Document: The formation and failure of natural dams
Section: GLACIER DAMS > Modes of Failure of Ice Dams
Type: figure
Figure

Image /page/23/Picture/0 description: A high-angle, black and white photograph captures a large, light-colored lake situated in a deep mountain valley. The surrounding mountains are steep and rugged, with patches of snow visible on their slopes, especially on the right side of the image. The mountainsides also show areas of darker rock and some vegetation. The lake fills most of the valley floor, and at its far end, a small inlet or river can be seen flowing into it. The foreground consists of the rocky or grassy terrain from which the photo was taken, looking down into the valley.


Document: The formation and failure of natural dams
Section: GLACIER DAMS > Modes of Failure of Ice Dams
Type: figure
Figure: Figure 8.--Photograph of a glacier-dammed lake in a tributary valley (type D, table 4), Coast Mountains, Alaska. Note the strand lines indicating previously higher lake levels. (Photograph by W. C. Bradley, University of Colorado)

Figure 8.--Photograph of a glacier-dammed lake in a tributary valley (type D, table 4), Coast Mountains, Alaska. Note the strand lines indicating previously higher lake levels. (Photograph by W. C. Bradley, University of Colorado)

data/('The formation and failure of natural dams', '.pdf')_extraction.md

GLACIER DAMS
Modes of Failure of Ice Dams


Document: The formation and failure of natural dams
Section: GLACIER DAMS > Modes of Failure of Ice Dams
Type: figure
Figure

Image /page/23/Picture/2 description: A black and white diagram illustrating various geographical features in a glacial environment. The diagram uses different patterns to distinguish between 'Ice' (speckled pattern), 'Land' (cross-hatched pattern), and water bodies. The image shows large areas of ice surrounding several islands of land. A large landmass is at the bottom. A 'Lake' is situated between an ice mass and the lower landmass, with a 'River valley' extending to the right. Several specific features are marked with letters A through H. A, B, C, and E are small, irregular shapes on land or in the ice. D represents a stream flowing from the ice onto the land. F is a channel through land connecting two ice masses. G is an area within the lake outlined by a dashed line. H is a channel flowing into the lake.


Document: The formation and failure of natural dams
Section: GLACIER DAMS > Modes of Failure of Ice Dams
Type: figure
Figure: Figure 9.--Diagram showing classification of glacial-ice dams, keyed to table 4. (Modified from Maag, 1969; and Blackut and Ballantyne, 1976)

Figure 9.--Diagram showing classification of glacial-ice dams, keyed to table 4. (Modified from Maag, 1969; and Blackut and Ballantyne, 1976)


Document: The formation and failure of natural dams
Section: GLACIER DAMS > Modes of Failure of Ice Dams
Type: text

The type of ice forming an ice dam (cold polar and subpolar ice versus warmer temperate ice) affects the potential failure mechanisms. Glacier dams formed of cold polar and subpolar ice are relatively tight and dense, have temperatures below the pressure melting point, are dry at their bases, and generally drain supraglacially or marginally by downmelting of an outlet channel (Marcus, 1960; Maag, 1969). Warmer temperate glacier dams are more fractured and less dense, have subglacial-meltwater flow, and tend to fail by sudden englacial or subglacial breaching and drainage (Blachut and Ballantyne, 1976).


Document: The formation and failure of natural dams
Section: GLACIER DAMS > Longevity and Controls of Glacier Dams
Type: text

Some glacier dams fail by means of non-catastrophic seasonal drainage outlets; the ice dams at Base Camp Lake, Greenland (Clement, 1984), are an example. Others fail catastrophically on an annual or near-annual basis. An example is the glacier dam that impounded Lake George, Alaska. This dam failed annually from at least 1918 until 1966 (Bradley and others, 1972). Some glacier dams fail several times during 1 year; others fail irregularly. Irregular damming and failure are believed related to local-climate control of glacier activity, filling rates, and lake temperatures. In some situations, the overflow outlet for a glacier-dammed lake is controlled by overflow across low bedrock cols around the perimeter of the lake (Sugden and others, 1985).

Numerous case studies of glacier-dammed lakes indicate that the frequency of glacier-dam failure is likely to change with time. Thorarinsson (1939) demonstrated that, as a glacier recedes and melts, failures increase in frequency but decrease in magnitude, eventually ending with establishment of a permanent outlet. This pattern of response to glacial recession, increasing ice-dam failure, and decreasing flood magnitudes has been documented for Tulsequah Lake, British Columbia (Marcus, 1960).

Some factors appear to predicate the failure of a glacier dam. Filling to some characteristic level is one example. A unique lake level may signal the beginning of hydrostatic flotation or overtopping of the ice barrier. A glacier dam with a cyclical history of failure may continue to fail, provided no change occurs in the size or shape of the ice dam. At Strandline Lake, Alaska, precursor indications of a glacier-dam failure include (1) rapid iceberg calving from the glacier, producing numerous icebergs; (2) filling of a number of small supraglacial pools; and (3) lake-level rise to a pre-identified failure level (Sturm and Benson, 1985).

Artificial controls of the levels of glacier-dammed lakes have been attempted in several locations. In the Alps, trenching across a glacier dam or tunneling through rock or ice has allowed control of dangerous lakes (Eisbacher and Clague, 1984). This is not always successful (Mathews, 1965). In Argentina, 500-kilogram bombs were dropped on a glacier dam in an unsuccessful attempt to destroy the dam (Nichols and Miller, 1952).

GLACIER DAMS
Longevity and Controls of Glacier Dams


Document: The formation and failure of natural dams
Section: MORAINE DAMS
Type: text

Many moraine dams occur throughout the world, but the most dangerous of these are restricted to alpine regions affected by the advances and retreats of valley glaciers in the last several centuries in steep mountain areas. Some excellent case studies have been made of moraine-dam failures in Peru (Lliboutry and others, 1977) and in British Columbia (Blown and Church, 1985), but relatively little is known about these natural dams compared to landslide or glacier dams. Moraine-dam failures have been reported from Napal (Galay, 1985; Vuichard and Zimmermann, 1987), India and Pakistan (Burgisser and others, 1982), the U.S.S.R. (Yesenov and Degovets, 1979), Oregon (Nolf, 1966; Laenen and others, 1987), Peru (Lliboutry and others, 1977), Canada (Clague and others, 1985), Austria (Eisbacher and Clague, 1984), and Argentina (Rabassa and others, 1979).


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Geomorphic Settings of Moraine Dams
Type: text

A globally synchronous readvance of glaciers during the last few centuries has been documented (Grove, 1979) and referred to as the Little Ice Age (Matthes, 1939) or late-neoglacial time (Porter and Denton, 1967). Neoglacial time ended in the late 19th Century, and since then many mountain glaciers have retreated significantly (Porter and Denton, 1967), leaving behind many moraine-dammed lakes.

These late-neoglacial and contemporary moraine-dammed lakes are hazards because (1) they are young and located at such high elevations that vegetation has not completely stabilized their slopes, (2) slopes are steep (some greater than 40 degrees), (3) thermal degradation melting an ice or snow core could render them unstable, and (4) they may be located close to an ice front and steep, rock-walled cirques and valleys. Anomalous meltwater inflow or ice and rock falls into the lakes may precipitate breaching and failure of these dams.

A variety of types of moraines may form in alpine areas. Push moraines originate when glacier ice advances and bulldozes sediment into a ridge, typically less than 9 meters high (Sugden and John, 1976). A typical push moraine has a shallow upstream dipping surface overridden by the glacier and a steeper downstream face formed by material cascading down the glacier surface. Because at least part of the moraine dam has been overridden by the glacier, some of the dam is partially compacted; this compaction adds an increment of stability that does not exist in other kinds of moraine dams. Push moraines are not common dams, or they only contain small lakes.

Ice-thrust moraines originate from the collection of sediment and debris erodes from the base of a glacier and thrust along shear planes to the front margin of the ice. Moraines can form to heights of 100 meters or more if the glacier is on a steep gradient, flows rapidly, and actively erodes material from its boundary. Ice-thrust moraine sediments are relatively compact and contain more fines than other types of moraine dams.

Dump moraines originate from the dumping down the ice front of sediment and debris from within the ice and on the ice surface. The form and size of dump moraines are controlled by rate of ice movement, rate of surface ablation, volume of sediment in and on the ice, and meltwater effects. A dump-moraine dam will be heterogeneous mixture because material moving from the ice to the moraine comes from diverse sources, lithologies, textures, and transport mechanisms.

Dump moraines typically have steep ice-contact upstream slopes where the moraine was buttressed by the glacier and flatter distal downstream-facing slopes (Andrews, 1975). For large dump moraines to form, forward motion of the glacier must balance the melting rate. Till in dump moraines tends to be relatively loose, uncompacted, and free of fines. Steep slopes, and uncompacted and noncohesive sediment make dump-moraine dams the least stable.

MORAINE DAMS
Geomorphic Settings of Moraine Dams


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Geomorphic Settings of Moraine Dams
Type: text

ice , and meltwater effects . A dump - moraine dam will be heterogeneous mixture because material moving from the ice to the moraine comes from diverse sources , lithologies , textures , and transport mechanisms . Dump moraines typically have steep ice - contact upstream slopes where the moraine was buttressed by the glacier and flatter distal downstream - facing slopes ( Andrews , 1975 ) . For large dump moraines to form , forward motion of the glacier must balance the melting rate . Till in dump moraines tends to be relatively loose , uncompacted , and free of fines . Steep slopes , and uncompacted and noncohesive sediment make dump - moraine dams the least stable .

Ice-cored moraines can originate when sediment at the base of a glacier is moved upward toward the ice surface by compression and thrusting. Ice-cored moraines can be as much as 90 percent ice by volume, with only thin till covers. The ice core can originate as glacial ice or snow (Ostrem, 1964). Ice cores in high-altitude or subpolar continental climates can last for 1,000 years (Andrews, 1975). Naturally, the melting of an ice-cored moraine impounding a lake can lead to the collapse and failure of the moraine dam. The presence of an ice-core in a moraine may be indicated by moist areas or seeps in moraine-dam walls above lake levels late in the summer or early autumn before sub-freezing temperatures occur.


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: text

Comprehensive descriptions of the failure of moraine dams are rare (table 5). The Cordillera Blanca and Cordillera Huayhuash of northcentral Peru are regions that have an especially large number of moraines enclosing narrow, steep valleys. Nearly all glaciers of the Cordillera Blanca lie behind large moraines of late-neoglacial age and contain lakes (Lliboutry and others, 1977). In 1941, Laguna Cohup, a proglacial moraine-dammed lake, drained rapidly when the moraine dam failed. The resulting flood of mud and water (known in Spanish as "alluvion") destroyed almost half the town of Juaraz and killed about 6,000 people (Ericksen and others, 1970; Eisbacher, 1982). This disaster led to a major effort toward control and lowering of lake levels in dangerous moraine-dammed lakes in Peru (Lliboutry and others, 1977).

The most comprehensive description of the failure of a moraine dam is the work of Blown and Church (1985) on the failure of the moraine dam of Nostetuko Lake, British Columbia, Canada (fig. 10). Another valuable account of a moraine-dam failure at Klattasine Lake, British Columbia, sometime between June 1971 and September 1973 was made by Clague and others (1985).

Most of the textural data from moraine dams indicates that the moraine material is silty, sandy, bouldery till, with minimal clay (less than 3 or 4 percent). Lake levels are controlled by seepage through the barrier and open overflow channels across the top of the moraine. Of the 14 documented cases of failures of moraine dams known to us, the mechanism of failure is known or can be reasonably estimated for nine. The most common failure mechanism is overtopping by a wave or series of waves generated by icefalls or rockfalls or by snow- or rock-avalanches into the lake basin. The wave overtops the moraine dam, and augmented flow in the outlet channel causes erosion of the channel that permits increased flow from the lake as it begins to drain. Another failure mechanism is overtopping by excessive runoff during glacial retreat, snowmelt, or intense rainfall. The moraine dam impounding Lago Tempanos, Argentina, failed sometime between 1942 and 1953 because of excessive meltwater accompanying a 352-meter retreat of the glacier (Rabassa and others, 1979).

MORAINE DAMS
Modes of Failure of Moraine Dams


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: table
Table: Table 5.--Well-documented examples of moraine dams that have failed

| Lake or site name | Location                       | Date failed           | Change in lake level (m) | Volume discharged (m3 x 104) | Flood peak (m3/s) | Failure mechanism               | Reference                                         |
|----------------------|--------------------------------|--------------------------|--------------------------------|------------------------------------|----------------------|------------------------------------|---------------------------------------------------|
| Madatschferner       | Austria                        | Aug. 5, 1874             | --                             | --                                 | --                   | --                                 | Eisbacher and Clague, 1984                     |
| Galrittferner        | Austria                        | Aug. 7, 1890             | --                             | --                                 | --                   | Ice fall                           | Eisbacher and Clague, 1984                     |
| Cohup                | Peru                           | Dec. 13, 1941            | --                             | --                                 | --                   | --                                 | Eisbacher, 1984; Lliboutry and others, 1977 |
| White Branch         | Oregon, U.S.A.              | July 1942                | --                             | --                                 | 360                  | --                                 | Laenen and others, 1987                        |
| Tempanos             | Argentina                      | 1942-1953                | --                             | --                                 | --                   | Excess meltwater                | Rabassa and others, 1979                       |
| Jancarurish          | Peru                           | 1950                     | 21                             | 600-1,000                          | 7,000-8,000          | Collapse of undercut glacier | Lliboutry and others, 1977                     |
| Artesoncoda          | Peru                           | July 16-17, 1951      | 113                            | --                                 | --                   | Ice fall                           | Lliboutry and others, 1977                     |
| Broken Top           | Oregon, U.S.A.              | Oct. 7, 1966             | 4.6                            | 18.9                               | 71                   | Ice fall                           | Nolf, 1966; this report                        |
| Squaw Creek          | Oregon, U.S.A               | Sept. 7, 1970            | 25.6                           | 33.3                               | 297                  | --                                 | Laenen and others, 1987                        |
| Safuna Alta          | Peru                           | 1970                     | 38                             | 490 (stored in lake)            | --                   | Earthquake- induced piping      | Lliboutry and others, 1977                     |
| Klattasine           | British Columbia, Canada | June 1971- Sept. 1973 | 13                             | 170                                | >1,000               | --                                 | Clague and others, 1985                        |
| Moraine #13          | Soviet Union                | Aug. 3, 1977             | 5.2                            | 8.64                               | 210                  | Melting of frozen soil          | Yesenov and Degovets, 1979                     |
| Nostetuko            | British Columbia, Canada | July 19, 1983            | 38.4                           | 650                                | 11,000               | Ice fall                           | Blown and Church, 1985                         |
| Dig Tsho             | Nepal                          | Aug. 4, 1985             | 22.5                           | 800                                | 2,000                | Ice fall                           | Galay, 1985; Vuichard and Zimmermann, 1987  |

Lake or site name
Location
Date failed
Change in lake level (m)
Volume discharged (m3 x 104)
Flood peak (m3/s)
Failure mechanism
Reference


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: figure
Figure

Image /page/28/Picture/0 description: A black and white, high-contrast aerial photograph of a rugged, mountainous landscape. The upper portion of the image is covered in snow and ice, with mountain peaks visible. Below the snowfields, a glacier flows down a valley between dark, steep mountain slopes. Patches of snow are scattered on the dark rock faces. In the lower right corner, a dark body of water, likely a glacial lake, can be seen at the base of the mountains.


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: figure
Figure: Figure 10-A.--Photograph of Nostetuko Lake, a neoglacial moraine-dammed lake, and Cumberland Glacier, British Columbia, Canada, in July 1977. (Photograph by J. M. Ryder, courtesy of Michael Church, University of British Columbia)

Figure 10-A.--Photograph of Nostetuko Lake, a neoglacial moraine-dammed lake, and Cumberland Glacier, British Columbia, Canada, in July 1977. (Photograph by J. M. Ryder, courtesy of Michael Church, University of British Columbia)


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: figure
Figure

Image /page/28/Picture/2 description: A black and white panoramic photograph, labeled as Figure 12, showing the Nastaklah and Cambria Glaciers in British Columbia, Canada, during August. The image captures a high-angle view of a vast, mountainous landscape. Large glaciers flow down from snow-covered peaks, converging in a valley below. The rugged mountains have dark, rocky slopes contrasting with the white snow and ice.


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: figure
Figure: Figure 10-B.--Photograph of Nostetuko Lake and Cumberland Glacier, British Columbia, Canada, in August 1983, after failure of the moraine dam. (Photographs by Michael Church, University of British Columbia)

Figure 10-B.--Photograph of Nostetuko Lake and Cumberland Glacier, British Columbia, Canada, in August 1983, after failure of the moraine dam. (Photographs by Michael Church, University of British Columbia)


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Modes of Failure of Moraine Dams
Type: text

In the Cordillera Blanca, Peru, all precisely dated moraine-dam failures occurred during the rainy season from October to April (Lliboutry and others, 1977). Failure during these wet months indicates that increased streamflow is an important factor in moraine-dam failures.

Settlement and subsequent failure of moraine dams accompanying earthquakes is another potential failure mechanism. The large Peruvian earthquake of May 1970 resulted in the release of water from at least two moraine-dammed lakes by piping in the Cordillera Blanca (Lliboutry and others, 1977).

A moraine dam in the Soviet Union collapsed from melting of frozen soil because of high air and water temperatures (Yesenov and Degovets, 1979). Although we know of no documented case, the failure of an ice-cored moraine dam from ice-melt also is a distinct possibility.


Document: The formation and failure of natural dams
Section: MORAINE DAMS > Longevity and Control of Moraine Dams
Type: text

The question of the longevity and stability of moraine dams is extremely complex. In our investigations, all of the known moraine dams that failed were late-neoglacial age or younger. Since ice or rock avalanches into moraine-dammed lakes are the primary failure mechanism of moraine dams, lakes located below steep, unstable, and highly-crevassed or fractured glaciers or rock slopes present obvious failure potential. In a study of the hypothetical failure and resulting flood from a late-neoglacial moraine dam in central Oregon, the annual probability of failure was estimated to be 1 to 5 percent because of the instability of the surrounding rock slopes and glacier and the history of previous moraine-dam failures in the area (Laenen and others, 1987). Ice-cored moraines or frozen-soil moraines may thaw for centuries and then become critically unstable (Ostrem, 1964; Andrews, 1975). Once this thaw occurs, the potential for a piping or overtopping failure of the moraine dam increases whenever a period of high runoff occurs.

Artificial controls of the levels of moraine-dammed lakes have been attempted in several locations. The most extensive efforts probably have been undertaken in Peru (Lliboutry and others, 1977). Control efforts include draining of lakes by tunnels through ice and rock, stabilization of lake outlets with paved revetments, increasing freeboard with low earthen dams, and construction of protective retention basins--if valley gradients are sufficiently low (Lliboutry and others, 1977; Yesenov and Degovets, 1979; Eisbacher, 1982).

MORAINE DAMS
Longevity and Control of Moraine Dams


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS
Type: text

Natural dams create the potential for two very different types of flooding: (1) upstream or backwater flooding as the reservoir fills and (2) downstream flooding as a result of failure of the dam.

Upstream flooding occurs because of the relatively slow rise of water behind the dam as the basin of the natural impoundment is filled. The threat of loss of life from this kind of flooding is minimal, but property damage can be substantial as entire developments disappear under water. Upstream-flood damage from glacier and moraine dams generally is not significant because these types of natural dams commonly occur in remote areas where development is minimal. Landslide dams, however, can present significant hazards from backwater flooding.

Because landslide dams tend to occur in mountainous areas, certain types of structures, such as hydroelectric plants, may be rendered inoperable because of inundation of intakes, flumes, generators, and transportation and transmission facilities (Schuster and Costa, 1986b). A recent example of the consequences of upstream flooding from a landslide dam occurred in April 1983, when a 22-million cubic meter landslide in central Utah dammed the Spanish Fork River and flooded the town of Thistle, Utah (Kaliser and Fleming, 1986; fig. 11).

It usually is possible to accurately estimate the extent and rate of upstream flooding from natural dams. Such estimates require knowledge of height of the dam crest, rates of streamflow or icemelt into the natural reservoir, rates of seepage through or beneath the dam, and information on topography upstream from the dam.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS
Type: figure
Figure

Image /page/30/Picture/2 description: A black and white aerial photograph shows a massive landslide in a mountainous region. A river or reservoir winds through a canyon on the left. On the right, a large portion of a mountainside has collapsed, leaving a wide, light-colored scar of exposed earth and rock that flows down into the valley. The debris from the landslide has spread out at the base, pushing into the river and altering its course.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS
Type: figure
Figure: Figure 11.--Photograph of the 1983 landslide at Thistle, Utah, and its temporary impoundment, Thistle Lake, in September 1983. The lake inundated the town of Thistle and was drained late in 1983 by a bedrock tunnel constructed through the mountain at lower left.

Figure 11.--Photograph of the 1983 landslide at Thistle, Utah, and its temporary impoundment, Thistle Lake, in September 1983. The lake inundated the town of Thistle and was drained late in 1983 by a bedrock tunnel constructed through the mountain at lower left.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: text

Floods resulting from the failure of natural dams usually are much larger than floods originating directly from snowmelt or rainfall. Compared with the failure of constructed dams, very little is known about the processes of natural-dam failure. Few accurate data exist about peak discharges, dimensions, or reservoir volumes of failed natural dams (Costa, 1985).

Many investigations have been conducted on the safety of constructed dams (Jansen, 1980; Committee on the Safety of Existing Dams, 1983), and numerous models have been proposed to determine the outflow hydrograph and downvalley routing of floodwaters resulting from failure of constructed dams (Land, 1980; Fread, 1980). However, few similar evaluations have been made for the failures of natural dams (Ponce and Tsivoglou, 1981; Clarke, 1982), which in many cases are different from those of constructed dams (Costa, 1985).

A dam failure is a complex hydrologic, hydraulic, and geologic phenomenon, controlled primarily by the failure mechanism and characteristics and properties of the dam. One way to compare different kinds of dam failures is to investigate the relationship between the specific energy of the lakewater behind the dam prior to failure and the flood-peak discharge from the failure of the dam (fig. 12). Potential energy of a lake behind a dam can be computed as the product of dam height (meters), volume (cubic meters), and specific weight of water (9,800 newtons/cubic meter). Dam-failure flood peak discharges used in figure 12 come from tables 3 and 5 and from Costa (1985).


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: figure
Figure

Image /page/31/Figure/2 description: A scatter plot showing the relationship between Potential Energy and Peak Discharge for different types of dams. The x-axis represents "POTENTIAL ENERGY, IN JOULES" on a logarithmic scale from 10^7 to 10^17. The y-axis represents "PEAK DISCHARGE, IN CUBIC METERS PER SECOND" on a logarithmic scale from 10 to 1,000,000. The plot includes data for four types of dams, indicated by a legend: black triangles for CONSTRUCTED DAMS (includes earth and rockfill dams), black circles for LANDSLIDE DAMS, circles with a dot for MORAINE DAMS, and black diamonds for GLACIER DAMS. The data points generally show a positive correlation, where higher potential energy corresponds to higher peak discharge. Several trend lines are drawn on the plot. An "Envelope curve" marks the upper boundary of the data. Separate trend lines are labeled for "Earth and rockfill dams", "Landslide dams", "Moraine dams", and "Glacier dams", indicating the general trend for each category.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: figure
Figure: Figure 12.--Graph showing potential energy versus peak discharge for various types of dam failures. Dashed lines are least-squares regression lines for earth- and rock-fill, moraine glacial ice, and landslide dams. Solid line is the envelope curve for all dam-failure data. Data from Costa (1985) and Table 5.

Figure 12.--Graph showing potential energy versus peak discharge for various types of dam failures. Dashed lines are least-squares regression lines for earth- and rock-fill, moraine glacial ice, and landslide dams. Solid line is the envelope curve for all dam-failure data. Data from Costa (1985) and Table 5.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: text

Regression analysis with potential energy as the independent variable produces different equations for landslide, glacier, moraine, and earth- and rock-fill dam-failure flood peaks, with standard errors ranging from 27 percent for moraine dams to 133 percent for landslide dams (table 6). The large scatter in the data in figure 12 reflects the diverse characteristics of individual dams within each category, as well as the difficulty in computing or measuring the peak discharge from a dam failure. Direct measurements of floods are nearly impossible; thus a variety of indirect estimation methods, such as drawdown rates or measurements based on post-flood channel surveys and hydraulic formulas, are used.

Concern about the large standard errors or overlapping of data from different types of dams is moot, since the variability of individual dam populations is so large and determination of the dependent variable (peak discharge) is so difficult. Individual regressions lines identify different kinds of dams in a physically meaningful way that provides insight into fundamental understanding of natural dams.

[Q = peak discharge,  $m^3/s$ ; PE = potential energy (joules)]


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: table
Table: Table 6.--Summary of regression equations to predict peak discharge from the failure of earth- and rock-fill, moraine, glacial-ice, and landslide dams

| Type of dam                | Equation                 | Number of data points | Coefficient of determination (r2) | Standard error (percent) |
|----------------------------|--------------------------|--------------------------|-----------------------------------------|--------------------------------|
| 1. Earth- and rock-fill | $Q = 0.0184 (PE)0.42$    | 26                       | 0.75                                    | 93                             |
| 2. Landslide               | $Q = 0.0158 (PE)0.41$    | 12                       | .81                                     | 133                            |
| 3. Moraine                 | $Q = 0.0000069 (PE)0.73$ | 6                        | .89                                     | 27                             |
| 4. Glacier                 | $Q = 0.0000055 (PE)0.59$ | 11                       | .80                                     | 75                             |

Type of dam
Equation
Number of data points
Coefficient of determination (r2)
Standard error (percent)


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: text

For the same potential energy at the time of dam failure, glacier-ice dams produce the smallest peak discharges. For potential energy less than about  $10^{11}$  joules, constructed earth- and rock-fill dams produce the largest peak discharges for a constant potential energy. For potential energy greater than about  $10^{11}$  joules, moraine-dam failures produce the largest peak discharges for a constant potential energy. Differences in peak discharges for constant potential energy of different kinds of natural and constructed dams originate because of differences in failure mechanisms, which are related to geometry and material characteristics of the dams.

Glacier-dam failures commonly involve the enlargement of subglacial or englacial tunnels during failure. This enlargement requires time and produces relatively small peak discharges compared with other kinds of natural and constructed dams with the same potential energy. Landslide dams are typically much wider than constructed earth- and rock-fill dams and involve much larger volumes of material. The Madison Canyon landslide that dammed the Madison River in 1959, forming Earthquake Lake, had a base width five to eight times as great as would have been used in building a rock-fill dam of the same height (Knight and Bennett, 1960). In figure 13, transverse sections of the Mayunmarca landslide dam in Peru and the Nostetuko moraine dam in British Columbia are compared to the Oroville Dam, a large earth-fill dam in California. The landslide dam in Peru is higher at the abutments and almost as high as the constructed dam in the center, but is over three times as wide. When a landslide dam is overtopped, much more earth material commonly is present for water to erode before a full breach is developed than is the case for constructed embankment dams. This is indicated by the parallel but displaced positions of landslide and earth- and rock-fill dam regression lines shown in figure 12.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: figure
Figure

Image /page/33/Figure/0 description: A black and white line drawing comparing the cross-sections of three dams: the Nostetuko Moraine Dam in British Columbia, the Oroville Dam in California, and the Mayunmarca Landslide Dam in Peru. The profiles are superimposed for direct comparison. The Nostetuko Moraine Dam is the smallest, shown as a triangular shape with a stippled pattern. The Oroville Dam is larger, depicted with a light gray shading, and its profile encompasses the Nostetuko dam. The "Center" and "Abutments" of the Oroville Dam are labeled. A dashed line is drawn above the peak of the Oroville Dam. The Mayunmarca Landslide Dam is the largest, represented by the overall outer contour line. A horizontal scale bar at the bottom of the diagram is marked from 0 to 1000 m.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: figure
Figure: Figure 13.--Diagram showing cross section of Mayunmarca landslide dam, Peru, and Nostetuko moraine dam, British Columbia, compared to the maximum cross section of a large earth-fill dam at Oroville, California. (Modified from Lee and Duncan, 1975)

Figure 13.--Diagram showing cross section of Mayunmarca landslide dam, Peru, and Nostetuko moraine dam, British Columbia, compared to the maximum cross section of a large earth-fill dam at Oroville, California. (Modified from Lee and Duncan, 1975)


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Comparison of Floods from the Failure of Different Kinds of Dams
Type: text

Moraine dams are relatively high and narrow compared to many landslide dams and have much steeper slopes. The predominate failure mechanism for moraine dams (wave overtopping) leads to rapid erosion of cohesionless sandy and gravelly till comprising many moraine dams. The rapid breach development in moraine-dam failures, compared to other kinds of dams, is primarily responsible for the location of the regression line in figure 12.


Document: The formation and failure of natural dams
Section: FLOODS FROM THE FAILURE OF NATURAL DAMS > Prediction and Reconstruction of Floods from Dam Failures
Type: text

For purposes of rapid prediction when potential loss of life or property is involved, a conservative peak-discharge estimate based on an envelope curve developed from historic failures of landslide, glacier, moraine, and constructed earth- and rock-fill dams can be made from knowledge of the potential energy of the lake behind the dam (fig. 12). The envelope curve that includes data points from all constructed and natural-dam failures for which reasonable estimates of peak discharge exist is defined by the equation

$$Q = 0.063 \text{ PE}^{0.42}$$

where Q is peak discharge, in cubic meters per second, and PE is potential energy, in joules.

For reconstructing past flood peaks from the failure of natural dams for paleohydrological or sedimentological investigations, regression equations for different kinds of natural dams with potential energy as the independent variable could be used (table 6).

A complicating factor in downstream routing of floods from natural-dam failures is the bulking and debulking of flood waters with sediment and debris as the flood moves downvalley. The use of envelope curves, regression equations, or dam-break models allows estimation of peak flood discharges at the dam. Most commonly, flood peak discharges attenuate downvalley (Costa, 1985; fig. 8). Sometimes, however, downstream peaks can be considerably larger because easily eroded sediment is added and subtracted from the flow. So much sediment can be added to the flood flow that a debris flow forms. This seems to be an especially important process in volcanic and glacial terrains (Scott, 1985; Yesenov and Degovets, 1979; Clague and others, 1985; Lliboutry and others, 1977). Fluctuations in peak discharge downstream from the failure of a moraine dam on the Kumbel River, U.S.S.R., in 1977 are well documented. Peak discharge from the dam failure was 210 cubic meters per second, but it had bulked to 11,000 cubic meters per second about 15 kilometers downstream (Yesenov and Degovets, 1979). This problem of bulking and debulking presents one of the most difficult unsolved problems in sediment transport today, and its consequences for hazard evaluation are enormous.


Document: The formation and failure of natural dams
Section: CONCLUSIONS
Type: text

Three kinds of natural dams present significant hazards to people and property: landslide dams, glacier dams, and moraine dams. Using a large data base of case studies, significant generalizations about individual kinds of natural dams can be made. Landslide dams form most commonly in steep narrow valleys in geologically active areas, but they have also formed in wide, open stream valleys. Avalanches, slumps and slides, and flows triggered by excessive snowmelt or rainfall or by earthquakes are the most common landslide processes. There are at least six morphologically different kinds of landslide dams, and the danger of failure and flooding varies with the kind of landslide dam.

Based on 73 examples of landslide-dam failures from around the world, landslide dams fail quickly after formation (half fail within 10 days of formation, and 15 percent last more than a year). By far the predominate failure mechanism is overtopping and breaching by headward erosion.

Glacier-dam failures have produced the largest documented historic and prehistoric flood-peak discharges known on Earth. The most dangerous glacier dams are formed when tributary-valley glaciers block main valleys and form large lakes behind them. Temperate glacier dams usually fail by erosion of subglacial or englacial tunnels under or through the ice dam, aided by the hydrostatic pressure of water behind the ice dam. Cold polarice dams fail most commonly by overtopping and erosion of a channel in the ice.

All the documented cases of moraine-dam failures occurred with moraine dams formed in the last few centuries (late neoglacial). The dams originated as dump or ice-thrust moraines and may or may not have ice cores. The predominate failure mechanism is overtopping and rapid erosion by a wave or series of waves generated by rock or ice avalanches into the lake from adjacent steep cirque or valley walls.

For the same potential energy at the dam site, the failures of glacier dams produce the smallest flood peak discharges because of the time required for enlargement of outlet tunnels and channels. For constant potential energy, landslide-dam failures produce smaller flood peaks than failures of constructed earth- and rock-fill dams because of the large volume of material to be eroded before full breach development. For potential energy greater than about  $10^{11}$  joules, late-neoglacial moraine dams produce the greatest flood peak discharges of any kind of natural or earth- or rock-fill dam because of rapid breach formation associated with wave overtopping and erosion of non-cohesive till on steep slopes.

An envelope curve defining the maximum discharge produced by the failure of natural and constructed earthen dams can be used as a rapid conservative approximation of the flood peak from the failure of a potentially dangerous natural dam. In areas with abundant loosely-consolidated surficial deposits in stream valleys downstream of natural-dam failures, peak discharges may not attenuate, but could actually increase many times by incorporation of easily erodible sediment and debris. This phenomenon of sediment bulking and debulking downstream from natural-dam failures remains one of the most pressing and difficult unsolved problems in sediment transport today.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: ABSTRACT
Type: text

There has been an increase in the adoption of Linked Data and subsequently representing data in the form of knowledge graphs across a wide spectrum of domains. There has also been significant interest in the remote sensing community to publish Earth Observation data in the form of Linked Data. As the geospatial Linked Data cloud on the internet grows, there arises a need for efficient methods of exploratory analysis of such information-rich geospatial knowledge graphs. Knowledge graph representation of remote sensing scenes has proved to add significant value for effective mining of implicit information in addition to seamless integration with other data sources. This work is geared towards visual exploration of semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). In this paper, we propose and implement an interactive web-based interface to visually explore and interact with RSS-KGs using Cesium. The proposed interface seeks to visualize the knowledge graph in the form of nodes and edges, mapped over the remote sensing scene consisting of different land use land cover regions and their inferred characteristics in addition to their spatial relationships with one another. It is envisaged that visualization in the form of nodes and edges would aid in visually validating the spatial relations in the knowledge graph, thus enhancing the understanding of the geospatial knowledge graph from the end user perspective. We demonstrate the efficacy of the interface through the visual exploration of an enriched geospatial knowledge graph of a remote sensing scene captured during an urban flood event.

*Index Terms*— visualization, exploration, knowledge graphs, remote sensing scenes, earth observation, linked data


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: text

Recently there has been a huge interest in publishing Linked Data on the web, backed by different governance initiatives [1]. The advantages of Linked Data in terms of seamless accessibility, interoperability and integration have been well understood by the research community. This has led to an increase in the size of the Linked Data cloud available on the web. The geospatial research community too has noticed and acknowledged the benefits of Linked Data and publishing


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: figure
Figure

Image /page/0/Figure/9 description: A flowchart titled 'Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene' illustrates a four-tiered process for enriching data. The layers, from bottom to top, are Data Mediation Layer, Spatial Knowledge Enrichment, Contextual Knowledge Enrichment, and Scene Knowledge Aggregation.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: text

1. \*\*Data Mediation Layer\*\*: This foundational layer produces a 'Linked Data Representation of Remote Sensing Scene Knowledge'. An example 'RSS-LD' shows entities like 'Vehicle "V1"' and 'Road "R1"'.

2. \*\*Spatial Knowledge Enrichment\*\*: This layer takes 'Spatial SWRL Rules' and a 'Remote Sensing Scene Ontology (RSSO)' as input. A 'Deductive Reasoner' processes them to create an 'Enriched Remote Sensing Scene Knowledge Graph (RSS-KG)' with 'Spatial', 'Directional', and 'Topological' components. An example 'RSS-KG' shows vehicles intersecting with a road ('geo:sfIntersects' -> 'Road "R1"').

3. \*\*Contextual Knowledge Enrichment\*\*: This layer uses 'Contextual SWRL Rules' and a 'Flood Scene Ontology (FSO)'. A 'Deductive Reasoner' enriches the knowledge graph, which now has 'Contextual' and 'Spatial' layers. The example 'RSS-KG' shows vehicles being 'On' an 'Unaffected Road "UR1"'.

4. \*\*Scene Knowledge Aggregation\*\*: The top layer starts with 'Spatio-Contextual Triple Aggregation'. A 'Deductive Reasoner' produces the final 'Enriched Remote Sensing Scene Knowledge Graph (RSS-KG)' with 'Aggregated', 'Contextual', and 'Spatial' layers. The example 'RSS-KG' shows an aggregated concept: 'Traffic Congestion "TC1"' is 'On' an 'Unaffected Road "UR1"'.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: figure
Figure: Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene Knowledge Graphs (RSS-KGs) as proposed by Sem-RSSU Framework [2]

Figure 1. Multi-level Semantic Enrichment of Remote Sensing Scene Knowledge Graphs (RSS-KGs) as proposed by Sem-RSSU Framework [2]


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: text

Geospatial Linked Data is largely being encouraged. With the adoption of Geospatial Linked Data, there is an increasing interest in leveraging such EO Linked Data through knowledge graph representation for mining the otherwise implicit knowledge. In addition to improving spatio-contextual inferencing over semantically enriched geospatial knowledge graphs there is a need for effective visual exploration of such information-rich sources.

This works builds over the Semantics driven Remote Sensing Scene Understanding (Sem-RSSU) Framework [2]. The Sem-RSSU framework advocates the transformation of remote sensing scenes as knowledge graphs. It facilitates this by proposing the Remote Sensing Scene Ontology (RSSO) – a core ontology for a generic remote sensing scene, formalizing the concepts and relations among regions in a scene. It also proposes the Flood Scene Ontology (FSO), formalizing the concepts and relations that transpire during an urban flood event. It introduces and demonstrates a holistic pipeline to translate remote sensing scenes to semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). Figure 1 depicts the multilevel Semantic Enrichment Layer of the Sem-RSSU framework for hierarchical spatial, contextual and aggregate enrichment of RSS-KGs.

This work is geared towards developing an intuitive interface for effective visual exploration of such information rich Remote Sensing Scene Knowledge Graphs. There have been some prominent research studies on visualization of geospatial linked data. Map4RDF [3] was developed as a faceted browser for visualization of RDF datasets with


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: figure
Figure

Image /page/1/Figure/0 description: A screenshot of a web application titled "Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)". The application is displayed in a web browser, with the URL visible as www.geosysiot.in/tools/rssKG-Explorer/. The interface is split into two main parts. On the left is a dark control panel with white text and toggle switches. This panel includes options like "RSS-Knowledge Graph in 3D," information about the data source, ontologies, and details of the remote sensing scene: "(Raster - WV02 [FCC]) | Srinagar, India | Sept. 17, 2014)". It also credits the developers from IIT Bombay. On the right is the main visualization area, which shows an aerial view of a city. A large portion of this view is overlaid with a false-color composite image, where vegetation appears bright red and water or flooded areas appear in shades of cyan, surrounding buildings. A major road runs diagonally through the scene.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: figure
Figure: Figure 2. Visualization of the Remote Sensing Scene of an Urban Flood Event in False Color Composite captured by WorldView 2, overlayed on Cesium's base map imagery

Figure 2. Visualization of the Remote Sensing Scene of an Urban Flood Event in False Color Composite captured by WorldView 2, overlayed on Cesium's base map imagery


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 1. INTRODUCTION
Type: text

support for visual exploration of GeoSPARQL endpoints. The LGD Browser [4] was another prominent tool for faceted browsing of structured information in OSM visualized on a slippy map. Sextant [5] has been a powerful visualization and editing tool for exploring time-evolving geospatial linked data. Another research study [6] aimed to visualize geospatial ontologies by exploiting the visualization capabilities of Cesium – a webGL based JavaScript library to render the globe in 3D on the web browser.

Although these research studies have developed powerful visualization tools for exploring geospatial linked data, none of them focus on the inherent granular graph nature of linked data consisting of nodes and edges. Nodes and edges are fundamental building blocks of a knowledge graph. This work proposes to enable intuitive visual exploration of nodes and edges of remote sensing scene knowledge graphs in addition to a region-based visualization through an interactive web-based interface. Thus, this work aims to enable users to develop a comprehensive understanding of a Remote Sensing Scene Knowledge Graph (RSS-KG) through the web-based interface that has been developed as part of this research.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 2. METHODOLOGY
Type: text

This work advocates the use of Cesium1 – a WebGL based JavaScript library for rendering the globe in 3D in the web browser. The geospatial visualization capabilities of Cesium

have been studied and understood. Thus, it has been found to be an apt candidate for visualizing and enabling interactive exploration of Remote Sensing Scene Knowledge Graphs (RSS-KGs).


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 2. METHODOLOGY > 2.1 JSON-LD
Type: text

JSON-LD stands for JavaScript Object Notation for Linked Data. JSON-LD is a data serialization format for Linked Data. It has been conceptualized to leverage the benefits of the interoperable JSON based data serialization to the world of Linked Data and Semantic Web. The serialization in JSON-LD is similar to JSON in terms of storing data in form of key-value pairs. It must be noted that each triple has an "@id" key associated with it pointing to the URI of the resource that it represents. Due to its ease of consumption by web-based systems, JSON-LD forms an apt choice for knowledge graph representation and visualization with Cesium. The proposed web-based interface visualizes the "Regions" in the Remote Sensing Scene as Nodes and the Spatial Relations - "Externally Connected" of the Region Connection Calculus (RCC8) as Edges. Thus, the spatial interaction of the regions among themselves has been effectively visualized. Consequently, this leads to the visual validation of inferred spatial relations in the knowledge graph, thus improving the understanding of the knowledge graph from an end user perspective.

1 https://cesium.com/cesiumjs/


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 2. METHODOLOGY > 2.1 JSON-LD
Type: figure
Figure

Image /page/2/Figure/0 description: A screenshot of a web application titled "Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)" running in a browser. The main view is a 3D visualization of a remote sensing scene of an urban area, overlaid with a complex network graph consisting of numerous blue circular nodes and yellow connecting lines (edges). On the left is a control panel with options for the visualization, including toggles for "RSS-Knowledge Graph in 3D (Nodes and Edges)" and "RSS-Knowledge Graph (Regions-Polygons)". This panel also provides details about the data: Data Source is "RSS-KG [JSON-LD]", Ontologies are "RSSO | FSO", the Remote Sensing Scene is from "Srinagar, India | Sept. 17, 2014", and the graph was generated by the "Sem-RSSU Framework". On the right, a panel titled "Selected Node" displays detailed information for a specific node, ID "http://www.geosysiot.in/semrssu/data#R6". The type of this node is identified as "ApplicationSchema#FloodWater". Its geographic coordinates are listed, including Centroid X: 74.77952999546662 and Centroid Y: 34.086458934296296. The panel also lists other externally connected nodes. The caption below the image reads: "Figure 2. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event."


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 2. METHODOLOGY > 2.1 JSON-LD
Type: figure
Figure: Figure 3. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event, with an Edge representing the "Externally Connected (EC)" Relation of the RCC8

Figure 3. Visualization of the Nodes and Edges in the Remote Sensing Scene Knowledge Graph (RSS-KG) in 3D for a RS Scene of an Urban Flood Event, with an Edge representing the "Externally Connected (EC)" Relation of the RCC8


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 2. METHODOLOGY > 2.1 JSON-LD
Type: text

The interface has been developed using HTML5, CSS3 and JavaScript. The Remote Sensing Scene Knowledge Graph (RSS-KG) in the JSON-LD form has been hosted on the server for consumption by the developed interface for visual exploration.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: text

The Remote Sensing Scene Knowledge Graph (RSS-KG) generated by the Sem-RSSU framework for the remote sensing scene of an urban flood event in Srinagar, India during September 2014, has been considered for this study. The snippet in Figure 4 depicts the RSS-KG in the JSON-LD format. The instance of "Region" class with "id" as "R0" has been depicted. The knowledge graph has been semantically enriched by inferring implicit concepts and relations using the Sem-RSSU framework. In that regard, "ResidentialBuilding", the inferred classes of "FloodedResidentialBuilding" "AccessibleResidentialBuilding" defined in the RSSO and FSO have been derived. The snippet also depicts the inferred spatial relation - "Externally Connected" of RCC8 between instances of "Region" classes. In addition to the LULC Classes and the Spatial Relations, the RSS-KG also constitutes the geometry-related data such as Centroid, Extent and Polygon Geometry represented in the Well-Known Text (WKT) representation.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: text

" R0 " has been depicted . The knowledge graph has been semantically enriched by inferring implicit concepts and relations using the Sem - RSSU framework . In that regard , " ResidentialBuilding " , the inferred classes of " FloodedResidentialBuilding " " AccessibleResidentialBuilding " defined in the RSSO and FSO have been derived . The snippet also depicts the inferred spatial relation - " Externally Connected " of RCC8 between instances of " Region " classes . In addition to the LULC Classes and the Spatial Relations , the RSS - KG also constitutes the geometry - related data such as Centroid , Extent and Polygon Geometry represented in the Well - Known Text ( WKT ) representation .

```
[ {
    "@id": "http://www.geosysiot.in/semrssu/data#RO",
    "@type": [
    "http://www.geosysiot.in/rsso/ApplicationSchema#Region",
    "http://www.geosysiot.in/rsso/ApplicationSchema#ResidentialBuilding",
    "http://www.geosysiot.in/fso/ApplicationSchema#ResidentialBuilding",
    "http://www.geosysiot.in/fso/ApplicationSchema#FloodedResidentialBuilding"],
    "http://www.geosysiot.in/fso/ApplicationSchema#hasInferredLULC":
    [ {
        "@value": "floodedBuilding"
      }, {
        "@value": "accessibleResidentialBuilding"
      }),
      "http://www.geosysiot.in/rsso/ApplicationSchema#hasURX": [ {
        "@type": "http://www.w3.org/2001/XMLSchema#double",
        "@value": "74.780932527"
      }),
      "http://www.geosysiot.in/rsso/ApplicationSchema#hasURY": [ {
        "@type": "http://www.w3.org/2001/XMLSchema#double",
        "@type": "http://www.w3.org/2001/XMLSchema#double",
        "@type": "http://www.so.org/2001/XMLSchema#double",
        "@type": "http://www.so.org/2001/XMLSchema#double",
        "@type": "http://www.geosysiot.in/rsso/ApplicationSchema#hasURY": [ {
        "@type": "http://www.so.org/2001/XMLSchema#double",
        "@type": "http://www.geosysiot.in/rsso/ApplicationSchema#hasURY": [ {
        "@type": "http://www.geosysiot.in/semrssu/data#R4"
      }, ...
} "http://www.opengis.net/ont/geosparql#rcc8ec": [ {
        "@id": "http://www.geosysiot.in/semrssu/data#R4"
      }, ...
} "####################################
```


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: figure
Figure: Figure 4. Snippet of the RSS-KG for a Remote Sensing Scene of an Urban Flood Event

Figure 4. Snippet of the RSS-KG for a Remote Sensing Scene of an Urban Flood Event


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: table
Table: Table 1. Time taken for visualizing Remote Sensing Scene Knowledge Graph (RSS-KG) for Nodes and Edges and Region Geometries

| Remote Sensing Scene Knowledge Graph (RSS-KG) Visualization | Number of Entities     | Time (seconds) |
|-------------------------------------------------------------------|---------------------------|-------------------|
| Nodes and Edges                                                   | Nodes: 554 Edges: 2153 | 5.78              |
| Region Geometries                                                 | Regions: 554              | 1.02              |


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: figure
Figure

Image /page/3/Figure/0 description: A screenshot of a web application titled "Visual Exploration of Semantically Enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs)". The main view displays a satellite image of a town, overlaid with a color-coded map showing regions in red, green, black, and brown, with a grey road running through the center. On the left is a control panel with various toggles and settings, including "RSS-Knowledge Graph in 3D" and "RSS-Knowledge Graph (Regions-Polygons)". It specifies the data source as "Remote Sensing Scene (Raster - WV02 [FCC] | Srinagar, India | Sept. 17, 2014)". On the right, a panel displays detailed information for a selected region, which is a road. The details include its ID, Type, Centroid coordinates (X: 74.7800655700565, Y: 34.08730995206955), corner coordinates, and a list of externally connected regions.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.1. Dataset
Type: figure
Figure: Figure 5. Visualization of Regions as Polygon Geometries in a Remote Sensing Scene Knowledge Graph (RSS-KG) for a RS Scene of an Urban Flood Event

Figure 5. Visualization of Regions as Polygon Geometries in a Remote Sensing Scene Knowledge Graph (RSS-KG) for a RS Scene of an Urban Flood Event


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 3. EXPERIMENTAL RESULTS > 3.2. Discussion
Type: figure
Figure: Figure 2, figure 3 and figure 5 depict screenshots of the developed visual exploration interface. Figure 3 depicts the knowledge graph visualization in the form of nodes and edges. The nodes and edges can be interacted with through mouse clicks to be presented with more information about the clicked entities. Similarly, the figure 5 depicts the visualization of polygon geometries of the "Region" instances in the knowledge graph. The "Region" instances are visualized in a color in accordance with the Land Use Land Cover Class (LULC) they belong to. These "Region" instances can also be interacted with individually to be presented with inferred concepts and relations from the RSS-KG. Table 1 depicts the time taken for visual rendering of the knowledge graph by the web browser. It was observed that the node-edge visualization took significantly more time as compared to region-geometry visualization for rendering, due to the overhead of visualizing the huge number of edges, in addition to nodes.

Figure 2, figure 3 and figure 5 depict screenshots of the developed visual exploration interface. Figure 3 depicts the knowledge graph visualization in the form of nodes and edges. The nodes and edges can be interacted with through mouse clicks to be presented with more information about the clicked entities. Similarly, the figure 5 depicts the visualization of polygon geometries of the "Region" instances in the knowledge graph. The "Region" instances are visualized in a color in accordance with the Land Use Land Cover Class (LULC) they belong to. These "Region" instances can also be interacted with individually to be presented with inferred concepts and relations from the RSS-KG. Table 1 depicts the time taken for visual rendering of the knowledge graph by the web browser. It was observed that the node-edge visualization took significantly more time as compared to region-geometry visualization for rendering, due to the overhead of visualizing the huge number of edges, in addition to nodes.


Document: Towards Visual Exploration Of Semantically Enriched Remote Sensing Scene Knowledge Graphs RSS-KGs
Section: 4. CONCLUSION
Type: text

This research proposed an intuitive interface using Cesium, for effective visual exploration of Remote Sensing Scene Knowledge Graphs (RSS-KGs) generated by the Sem-RSSU framework. The knowledge graphs comprehensively represent the different Land Use Land Cover regions contained in remote sensing scenes along with inferred spatio-contextual concepts and relations. Thus, this research aimed to enhance the comprehensive understanding of a remote sensing scene from an end user

perspective. The visual exploration interface developed as a part of this research has been hosted on the web (http://www.geosysiot.in/tools/rssKG-Explorer/).

Future work on this interface would involve enabling support for user uploaded geospatial knowledge graphs in addition to support for remote GeoSPARQL endpoints.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Type: text

Abstract On June 25, 2020, Jinweng Co in Yiga, Tibet, experienced an outburst flood that resulted in catastrophic damage to farmland and roads. The complex causal factors for glacial lake outburst flooding (GLOF) are not fully understood. This paper provides a systematic analysis of the contributing factors leading to the GLOF disaster in terms of meteorological triggering, glacial activity, lake expansion, landslide, and glacial collapse. The analysis is based on multi-source remote sensing approaches. Pixel offset tracking of Sentinel-1 images shows changes in the glacier flow velocity from 2017 to 2020. Sentinel-2 and Landsat-8 images and inventory data have revealed the expansion of the lake since 1998. The satellite precipitation measurements revealed that the highest daily rainfall in recent years occurred approximately 4 days before the GLOF. Time series synthetic aperture radar (SAR) backscattering images and interferograms suggest that a landslide had occurred from the western lateral moraine into the lake. Additionally, SAR images suggest possible ice collapse from the glacier tongue into the lake. The causal mechanism for the June 2020 GLOF event was likely the dam failure triggered by heavy rainfall and combined with landslides and ice collapses. Our research can provide a reference to identify and mitigate glacial lake outburst disasters in mountainous regions based on satellite optical and radar images.

**Keywords** Glacial lake outburst flood (GLOF)  $\cdot$  Glacier three-dimensional flow velocity  $\cdot$  Outburst mechanism  $\cdot$  Multi-source remote sensing

data/('triggering factors of glacial lake_SAR_optical_image', '.pdf')_extraction.md


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Introduction
Type: text

Glacial lake outburst flood (GLOF) is a sudden and rapid downstream discharge of a significant volume of water. Previous studies (Evans and Clague 1994; Nadim et al. 2006; Yamada 1998; Emmer and Cochachin 2013a, Emmer and Vilímek 2013b, Emmer 2017) identified two groups of factors that induce lake outbursts. The first set consists of dynamic causes, such as slope movement into the lake, earthquakes, heavy rainfall, or snowmelt. The second set includes long-term factors such as the melting of buried ice, hydrostatic pressure changes, and other long-term degradation processes. GLOFs often have catastrophic consequences for communities and infrastructure located downstream. Glacier recede and thinning in high mountains generate the formation and development of glacial lakes (Richardson and Reynolds 2000; Haeberli et al. 2001, 2002). Monitoring-related glacial movement and changes in glacier lakes and the surroundings help us better understand how the ice mass propagates in space, how their movements evolve over time, and how external factors control their

behaviors (Satyabala 2016; Ma et al. 2020). Owing to the inherently challenging landscapes, comprehensive remote sensing methods are needed to study and monitor GLOFs.

The outburst of Jinweng Co ("Co" refers to "lake" in Tibetan) in Yiga village, Tibet, caused intense and sudden flooding on June 25, 2020. This grave disaster inundated and destroyed 382.43 km2 of farmland, washed away over 43.9 km of roads, and flooded 45% of the Yiga Scenic Area project (Wang et al. 2020a). Over 2,000 glacial lakes have been detected across the Himalayas (Fujita et al. 2013), so the potential for further hazards is significant. Moreover, the eastern Himalayas are considered as one of the most severely deformed areas due to intense tectonic activities and earthquakes. The melting, thinning, and receding of temperate glaciers leads to lake expansion and is related to slope movement. Extreme precipitation plays an important role in triggering slope movement, while an increase in temperature can result in increased melting, permafrost degradation, and rockfalls (Hu et al. 2019a; Wang et al. 2020a, b; Lu and Kim 2021).

Many studies have documented that snow/ice accumulation/ avalanches and landslides significantly impact outburst flooding (Allen et al. 2016; Clague and Evans 2000; O'Connor et al. 2001; Worni et al. 2012). The Jinweng Co disaster originated from a parent glacier and included an outburst of the proglacial lake. The real-time monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF (Wang et al. 2020a). However, the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated. The evolution of the glacier, the proglacial lake, and the surrounding area prior to the GLOF event has not yet been analyzed. A complete assessment of triggering factors (glacier activity, precipitation, air temperature, slope movement, and ice collapse) is still lacking.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Introduction
Type: text

originated from a parent glacier and included an outburst of the proglacial lake . The real - time monitoring and analysis conducted shortly after the disaster suggested that the intensification of climate warming and cryosphere instability stimulated the occurrence of the GLOF ( Wang et al . 2020a ) . However , the mechanism and triggering factors of the Jinweng Co GLOF process chain should be further investigated . The evolution of the glacier , the proglacial lake , and the surrounding area prior to the GLOF event has not yet been analyzed . A complete assessment of triggering factors ( glacier activity , precipitation , air temperature , slope movement , and ice collapse ) is still lacking .

Thus, this study addresses the following key question: Can we identify precursory characteristics of the 2020 GLOF at Jinweng Co in eastern Tibet? To address this question, we aimed to understand the movement of glaciers and the change in moraine-dammed lakes, as well as temperature and precipitation records in the study area. We used spaceborne C-band Sentinel-1 data to retrieve the time series two-dimensional (2D) displacement and three-dimensional (3D) glacier flow velocity. The interpretation was then augmented with Sentinel-2 and Landsat-8 optical images and inventory data to construct changes to the area of the lake before and after the event. Meteorological observations were examined to understand the climatic properties of the region. Additionally, we analyzed SAR intensity images and interferograms to infer the

Published online: 30 January 2022 Landslides

glacier tongue changes and identify landslide activity. This led to further clarification regarding the outburst triggering mechanisms.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Study area
Type: text

The study region is within the Tanggula Mountains, which are home to some of the highest mountains in the world. It is also the most humid region of the southeastern Qinghai-Tibet Plateau (QTP) (Fig. 1). The topography of the study area is affected by the compression of the Indian Ocean-Eurasian continental plate, uplift of the QTP, strong faulting activity, and fluvial incision. The climate is influenced by both East Asian and Indian monsoons (Ding 2013). Precipitation during the rainy season is mainly caused by airflow from the southwest, west, and northwest (Li et al. 2009). The temperate glaciers in this study area are small and primarily oriented in the north-south direction with large ice caps (You and Yang 2013). Consequently, the area is subject to glacial hazards such as glacier debris flows, GLOFs, and glacier surges, which are further increased by climate change (Allen et al. 2016; Bazai et al. 2021; Chiarle et al. 2007; Wang et al. 2020a, b; Kääb et al. 2021). Generally, a glacial lake outburst results from external forces such as ice or rock fall, snow avalanches, landslides, rainstorms, glacier surges, rapid snow melting, and/or internal processes such as the ablation of buried ice in moraines and the release of lake water inside the ice body (Wang et al. 2020a).

Jinweng Co (30.356 N°, 93.631 ° E) is a proglacial moraine-dammed lake (Fig. 2) and is also the largest glacial lake within the Nidou Zangbo basin ("Zangbo" refers to rivers in Tibetan) (Zheng et al. 2021). The lake is oriented in a north–south direction and is characterized by steep slopes and lateral moraines. The slopes are approximately 40°, and the average elevation of lateral moraines is more than 100 m above the lake level (Zheng et al. 2021). As shown in Fig. 2d, the length is approximately 1.8 km, and its width near the glacier tongue is 0.24 km, while the width near the dam was 0.33 km before the lake outburst. The parent glacier tongue enters

**Fig. 1** Overview of the study area showing Jinweng Co and coverage of multiple datasets outlined in purple (Sentinel-2), orange (Landsat-8), blue (Ascending Sentinel-1), and red (Descending Sentinel-1)

the lake after passing through a steep ice cliff approximately 350 m long with an average slope of approximately 35 ° (Fig. 2). The parent glacier is a temperate glacier with a mean annual temperature of approximately 0 °C. The snow basin at high altitude serves as the accumulation zone, while the tongue is the ablation zone. This study investigates the triggering factors for the June 2020 GLOF event in Jinweng Co by analyzing the movement of the parent glaciers and changes to the lake and surroundings using multi-satellite remote sensing datasets.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods
Type: text

The multi-track C-band Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 and Landsat-8 optical images were used to investigate the triggering factors of the 2020 GLOF event (Fig. 3). The Copernicus Sentinel-1A/B and Sentinel-2 Level-1C images for this study are available free of cost from the Sentinel Scientific Data Hub of the European Space Agency. Multi-temporal Landsat images were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/). In addition, daily and monthly precipitation measurements from the satellite Global Precipitation Measurement (GPM) (Skofronick 2017) were obtained to analyze the meteorological factors. In addition, inventory data from 1975 to 2018 were obtained from the National Tibetan Plateau Data Center (TPDC) to evaluate temporal variations in Jinweng Co (https://data.tpdc.ac.cn) (Wang 2015).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Satellite datasets
Type: text

Ninety-eight interferometric wide (IW) swath mode ascending Sentinel-1 and eighty-two descending Sentinel-1 images from March 2017 through October 2020 were acquired. The pixel spacing in the ground range and azimuth direction was ~4 m and ~14 m, respectively. Sentinel-2 data with multi-spectral instrument (MSI) images were acquired on May 1 and July 27, 2020, and provided a spatial resolution of 10 m. Landsat-8 MSI images from 2018 to 2019 with a spatial resolution


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Satellite datasets
Type: figure
Figure

Image /page/1/Figure/11 description: A satellite map of a mountainous region, with longitude ranging from 93°25' to 93°55' and latitude from 30°15' to 30°35'. The map displays snow-covered mountains, glaciers, and lakes. Several colored outlines indicate the coverage areas of different satellites: a blue polygon for 'Ascending Sentinel-1', a red polygon for 'Descending Sentinel-1', an orange rectangle for 'Landsat-8', and a purple rectangle for 'Sentinel-2'. A legend in the bottom-left corner defines symbols for Glacier, Lake, and Main city. Key locations marked include the main city 'Yiga' and the lake 'Jinweng Co'. A blue arrow indicates the 'Flood direction' from west to east. A scale bar in the bottom-right shows a scale of 0 to 6 km.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Satellite datasets
Type: text

Fig. 2 Overview of Jinweng Co and its parent glacier. a An overall view of Jinweng Co and surroundings. **b** The parent glacier before the GLOF event. **c** The crevassed glacier tongue terminated in Jinweng Co. d Morphological parameters of Jinweng Co. e The eastern lateral moraine and steep slopes. f The landslide zone at the western lateral moraine. **g** The parent glacier after the GLOF event. Photos: Q. Quying Source: Guoxiong Zheng (used with permission)


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Satellite datasets
Type: figure
Figure

Image /page/2/Figure/1 description: A collage of seven images, labeled (a) through (g), showing various aerial and landscape views of a glacial environment. Each image includes a compass rose indicating the direction of North.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Satellite datasets
Type: text

Image (a) is a wide aerial view of a valley with snow-capped mountains. A glacier at the far end feeds into a lake labeled "Jinweng Co". A dashed white line with an arrow indicates the "Flow direction" down the valley.

Image (b) is a top-down aerial view of a mountainous, snow-covered landscape partially obscured by clouds.

Image (c) shows a glacier terminating in a milky green proglacial lake, with steep, rocky mountain slopes surrounding it.

Image (d) provides a view of the glacial lake with its dimensions annotated: 1.8 km in length, 0.33 km in width at the far end, and 0.24 km in width at the near end.

Image (e) is a similar view to (d), with a dashed white line highlighting the "Lateral moraine" along the side of the lake.

Image (f) shows the same lake and mountains, with a red dashed line and text identifying a "Landslide zone" on the slope adjacent to the lake. There is snow in the foreground.

Image (g) is another high-altitude aerial view looking down on a glacier and surrounding mountains, with some cloud cover.

of 30 m were added to derive the temporal changes of Jinweng Co. Information on the Sentinel-1/2 and Landsat data are listed in Table 1, and the data coverages are shown in boxes in Fig. 1.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Mapping slope movement with SAR intensity images and interferograms
Type: text

SAR intensity images can be used to detect landscape changes due to their sensitivity to terrain slope, surface roughness, and dielectric constant (e.g., Lu and Meyer 2002; Kim et al. 2017; Zribi and Dechambre 2017). Multi-temporal SAR backscattering intensity images were used in this study to infer changes in glaciers, lakes, and surroundings before the lake outburst event. Interferometric synthetic aperture radar (InSAR) can map surface deformation in the spatial–temporal view, which is a direct manifestation of slope movement (Hu et al. 2019a; Lu and Meyer 2002; Lu et al. 2003; Lu and Kim 2021).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Change detection with optical images
Type: text

The interpretation of optical images is commonly employed to support GLOF mapping and inventory (Strozzi et al. 2010). There are countless glaciers in this study area, which induce enormous glacial lakes as glacier recedes. The Sentinel-2 and Landsat-8 images

allow us to recognize geomorphological features related to ice mass movements, such as crevasses, debris flows, and outburst flooding. For instance, a glacial lake may have green patterns with fine texture in one optical image, and after the glacial lake outbursts or shrinks, its edge will form a gray-white submerged zone with obvious breaches and colluvial deposits. Using multi-temporal images, we can interpret the changes in glacial lakes and analyze the possibility of their collapse.

Data and methods
Change detection with optical images


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Offset tracking method
Type: text

We carried out the offset tracking procedure implemented by GAMMA software to estimate the two-dimensional displacement (Wegmuller et al. 1998; Werner and Wegmuller 2000; Gomez et al. 2019). Offset tracking includes intensity tracking and speckle tracking based on maximizing the cross-correlation of SAR image patches (e.g., Strozzi et al. 2002; Pritchard et al. 2005) to derive glacier displacement for pairs of images acquired at different times. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used to assist the co-registration of the SAR images to minimize geometric artifacts in offset-tracking displacements due to high topography (Kobayashi et al. 2009; Liu et al. 2020). A template of  $64 \times 64$  and  $4 \times 1$  was adopted in this study. Thresholds of amplitude correlation range between 0.2 and 0.4.

Table.1 Basic parameters of the SAR and optical datasets used in this study


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Offset tracking method
Type: table
Table

| Data                            | Sentinel-1            |                       | Sentinel-2            | Landsat-8             |
|---------------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| Pixel Spacing (azimuth × range) | 14 m × 4 m            |                       | 10 m                  | 30 m                  |
| Number of SAR images            | 98(ascending)         | 82(descending)        | 2                     | 2                     |
| Acquisition period              | 2017/03/21-2020/10/31 | 2017/03/16-2020/10/26 | 2020/05/01-2020/07/27 | 2018/06/06-2019/06/25 |


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Offset tracking method
Type: text

**Fig. 3** Flow chart of remote sensing methods used in this study


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Offset tracking method
Type: figure
Figure

Image /page/3/Figure/2 description: A flowchart titled 'Recent Landslides' illustrating a process for analyzing geological data from three different sources. The flowchart is organized into three parallel columns, each starting with a different data input and leading to a specific output. The first column starts with 'External DEM' (Digital Elevation Model), represented by a colorful topographic image. This data goes through 'Offset tracking', 'Stacking', and '3-D inversion' to produce 'Derivation of 3D annual flow velocity', shown as a 3D map with red and yellow areas. The middle column begins with 'Ascending and Descending SAR images', depicted as stacks of grayscale images. This data undergoes 'Co-registration' to create 'Original interferograms', which are then processed through 'Interferograms filtering' to result in 'Deformation interferograms', illustrated by a colorful, noisy-textured image. The third column uses 'Optical images', shown as a stack of color images, as input. This path involves 'Change detection', leading to 'Intensity change' (shown as a stack of grayscale images), and finally results in 'Optical interpretation', which is represented by two side-by-side maps showing changes in a landscape, possibly a glacier.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Pixel offset tracking small baseline subsets (PO-SBAS)
Type: text

Pixel offset (PO) tracking small baseline subsets (SBAS) (PO-SBAS) follows the same motivation as the SBAS-InSAR technique. A sequence of small baseline SAR image pairs that have been previously co-registered with respect to a common reference image was obtained as a starting point (Sansosti et al. 2006; Casu et al. 2011). Subsequently, instead of the phase difference of the selected SAR images, we used their intensities to calculate the pixel offset for both the line-of-sight (LOS) and azimuth directions. Then, the singular value decomposition (SVD) inversion method was applied for the estimated relative LOS and azimuth offsets to generate the corresponding offset-based deformation time series (Sansosti et al. 2006; Casu et al. 2011).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Data and methods > Derivation of 3D Glacier velocity
Type: text

To retrieve the 3D glacier flow velocity from ascending and descending SAR images, we first obtained displacement measurements in the azimuth and LOS directions based on small baseline image pairs from both descending and ascending tracks.

The 3D velocity field can be obtained from velocity measurements in four distinct directions (Li et al. 2018; Yang et al. 2020):

$$\left\{ \begin{array}{l} V_{LOS}^{i} = -V_{U}cos\:\theta^{i} + \bullet V_{E}sin(\alpha^{i} - 3\pi/2)sin\:\theta^{i} + V_{N}cos(\alpha^{i} - 3\pi/2)sin\:\theta^{i} \\ V_{AZ}^{i} = -V_{E}cos(\alpha^{i} - 3\pi/2) + V_{N}sin(\alpha^{i} - 3\pi/2) \end{array} \right.$$

where i stands for the orbit direction (A indicates the ascending track, and D indicates the descending track),  $\theta$  is the incidence

angle, and  $\alpha$  is the azimuth angle. E, N, and V refer to the east, north, and vertical directions, respectively. The matrix form is as follows:

$$BX = V (2)$$

 $X = \begin{bmatrix} V^{\text{U}} & V^{\text{E}} & V^{\text{N}} \end{bmatrix}^{\text{T}}$ ;  $L = \begin{bmatrix} V_{\text{LOS}}^{\text{A}} & V_{\text{AZ}}^{\text{A}} & V_{\text{LOS}}^{\text{D}} & V_{\text{AZ}}^{\text{D}} \end{bmatrix}^{\text{T}}$ ; and B is a design matrix composed of imaging geometry parameters.

Data and methods
Derivation of 3D Glacier velocity


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes in glacier velocity
Type: text

In this study, the 2D and 3D glacial movements of Jinweng Co from 2017 to 2020 were derived (Fig. 4). The 3D glacier flow velocity during this period is shown in Fig. 4a. The vector arrows indicate the horizontal velocity, whereas the color shows the vertical velocity. In the horizontal direction, the north directed velocity mainly appeared in the glacier trunk of Jinweng Co, with a mean flow velocity of up to 200 m/year. In the vertical direction, the parent glacier moved downward at a velocity of 40 m/year. Owing to sparse acquisitions from descending Sentinel-1, we only obtained the long time series displacement from the ascending track to show the temporal variation (Fig. 4b). As shown in Fig. 4b, P1 was located at the glacier tongue near the lake. The mass transport is evident in the azimuth direction (approximately in the northern direction), as seen in the cumulative displacement of up to 18.4 m. Interestingly, the total displacement during the 2019-2020 cycle (October 2019 to June 2020) at the lower part of the glacier (close to the proglacial lake) is much larger than that observed in the previous years (Fig. 4b).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes in glacier velocity
Type: figure
Figure

Image /page/4/Figure/0 description: The image contains two plots, labeled (a) and (b).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes in glacier velocity
Type: text

Plot (a) is a 3D surface plot illustrating velocity in a geographical area. The surface is colored according to a 'Vertical velocity (m/year)' scale, which ranges from -40 (blue) to 40 (red). The plot also displays black arrows indicating 'Horizontal velocity (m/year)', with a scale arrow representing 200 m/year. A location on the map is labeled 'Jinweng Co'. The x-axis ranges from approximately 93.64 to 93.665, and the y-axis ranges from approximately 30.325 to 30.365. A compass indicates the direction of North.

Plot (b) is a scatter plot showing 'Displacement (m)' on the y-axis versus 'Date (yyyy/m/d)' on the x-axis. The time period spans from 2017/3/1 to 2020/9/1. There are two data series: 'Azimuth', represented by red circles, and 'LOS', represented by blue triangles. The 'Azimuth' displacement shows a steady increase from about 2 m to over 17 m. An event labeled 'Outburst' is marked on the 'Azimuth' data around mid-2020. The 'LOS' displacement remains much lower, fluctuating between 0 m and approximately 3 m over the same period. The y-axis for displacement ranges from 0 to 20 m.

Fig. 4 a 3D glacier flows velocity during 2017–2020. The color indicates vertical velocities, and the arrow indicates horizontal velocities. **b** 2D time series displacement for P1 from the ascending track. The location of P1 is shown in a


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes to Jinweng Co
Type: text

To investigate the historical evolution of Jinweng Co, we analyzed its extent of variation and frontal recede using the inventory data from TPDC and by interpreting optical images. Figure 5 shows the temporal variations in Jinweng Co from 1998 to 2020. By superimposing the lake boundaries in the image of 2015 (Fig. 5a), we clearly see that the lake expanded upstream, and the glacier tongue receded. From 1998 to 2020, the proglacial lake expanded dramatically at a mean rate of 21.73 m²/year, and the glacier receded at a mean rate of 0.13 m/year (Fig. 5b).

To better understand the changes to Jinweng Co before and after the catastrophic GLOF in June 2020, we interpreted four summertime optical images, including Sentinel-2 images and Landsat images, and analyzed the variations to the boundary of Jinweng Co, as shown in Fig. 6. The lake area experienced an increasing trend from 2018 to 2020. It showed 0.52 km² on June 6, 2018; 0.55 km² on June 25, 2019; and then 0.57 km² on May 1, 2020. After the GLOF event in June 2020, the lake area decreased to 0.32 km², as reflected in the July 2020 image. This resulted in a massive (~0.25² km) downstream water flow from the lake dam (Fig. 6d).


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes to Jinweng Co
Type: figure
Figure

Image /page/4/Figure/5 description: A two-part figure, labeled (a) and (b), illustrating the changes in a glacial lake over time. Part (a) is a satellite image showing the expansion of a lake between 1998 and 2020. The image is marked with latitude and longitude coordinates (30°21' to 30°22' N, 93°37' to 93°38' E). A legend titled 'Acquisition Data (yyyy/mm/dd)' shows colored outlines for the lake's extent on different dates: 1998/10/19 (light blue), 2001/10/21 (green), 2011/11/10 (yellow), 2015/11/15 (red outline), 2018/10/15 (darker red outline), 2019/06/25 (pink outline), and 2020/05/01 (purple outline). An inset map shows a wider view of the lake dated 2020/7/27. Part (b) is a scatter plot showing the change in lake area and retreat distance over time, from 1998 to 2020. The x-axis represents the date. The left y-axis represents 'Lake area (km²)' and the right y-axis represents 'Retreat distance (m)'. There are two data series plotted. Red squares represent the lake area, which increases from about 0.4 km² to nearly 0.6 km². The linear fit for this data has a slope of 21.73 m²/year. Green circles represent the retreat distance, which increases from about 0.5 m to 1.2 m. The linear fit for this data has a slope of 0.13 m/year. Both linear fits are shown with their 95% confidence bands. The legend clarifies the symbols: red squares for 'Lake area (km²)', green circles for 'Retreat distance (km)', and lines and shaded areas for the linear fits and confidence bands.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Results > Changes to Jinweng Co
Type: text

**Fig. 5** Temporal evolution of the Jinweng Co during 1998–2020. **a** Lake boundaries derived from inventory data (1998, 2001, 2011, 2015) and interpretation of Landsat-8/Sentinel-2 images (2018, 2019, 2020); the inset map shows the lake boundary after the collapse. **b** Time series of the lake areas and glacier recede from 1998 to 2020. Red squares are measurements of the lake area in km2; the red line

is a linear regression fit of the area measurements, and aqua shading surrounding the red line refers to the 95% confidence interval of the lake area. Green circles are distance in km of parent glacier frontal recede; the green line is a linear regression fitting within a 95% confidence interval, indicted by pink shading


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Recent Landslides
Type: figure
Figure

Image /page/5/Figure/1 description: A series of four satellite images, labeled (a) through (d), showing a glacial landscape at different points in time. Each image includes a legend and scale. The dates for the images are: (a) 2018/6/6, (b) 2019/6/25, (c) 2020/5/1, and (d) 2020/7/27. The images depict a large glacier in shades of cyan, with surrounding land in brown, green, and red. A large, dark blue glacial lake is prominent in the upper left of each frame. A smaller, higher-elevation lake is visible in panel (a), but appears to have drained in the subsequent images. Key features are labeled, including the city of Yiga and elevations such as 3800m, 4845m, and 6838m. A legend in the bottom left of each panel defines symbols for City, Elevation, Glacier area, Lake area, Ice dam, Natural drainage channel, and Conduit. The legend for panel (d) also includes a symbol for Breach.


Document: Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co, Tibet, China
Section: Recent Landslides
Type: text

Fig. 6 A comparison of the Jinweng Co area and the surrounding glaciers before and after the GLOF: **a** June 6, 2018, Landsat-8 image; **b** June 25, 2019, Landsat-8 image; **c** May 1, 2020, Sentinel-2 image; and **d** July 27, 2020, Sentinel-2 image