Document: Author(s):
Section: Development and implementation of a GLOF Early Warning System > Response capability
Type: text

The ability of people at risk to appropriately respond to the levels of warning issued is possibly the most critical element of an EWS – it is also the most susceptible to failure as the last element of the EWS chain. Failures or errors along the monitoring and warning chain have to be accommodated such that this last element is not adversely affected or threatened.

For the Laguna 513 EWS, information sessions were held with the population at risk. During these, the concept and functionality of the EWS, as well as its potentials and limitations, and clear instructions on actions that have to be taken in case of an alarm were explained and discussed. The instructions include the directive to immediately escape the endangered zones, and a clear indication of the evacuation routes and safety zones. A detailed map with all evacuation routes

was prepared by the civil defense of Carhuaz on the basis of the hazard map developed in the risk knowledge phase of the EWS GLOF design. Emergency simulations are scheduled several times a year for the entire country as Peru's seismic risk is very high. Such simulations, some of them taking place at nighttime, have been used to expose both the population as well as the responsible authorities to a test evacuation und near-realistic conditions, and to familiarize them with the EWS of Laguna 513.


Document: Author(s):
Section: Operational aspects and lessons learned
Type: text

In 2010, when discussions and activities related to the GLOF EWS started, Carhuaz was the main local actor and the centre for data and information reception. However, the technical, operational and social dimensions of the EWS were beyond the capacity of such a small city. Long-standing national and international expertise – necessitating regular presence on site and permanent joint capacity building and exchange with local people and authorities – was indispensable to address the challenges. In July 2015, full responsibility of the EWS was handed over to the local authorities in a ceremony attended by representatives from the local, provincial and national governments of Peru, from the Swiss government, and from local communities and schools as well as national and international experts. By that time, the EWS had made headlines in the Peruvian, Swiss and international media.

In 2016, much of the central tropical Andes, including the Cordillera Blanca region, was affected by a strong drought. In normal years, after a long dry Austral winter season, farmers count on the start of the rainfall season in October. In 2016, no rainfall was recorded in October and November.

After first requests to remove the EWS by some local inhabitants (cf. Fraser, 2017), farmers got desperate and rumors started to spread that the rain gauges and antennas of the EWS at Laguna 513 were responsible for the lack of rainfall. In a rather dramatic turn of events – driven by community-level power policy factors and weak communications from authorities on the extraordinary meteorological events – a large number of locals gathered at Laguna 513 on 24 November and decided to dismantle the EWS station at the lake. Reactions at the local, national and international levels were vigorous. There was animated disorderly mix of supporters of the EWS on social media. Others expressed incomprehension, disgust, shame and critiqued the voluntarily exposure of lives at risk by this destructive action.

The destruction of the station affected the monitoring and warning components of the EWS from a technical and operational point of view. But service could be maintained thanks to the wardens located at the intermediate site (Pampa Shonquil). Response capabilities and institutional mechanisms were not affected. However, it was crucial to understand the root causes of this action.

The results of intensive research into the incident, which centred on social sciences, are summarized below. The lessons learned are relevant for the development of climate and warning services beyond Peru.

The dismantlement of meteorological and EWS stations by local people is not unique to this site, nor to Peru. Similar incidents have occurred in other regions, such as Himalayas, Andes and Alps of Europe, though those experiences were poorly documented.

Local intra and inter-community conflicts, as well as distrust and biases against the participation of, and installations from, external institutions, can have a strong, but invisible, impact on acceptance.

The relation of local (risk exposed) people to their natural environment and their perceptions of different risks strongly determines their attitude towards risk reduction efforts. Local perspectives

may differ substantially from government or technical and scientific perspectives. For instance, local people can have intimate relations with mountains, glaciers and lakes as places of spirituality and the origin of life. Hence, a GLOF may be understood as a reaction of, for example, a glacier (as a mountain spirit) and a lake (as a being) to human disturbance or inappropriate human behavior. Traditional knowledge and narratives have to be recognized and acknowledged as part of a constructive dialogue and in finding acceptable solutions.

data/('Glacier Lake 513 Peru_ Lessons for early warning service development', '.pdf')_extraction.md


Document: Author(s):
Section: Operational aspects and lessons learned
Type: text

) people to their natural environment and their perceptions of different risks strongly determines their attitude towards risk reduction efforts . Local perspectives may differ substantially from government or technical and scientific perspectives . For instance , local people can have intimate relations with mountains , glaciers and lakes as places of spirituality and the origin of life . Hence , a GLOF may be understood as a reaction of , for example , a glacier ( as a mountain spirit ) and a lake ( as a being ) to human disturbance or inappropriate human behavior . Traditional knowledge and narratives have to be recognized and acknowledged as part of a constructive dialogue and in finding acceptable solutions .

As a consequence, the acquisition of a profound understanding of the social, political and cultural conditions, particularly in terms of power dynamics, is a prerequisite for early warning as well as more generally for climate adaptation service development. It is necessary for collaboration among diverse people, actors and experts, including local populations, physical and social scientists, engineers, local governments, technical governmental institutions, and nongovernmental organization (NGOs). It is encouraged to give the social sciences a more prominent role.

Authorities often believe that an EWS is primarily a technical measuring and data transmission system. The recognition that an EWS also consists of institutional, social, cultural and political components is fundamental because an EWS can only be operational if all components fulfill their function. Furthermore, it is critical that the local authorities and people understand that an EWS cannot reduce risks to zero – its main objective is to avoid harm to human lives. Therefore, it needs to be accompanied by other risk reduction measures, in particular appropriate land-use planning.


Document: Author(s):
Section: Conclusion
Type: text

EWS in extreme environments, such as glacier lakes, bring many challenges. The system needs to be carefully designed to achieve robust energy provision, smooth and reliable data transmission, measurement of critical physical variables and the required degree of redundancy. Many months of system calibration are indispensable. Local authorities must clearly understand this. In addition, maintenance of the EWS needs to be budgeted annually by the local authorities to guarantee the sustainability of the system.

The Laguna 513 EWS has become the model for several other EWS in the Peruvian Andes (e.g. Huaraz-Palcacocha, Urubamba-Chicón) and beyond. While the experience and capacity development can be replicated, it is also crucial to acknowledge that every location is an individual case with special characteristics that need appropriate attention.

#


Document: Glacier Lakes & Floods
Section: Glacier Lakes & Floods
Type: text

Glacier lakes are indirectly a result of climatic changes expressed by glacier fluctuations. During the earth history glacier lakes have survived only a short time period, but glacier lake outbursts had a profound impact in shaping the landscape. The dominant type of glacial lakes may shift over time from glacier-dammed lakes to moraine-dammed lakes in different glaciation situations in dependence of the topographic setting. In the course of deglaciation, the types of glacier will change and therefore the type of glacial lakes. Glacier-dammed lakes occur mainly in times of glacier advance, but there are also some constellations in which they are the result of glacier retreat. Moraine-dammed lakes are mainly linked to glacier retreat regimes.

Glacier lake outbursts from these lakes can attain extremely high peak discharges by sudden dam failure. As a consequence of topographical and climatic conditions, a characteristic distribution pattern of glacier lake types can be recognized in individual mountain areas. In terms of natural hazards, sudden outbursts from small lakes with high peak discharges may have a more severe impact on human settlement than the gradual drainage of large ice-dammed lakes. Not all glacier lake outbursts have to be necessarily released as water floods. They may also occur as debris flow with a high hazard potential.

data/('Glacier Lake Management_ GLOF Early Warning System', '.pdf')_extraction.md


Document: Glacier Lakes & Floods
Section: Glacial Lake Outburst Flood (GLOF)
Type: text

The acronym GLOF is used for glacier floods caused by the drainage of naturally dammed lakes in the glacier, on or at the margin of glaciers. "Glacier floods represent in general the highest and most far reaching glacial risk with the highest potential of disaster and damages". A lake outburst can be triggered by several factors: ice or rock avalanches, the collapse of the moraine dams due to the melting of ice buried within, the washing out of fine material by springs flowing through the dam (piping), earthquakes or sudden inputs of water into the lake e.g. through heavy rains or drainage from lakes further up-glacier. The glacier lake may breach at the end-moraine or at the lateral moraine.


Document: Glacier Lakes & Floods
Section: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State
Type: text

Through extensive research, CDAC has deployed India's first GLOF Early Warning System for Sikkim to predict GLOFs, which will help the Government authorities in case of an impending GLOF event. The GLOF Early Warning System has the following capabilities:


Document: Glacier Lakes & Floods
Section: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State
Type: figure
Figure

Image /page/1/Picture/10 description: A screenshot of a software application titled "GLOF Information System". The main window features a scenic background image of snow-capped mountains rising above a layer of clouds under a clear blue sky, with a dark mountain silhouette on the right. Overlaid on this image in large white text is the title "Glacial Lake Outburst Flood Management System". The application has a menu bar with the options: Home, GLOF Analysis, Settings, Help, and About Us. At the bottom, it displays "Powered by ICIMOD 2012", "Version 1.3", and a red button for "Licensing Policy".


Document: Glacier Lakes & Floods
Section: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State
Type: figure
Figure

Image /page/1/Picture/11 description: A simple, green icon of a person standing with their arms raised and spread out to the sides, and their legs apart, against a white background. The figure is slightly blurry.


Document: Glacier Lakes & Floods
Section: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State
Type: text

Water Level Sensors developed indigenously by C-DAC, has been deployed at Shakho Chho Glacial Lake, North Sikkim.

The near-real time sensors transmit data through INSAT satellite to control centre at Gangtok (SSCST).

The flood simulation model runs the simulation for various GLOF scenarios. The model is capable of presenting Flood Simulation, Inundation information and Flood Arrival Time, in the event of a glacier lake outburst flood.


Document: Glacier Lakes & Floods
Section: GLOF - Monitoring & Early Warning System C-DAC's GLOF Early Warning System for Sikkim State
Type: figure
Figure

Image /page/2/Picture/4 description: A screenshot of a software application titled 'GLOF Information System'. The 'Lake Information' tab is selected, displaying data for the year 2010. The selected lake is '78A013A1084027 (South Lhonak Chhu)'. The 'Lake Details' section provides the following information: Lake ID: 78A013A1084027, Lake Name: South Lhonak Chhu, alternate Name: South Bonak chhu, Lake Type: C, Lake Class: M, Lake Condition: Overtopping, Past GLOF occurrence: Yes, Dam Type: --NA--, Dam Condition: --NA--, Perimeter [mtr.]: 4957.20, Area [sq. mtr.]: 1009353.73, and Sensor Installed: --NA--. To the right, there is a small satellite image of the lake and a green box indicating 'Total Lakes in Sikkim: 203'.


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: text

Analysis, Glacial Lake Management & Flood Simulation Statistics.

The GLOF Analysis Tool has three sub-tools: Lake Information, Simulation & the Sensor Information. The "Lake Information" tool gives basic information about the glacial lake selected by the user from the drop-down menu, such as, Lake Name, Lake Condition, etc. The satellite image of the glacial lake is shown on the right hand side.

The Simulate tool gives information regarding the GLOF simulation progress and the outputs of the simulation. The user will be able to see the time at which the GLOF at a particular glacial lake occurred and the period of GLOF simulation.


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: figure
Figure

Image /page/2/Picture/9 description: A screenshot of a software application titled "GLOF Information System". The window displays the "Sensor Information" tab. On the left, there are buttons for "View", "Add", "Edit", and "Delete". The main area shows details for a sensor. Under "View Sensor Information", a dropdown for "Sensor Installed on Lake" shows "70A0934184040 [ ]". Under "Sensor Details", the "Sensor ID" is "SM516", the "Sensor Location" Easting is "27.8709000000" and Northing is "86.6103400000". The fields for "Sensor Model", "Sensor Type", and "Sensor Accuracy" are blank. A green button indicates "Number of Lakes with sensor: 1".


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: text

The user can choose to see the simulation results such as Simulation video, Inundation map, Inundation report & Hydrographs.

The Sensor Information tool gives basic information about the sensors deployed at the glacial lakes for monitoring water-levels. Each sensor has a Sensor ID linked to the Lake ID. The user may add information of new sensors when sensors are deployed on new glacial lakes in future or delete existing ones, in case if they are decommissioned.

The Settings tool gives the user the flexibility to load the input data from any location on the computer. The data will be copied to an in-built database system for computations.


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: figure
Figure

Image /page/3/Picture/3 description: A screenshot of a software application titled "GLOF Information System". The window has a menu bar with options for "Home", "GLOF Analysis", "Settings", "Help", and "About Us". The main content area features buttons to "Connect to Database" and "Activate the Model". Below these are several sections, including "Input Settings", "Path Settings", "Database Settings", and "Simulation Settings". The "Input Settings" section is expanded, showing a field for "Lake Shapefile" with the "Year" set to "1990" and an "Upload" text box. A lower section, "Summary of Settings Made", displays a list of configurations:
1. GIS Path: C:/Program Files/Quantum GIS Wroclaw/bin/qgis.bat
2. Lakes in Year 1962: F:/All\_Glacier\_Data\_for\_QC/Lake Layers/Other Lakes/TA\_29Oct1962.shp


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: text

3. DB Host: localhost
4. Lakes in Year 1990: F:/All\_Glacier\_Data\_for\_QC/Lake Layers/Other Lakes/TA\_12Oct1990.shp
At the bottom of the window are buttons for "Save current Settings", "Restore last saved Settings", and "Delete".


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: figure
Figure

Image /page/3/Picture/4 description: A low-resolution screenshot of a mapping or GIS software interface. The main window displays a map with a large, irregularly shaped yellow polygon in the center. Inside the polygon, the text "Kanchipuram Taluk" is visible. On the left side of the screen, there is a control panel with various options and a data table. The top part of the panel is titled "Route and Locate" and has fields for "View" and "Layer," with "Taluk Name" selected in the Layer field. Below this, a table shows attribute data with columns such as "OBJECTID" and "Taluk Name".


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF
Type: text

The Query tool gives the user three options based on his expertise or authority in using the system. The "Ready-made Queries" option is for user who wishes to know basic information about the glacial lake of interest on the GIS interface. The queries are tailor-made for users who do not have expertise in GIS. In this mode, only the basic functionalities such as zoom in/out, info tool, select feature, etc. are only available on the GIS interface.

The Custom Query option invokes the "Advanced Query" tool for users who are familiar with the GIS query system. The needs to select the query of interest from the list of attributes displayed on the left panel. The output will be displayed as a map.

The Advanced Mode option invokes all the tools and features available on the GIS interface for the advanced users familiar with GIS.


Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),
Type: text

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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Contact Us > Centre for Development of Advanced Computing (C-DAC),
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CENTERS
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Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > LINKS
Type: text

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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > CONTACT US
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,
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Document: Glacier Lakes & Floods
Section: The Glacial Lake Outburst Flood Management System - Interface & functionalities This is the main interface of the GLOF Management System. It features tools for GLOF > Centre for Development of Advanced Computing C-DAC Innovation Park,
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Document: Glacier Retreat and Glacial Lake Outburst Floods (GLOFs)
Section: Summary
Type: text

Glacier retreat is considered to be one of the most obvious manifestations of recent and ongoing climate change in the majority of glacierized alpine and high-latitude regions throughout the world. Glacier retreat itself is both directly and indirectly connected to the various interrelated geomorphological/hydrological processes and changes in hydrological regimes. Various types of slope movements and the formation and evolution of lakes are observed in recently deglaciated areas. These are most commonly glacial lakes (ice-dammed, bedrock-dammed, or moraine-dammed lakes).

"Glacial lake outburst flood" (GLOF) is a phrase used to describe a sudden release of a significant amount of water retained in a glacial lake, irrespective of the cause. GLOFs are characterized by extreme peak discharges, often several times in excess of the maximum discharges of hydrometeorologically induced floods, with an exceptional erosion/transport potential; therefore, they can turn into flow-type movements (e.g., GLOF-induced debris flows). Some of the Late Pleistocene lake outburst floods are ranked among the largest reconstructed floods, with peak discharges of up to 107 m3/s and significant continental-scale geomorphic impacts. They are also considered capable of influencing global climate by releasing extremely high amounts of cold freshwater into the ocean. Lake outburst floods associated with recent (i.e., post-Little Ice Age) glacier retreat have become a widely studied topic from the perspective of the hazards and risks they pose to human society, and the possibility that they are driven by anthropogenic climate change.

Despite apparent regional differences in triggers (causes) and subsequent mechanisms of lake outburst floods, rapid slope movement into lakes, producing displacement waves leading to dam overtopping and eventually dam failure, is documented most frequently, being directly (ice avalanche) and indirectly (slope movement in recently deglaciated areas) related to glacial activity and glacier retreat. Glacier retreat and the occurrence of GLOFs are, therefore, closely tied, because glacier retreat is connected to: (a) the formation of new, and the evolution of existing, lakes; and (b) triggers of lake outburst floods (slope movements).

**Keywords:** glacier retreat, GLOFs, natural hazards, slope movement, climate change

**Subjects:** Floods, Glacial Lake Outburst (GLOFs)

data/('Glacier Retreat and Glacial Lake Outburst Floods (GLOFs)', '.pdf')_extraction.md


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Type: text

The GLOF Hazard Landscape: Formation and Dam Mechanics

data/('GLOF Early Warning System Factors_gemini', '.pdf')_extraction.md


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Defining the GLOF Phenomenon
Type: text

A Glacial Lake Outburst Flood (GLOF) is the sudden and catastrophic release of a significant volume of water from a lake fed by glacier melt. These lakes can form at the side, in front, within, beneath, or on the surface of a glacier, impounded by natural dams composed of moraine complexes, glacial ice, or bedrock. GLOFs are distinguished from more common hydrometeorological floods by their defining characteristics: extreme peak discharges that can be several times greater than those of rainfall-induced floods, and an exceptional capacity for erosion and sediment transport. This high energy allows them to mobilize vast quantities of rock, ice, and mud, often transforming into highly destructive debris flows as they travel downstream. These events represent one of the most severe and unpredictable hazards in high-mountain regions, posing a significant threat to human lives, livelihoods, and critical infrastructure such as roads, bridges, and hydropower facilities. The rapid onset and immense destructive power of GLOFs necessitate the development of specialized and highly reliable Early Warning Systems (EWS) to mitigate their devastating impacts.

Glacial Lake Genesis in a Warming Climate

The formation and proliferation of glacial lakes are a direct and highly visible consequence of global climate change.5 As atmospheric temperatures rise, glaciers worldwide are retreating at an accelerated rate.7 This process of deglaciation uncovers over-deepened basins and depressions in the landscape, often located between the retreating glacier terminus and a proglacial moraine—a ridge of unconsolidated debris deposited by the glacier at its furthest extent.1 These depressions subsequently fill with meltwater, giving birth to new glacial lakes.1

This process is not static; it is a dynamic feedback loop. The formation of a glacial lake can itself accelerate further ice loss from the parent glacier through calving (the breaking off of ice chunks from the glacier front) and subaqueous melting at the ice-water interface. Consequently, as the climate continues to warm, existing lakes expand and new lakes form, leading to a rapid global increase in the number, total area, and stored volume of glacial water. This escalates the potential energy available for release, inherently increasing the potential magnitude and frequency of future GLOF events. In High Mountain Asia, for instance, a warming rate of 0.32 °C per decade is driving this widespread expansion of glacial lakes, amplifying the regional GLOF hazard.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics
Type: text

The nature of a GLOF event—its likelihood, trigger mechanism, and recurrence potential—is fundamentally dictated by the type of dam impounding the glacial lake. Understanding the distinct mechanical properties and failure modes of the two primary dam types, moraine and ice, is therefore a prerequisite for designing an effective and appropriately targeted EWS.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Moraine-Dammed Lakes
Type: text

Moraine-dammed lakes are the most common type in many of the world's most vulnerable mountain ranges, including the Hindu Kush Himalaya. The dams are composed of moraine: a heterogeneous and unconsolidated mixture of boulders, gravel, sand, and fine sediment left behind by a retreating glacier. These structures are inherently weak, poorly sorted, and often highly permeable, making them susceptible to various failure mechanisms. Furthermore, many moraine dams contain a core of buried glacial ice, which can melt over time, compromising the dam's structural integrity from within.

The failure of a moraine dam is typically a singular, catastrophic event. Mechanisms such as overtopping by a large wave or excessive inflow can initiate a process of "breach incision," where flowing water rapidly erodes a channel through the loose dam material.4 This process creates a positive feedback loop—a deeper channel allows a higher discharge, which in turn accelerates erosion—leading to a complete and often explosive failure of the dam structure.4 Because the dam is often destroyed in the process, the threat of repeated GLOFs from the same lake is generally low.4 The primary hazard is therefore a single, high-magnitude flood.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dam Typology and Associated Failure Characteristics > Ice-Dammed Lakes
Type: text

Ice-dammed lakes form when a glacier itself blocks the natural drainage of a valley.17 These lakes are responsible for a large number of GLOF events globally, particularly in regions like Alaska and Patagonia.4 Unlike moraine dams, ice dams do not typically fail through erosional breaching. Instead, they are subject to unique failure mechanisms driven by the physical properties of ice. The most common mechanism is flotation, which occurs when the water level in the lake reaches approximately 90% of the ice dam's height. Due to the density difference between water () and ice (), the buoyant force of the water can physically lift the glacier off its bed, allowing water to escape through subglacial tunnels.4

A critical characteristic of ice-dammed GLOFs is their potential for recurrence. As the lake drains and the water level drops below the 90% threshold, the glacier settles back onto its bed, and the subglacial conduits can close up, resealing the dam.4 This allows the lake to refill, setting the stage for future outburst events.17 Ice-dammed lakes can therefore produce cyclic GLOFs, posing a persistent, long-term hazard that may recur annually or over several years.4

The fundamental distinction between the failure modes of moraine and ice dams necessitates tailored EWS philosophies. An EWS designed for a moraine-dammed lake, which is prone to a single, catastrophic failure, must prioritize the detection of immediate, high-energy triggers like landslides or extreme rainfall that can initiate a breach. The focus is on capturing the precursors to a singular, high-consequence event. Conversely, an EWS for an ice-dammed lake, which can fail and reform cyclically, requires a design focused on long-term, continuous monitoring of the recurring hazard cycle. This system must track the slower buildup of hydrostatic pressure and lake level over multiple seasons to predict the next drainage event. This critical difference impacts the required sensor longevity, power management strategies, data analysis algorithms, and the nature of community preparedness and response planning.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers
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Meteorological conditions are a fundamental driver of GLOF activity, acting as both long-term conditioning factors that increase overall hazard levels and as acute, short-term triggers that can initiate a dam failure. An effective EWS must be capable of monitoring and interpreting these atmospheric influences across different timescales.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > The Role of Temperature
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Rising atmospheric temperature is the primary climate-driven force increasing GLOF risk globally. Its influence is multifaceted, affecting the entire glacial environment from the ice mass itself to the stability of the surrounding landscape.

Long-Term Conditioning: Sustained warming trends are the principal cause of glacier mass loss, which directly leads to the formation of new glacial lakes and the expansion of existing ones. This relentless increase in the number and volume of lakes inherently elevates the potential GLOF hazard across a region. Beyond the lakes themselves, rising temperatures have a profound impact on the geotechnical stability of high-mountain terrain. As temperatures increase, permafrost—perennially frozen ground that acts as a cement binding rock and soil together—begins to thaw and degrade. This process significantly reduces the stability of rockwalls and moraine slopes surrounding glacial lakes, "pre-conditioning" the landscape and increasing the probability of rockfalls, avalanches, and landslides. These mass movements are a primary GLOF trigger, as detailed in Section 4.

**Short-Term Triggering:** Superimposed on the long-term warming trend are short-term extreme temperature events or heatwaves. These periods of intense warmth can cause a rapid and dramatic acceleration of snow and ice melt.7 The resulting surge in meltwater runoff can overwhelm a glacial lake's natural outflow capacity, causing the water level to rise rapidly over a period of hours to days.2 This rapid increase in lake volume can directly trigger an outburst flood through the mechanism of dam overtopping, particularly in moraine-dammed lakes.7 Analysis of historical GLOF events indicates a strong correlation, with outbursts being significantly more likely to occur during anomalously warm periods or during abrupt climatic transitions from cold to warm conditions.18


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Intense Precipitation and Snowmelt Dynamics
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While temperature sets the long-term stage, intense precipitation events are a common and powerful proximate trigger for GLOFs. The link between climate change and such events is well-established; a warmer atmosphere can hold more moisture (approximately 7% more for every 1°C of warming), which increases the potential for heavier downpours and more extreme rainfall events.20

**Mechanism of Failure:** An episode of intense, heavy rainfall or an event combining rain with rapid snowmelt can deliver a massive volume of water into a glacial lake's catchment area in a very short time.2 This sudden influx causes the lake level to rise swiftly, potentially leading to the overtopping of the dam crest.4 This mechanism is particularly critical for moraine dams due to their unconsolidated nature.

The Positive Feedback Loop of Breach Incision: The process does not stop at simple overtopping. Once water begins to flow over the crest of a moraine dam, its erosive power starts to cut a channel into the loose material.4 This initial incision creates a pathway for a greater volume of water to be discharged. This increased flow possesses even more erosive energy, which deepens and widens the breach channel further. This self-reinforcing, or "positive feedback," cycle can cause the dam to fail catastrophically in a matter of hours, releasing the entire volume of the lake in a devastating flood wave.4

**Link to Weather Systems:** In many high-mountain regions, these extreme precipitation events are associated with large-scale meteorological patterns. In the Himalayas, for example, the summer monsoon can bring vigorous, moisture-laden systems that result in orographic lifting—where air is forced upward by mountain slopes, causing it to cool and release its moisture as extremely heavy rainfall. The 2013 Kedarnath disaster in the Indian Himalaya was a tragic example of a GLOF from Lake Chorabari triggered by exceptionally heavy rainfall.

The interplay between these meteorological factors creates a classic cascading hazard chain that a sophisticated EWS must be designed to monitor and interrupt. The causal sequence is not a simple linear progression from rain to flood. It begins with anthropogenic climate change, which leads to a warmer atmosphere capable of holding and delivering more moisture. This increases the frequency and intensity of extreme precipitation events. When such an event occurs over a glacial catchment, it causes a rapid rise in the lake level, leading to dam overtopping. This, in turn, initiates the critical positive feedback loop of breach incision, culminating in a GLOF. Simultaneously, the same long-term warming that fuels these storms is also degrading permafrost on adjacent slopes, increasing the likelihood of a landslide trigger. Therefore, an effective EWS cannot rely on a single parameter like a rainfall gauge. It must adopt a holistic approach, integrating meteorological forecasts to predict intense precipitation, temperature sensors to monitor melt rates and long-term warming trends, and geotechnical sensors to assess slope stability, recognizing that these factors are deeply interconnected and can amplify one another's hazardous effects.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Meteorological Drivers as Primary GLOF Triggers > Atmospheric Pressure: An Assessment of Influence
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The user query specifically requested information on the role of atmospheric pressure as a GLOF trigger. A thorough review of the scientific literature indicates that while atmospheric pressure is an integral component of weather systems, it is not considered a direct physical trigger for the failure of a glacial dam.1

The hazardous weather conditions associated with GLOFs, such as intense storms, are indeed characterized by low-pressure systems.19 However, the dam failure is caused by the physical impacts of the associated phenomena—namely, the mass of water from heavy precipitation or the rapid influx from temperature-driven melt—rather than the change in barometric pressure itself. The direct force exerted by atmospheric pressure changes on the lake surface or dam structure is negligible compared to the immense forces of hydrostatic pressure from the water column or the kinetic energy of an avalanche.

For the purpose of designing an EWS, monitoring atmospheric pressure is highly valuable, but its role is predictive rather than causative. As a key parameter measured by any standard Automatic Weather Station (AWS), barometric pressure trends are crucial for forecasting the approach of storm systems that could bring triggering conditions (i.e., heavy precipitation). Therefore, it serves as an important input for meteorological forecasting models that can provide advance warning of hazardous weather, but it should not be mistaken for a direct physical trigger of dam failure. The critical pressure that must be monitored for direct risk assessment is the **hydrostatic pressure** exerted by the lake's water, which is a function of water depth.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity
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While external meteorological and geotechnical events often act as the final trigger, the inherent state of the glacial lake and its dam determines the system's underlying susceptibility to an outburst. Factors such as the water level, the resulting hydrostatic pressure, and the long-term structural integrity of the dam are critical parameters that an EWS must continuously monitor.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Hydrostatic Pressure and Lake Level Thresholds
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The most direct and quantifiable threat posed by a growing glacial lake is the increase in hydrostatic pressure. As a lake fills with meltwater, its volume and depth increase, exerting a progressively greater force on the inner face of the impounding dam.1 A GLOF can occur through "dam self-destruction" when this hydrostatic pressure—the force exerted by the weight of the water column—exceeds the lithostatic pressure and shear strength of the dam material, causing a structural failure without a dynamic external trigger.27 This risk is amplified in deep lakes and where the dam has been weakened by other long-term degradation processes.27

Crucially for the design of a quantitative EWS, empirical research has identified specific, monitorable thresholds that correlate with a high probability of an outburst flood, particularly for ice-dammed lakes. Studies of recurring GLOFs have shown that an outburst becomes highly likely when the lake's **normalized depth (n')**—the ratio of the water depth to the dam height—exceeds a value of **0.60**.28 This critical depth corresponds to a typical water pressure on the dam face of approximately **510 kPa**.28

These empirically derived values are of paramount importance for EWS development. They provide a concrete, physics-based foundation for moving beyond qualitative hazard assessment to a quantitative, automated alarm system. A monitoring system equipped with water level and pressure sensors can be programmed with these thresholds to automatically escalate alert levels. For example, a normalized depth approaching 0.60 or a pressure nearing 510 kPa could trigger a "Yellow Alert" or "Warning" status, prompting heightened surveillance and preparedness activities. A continued rise beyond these thresholds could trigger a "Red Alert" or "Evacuation" order. These quantifiable parameters represent the most direct and reliable indicators of imminent risk from pressure-induced dam failure.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Lake-Centric Triggers: Water Level, Pressure, and Dam Integrity > Long-Term Dam Degradation Processes
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The structural integrity of a moraine dam is not static; it can degrade over time through slow, often invisible processes. These long-term mechanisms weaken the dam, reducing its ability to withstand hydrostatic pressure and making it susceptible to failure from a much smaller or less intense trigger event.

**Melting of Buried Ice:** Many moraine dams, particularly those formed during the Little Ice Age, contain significant cores of buried glacial ice. This ice, insulated by the overlying debris, can persist for centuries. However, as regional atmospheric temperatures rise due to climate

change, this buried ice begins to melt.2 The melting of the ice core causes a loss of volume and structural support within the dam, leading to subsidence, the formation of internal voids, and a general weakening of the entire structure.4 This internal degradation is a critical long-term destabilization mechanism that can ultimately lead to the dam's collapse.12

**Piping and Seepage:** Moraine dams are composed of unconsolidated materials with varying permeability. This allows water from the lake to seep into and through the dam structure. This seepage can create preferential flow paths within the dam. If the velocity of this internal flow is sufficient, it can begin to erode and transport fine-grained sediment from the dam's core, a process known as "piping". Over time, this internal erosion can create large conduits or "pipes" through the dam, progressively weakening its structure until it can no longer support the pressure of the impounded water, leading to a sudden and catastrophic failure.

The existence of these quantifiable pressure and depth thresholds provides a powerful tool for building an automated EWS. However, the "hidden" nature of long-term dam degradation processes like ice core melt and internal piping reveals a potential vulnerability in a monitoring strategy that relies solely on surface-level measurements. A dam that has been significantly weakened from within might fail at a hydrostatic pressure *below* the established 510 kPa threshold, meaning an alarm based on that value alone might come too late or not at all. This implies that a truly comprehensive EWS for a high-risk, moraine-dammed lake must be informed by a deeper understanding of the dam's internal composition and integrity. This requires a preliminary geotechnical assessment, potentially using techniques like Ground Penetrating Radar (GPR) to map the presence and extent of any buried ice core.32 The results of such a survey can provide a more accurate picture of the true risk level and allow for the calibration of alarm thresholds that are tailored to the specific structural condition of the dam, closing a critical blind spot in the monitoring strategy.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity
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While meteorological and lake-centric factors create and condition the GLOF hazard, the most common and often most violent triggers are dynamic geotechnical events. These triggers are characterized by their rapid onset, typically occurring over seconds to minutes, and their immense energy, which can overwhelm even a relatively stable dam.

Mass Movement Impacts: Avalanches, Landslides, and Displacement


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Waves
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The most frequently documented trigger for GLOFs, particularly from moraine-dammed lakes, is the sudden impact of a large mass movement into the lake basin.15 These events include ice avalanches from hanging glaciers, rockfalls from destabilized valley walls, or debris-laden landslides.4

**Mechanism of Displacement Wave Generation:** When a substantial volume of ice, rock, or debris rapidly enters a lake, it displaces a massive volume of water, generating a high-energy impulse wave, also referred to as a seiche or a tsunami-like wave. The size of this wave is a function of the volume, velocity, and geometry of the incoming mass. These waves can be exceptionally large, with observed or modeled heights reaching tens of meters. 35

Frequency and Dam Failure: This trigger mechanism is remarkably common. In the Himalayas, displacement waves from ice or rock avalanches are responsible for nearly half of all recorded moraine-dam failures. 12 Globally, it is the most common dynamic cause of GLOFs. 34 The resulting displacement wave can easily overtop the freeboard (the height of the dam crest above the water level). This initial overtopping can be powerful enough to cause immediate dam rupture, but more commonly, it initiates the catastrophic breach incision process. The initial surge of water over the dam crest begins to erode the unconsolidated moraine, and the subsequent reflected waves continue to overtop the dam, rapidly carving a breach that leads to the complete and violent drainage of the lake. 4

The prevalence of mass movements as the primary GLOF trigger has a profound implication for EWS design: a GLOF EWS cannot be merely lake-focused; it must be *catchment-focused*. A monitoring system that only includes sensors at the lake itself, such as water level gauges, would only detect the *consequence* of the trigger—the arrival of the displacement wave at the dam. This would provide, at best, only seconds to minutes of warning time, which is insufficient for any meaningful downstream evacuation. To provide a true *early* warning, the system must be capable of detecting the precursor to the trigger: the instability of the slope *before* it fails. This fundamentally expands the spatial scope and technological complexity of the required sensor network, necessitating the monitoring of the stability of surrounding slopes, hanging glaciers, and permafrost zones with instruments like geophones, extensometers, or remote sensing techniques like InSAR.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms
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In tectonically active mountain ranges like the Himalayas and the Andes, earthquakes are

another significant GLOF trigger. Seismic events can initiate dam failure through both direct and indirect pathways.

**Direct Impact:** Intense ground shaking during an earthquake can directly compromise the structural integrity of a moraine dam. The seismic waves can cause liquefaction of saturated sediments within the dam or induce cracking and slumping, leading to a rapid structural failure.2

**Indirect Impact (More Common):** More frequently, earthquakes act as an indirect trigger by initiating the very mass movements described in the preceding section.4 The seismic energy can be the final catalyst that destabilizes an already precarious slope, causing a massive ice or rock avalanche to plunge into the lake, thereby generating a displacement wave.39

Post-Earthquake Hazard Amplification: The GLOF hazard in a region can be dramatically amplified for months or even years following a major earthquake. A large seismic event can trigger thousands of landslides throughout a mountain range, depositing vast quantities of loose, unconsolidated sediment onto valley floors and slopes. This material then becomes readily available for entrainment by any subsequent flood. A GLOF that might have otherwise been a relatively clean water flood can scour and incorporate this loose debris, transforming into a much larger, denser, and more destructive debris flow. This means that even a small glacial lake, previously considered low-risk, could produce a catastrophic debris flow if its outburst path is now loaded with earthquake-induced landslide deposits. Hazard assessments must therefore be re-evaluated in the aftermath of any significant regional earthquake.

A second, paradigm-shifting realization for EWS design comes from the analysis of the seismic signals generated by GLOFs themselves. Detailed studies of past events, such as the 1994 GLOF from Lugge Tsho in Bhutan, have revealed that the flood's highly turbulent flow and the transport of boulders and sediment along the riverbed generate a continuous, high-frequency seismic signal. This GLOF-induced ground vibration is distinct from the short, sharp signal of an earthquake and can be detected by seismometers located up to 100 kilometers away. By using arrays of seismometers and applying signal processing techniques like cross-correlation, it is possible to not only detect the onset of a GLOF but also to track the location of the flood front as it propagates down the valley in real-time. This transforms the role of seismometers from merely being detectors of a potential trigger (an earthquake) to being a powerful, remote tool for detecting and tracking the hazard itself. This provides a robust, wide-area monitoring capability that is less vulnerable to being destroyed by the flood and can provide hours of warning time to downstream communities.

The following table synthesizes the primary GLOF triggers, their physical mechanisms, and the key parameters that can be monitored by an EWS.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Dynamic and Geotechnical Triggers: Mass Movements and Seismicity > Seismic Activity: Direct and Indirect Triggering Mechanisms
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Table

| Trigger Category           | Specific Trigger                                                                                | Physical Mechanism                                                                                | Primary Monitorable Parameters                                                         | Secondary/ Proxy Parameters                                                  | Typical Onset Time                                                       |
|-------------------------------|-----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------|
| Meteorological                | Intense Rainfall / Rapid Snowmelt                                                                            | Dam overtoppin g leading to positive feedback of breach incision.4                                                   | Precipitatio n intensity (); Rate of lake level rise ()                          | Air temperatur e; Snow line elevation; Barometric pressure trend | Hours to Days                                                            |
|                               | Sustained High Temperatures                                                                                | Accelerated glacier and snow melt increasing lake volume; Permafrost degradatio n weakening slopes.10 | Air temperatur e anomalies; Cumulative positive degree days             | Solar radiation; Glacier surface velocity                              | Days to Weeks (trigger); Years to Decades (conditionin g) |
| Lake-Centric                  | Hydrostatic Pressure                                                                                | Water pressure exceeds the structural strength (lithostatic pressure) of the dam, causing failure.27  | Lake water level/depth; Sub-surfac e water pressure (hydrostatic pressure) | Lake volume; Normalized depth ()                                          | Weeks to Months                                                          |
|                               | Dam Degradation                                                                                | Melting of buried ice core or internal erosion                                                                          | Dam surface temperatur e; Seepage rate/turbidit y                             | Ground-pe netrating radar (GPR) survey results (for                    | Years to Decades                                                         |
|                               |                                                                                | (piping) weakens the dam structure, lowering its failure threshold. 4                                                               | y; Surface subsidence (via InSAR)                                                      | ice core presence)                                                              |                                                                             |
| Dynamic / Geotechni cal | Ice/Rock Avalanche or Landslide                                                                              | Rapid mass entry into lake generates a large displaceme nt (seiche) wave, causing dam overtoppin g and breach. 1                    | Ground vibration (geophone/ seismomete r); Acoustic signal                                   | Slope deformatio n rate (InSAR, extensomet ers); Rock/ice temperatur e             | Seconds to Minutes                                                       |
|                               | Earthquake  Ground shaking directly compromis es dam stability or indirectly triggers mass movements into the lake. 7 |                                                                                | Seismic P- and S-wave arrival; Peak Ground Acceleratio n (PGA)                | Regional seismic hazard maps                                              | Seconds                                                                     |

Trigger Category
Specific Trigger
Physical Mechanism
Primary Monitorable Parameters
Secondary/ Proxy Parameters
Typical Onset Time


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS)
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The complex, multi-causal nature of GLOFs dictates that a reliable EWS cannot be based on a single sensor or parameter. Instead, a robust framework must be built upon an integrated,

multi-sensor approach that provides detection, verification, and redundancy.36 This framework should be structured around the four internationally recognized components of a modern EWS: Risk Knowledge, Monitoring and Warning, Dissemination and Communication, and Response Capability.36 The following sections focus on the technological aspects of the Monitoring and Warning component.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > A Multi-Sensor, Integrated Approach
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The optimal EWS design is a hierarchical, multi-scale system that intelligently allocates monitoring resources based on risk. This approach avoids the financially and logistically prohibitive task of installing dense in-situ sensor networks at every one of the thousands of glacial lakes in a region.1

The framework begins with wide-area **surveillance** using freely available, moderate-resolution satellite data (e.g., Landsat, Sentinel) to create and regularly update comprehensive glacial lake inventories. This regional-scale analysis identifies "hotspots"—lakes that are expanding rapidly, are situated beneath potentially unstable slopes, or exhibit other characteristics associated with high risk. These identified hotspots are then prioritized for more focused and resource-intensive **intensive care** monitoring. This may involve tasking high-resolution commercial satellites for more detailed analysis or, for the most critical and potentially dangerous glacial lakes, deploying a ground-based network of in-situ sensors. This tiered strategy ensures that the most sophisticated monitoring efforts are directed where the risk is greatest, creating a scalable and economically viable national or regional EWS.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > In-Situ Monitoring Technologies and Parameters
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In-situ, or ground-based, sensors are the cornerstone of a real-time GLOF alarm system. They provide high-frequency, continuous data directly from the hazard source, enabling the detection of rapid-onset triggers and the tracking of critical parameters against predefined alarm thresholds.

- Automatic Weather Stations (AWS): An AWS is fundamental for monitoring meteorological triggers. It should be equipped to measure air temperature, precipitation (type and intensity), solar radiation (a key input for melt models), wind speed, and barometric pressure (for weather system forecasting).50
- Water Level and Pressure Sensors: These are the most direct way to monitor the state

of the lake. Technologies include non-contact sensors like ultrasonic or radar gauges, which are mounted above the water surface, and submersible pressure transducers placed in the lake.32 They provide continuous data on the lake's water level, allowing for the calculation of the rate of rise and the real-time tracking of hydrostatic pressure against critical thresholds.53

- Geophones and Acoustic Sensors: Geophones are sensitive instruments that detect
  ground vibrations. Deployed on or near the moraine dam and on surrounding slopes, they
  can detect the high-frequency seismic signals generated by rockfalls, ice avalanches, or
  the initial dam breach, providing an immediate warning of a dynamic trigger event.36
  Acoustic sensors can similarly detect the audible sound of these events.
- Seismic Sensors (Seismometers and Accelerometers): These instruments serve a dual purpose. A local network can detect regional earthquakes that may act as triggers.32
   More significantly, a regional network of seismometers can be used to detect the distinct, continuous ground vibrations generated by the GLOF itself as it moves downstream, providing a powerful tool for remote detection and tracking.41
- Ground-Based Cameras and Radar: Automatic, solar-powered cameras can provide invaluable visual information, capturing images of the glacier terminus to monitor calving activity, the dam face to identify seepage or slumping, and the lake surface to detect waves.36 Ground-based interferometric radar can be used for high-precision monitoring of movement on the dam or critical slopes.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring
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Remote sensing from satellite platforms provides the essential broad-area context and long-term monitoring capability that is impossible to achieve with in-situ sensors alone. Much of this data is freely and publicly available, providing a cost-effective foundation for regional risk assessment.

- Optical Satellite Imagery: Platforms like the Landsat series (USGS) and Sentinel-2 (ESA) provide multispectral imagery that is crucial for creating lake inventories and monitoring long-term changes in lake area, number, and color (which can indicate turbidity and sediment load).3 The increasing application of machine learning and deep learning algorithms to this data allows for the automated and rapid delineation of thousands of lakes, making large-scale analysis feasible.54
- Synthetic Aperture Radar (SAR): SAR satellites, such as Sentinel-1, are indispensable for GLOF monitoring due to their ability to acquire images through clouds and at night.7 This overcomes the primary limitation of optical imagery in persistently cloudy mountain regions. SAR data is used to monitor changes in lake area and can detect the presence of floating ice.56 An advanced technique, Interferometric SAR (InSAR), uses pairs of SAR

- images to detect and measure ground surface deformation with millimeter-level precision, making it an exceptionally powerful tool for monitoring the stability of moraine dams and surrounding slopes for precursor movements.55
- Digital Elevation Models (DEMs): DEMs provide the 3D topographic data necessary for GLOF hazard assessment. They are used to characterize the geometry of the lake basin and dam, estimate lake volume, and, crucially, to run hydrodynamic models that simulate potential flood paths, inundation depths, and travel times for downstream communities.46
   Several global and regional DEMs are freely available for this purpose.

The following table provides a comparative overview of key sensor technologies, detailing the parameters they measure, their operational characteristics, and their availability. This information is intended to guide the selection of an appropriate suite of technologies for a comprehensive EWS.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Framework for a GLOF Early Warning System (EWS) > Remote Sensing and Data Integration for Comprehensive Monitoring
Type: table
Table

| Technology Type                       | Specific Instrument                                           | Parameter(s) Measured                               | Relevance to GLOF Trigger                                                                         | Advantages                                                                        | Disadvantages/Constraints                                                              | Data Availability                                                                   |
|---------------------------------------|---------------------------------------------------------------|-----------------------------------------------------|---------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|
| <b>In-Situ Sensor</b>             | Radar Water Level Sensor                             | Lake water level, rate of rise             | Monitors hydrostatic pressure, overtopping risk52                                        | High temporal resolution, real-time data, non-contact              | High cost, difficult installation/maintenance, power requirements       | Commercial Purchase                                                              |
|                                       | Submersible Pressure Transducer                         | Hydrostatic pressure, water temperature    | Direct measurement of pressure on dam53                                                  | High accuracy, continuous data                                              | In-water deployment risk (icebergs, currents), cable damage             | Commercial Purchase                                                              |
|                                       | Geophone                                                      | High-frequency                                      | Detects rock/ice                                                                               | Highly sensitive                                                               | Can be sensitive                                                                    | Commercial                                                                          |
|                                       |                                                               | ground vibration                                 | avalanches, dam breach onset36                                                              | to impact events, provides immediat e trigger alert                | to noise (wind, animals), requires careful placemen t                | Purchase                                                                            |
|                                       | Seismom eter                                               | Broad-sp ectrum ground motion              | Detects earthqua ke triggers and tracks GLOF propagati on41               | Can detect events remotely (100+ km), wide-are a coverage | Requires specializ ed analysis, high initial cost                    | National/ Global Networks (often public); Commerc ial Purchase |
|                                       | Automati c Weather Station (AWS)                  | Temperat ure, precipitat ion, radiation | Monitors meteorol ogical triggers and melt condition s50                        | Provides essential data for forecasti ng and melt modeling      | Requires regular maintena nce/calibr ation in harsh environm ents | Commerc ial Purchase                                                          |
| Remote Sensing                     | Optical Satellite (e.g., Sentinel- 2, Landsat) | Lake area, color, snow cover            | Long-ter m monitorin g of lake expansio n and catchme nt condition s45 | Wide area coverage , long historical archive, free data         | Obscure d by clouds, limited use in winter/da rkness                 | Freely available (ESA/US GS)                                               |
|                                       | SAR Satellite (e.g.,                                    | Lake area, surface                            | All-weath er lake/slope                                                                     | All-weath er, day/night                                                     | Complex data processin                                                           | Freely available (ESA)                                                        |
| Sentinel-1)                           | deformation (InSAR), surface texture                          | monitoring7                                         | capability, detects subtle ground movement                                                        | geometric distortions in steep terrain                                            |                                                                                |                                                                                |
| High-Resolution Commercial Satellites | Detailed imagery of dam, slopes, glacier terminus             | Detailed site inspection, change detection          | Very high spatial detail (<1 m)                                                                   | High cost per image, requires specific tasking                                    | Commercial Purchase                                                                    |                                                                                |

Technology Type
Specific Instrument
Parameter(s) Measured
Relevance to GLOF Trigger
Advantages
Disadvantages/Constraints
Data Availability


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies
Type: text

The theoretical principles of EWS design are best understood through their application in real-world, high-risk environments. Case studies from around the globe provide invaluable lessons on successful strategies, technological challenges, and the critical importance of integrating technology with institutional and community frameworks.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Kyagar Glacier, China
Type: text

The EWS implemented at the ice-dammed Kyagar Lake in the Karakoram Mountains represents a model of an effective, integrated system designed for a remote and recurring hazard. 50 The system combines remote sensing with a network of terrestrial stations. SAR satellite imagery is used to monitor the seasonal filling of the lake and estimate its volume. 50 This is complemented by a solar-powered, automatic observation station located near the ice dam, which records meteorological data and takes daily images of the glacier terminus. 50 Crucially, the system includes two additional monitoring stations with radar water level sensors located far downstream on the Yarkant River. This distributed network provides exceptional warning lead times: an outburst detected at the glacier station provides 34–36 hours of warning, while the downstream stations provide 22 hours and 7.5 hours of warning, respectively. 50 When a GLOF is detected, an automated alarm is sent via satellite to the mobile phones of authorities, enabling timely emergency response. The Kyagar system demonstrates

the power of a layered monitoring network to maximize warning time.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Laguna 513, Peru
Type: text

The EWS for Laguna 513 in the Cordillera Blanca was specifically designed to address the region's primary trigger: ice and rock avalanches from the steep slopes of Mount Hualcán.36 The core of the monitoring component consists of four geophones installed on the moraine dam to detect the ground vibrations from an avalanche impact, and two cameras providing a constant visual feed of the glacier face and the dam.36 This data is transmitted in near real-time to a data center in the downstream city of Carhuaz. The system is built on a clear, tiered warning protocol with defined thresholds for geophone measurements that trigger different alert levels (yellow, orange, red).36 This case highlights a system tailored to a specific, dominant trigger mechanism. It also underscores the importance of a well-defined institutional framework; by law, only the mayor can authorize an evacuation, so the EWS is designed to provide actionable information to a specific decision-maker.36


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Thorthormi Lake, Bhutan
Type: text

The experience at Thorthormi Lake in the Bhutan Himalayas serves as a critical lesson in the operational challenges of maintaining an EWS in one of the world's most extreme environments. During a GLOF event in October 2023, the Automatic Water Level Station (AWLS) at the lake was completely damaged and washed away by the flood. 59 This event highlights the extreme vulnerability of in-situ equipment and underscores the absolute necessity of designing hardened, robust systems and ensuring redundancy. If a single point of failure (the destruction of the primary sensor) can disable the entire warning capability, the system is flawed. Furthermore, a study of a 2019 GLOF event at the same location revealed that the first warning for many residents came not from the official EWS, but from natural indicators—the unusually loud sound of the river and ground vibrations. 60 This demonstrates that community-based knowledge and observation are vital components of a resilient warning system and must be formally integrated with technical monitoring. The recommendations following the 2023 event—to relocate the AWLS, add more sensors for redundancy, and enhance community planning—point to a continuous process of adaptation and improvement. 59


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > EWS in Practice: Insights from Global Case Studies > Imja Tsho and Tsho Rolpa, Nepal
Type: text

The cases of Imja Tsho and Tsho Rolpa in Nepal illustrate the long-term evolution and challenges of GLOF risk management. Tsho Rolpa was one of the first lakes in the region to have a technical EWS installed in the late 1990s, which included water level sensors and a series of 19 downstream siren towers. However, over two decades later, this system has reportedly fallen into disrepair due to a lack of sustained funding for maintenance, leaving the community vulnerable once again. This is a stark reminder that the financial commitment for an EWS cannot end with its installation; a long-term operational and maintenance budget is non-negotiable. In contrast, a pilot project at Imja Lake has demonstrated the potential of modern, lower-cost technologies. A Wi-Fi-based network was established to connect monitoring devices, including a field server collecting meteorological data and water levels, and to provide internet connectivity to remote villages. This approach not only facilitates data transmission for the EWS but also provides co-benefits to the community, which can help foster local ownership and support for the system's maintenance.

Synthesizing the lessons from these diverse case studies reveals a critical conclusion: technology alone is insufficient to create a successful EWS. The most sophisticated sensor network is rendered useless if it is not supported by a robust institutional framework, a sustainable long-term maintenance plan, and deep engagement with the at-risk community. The Kyagar and Laguna 513 systems succeed because their technology is embedded within a clear operational protocol involving specific authorities. The Thorthormi case demonstrates that technology is fragile and that local, traditional knowledge can be a life-saving form of redundancy. The Nepal cases starkly illustrate that an EWS is a long-term commitment, not a one-time project; without sustained funding for upkeep, the initial investment can be entirely lost. Therefore, the design process for an EWS must extend beyond hardware and software specifications. It must include a multi-year budget for operations and maintenance, a plan for co-designing warning and response protocols with local emergency managers, and a comprehensive strategy for community training, education, and drills to build trust and ensure that a warning, when issued, leads to effective, life-saving action.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation
Type: text

Based on the comprehensive analysis of GLOF triggers, monitoring technologies, and practical case studies, the following recommendations are provided to guide the design and

implementation of a robust and effective GLOF Early Warning System.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Prioritizing Key Monitoring Parameters for a Tiered Warning System
Type: text

A scalable and resource-efficient strategy should be adopted, focusing monitoring efforts in a tiered approach based on assessed risk levels.

- Tier 1 (Regional Surveillance): This foundational layer should involve the systematic, continuous monitoring of all glacial lakes within a given region using freely available satellite data. Annual or bi-annual inventories should be created using optical (Landsat, Sentinel-2) and SAR (Sentinel-1) imagery to track changes in lake area and number. This wide-area surveillance identifies emerging threats and allows for the prioritization of resources.
- Tier 2 (Targeted Monitoring): For lakes identified in Tier 1 as high-risk (e.g., rapid expansion, proximity to critical infrastructure, location beneath unstable slopes), a more intensive monitoring schedule should be implemented. This includes more frequent analysis of high-resolution commercial satellite imagery and the application of advanced techniques like InSAR to detect and quantify slope deformation and dam subsidence, which are precursors to failure.
- Tier 3 (In-Situ Alarm System): For the most critical, high-consequence lakes designated as "Potentially Dangerous Glacial Lakes" (PDGLs), a permanent, real-time in-situ sensor network is warranted. This network must be designed with redundancy and should prioritize the continuous monitoring of the most immediate trigger parameters:
  - 1. Rate of Lake Level Rise: The most direct indicator of rapid inflow from melt or rainfall.
  - 2. **Ground Vibration:** Geophones placed on the dam and surrounding slopes to provide an instantaneous alert for an avalanche trigger.
  - 3. **Real-time Meteorological Data:** An on-site AWS to correlate lake level changes with temperature and precipitation events.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Integrating Data Streams for Predictive Modeling and Anomaly Detection
Type: text

An advanced EWS should move beyond simple, single-parameter threshold alarms. All data streams—from remote sensing platforms and in-situ sensors—should be transmitted to a central data processing hub. This integrated dataset enables more sophisticated analysis. Machine learning algorithms can be developed and trained on this multi-parameter data to

identify complex correlations and anomalies that may signal an impending GLOF. For example, a model could learn to recognize a hazardous signature composed of a combination of factors—such as several days of above-average temperatures, the onset of intense rainfall, and a subtle increase in microseismic activity on a nearby slope—that would be missed by individual sensor alarms. This data fusion approach enhances predictive capability and can increase warning lead times.


Document: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems
Section: A Comprehensive Analysis of Glacial Lake Outburst Flood (GLOF) Triggers for the Development of Advanced Early Warning Systems > Recommendations for EWS Design and Implementation > Establishing Actionable Thresholds and Robust Warning Protocols
Type: text

The ultimate purpose of the technology is to trigger a timely and effective human response. This requires a clear, unambiguous warning protocol.

- Calibrate Actionable Thresholds: The system's alarm logic should be based on
  empirically derived, physically meaningful thresholds. The normalized depth of >0.60 and
  hydrostatic pressure of >510 kPa provide an excellent starting point for calibrating alarms
  related to dam stability failure.28 Thresholds for other parameters, such as rainfall
  intensity () or the magnitude of a seismic signal from a geophone, must also be
  established through site-specific analysis and modeling.
- Develop a Multi-Level Warning Protocol: A tiered warning system, similar to the one
  used in Peru (Green for normal, Yellow for alert, Orange for prepare to evacuate, Red for
  evacuate), should be developed in close collaboration with local and national disaster
  management authorities.36 Each level must be tied to specific sensor readings or model
  outputs and must have a corresponding, pre-agreed set of actions for authorities and
  the public.
- Ensure Redundancy in Monitoring and Communication: The system design must assume that components will fail. Redundancy is critical. This means deploying multiple sensors to measure the same parameter at critical locations and, equally important, establishing multiple channels for disseminating warnings. A robust communication plan should utilize sirens, mobile phone alerts (SMS and cell broadcast), radio broadcasts, and designated community messengers to ensure the warning reaches everyone at risk, even if one communication pathway fails.36 Given that some GLOF triggers provide only minutes of warning, the entire system from detection to dissemination must be automated and optimized for speed.44


Document: The Google Flood Forecasting Initiative
Section: The Google Flood Forecasting Initiative
Type: text

Sella Nevo $^{1[0000-0002-4743-3634]}$

Google Research, Tel Aviv

Abstract. The Google Flood Forecasting Initiative is the world's first large-scale machine-learning-based operational flood forecasting system. It currently covers more than 360 million people, and provides flood warnings to governments, disaster management organizations, and individuals at risk. It combines two separate models - a hydrologic model and an inundation model - which together can provide high accuracy, actionable forecasts for flooding in upcoming days.

**Keywords:** Flood forecasting · Hydrology · LSTM.


Document: The Google Flood Forecasting Initiative
Section: 1 Introduction
Type: text

Floods are responsible for 5,000 to 60,000 fatalities [5, 18, 4, 17, 12, 11], affect between 95 million and 250 million people [5, 13, 12, 17, 4, 22], and cause between \$21 and \$33 billion in economic damages annually [3–5]. The majority of these harms are caused by riverine floods [17] - where a river overflows its banks and inundates the floodplain around it. The frequency and severity of floods has been increasing in recent decades [21, 10], and are expected to rise significantly in the future due to climate change, land use changes and other long term processes [16, 15]. On a brighter note, reliable early warning systems have been shown to prevent up to 43% of fatalities [2, 26], and up to 50% of economic damages [23, 1]. They have also been shown to have an impressive cost-benefit ratio of 1:9 [6], and much higher in low and middle-income countries (LMICs) [25], making them a top climate change adaptation policy recommendation by the United Nations and the World Bank.

Human costs of flooding are heavily concentrated in LMICs - for example, about half of all flood-related deaths globally occur in India and Bangladesh [17]. Despite the promise of early warning systems, many of these countries lack the resources for the comprehensive data collection, ongoing recalibration and maintenance, and computational resources that classic flood forecasting systems demand.

The Google Flood Forecasting Initiative aims to provide accurate and actionable flood warnings globally. It utilizes machine learning to create forecasting systems that are both more accurate and more scalable than classic flood forecasting systems, and then disseminates the resulting warnings via Google's various interfaces (Search, Maps, and smartphone notifications) as well as specialized interfaces to support governments and NGOs engaged in disaster management. At the time of writing, the system provides flood forecasting systems to regions covering more than 360 million people.


Document: The Google Flood Forecasting Initiative
Section: 2 Problem Statement
Type: text

Flood forecasting systems aim to provide governments, NGOs and individuals in the affected region reliable and actionable information about upcoming floods. The modeling task of riverine flood forecasting systems can often be divided into two main sub-tasks.

The first is forecasting conditions within the river, sometimes described as hydrologic modeling. In this task one traditionally uses inputs such as precipitation, temperature, soil moisture and others throughout the river basin, and produces as an output either the river discharge or the water level at specific points within the river at a specific time, usually up to two weeks into the future. Traditional hydrologic models are "conceptual" models - they are inspired by the actual physical processes involved, though usually are vastly simpler than the actual processes they describe. They will have somewhere between several to several dozen parameters, which are calibrated to produce outputs that fit the historical measurements in the specific river.

The second task is forecasting behavior across the floodplain, sometimes described as inundation modeling. Here we assume we already know the discharge or water level in the river (provided by a hydrologic model or a real-time measurement), and model the movement of water across the floodplain. The goal is to produce a spatially accurate map of flood extent (which areas are flooded) or flood depth (how deep is the water in each point on the map). The most common practice for these types of models are called hydraulic models - these are physics-based models which find finite-element solutions to a set of differential equations (specifically the St. Venant equations [8]) on a grid. These models can describe water behavior across the floodplain very accurately, when all their input data is accurate and they are run at sufficient resolution.

The case for incorporating AI into these two types of models is not identical, but similar principles apply. Both traditional models (hydrologic and hydraulic models) require significant manual calibration, and often continuous recalibration based on new information. This leads to high costs of deployment and operation, up to hundreds of thousands of dollars for a single basin - which significantly limits their availability in the regions that need warning systems the most, and prevents scale-up of high-quality systems to truly global scales. Relatedly, both have extremely limited spatial transferability - researchers have been largely unsuccessful in utilizing models trained in one location to improve performance in other locations, e.g. ones with less data available. Computational costs can also act as a critical limitation - especially for the hydraulic model, which is incredibly sensitive to resolution yet requires computation proportional to one over the resolution cubed. Finally, both models share a limitation that is common in physics-based and conceptual models - they can be overly rigid. Even when repeated historical data shows some assumption of the model to be incorrect, it is often difficult (and sometimes impossible) to change the model to correct its errors.


Document: The Google Flood Forecasting Initiative
Section: 3 Method
Type: text

Our flood forecasting system uses ML-based methods to tackle each of the tasked described above. We will describe our models for each task separately. For each task, we currently have two different architectures in production - used in different circumstances, e.g. depending on data availability and performance. See figure 1 for an overview of the full system including all components described below, and other components beyond the scope of this chapter.


Document: The Google Flood Forecasting Initiative
Section: 3 Method
Type: figure
Figure

Image /page/2/Figure/4 description: A flowchart illustrating a four-stage process for a flood alert system. The stages are labeled from left to right: Data management, Hydrologic modeling, Inundation modeling, and Alerts.


Document: The Google Flood Forecasting Initiative
Section: 3 Method
Type: text

1. \*\*Data management\*\*: This stage takes three inputs: "Stream gauge measurements," "Precipitation measurements," and "Precipitation forecast." These inputs feed into a process labeled "Data: Ingestion, Quality Control, Correction."

2. \*\*Hydrologic modeling\*\*: The output from Data management flows into this stage. It contains two models in blue boxes: "Linear" and "LSTM." A separate input, "External stream gauge stage forecasts," points to a pink vertical box labeled "Warning thresholds," which is positioned next to the Linear and LSTM models.

3. \*\*Inundation modeling\*\*: The output from Hydrologic modeling flows into this stage. It contains two methods in green boxes: "Thresholding" and "Manifold."

4. \*\*Alerts\*\*: The output from Inundation modeling flows into the final stage. This stage lists three types of alerts that are sent out: "Alerts to responsible authorities," "Alerts to emergency units," and "Alerts to population."

Each stage title is accompanied by a small illustrative icon: a green checkmark for Data management, a line graph for Hydrologic modeling, a map of a river delta for Inundation modeling, and three smartphones displaying notifications for Alerts.

Fig. 1. A high-level overview of the Google flood forecasting system.