and propagation time"]
    E --> F["Warning time in Nyalam and at the Nepal border"]

Getting started: reproducing this type of analysis with open tools

You don’t need to work at the Institute of Tibetan Plateau Research to start exploring glacial lake risk with open data. The basic recipe combines a public DEM, a geospatial library in Python, and, if you want to simulate the trigger, GRASS GIS with the r.avaflow add-on.

For the DEM, Copernicus GLO-30 (30-meter resolution, global coverage, and an open license) is the most practical starting point. The first step is simply to read it and calculate the terrain slope, to pinpoint where a rockfall could accelerate toward the lake:

pip install rasterio numpy

python3 - <<'EOF'
import rasterio
import numpy as np

with rasterio.open("nyalam_dem_30m.tif") as dem:
    elevacion = dem.read(1).astype(float)
    dy, dx = np.gradient(elevacion, dem.res[0])
    pendiente = np.degrees(np.arctan(np.sqrt(dx**2 + dy**2)))
    print(f"Maximum slope: {pendiente.max():.1f} degrees")
    print(f"Average slope: {pendiente.mean():.1f} degrees")
EOF

That script opens the elevation raster, calculates the gradient at each cell, and converts it to slope degrees. With that, you can already identify the steepest slopes above the lake, candidates for a rock or ice fall.

The next level is simulating the trigger and its propagation with r.avaflow, the open source GRASS GIS add-on designed for multiphase avalanches and debris flows:

# Windows (OSGeo4W shell)
osgeo4w-setup.exe -P grass -P grass-addons
grass83 --tmp-location EPSG:32645 --exec g.extension extension=r.avaflow

# macOS (Homebrew)
brew install grass
grass --tmp-location EPSG:32645 --exec g.extension extension=r.avaflow

# Linux (Debian/Ubuntu)
sudo apt install grass grass-dev
grass --tmp-location EPSG:32645 --exec g.extension extension=r.avaflow

# Worst-case simulation: 20 million m3 avalanche over the lake
r.avaflow elevation=dem_nyalam release_hydrograph=galongco_release.txt \
  friction=voellmy phases=s-w cores=8 prefix=galongco_worstcase

The phases=s-w flag tells r.avaflow to model two phases (solid and water), the multiphase approach used in much of the recent literature on avalanche-triggered GLOFs. The output is a set of rasters showing maximum depth, velocity, and arrival time of the flow at each cell in the basin.

💡 Tip: the same pipeline (open DEM + GRASS GIS + r.avaflow) applies directly to Andean glacial lakes like those in the Cordillera Blanca, in Peru, where GLOF risk is also actively monitored.

To confirm the simulation finished correctly and produced useful data, the simplest approach is to inspect the maximum flow depth raster with GDAL:

gdalinfo -stats galongco_worstcase_results/galongco_worstcase_ascii/galongco_worstcase_hflow_max.tif

If the command reports a STATISTICS_MAXIMUM greater than zero along the expected channel, the simulation produced a coherent flow. If the entire raster is zero, either the trigger didn’t manage to mobilize water or the DEM doesn’t accurately represent the basin.

Impact and analysis: why the Jialongco mitigation project isn’t enough

An uncomfortable finding from the study: the mitigation measures already carried out (artificially lowering the water level at Jialongco) would have little effect on the downstream impact in a worst-case event, particularly in Nyalam. Lowering the lake level reduces the volume available for a moderate breach, but when the trigger is an avalanche larger than 20 million m3, the impact of the mass falling into the water outweighs the volume that was trimmed.

The problem is compounded because Nyalam developed significant infrastructure directly inside what the study calls the high-intensity flood zone: the stretch of the valley that a large GLOF would flood with the greatest depth and flow velocity. Building there isn’t an isolated miscalculation, it’s a land-use decision that this study’s evidence calls into question.

⚠️ Heads up: lowering a glacial lake’s level reduces the risk of moderate events, but the study shows it does little against a worst-case avalanche.

The authors aren’t proposing abandoning physical mitigation works, but combining them with two pieces that are currently missing: land-use zoning that keeps critical infrastructure out of the high-intensity zone, and training programs so the local population knows how to react within the 5 to 11 minutes of available warning, without relying exclusively on an automated system.

What’s next: modeling the hazard before the lake exists

The most relevant part for anyone working with data and models, beyond the specific geography of the Himalayas, is the anticipatory approach: instead of waiting for a dangerous glacial lake to appear before assessing it, the team simulated one that doesn’t exist yet, using the known trajectory of glacier retreat to project where and when it might form.

That same approach, projecting the basin from the rate of glacier retreat instead of just inventorying current lakes, can be exported to any mountain range with retreating glaciers, including the Cordillera Blanca in Peru, where Lake Palcacocha is already under surveillance due to its historical record of GLOFs. The availability of open global DEMs and software like r.avaflow or BASEMENT means replicating this type of analysis no longer depends on access to institutional supercomputing.

📖 Summary on Telegram: View summary

Try it yourself: install r.avaflow with g.extension extension=r.avaflow on an open DEM of your own mountain range and run your own worst-case simulation in an afternoon.

Frequently Asked Questions

What is a GLOF, or glacial lake outburst flood?

It’s the sudden release of water held in a glacial lake, usually because the natural dam (moraine, ice, or loose rock) breaks all at once. It rushes down the valley as a flash flood loaded with sediment.

Why do the Himalayas concentrate so much GLOF risk?

Because accelerated glacial retreat has formed and expanded hundreds of new glacial lakes over the last few decades, many held back by unstable moraines in basins with growing population and infrastructure.

What’s the difference between this study and a conventional early warning system?

A conventional early warning system reacts to an event already underway. This study models the worst possible case in advance, including lakes that don’t exist yet, to size warning systems and zoning before the event happens.

How is a worst-case scenario defined in this type of simulation?

The most severe physically plausible trigger is set (in this case, a rock and/or ice avalanche larger than 20 million m3) and the full chain is simulated: impact on the lake, breach, and downstream propagation.

Does this approach apply to the Andes, or only to the Himalayas?

It applies to any mountain range with retreating glaciers and moraine-dammed lakes, including the Cordillera Blanca in Peru, where lakes like Palcacocha are already under active surveillance.

What open source tools exist for modeling this risk?

GRASS GIS with the r.avaflow add-on for the trigger and multiphase propagation, BASEMENT for the purely hydrodynamic part, and QGIS or Python (rasterio, numpy) for preliminary terrain analysis.

References

• Allen et al., NHESS 22, 3765-3785 (2022): the original study with the methodology and results of the three simulated scenarios.
• Wikipedia: Glacial lake outburst flood: general background on what a GLOF is and historical examples across different mountain ranges.
• avaflow.org: official documentation for r.avaflow, the open source GRASS GIS add-on for simulating avalanches and debris flows.
• QGIS.org: official site for the open source GIS used to prepare and visualize terrain data prior to the simulation.

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Imagen destacada: Foto de Joanne Tang en Unsplash