Document: 1 Introduction
Section: 3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity
Type: text

Satellite imagery had a spatial resolution of 0.8 to 30 m (Table S1). High-resolution imagery is used with the aim of obtaining accurate lake surface levels. The images were selected based on the visibility of the glacier surface and lake areas, and overall, 23 ice-dammed lakes from eight surge events were identified related to the Khurdopin, Kyagar, and Shishper glaciers (Table 1, Fig. 1a). The presence of lakes was determined based on the normalized difference water index (NDWI) (McFeeters, 1996), and the outlines of all 23 lakes were digitized manually using Landsat false-color composites (near-infrared, red, and green bands) to distinguish waterbodies from other objects (Huggel et al., 2002). The extents of six Shishper and Khurdopin lakes that occurred after 2017 were obtained in the field using GPS (G639; accuracy: single, 1-3 m; Satellite Based Augmentation System: 0.6 m) survey points along the lake shorelines (Fig. 2a-d), as well as UAV-generated digital surface models (DSMs). Alternatively, high-resolution satellite imagery from Planet (3 m), GF-1 and GF-2 (0.8 and 4 m resolution, respectively), and SPOT 6 and SPOT 7 (1.5 m) was used to extract the lake boundaries. The coupled lake extent and outlines help reduce the uncertainty in the lake extent obtained for Landsat 2-Landsat 5 images. The above-described method was used to extract the extent of the Khurdopin and Kyagar glacial lakes previously reported (Bazai et al., 2021, 2022), the data of which are incorporated into the current analysis.

The Khurdopin, Kyagar, and Shishper glaciers are surgetype glaciers (Copland et al., 2011; Hewitt, 1998). Since 1972, eight surge events have occurred in total at Khurdopin (three surges), Kyagar (three surges), and Shishper (two surges) (Table 1) at an interval of 17–20 years for each glacier. The Landsat 2–Landsat 4 images from 1970 to 1990 have errors in the selected glacier area. Therefore, the initial surges for Khurdopin and Kyagar between 1970 and 1989 were not considered when estimating the annual velocity. Orthorectified Landsat scenes from the Thematic Mapper (TM) and OLI-2 and Sentinel-2 were used to estimate the yearly and event-based velocities of all three glaciers from 1989 to 2022 to obtain information about the surge events and glacier front changes. Within this period, cloud-free images were chosen each year, although some satellite images were absent. Glacier velocities were recorded as annual averages, although daily measurements of glacier velocity were also determined to assess any effect on lake volume given the possible velocity sensitivity to the triggering time of GLOFs.


Document: 1 Introduction
Section: 3 Data and methods > 3.2 Glacier lake surface area mapping and glacier surface velocity
Type: text

and Kyagar between 1970 and 1989 were not considered when estimating the annual velocity . Orthorectified Landsat scenes from the Thematic Mapper ( TM ) and OLI - 2 and Sentinel - 2 were used to estimate the yearly and event - based velocities of all three glaciers from 1989 to 2022 to obtain information about the surge events and glacier front changes . Within this period , cloud - free images were chosen each year , although some satellite images were absent . Glacier velocities were recorded as annual averages , although daily measurements of glacier velocity were also determined to assess any effect on lake volume given the possible velocity sensitivity to the triggering time of GLOFs .

The surface velocities were extracted along the central line of the Khurdopin, Shishper, and Kyagar glaciers, highlighting the quiescent and surge phases obtained from published data (Bazai et al., 2021, 2022) using the image-to-image correlation, open-source software COSI-Corr (Leprince et al., 2012, 2007). The software effectively assesses the glacier surface velocity (Leprince et al., 2012; Steiner et al., 2018). Utilizing a displacement calculation, this technique was used to co-register and correlate surface features (Bazai et al., 2021; Steiner et al., 2018). The surface velocity and overall movement during the surge were measured by observing changes in the GPS-registered glacier front positions every 3 months from March 2019, and these changes were measured for 3 years for the Shishper Glacier in the field as well as for the Khurdopin Glacier from June 2017 to July 2019. When coupled with COSI-Corr-measured velocities, these latter procedures gave accurate results. The velocity estimation procedure generally yields an accuracy of 1/4 of a pixel (Sattar et al., 2019). Velocity root-mean-square errors (RM-SEs) were assessed to justify image processing accuracy. Examples of output are given in Fig. 1b-d.

data/('Refining lake volume estimation and critical depth identification', '.pdf')_extraction.md


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: text

Six lakes were regularly monitored: four from the Shishper Glacier and two from the Khurdopin Glacier. Data for 23 GLOF events from eight surge cycles that occurred during or following the first year of each surge are presented in Table 1, with lakes resealing after each GLOF. The field data for six events from the Khurdopin and Shishper glaciers helped to reduce the uncertainty or validated data for 17 lakes for which data were obtained through remote sensing techniques (as explained in Sect. 3.2).

For the Kyagar Glacier, Li et al. (2023) suspected that the drainage conduit may not have been at the deepest part of the lake basin, and its configuration changed between GLOF events. All the previously recorded lakes from Khurdopin and Shishper were drained via single subglacial conduits with stable inlet positions (i.e., the inlet was seen to be in the same place on each occasion) and varying outlet positions and conduit lengths. Therefore, as closely as possible, we identified the inlet and outlet positions of the drainage conduits. As is shown in the Results section, the inlet position of the conduit in the ice-dammed lake basin was always in the deepest position. The lowest ice dam height also tended to be in the vicinity of the conduit. The conduit inlet positions were geolocated within the empty lake basins using GPS, and the lake depths were calculated for these locations with reference to shoreline elevations (Table 1 and Fig. 2). From the field survey, we noted that the presence of alignments of surface depressions in the glacier indicated the


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: table
Table: Table 1. GLOF and surge date and lake volume measurement since 1970 obtained using remote sensing techniques. The average surge velocity and volume related to each of the 23 GLOFs from eight surge cycles are presented; other detail is in Table S3 in the Supplement.

| Glacier name | Lake surface elevation (m) | Date (mm/dd/yyyy) | Lake area (km2) | ASTER/UAV lake volume estimate (106 m3) | Lake vol. uncertainty (±106 m3) | Vol. after GLOF (106 m3) | Average velocity (± md-1) | Date of next clear image after GLOF (mm/dd/yyyy) | Surge cycle and resealed GLOF | Surge duration in months           |
|--------------|----------------------------|-------------------|-----------------|-----------------------------------------|---------------------------------|--------------------------|---------------------------|--------------------------------------------------|-------------------------------|------------------------------------|
| Khurdopin    |                            | 20/08/1977        |                 |                                         |                                 |                          |                           |                                                  | 1977–1979                     | May 1977 to Aug 1979; 27 months |
| Khurdopin    |                            | 15/08/1999        |                 |                                         |                                 |                          | 0.33                      |                                                  | 1998–1999                     | Jan 1995 to Sep 2002; 92 months |
| Khurdopin    | 3440                       | 05/30/2000        | 1.87            | 186                                     | 2.1                             | —                        | 0.33                      | 08/26/2000                                       | Resealed                      |                                    |
| Khurdopin    | 3416                       | 04/07/2001        | 0.295           | 19.5                                    | 1.5                             | —                        | 0.44                      | 06/26/2001                                       | Resealed                      |                                    |
| Khurdopin    | 3420                       | 07/15/2002        | 0.60            | 52.1                                    | 1.6                             | 2.2                      | 0.87                      | 08/16/2002                                       | Resealed                      |                                    |
| Khurdopin    | 3415                       | 07/28/2017        | 0.180           | 16.2                                    | 1.4                             | —                        | 1.41                      | 08/01/2017                                       | 2016–2018                     | Jun 2006 to Aug 2009; 38 months |
| Khurdopin    | 3418                       | 03/18/2018        | 0.402           | 19.8                                    | 0.9                             | —                        | 0.53                      | 02/25/2018                                       | Resealed                      |                                    |
| Kyagar       | 4785                       | 08/01/1977        | 1.181           | 40.73                                   | 5.8                             | —                        |                           | 10/14/1977                                       | 1976–1977                     | Jan 1975 to Aug 1978; 43 months |
| Kyagar       | 4810                       | 07/18/1978        | 2.17            | 82.12                                   | 15.6                            | —                        |                           | 06/07/1979                                       | Resealed                      |                                    |
| Kyagar       | 4823                       | 03/08/1997        | 3.30            | 127.3                                   | 2.9                             | —                        | 0.4                       | 04/09/1997                                       | 1994–1996                     | Jan 1995 to Sep 2002; 92 months |
| Kyagar       | 4825                       | 09/10/1998        | 3.32            | 133.5                                   | 3.5                             | —                        | 0.3                       | 10/11/1998                                       | Resealed                      |                                    |
| Kyagar       | 4813                       | 09/07/1999        | 2.19            | 86.12                                   | 1.23                            | —                        | 0.46                      | 08/17/1999                                       | Resealed                      |                                    |
| Kyagar       | 4778                       | 06/25/2000        | 0.91            | 23.48                                   | 1.12                            | —                        | 0.49                      | 08/03/2000                                       | Resealed                      |                                    |
| Kyagar       | 4819                       | 09/08/2002        | 2.93            | 115.19                                  | 1.09                            | —                        | 1.29                      | 10/09/2002                                       | Resealed                      |                                    |
| Kyagar       | 4811                       | 06/14/2008        | 1.45            | 94.95                                   | 1.65                            | —                        | 0.61                      | 23/06/2008                                       | Resealed                      | Jun 2006 to Aug 2009; 38 months |
| Kyagar       | 4808                       | 07/28/2009        | 1.39            | 91.35                                   | 1.56                            | —                        | 0.56                      | 04/08/2009                                       | Resealed                      |                                    |
| Kyagar       | 4800                       | 07/16/2015        | 1.56            | 53.5                                    | 0.87                            | —                        | 1.14                      | 05/08/2015                                       | Resealed                      | Jan 2013 to Aug 2018; 67 months |
| Kyagar       | 4804                       | 07/14/2016        | 1.63            | 45.89                                   | 1.49                            | 2.9                      | 0.53                      | 30/07/2016                                       | 2014–2016                     |                                    |
| Kyagar       | 4806                       | 07/31/2016        | 1.48            | 44.32                                   | 1.23                            | —                        | 0.38                      | 08/09/2016                                       | Resealed                      |                                    |
| Kyagar       | 4815                       | 08/10/2017        | 2.91            | 113.99                                  | 0.73                            | 11.9                     | 0.38                      | 26/08/2017                                       | Resealed                      |                                    |
| Kyagar       | 4807                       | 08/06/2018        | 2.38            | 87.98                                   | 0.53                            | —                        | 0.29                      | 08/29/2018                                       | Resealed                      |                                    |
| Shishper     | 2650                       | 06/23/2019        | 0.37            | 24.10                                   | 2.1                             | —                        | 0.95                      | 07/13/2019                                       | 2017–2019                     | Dec 2018 to Jun 2022; 41 months |
| Shishper     | 2636                       | 05/29/2020        | 0.50            | 24.90                                   | 1.5                             | —                        | 0.46                      | 06/22/2020                                       | Resealed                      |                                    |
| Shishper     | 2638                       | 05/16/2021        | 0.52            | 25.77                                   | 1.4                             | —                        | 0.29                      | 07/15/2021                                       | Resealed                      |                                    |


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: text

\* Only the date of the GLOF is available; other details are not accessible.


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: figure
Figure

Image /page/5/Figure/2 description: A multi-panel figure displaying maps of the Karakoram Range. The main, largest panel, labeled (a), is a map showing the extent of glaciers (in light blue) within the Karakoram Boundary. The map spans from 74°0'0"E to 78°0'0"E longitude and 35°0'0"N to 37°0'0"N latitude. It highlights three specific ice dam locations: Shishper, Khurdopin, and Kyagar, each marked with a black dot and a red glacier outline. The Indus River is also shown. A legend in the top left clarifies the symbols for Ice Dam, Glacier Outline, Glaciers, Indus River, and Karakoram Boundary. A scale bar at the bottom indicates distances up to 100 km. An inset map in the top right shows the location of the study area within the broader High Mountain Asia (HMA) region, with an elevation legend ranging from a low of -102 to a high of 8685. At the bottom, there are three smaller inset images, labeled (b), (c), and (d), corresponding to the Shishper, Khurdopin, and Kyagar glaciers, respectively. These insets show glacier velocity in meters per year (m/year) for different time periods, using a color scale that ranges from 0.01 (purple) to 15 (red). Image (b) is for 2016-2017, image (c) is for 2015-2016, and image (d) is for 2013-2014. Each of these insets indicates a 'Dam site'.


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: figure
Figure: Figure 1. Overview of the study site in the Karakoram (a) and the high-mountain Asia (HMA) region; panels (b)–(d) present the extent of each glacier at a given time when surge speed has led to ice-dammed lake formation. The associated ice flow velocities are indicated. The background of panels (b)–(d) is from © Google Earth images. Publisher's remark: please note that the above figure contains disputed territories.

Figure 1. Overview of the study site in the Karakoram (a) and the high-mountain Asia (HMA) region; panels (b)–(d) present the extent of each glacier at a given time when surge speed has led to ice-dammed lake formation. The associated ice flow velocities are indicated. The background of panels (b)–(d) is from © Google Earth images. Publisher's remark: please note that the above figure contains disputed territories.


Document: 1 Introduction
Section: 3 Data and methods > 3.3 Field observation and lake volume measurement
Type: text

approximate position of curvilinear conduits, from which we estimated the conduit lengths between the inlet and outlet.

In addition, we used a UAV (DJI Mavic 2 Pro) equipped with a high-resolution camera (4000 pixels × 2250 pixels) to obtain multiple aerial photographs with a minimum of 85 % image overlap (Entwistle and Heritage, 2017, 2019; Tonkin and Midgley, 2016). The UAV flew at a low uniform height (500 m - to reduce the image distortion) to generate high-resolution orthomosaics and DSMs of the glacier lake surfaces, empty lake basins, and glacier termini. In addition to UAV data, we utilized data from KH-9 (1974), ASTER (2000–2019), PALSAR DEM (June 2008), and SRTM (February 2000) for the computation of lake volumes (Table S2). The SRTM DEM without voids serves as the reference dataset, and the vertical uncertainties in the SRTM DEM are reported to be  $\pm 10 \,\mathrm{m}$  (Rodriguez et al., 2006). The corrected DEMs from the Karakoram region are those used by Bazai et al. (2021) and Gardelle et al. (2013).


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: text

Although, in this study, we have field-derived estimates of lake depth, basin geometry, and lake surface area to calculate lake volumes, in many other applications, only remote sensing data are available for undrained lakes. Consequently, considering that the lake area in satellite images often exhibits a triangular planform (e.g., Figs. 2a and 3a), we explored the possibility of using a geometric shape to approximate the volume of undrained lake basins. Such an approach would be valuable where the depths of lakes are unknown. To this end, we employ NDWI (as in Sect. 3.2) to identify lake outlines through Landsat false-color composites, which use near-infrared, red, and green bands to distinguish waterbodies from other features. We employ standard connected component analysis (Dillencourt et al., 1992) to manually calculate each lake's area, perimeter, and other surface dimensions (as given in Fig. 3b). Initial calculations are pixelbased and are later converted into metric units by multiplying pixel counts by their respective pixel sizes. The pixel size for high-resolution images varied from 0.8 to 3 m. The output was cross-validated with Khurdopin and Shishper glacial

3 Data and methods
3.4 Geometry of lake basin


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: figure
Figure

Image /page/6/Figure/2 description: A composite image with five panels, labeled (a) through (e), showing photographs of Shishper Lake and Khurdopin Lake at different times. Panel (a), titled "Shishper Lake March 2019", is an aerial view of a murky green lake with a blue arrow indicating flow direction. Panel (b), titled "Shishper Lake Shoreline", shows a rocky slope with dashed lines indicating the shoreline for 2019 (blue, highest), 2020 (red, middle), and 2021 (green, lowest). Panel (c), titled "Khurdopin Lake February 2018", shows a glacier-filled valley with a river flowing through it. Panel (d), titled "Khurdopin Lake March 2018", shows a close-up of the broken, icy surface of the glacier. Panel (e), titled "Khurdopin Lake Shoreline", shows a rocky slope with dashed lines indicating the shoreline for 2017 (black, lower) and 2018 (yellow, higher).


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: figure
Figure: Figure 2. Shishper and Khurdopin glacial lake views in the field. (a) Oblique view from a helicopter in March 2019 (image captured during lake monitoring by Gilgit-Baltistan Disaster Management Authority); (b) the Shishper shoreline elevations of four lakes that burst out in the given years; (c, d) successive oblique views of the Khurdopin lake in the field; (e) Khurdopin lake elevations in the given years. Wiggly blue lines are flow directions.

**Figure 2.** Shishper and Khurdopin glacial lake views in the field. (a) Oblique view from a helicopter in March 2019 (image captured during lake monitoring by Gilgit-Baltistan Disaster Management Authority); (b) the Shishper shoreline elevations of four lakes that burst out in the given years; (c, d) successive oblique views of the Khurdopin lake in the field; (e) Khurdopin lake elevations in the given years. Wiggly blue lines are flow directions.


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: text

lake UAV data that had a pixel size of 0.063 m and with field survey evidence. Trials demonstrated that the known volume of the lakes determined using DEMs of the lake basins once drained (Sect. 3.3) could be approximated if the length of the lake from the upstream inlet to the ice dam face (Z) and the breadth of the lake at the ice dam (C) were known. Given the reported image resolution, uncertainties in the characteristic length measurements (Fig. 3b), measured using 3D GIS interpolation, would translate into uncertainty in lake volume estimates of only 3% when applying Eqs. (1) and (2) if lake depth were known exactly. For undrained lakes, assuming the depth is the same as the width of the lake at the dam face (Fig. 3b) will likely overestimate lake volume. It might be expected that geometric estimates based on lake surface area alone would be improved if the lake's depth (h)is known at the deepest point close to the dam face. However, in our examples, there is uncertainty in the values of h obtained from DEMs of the drained basins such that the errors in lake depth estimates translate into errors in lake volume estimates of < 14 \%. Alternatively, where a lake is present, this latter parameter can be obtained by plumbing the depth from a boat.

Given the triangular shape of the lake surface areas, the first consideration with regard to lake geometry was whether the valley sides might be regarded as providing a V-shaped lake cross-section or a rectangular cross-section (Fig. 3b); in either case, regular geometric shapes might provide an esti-

mate of the lake volumes. A rectangular cross-section would be closer to the U-shaped valley cross-sections commonly associated with glaciated valleys. Thus, assuming a V-shaped valley, lake volume (V) can be approximated by an irregular tetrahedron (Fig. 3b, left-hand diagram) where the depth (h) is unknown but the distance from A to B (X in Fig. 3b) and the length C are known values. Assuming the lake surface is an isosceles triangle and the vertical face at the dam wall is an equilateral triangle, the volume can be obtained from

$$V = \sqrt{V^2}, \qquad (1)$$
$$\begin{aligned} V^2 = \frac{1}{144} \biggl[ & Y_1^2 D^2 (Z_2^2 + X^2 + C^2 + E^2 - Y_1^2 - D^2) \\ & + Y_2^2 E^2 (Y_1^2 + X^2 + C^2 + D^2 - Y - E^2) \\ & + Z_2^2 C^2 (Y_1^2 + Y + D^2 + E^2 - Z^2 - C^2) \\ & - Y_1^2 Y_2^2 C^2 - Y_2^2 Z_2^2 D^2 - Y_1^2 Z_2^2 E^2 - C^2 D^2 E^2 \biggr], \end{aligned}$$

where the values for lakeside lengths  $Y_1$  and  $Y_2$ , the main length Z, lakeside length D, and lakeside length E are defined in Fig. 3b and obtained from geometry.

Alternatively, considering a pentahedral, the volume is


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
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+ Y + D ^ 2 + E ^ 2 - Z ^ 2 - C ^ 2 ) \ \ & - Y_1 ^ 2 Y_2 ^ 2 C ^ 2 - Y_2 ^ 2 Z_2 ^ 2 D ^ 2 - Y_1 ^ 2 Z_2 ^ 2 E ^ 2 - C ^ 2 D ^ 2 E ^ 2 \ biggr ] , \ end { aligned } $ $ where the values for lakeside lengths $ Y_1 $ and $ Y_2 $ , the main length Z , lakeside length D , and lakeside length E are defined in Fig . 3b and obtained from geometry . Alternatively , considering a pentahedral , the volume is

$$V = \frac{1}{3}(h^2)X. \tag{2}$$

These shape assumptions are addressed within the Results section.


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: figure
Figure

Image /page/7/Figure/2 description: A composite image with two panels labeled (a) and (b). Panel (a) is an aerial photograph of a glacial lake in a steep, rocky mountain valley. The lake has a light turquoise color and contains floating ice. It is dammed by a large deposit of dark rock and debris. Panel (b) contains two schematic diagrams illustrating the cross-section of a valley with an ice dam. A dashed line separates the 'OPEN VALLEY' above from the 'ICE DAM' below. The first diagram shows a V-shaped valley cross-section. The water-filled portion in the open valley is a triangle with apex A, sides Y1, and a vertical line Z. The ice dam below is also V-shaped with sides D and E and height h. An arrow labeled 'Inflow' points to apex A. The second diagram shows a similar triangular water body in the open valley, with a vertical line X, but the ice dam below is depicted as a rectangle with height h.


Document: 1 Introduction
Section: 3 Data and methods > 3.4 Geometry of lake basin
Type: figure
Figure: Figure 3. (a) Example of Shishper glacier-dammed lake, with the surface exhibiting a roughly triangular 2D shape (see also Fig. 2a); (b) diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall.

**Figure 3.** (a) Example of Shishper glacier-dammed lake, with the surface exhibiting a roughly triangular 2D shape (see also Fig. 2a); (b) diagram for calculating the volume of the lakes assuming (left) an irregular tetrahedral shape and (right) an irregular pentahedral shape. The blue shading represents the horizontal surface of the lake, and the white area represents the vertical ice wall.


Document: 1 Introduction
Section: 4 Results > 4.1 Surge velocity and ice-dammed lake volume
Type: figure
Figure: Figure 4a-c present the relationship between the glacier surge velocity (Khurdopin, Kyagar, and Shishper) and 23 GLOFs. The relationship between surge and GLOF was developed using annual average velocity data. The glacier's daily velocity was recorded on the day the GLOF was initiated, as detailed in Table 1. In Fig. 4 panels (a) to (c), it can be seen that the GLOF occurred after the peak of the glacier surge and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted for 2-4 years after the surge peak. Thus, GLOFs occur toward the end of a surge period or immediately afterward; the detail is presented in Table 1. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 4a-c, wherein the dates of the GLOFs are given as the month in the year. The three Karakoram glaciers can be used as regional examples of surge behavior controlling GLOF occurrence, as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 4a-d). This pattern of behavior prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed lakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice is low, the ice mass would be conserved or increased, and the fracturing of the ice would be reduced. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits (Gao et al., 2024). This sequence of events is shown schematically within Fig. 5. A thinning glacier also minimizes potential lake depth and might increase the likelihood of a GLOF occurring over the top of the ice dam.

Figure 4a-c present the relationship between the glacier surge velocity (Khurdopin, Kyagar, and Shishper) and 23 GLOFs. The relationship between surge and GLOF was developed using annual average velocity data. The glacier's daily velocity was recorded on the day the GLOF was initiated, as detailed in Table 1. In Fig. 4 panels (a) to (c), it can be seen that the GLOF occurred after the peak of the glacier surge and the resealed lake formed while the surge velocity declined. These responses to slowing of the glacier velocity lasted for 2-4 years after the surge peak. Thus, GLOFs occur toward the end of a surge period or immediately afterward; the detail is presented in Table 1. The relationship between the timing of glacier surges and the timing of GLOFs is shown in Fig. 4a-c, wherein the dates of the GLOFs are given as the month in the year. The three Karakoram glaciers can be used as regional examples of surge behavior controlling GLOF occurrence, as there is a temporal relationship between the occurrence of periods of glacier surging and the occurrence of GLOFs (Fig. 4a-d). This pattern of behavior prompted the hypothesis that glacier thickening and thinning during surging might control the development of ice-dammed lakes (Bazai et al., 2022). Lake volumes would increase when the speed of the ice is low, the ice mass would be conserved or increased, and the fracturing of the ice would be reduced. The corollary pertains to when the ice speed increases, the glacier thins, and the fracturing of the ice mass increases, providing hydraulic drainage conduits (Gao et al., 2024). This sequence of events is shown schematically within Fig. 5. A thinning glacier also minimizes potential lake depth and might increase the likelihood of a GLOF occurring over the top of the ice dam.


Document: 1 Introduction
Section: 4 Results > 4.1 Surge velocity and ice-dammed lake volume
Type: text

As a first attempt to relate glacier behavior in a predictive sense to lake formation, we sought to determine the relation-

ship between the resulting lake volume from the prior surge speed. Lake volume should be high when the glacier velocity is low and the ice mass thickens and vice versa; there is some support for this assertion (Fig. 6). Within Fig. 6, considering all the data (excluding the three drained lakes), the broad data spread prevents the fitting of a significant least-squares regression function. Nonetheless, trial curve fitting showed that a negative power function would be the best fit.

If the volume of a lake decreases as the glacier surge speed increases, as a negative power function would imply, both the lake depth and the surface area decrease; then, from an analogy with a pentahedron, the volume of a pentahedron reduces as the square of the characteristic length (Eq. 2), here the water depth. Consequently, assuming the pentahedral analogy applies, a least-squares trend line fitting procedure was used to define the constant  $\alpha$  in the following function:  $V_{\rm DEM} = \alpha U_s^{-2}$  fitted to all the data excluding the drained volumes. This equation, with  $\alpha = 13.6$ , is shown in Fig. 6 and visually is a good fit through data for a range of low values of  $U_s$  when lake depths will be greatest. Given the data dispersion and the small sample number, there are no statistical outliers (defined objectively; Carling et al., 2022).

An eye-fitted power function has been added to Fig. 6 to tentatively define the lower limit of the data spread. These results, although clearly not definitive, indicate that there probably is a relationship between the volume of the lake and the control of the lake water level exerted by the surge speed. Therefore, surge speed should exert some control on lake depth, lake volume, and potential GLOF volumes.

Given the scatter in the data within Fig. 6, additional data would be required to determine if the relationship between surge speed and lake volume does follow a negative power trend, as we have suggested.


Document: 1 Introduction
Section: 4 Results > 4.1 Surge velocity and ice-dammed lake volume
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Figure

Image /page/8/Figure/2 description: The image contains four graphs, labeled (a), (b), (c), and (d), analyzing glacier velocity and Glacial Lake Outburst Flood (GLOF) events over time.


Document: 1 Introduction
Section: 4 Results > 4.1 Surge velocity and ice-dammed lake volume
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Graphs (a), (b), and (c) are time-series plots. Each plots 'Average Velocity m yr⁻¹' on the left y-axis as a red line with square markers, and the 'Month of GLOF Trigger' on the right y-axis, with GLOF events shown as colored circles. The x-axis represents 'Years'. Shaded gray areas indicate 'Surge periods' of high velocity.

- Graph (a) shows data from roughly 1993 to 2020. The average velocity is 79 m yr⁻¹. There are two surge periods: 1998-2002, with a peak velocity around 300 m yr⁻¹, and 2016-2020, with a peak velocity over 350 m yr⁻¹. GLOF events are marked with blue circles.

- Graph (b) shows data from roughly 1993 to 2019. The average velocity is 29.9 m yr⁻¹. It features a surge period from 1994-1998 with a peak velocity around 300 m yr⁻¹, a 'Mini Surge' around 2008, and another surge period from 2013-2016 with a peak velocity over 400 m yr⁻¹. GLOF events are marked with orange circles.

- Graph (c) shows data from roughly 1995 to 2020. The average velocity is 87 m yr⁻¹. It has two surge periods: 1997-2002, with a peak velocity just under 300 m yr⁻¹, and 2017-2020, with a peak velocity around 350 m yr⁻¹. GLOF events are marked with gray circles.

- Graph (d) is a timeline plotting GLOF events against 'Cumulative Days' from 0 to over 12,000. A legend indicates that blue circles represent Khurdopin, orange circles represent Kyagar, and gray circles represent Shishper. Horizontal bars at the top indicate surge periods for each location, and lines at the bottom distinguish between 'Winter' (blue) and 'Summer' (red) occurrences.


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Figure: Figure 4. Relationship between glacier surges and GLOFs, with average annual glacier velocity during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyagar, and (c) Shishper. GLOFs for these glaciers occurred between the months of March and November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) and related periods of glacier surging (bars) as cumulative days since 1 January 1990. Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (October to April) and summer (May to September) seasons, respectively, with the GLOFs occurring predominantly in the summer months. Within panels (a) to (c), the average surge velocity is given as the red curves, and the average velocity during the study period is given in blue text.

**Figure 4.** Relationship between glacier surges and GLOFs, with average annual glacier velocity during the surge and quiescent phases for three glaciers: (a) Khurdopin, (b) Kyagar, and (c) Shishper. GLOFs for these glaciers occurred between the months of March and November. The combined analysis is presented in (d), illustrating the occurrences of GLOFs (dots) and related periods of glacier surging (bars) as cumulative days since 1 January 1990. Some points are plotted below the timeline to avoid coincident positions. The blue and red lines show the winter (October to April) and summer (May to September) seasons, respectively, with the GLOFs occurring predominantly in the summer months. Within panels (a) to (c), the average surge velocity is given as the red curves, and the average velocity during the study period is given in blue text.


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Section: 4 Results > 4.2 Tetrahedron assumption for lake volume
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Using Eq. (1) and assuming the ice dam face was an equilateral triangle, only the values X and C are required such that the volume of the "tetrahedron" lakes is around 10 times greater than the volume of the lakes determined using the DEMs (Fig. 7a). This result indicates that the actual depth of the lake (h) must be much less than that value associated with an equilateral triangle of side length C (Fig. 3b, left-hand side). Nevertheless, this procedure provides a means to estimate lake volume from plan-view data alone.

In contrast to the assumption of an equilateral triangle at the dam face, improved lake volume estimates were obtained considering the measured DEM-derived values of h along with the values of X and C. Once again, assuming an irregular tetrahedron as in Fig. 3b, the analysis demonstrated that the tetrahedral lake volume was roughly half that of the DEM volume (not illustrated). This latter result suggests that treating the valley cross-section as U-shaped (roughly quadrilateral) rather than V-shaped means that doubling the area of the triangular dam face section to form a quadrilateral should provide lake volume estimates, with the shape defined as an irregular pentahedron (square-based pyramid) (Fig. 3b, right-hand side), closer to the DEM-derived volume estimates.


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Section: 4 Results > 4.3 Pentahedron assumption for lake volume
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The pentahedral volume estimates (Eq. 2), as shown in Fig. 7b, are preferable to those values shown in Fig. 7a. They result in a nearly 1:1 relationship between  $V_{\rm PEN}$  and  $V_{\rm DEM}$  but require knowledge of the parameter depth h, as well as X and C. If it were assumed that a rectangular base of the pentahedron provides an exact match for the DEM volume, the correlation coefficient value would be unity. Thus, the coefficient of 0.88 reflects the deviation of the cross-sectional shape of the lake at the dam face from a rectangle. Note that the relationships between both determinations of lake volume (Fig. 7a and b) progressively deviate from a 1:1 relationship as lake volume increases. This trend might indicate that larger lakes are less well defined as tetrahedrons or pentahedrons as the volumes increase.

When assuming a tetrahedral shape for a lake, the lake volume can be estimated from remote sensing images alone as only the length of the lake (X) and the breadth of the lake (C) at the ice dam are needed to estimate the lake volume. When assuming a pentahedral shape, the depth (h) of the lake at the ice dam is required as well. Although, subsequent to GLOF drainage, h can be measured from a DEM or field survey,


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Figure

Image /page/9/Figure/2 description: A five-panel diagram illustrating the mechanism of glacier surge and its control on lake formation and outburst floods.


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Panel (a), titled 'Glacier surge initiation (Reference glacier surface elevation)', shows a glacier with a terminus and an accumulation zone. Arrows indicate a low to medium glacier velocity.

Panel (b), 'Glacier surge peak -> River blocked -> Lake formed', depicts the glacier surging forward, creating an ice dam that blocks a river and forms a lake with a level H1. The glacier has many crevasses, a high velocity, and a terminus thickness of T1.

Panel (c), 'Lake outburst flood -> Terminus thin and advancement -> Peak velocity', shows the lake has drained, causing a Glacial Lake Outburst Flood (GLOF). A conduit through the glacier is now open, and the glacier is at its peak velocity. The terminus thickness is still T1.

Panel (d), 'Lake re-established -> Terminus thicker -> Glacier velocity decrease', illustrates the formation of a second, larger lake with a level H2. The glacier terminus is now thicker (T2), and the glacier's velocity has decreased.

Panel (e), 'Conduit open -> Outburst flood', shows the second lake draining in another outburst flood. The terminus thickness remains T2.

A legend at the bottom explains the symbols used for glacier velocity and direction, glacier debris, crevasses, open conduit, lake level (H1 < H2), and glacier thickness (T1 < T2). A vertical scale next to panel (a) shows glacier velocity from low (blue) to medium (orange) to very high (red).


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Figure: Figure 5. The mechanism of glacier surge controls lake formation: (a) prior to the surge, there is no stream blockage in front of a thick terminus; (b) in the first year of the surge, stream blockage occurs, leading to lake formation behind a thinning terminus; (c) during the first year, peak glacier velocity is reached, the lake drains, and the terminus thins; (d) as surge velocity decreases, the lake reforms and the terminus thickens; (e) the second lake can drain as velocity continues to decrease and the terminus thickens.

**Figure 5.** The mechanism of glacier surge controls lake formation: (a) prior to the surge, there is no stream blockage in front of a thick terminus; (b) in the first year of the surge, stream blockage occurs, leading to lake formation behind a thinning terminus; (c) during the first year, peak glacier velocity is reached, the lake drains, and the terminus thins; (d) as surge velocity decreases, the lake reforms and the terminus thickens; (e) the second lake can drain as velocity continues to decrease and the terminus thickens.


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for the purposes of mitigation, a warning prior to a GLOF occurring is preferable.


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The timing of a GLOF remains difficult to determine, but for tunnel drainage, the main driver is the critical depth

(Thorarinsson, 1969). The critical depth is important as it is the depth that exerts sufficient pressure at the ice dam wall to induce completed connectivity within the subglacial GLOF drainage conduit (Gao et al., 2024; Yasuda and Furuya, 2013). For the cases of Shishper, Khurdopin, and Kyagar, the glacial lake depths (h) have been normalized by dividing them by the minimum value of each ice dam height


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Figure

Image /page/10/Figure/2 description: A scatter plot showing the relationship between Lake Volume (V\_DEM) and Glacier surge speed (U\_s). The y-axis, labeled "Lake Volume, V\_DEM (M m³)", ranges from 0.0 to 200.0. The x-axis, labeled "Glacier surge speed, U\_s (m d⁻¹)", ranges from 0 to 1.6. The plot includes data points for three different locations: Shishper (black circles), Kyagar (red circles), and Khurdopin (blue circles), as well as points for when the lakes drained: Khurdopin drained (light blue triangle) and Kyagar drained (red triangle). Two downward-sloping trend lines are shown. The upper green dotted line is represented by the equation V\_DEM = 13.69U\_s⁻². The lower purple dotted line is represented by the equation V\_DEM = 10U\_s⁻⁰.⁸. The data points generally show that as glacier surge speed increases, the lake volume tends to decrease. The Khurdopin data includes a point with a high lake volume of approximately 185 M m³ at a low surge speed of about 0.3 m d⁻¹. The Kyagar data points are scattered across a wide range of volumes and speeds. The Shishper data points are clustered at lower volumes and speeds. The 'drained' data points are all located at very low lake volumes, close to zero.


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Figure: Figure 6. Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three drained lakes had residual volumes (triangles). A -0.8 power function (purple curve) defines the lower limit to the data spread, while a -2.0 power least-squares function (green curve) defines the central tendency of the data trend (see text for explanation).

Figure 6. Variation in glacial lake volume as a function of the glacier surge speed. Data from three glaciers. Most lakes drained completely, but three drained lakes had residual volumes (triangles). A -0.8 power function (purple curve) defines the lower limit to the data spread, while a -2.0 power least-squares function (green curve) defines the central tendency of the data trend (see text for explanation).


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Figure

Image /page/10/Figure/4 description: An image displaying two scatter plots, labeled (a) and (b).


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Plot (a) shows "Lake Volume from DEM, V\_DEM (M m^3)" on the y-axis versus "Volume of Irregular Tetrahedron/10, V\_TET (M m^3)" on the x-axis. The y-axis ranges from 0 to 200, and the x-axis ranges from 0 to 100. The plot contains data points for three locations: Shishper (black circles), Khurdopin (blue circles), and Kyagar (red circles). A red dotted trendline is fitted to the data with the equation V\_DEM = 1.3525V\_TET and an R-squared value of 0.88. A "1:1" label is also present on the plot.

Plot (b) shows "Lake Volume from DEM, V\_DEM (M m^3)" on the y-axis versus "Volume of Pentrahedron, V\_PEN (M m^3)" on the x-axis. Both axes range from 0.0 to 250.0. The plot contains a single series of grey circular data points. A blue dotted trendline is fitted to the data with the equation V\_DEM = 0.8828 V\_PEN and an R-squared value of 0.932. A second blue dotted line represents the 1:1 relationship.


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Figure: Figure 7. (a) Relationship between the volumes of irregular tetrahedrons / 10, derived from Eq. (1), and the volumes of the lakes determined using DEMs. (b) The relationship between the volumes of irregular pentahedrons and the volumes of the lakes determined using DEMs.

**Figure 7. (a)** Relationship between the volumes of irregular tetrahedrons / 10, derived from Eq. (1), and the volumes of the lakes determined using DEMs. (b) The relationship between the volumes of irregular pentahedrons and the volumes of the lakes determined using DEMs.


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to give values (n') of normalized lake depths that range between 0 for a fully drained lake and a hypothetic value of 1 if the lake level reaches the height of the ice barrier. At the approximate time of GLOF occurrence, the resulting values of n' range between 0.32 and 0.95 (Fig. 8a–c). Most GLOFs occur for a range of n' values between 0.61 and 0.95 (Fig. 8d).

Thus, n' = 0.60 can be regarded as a warning level value with the potential for a GLOF occurring imminently increasing as n' approaches unity.

As the water pressure (P) at the dam face increases linearly with water depth in each lake, any variation in the pressure with n' that deviates from the linear trend reflects


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Figure

Image /page/11/Figure/2 description: A figure with four panels, (a), (b), (c), and (d), analyzing glacier lake outburst floods (GLOFs) for three different glaciers: Shishper, Khurdopin, and Kyagar.


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Panels (a), (b), and (c) are cross-sectional profiles of the lakebeds for Shishper, Khurdopin, and Kyagar glaciers, respectively. Each plot has a primary x-axis for 'Cross-section (m)' and a primary y-axis for 'Elevation (m)'. A secondary top x-axis shows 'Volume 10^6 m^3', and a secondary right y-axis shows 'n''. The profiles depict the lakebed, conduit inlet/outlet, and the glacier. Each panel includes an inset satellite map showing the respective glacier and the location of the cross-section.
- Panel (a) for Shishper shows a cross-section up to about 2200 m, with elevations from 2520 m to 2660 m. The volume scale goes up to 32 x 10^6 m^3.
- Panel (b) for Khurdopin shows a cross-section up to about 3300 m, with elevations from 3360 m to 3440 m. The volume scale goes up to 180 x 10^6 m^3.
- Panel (c) for Kyagar shows a cross-section up to about 6500 m, with elevations from 4750 m to 4830 m. The volume scale goes up to 130 x 10^6 m^3.

Panel (d) is a scatter plot showing the relationship between 'Water Pressure, P (kPa)' on the y-axis (0 to 1600) and 'Normalized glacier lake depth, n' (-)' on the x-axis (0.0 to 1.0). It includes data for:
- Shishper (black dots), with a trendline P = 1514.9 n'.
- Khurdopin (blue dots), with a trendline P = 977.1 n'.
- Kyagar (red dots), with a trendline P = 835.5 n'.
A light blue shaded area labeled 'Critical range' covers the region where n' is approximately greater than 0.6.


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Figure: Figure 8. The relationship between lake volume and elevation and the critical normalized lake depths for GLOFs: (a) Shishper, (b) Khurdopin, and (c) Kyagar. Red lines connect the relevant elevation and volume in each instance. (d) Water pressure at dam face as a function of n'. The cross-sections are denoted by A' and A. The serial number and date of each lake flood event are shown in Table 1. The straight solid red lines relate specific lake elevations to volumes. A UAV photograph captured in the field was used for panel (a), the images captured by GF-2 were used for panel (b), and Landsat 8 OLI was used for panel (c).

**Figure 8.** The relationship between lake volume and elevation and the critical normalized lake depths for GLOFs: (a) Shishper, (b) Khurdopin, and (c) Kyagar. Red lines connect the relevant elevation and volume in each instance. (d) Water pressure at dam face as a function of n'. The cross-sections are denoted by A' and A. The serial number and date of each lake flood event are shown in Table 1. The straight solid red lines relate specific lake elevations to volumes. A UAV photograph captured in the field was used for panel (a), the images captured by GF-2 were used for panel (b), and Landsat 8 OLI was used for panel (c).


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changes in the height of the ice dam (Fig. 8d). Thus, for example, the values of pressure for the Shishper lakes around an n' value of 0.8 reflect greater overall deeper lakes and higher ice dam heights in contrast to the Kyagar lakes for similar values of n'. The two values of low pressure for n' < 0.6 are associated with relatively low ice dams and consequently reflect presumed low structural integrity within the ice mass, allowing ready conduit development. In the present examples, low values of n' (< 0.6) probably are associated with shallow lakes of low hazard potential. Overall, the Kyagar data (Fig. 8d) indicate that a minimum water pressure of around 510 kPa should be regarded as a threshold for general concern for GLOF occurrence in the region. However, consideration should be given to local conditions when applying the findings of this study to other locations around the globe.


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Although predictive models related to ice-dammed lake development and subsequent GLOF hazard would best be based on modeling the physics of the systems, the controlling parameters are numerous and complex. For example, the mechanisms of glacial sliding, overburden pressure, and tensile and driving stresses require consideration, as do flexure and ice fracture mechanics, thermal erosion, and water pressure, amongst other controls (Carrivick et al., 2020), along with climatic influences (Ng et al., 2007; Richardson and Reynolds, 2000). Few of these controls are understood well, and, importantly, even where there is an adequate theory, the field data required to inform modeling are absent for specific potential GLOF locations. In this study, the glacier and lake interactions and their empirical relationships have been explored, and their effect on lake volume and draining processes has been examined. Understanding glacier surges, lake formation, and the interactions between lakes and glaciers is crucial for advancing knowledge and developing empirical or numerical GLOF models in mountainous regions (Carrivick et al., 2020; Quincey and Luckman, 2014). Glacier surge speed is routinely determined using remote sensing imagery (Paul, 2015), as is lake surface area (Quincey and Luckman, 2014). Thus, remote sensing provides a means to develop graphs similar to the graph of Fig. 6 for specific locations around the globe where ice-dammed lakes form due to glacier surging. Although the data within Fig. 6 are scattered, a negative relationship between surge velocity and lake volume is implied. Specifically, data points scatter around a median trend according to a theoretical -2power function (Fig. 6). Clearly, more data points within Fig. 6 would be desirable so that the relationship (if any) between ice surge velocity and lake volume might be better defined.

There is an urgent need for simpler methods to predict the probable triggering water levels that lead to GLOFs and the likely volume of the ice-dammed lakes that translate into GLOF hydrographs. Given that requirement, it is acknowledged that the relationships proposed herein are empirical and apply specifically to glaciers within the Karakoram region. However, there is no reason to suppose that similar functions based on geometric considerations (Zhang et al., 2023) and a critical depth (Zhao et al., 2017) might not be developed elsewhere, including for moraine-dammed lakes (Yao et al., 2010). Below, the approach is explored for glacial ice-dammed lakes worldwide.

Despite the absence of long-term records, those that are available indicate that glacial ice-dammed lakes worldwide exhibit consistent behavior in terms of lake formation, filling, and volume gain in response to low glacier velocity (Bazai et al., 2021). Additionally, specific water pressure and critical normalized lake depth values for initiating outburst floods are evident (Fig. 8).


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and a critical depth ( Zhao et al . , 2017 ) might not be developed elsewhere , including for moraine - dammed lakes ( Yao et al . , 2010 ) . Below , the approach is explored for glacial ice - dammed lakes worldwide . Despite the absence of long - term records , those that are available indicate that glacial ice - dammed lakes worldwide exhibit consistent behavior in terms of lake formation , filling , and volume gain in response to low glacier velocity ( Bazai et al . , 2021 ) . Additionally , specific water pressure and critical normalized lake depth values for initiating outburst floods are evident ( Fig . 8 ) .

Building upon the information presented in Fig. 8 for the Karakoram, Fig. 9, based on 50 GLOF events from 10 glacial ice-dammed lakes, offers a depiction of the conditions under which glacier lake volume measurements are estimated with high accuracy. Considering lakes other than those within the Karakoram, the lake elevation (Fig. 9a) at the time of a GLOF was available for five lakes that have triggered a GLOF more than twice (Chilinji, Medvezhiy, Merzbacher, Russell, Rio Colonia), enabling the calculation of the normalized lake depths (Fig. 9b). The data points in Fig. 9c represent 27 lakes within the Pamir, Tian Shan, Greenland, and northern Patagonia for which lake volume data are available in the literature. These latter data were used to establish a relationship between the lake volume estimated using Eq. (2) and the reported volumes ( $R^2 = 0.972$ ; Fig. 9c).


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Figure: Figure 9a and b serve as key components and summaries for this discussion, identifying the lake volumes, elevations, and critical normalized depth values for GLOF outbursts. Critical normalized lake depth values (n') exceed 0.60 in all

Figure 9a and b serve as key components and summaries for this discussion, identifying the lake volumes, elevations, and critical normalized depth values for GLOF outbursts. Critical normalized lake depth values (n') exceed 0.60 in all


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cases of GLOFs (Fig. 9b). From this result, we infer that a safe lake level can be defined as < 0.60, while the trigger level is  $\ge 0.60$ . Values of n' < 0.60 were associated with slow, non-catastrophic lake drainage. Therefore, in the case of future ice-dammed lakes, values of critical depth (n') exceeding 0.60 should be a cause for concern, and 0.60 would serve as a warning level.

Estimating the volume of an undrained ice-dammed lake from a field survey is dangerous due to floating ice, rugged terrain, and sudden drawdowns. The utilization of DEM measurements for lake volume estimation may also introduce high uncertainties or errors due to the difficulty in defining the lake depths (Carrivick et al., 2020; Emmer, 2018). However, for rapid response or mitigation policy purposes, the empirical model (Eq. 1 or Eq. 2) used in the current study proves to be quite efficient when applied to estimate the lake volume before a GLOF, not least because the errors in measurements from both satellite and UAV images are now quite small, as noted within the "Data and methods" section.

Although GLOFs cannot be predicted from this approach, the likely volume of water that might be released catastrophically can be determined. For sites that are deemed to pose a threat to human life and infrastructure, once the lake volume is better constrained, through either DEM analysis or geometric considerations, the value of n' for any specific lake provides a ready indicator of the probability of an imminent GLOF. In contrast to the lower trend for water pressures associated with the Kyagar lakes, the higher water pressures required to cause the Khurdopin and Shishper lakes to empty may reflect greater structural integrity, possibly related to a greater downstream extent of the glacier dams. These structural issues can be examined in the future. Still, at this stage, if n' exceeds 0.6, an initial general warning could be issued to communities downstream of the ice dam. In principle, the estimated volume of a potential GLOF can then be routed downstream using standard hydrodynamic flood routing procedures to determine the timing, depth, and extent of flooding at locations where inundation is forecast. Thus, the severity of the likely impact on humankind can be determined, and specific warning times can be derived from the modeled rate of travel of the GLOFs. These results represent a step forward from the observations made by Carrivick et al. (2020), who proposed the exploration of the interaction between lake water and glaciers to understand the lake formation process and identify lake depth, level, and volume. Based on this understanding, empirical models can be generated to predict GLOFs in a timely manner.


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Despite an escalating hazard from glacier lake outburst floods (GLOFs), understanding these hazards remains limited. It is imperative to determine the causes of these hazards, make timely predictions, and formulate new mitiga-


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Figure

Image /page/13/Figure/2 description: The image contains three plots labeled (a), (b), and (c), which analyze data related to Glacial Lake Outburst Floods (GLOFs).


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Plot (a) displays a series of horizontal bar charts for different glacier lakes: Kyagar, Khurdopin, Merzbacher, Chilinji, Medvezhiy, Shishper, and an inset for Russel and Rio Colonia. The y-axis represents Elevation in meters above sea level (m.a.s.l.), and the x-axis represents Volume in million cubic meters (M m³). Each bar is divided into a light blue 'Safe lake level' and a red 'Triggering lake level'. Black dots indicate the volume and elevation of past GLOF events. Each lake has an associated n' value: Kyagar (n'=0.67), Khurdopin (n'=0.76), Merzbacher (n'=0.83), Chilinji (n'=0.60), Medvezhiy (n'=0.71), Shishper (n'=0.67), Russel (n'=0.80), and Rio Colonia (n'=0.79).

Plot (b) is a vertical bar chart illustrating the 'Normalized Glacier Lake depth (n')'. The bar is color-coded, with a blue 'Safe Zone' for n' values up to approximately 0.6 and a red 'High Risk and Critical Zone' for values above 0.6 up to 1.0. Horizontal lines connect the n' values for each specific lake to their position on this risk scale, showing their triggering thresholds.

Plot (c) is a scatter plot comparing 'Reference Volume, V\_REF (M m³)' on the y-axis with 'Volume of Irregular Pentahedron, V\_PEN (M m³)' on the x-axis. The plot includes data points from various locations, such as Medvezhiy-Pamir, Grænalón-Iceland, and Merzbacher-TianShan. The data points show a strong linear relationship, represented by a dashed red line with the equation V\_REF = 1.0689 \* V\_PEN and an R² value of 0.972.


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Figure: Figure 9. (a) The maximum volume of ice-dammed glacier lakes that results in GLOFs related to water surface elevation. In this representation, the red bands are defined by the range of n' values recorded just before each GLOF event was initiated. The blue bands represent lakes with volumes below the normalized lake depth of 0.60. (b) Normalized critical lake depths and the high-risk and critical zones for GLOFs, with an inferred gradation for risk within the safe zone, shown by shades of blue. (c) The relationship between the reference volume ( $V_{\text{REF}}$ ) and the measured volume ( $V_{\text{PEN}}$ ) obtained through a geometric approach. The locations of these glaciers are presented in Fig. S1 in the Supplement.

**Figure 9.** (a) The maximum volume of ice-dammed glacier lakes that results in GLOFs related to water surface elevation. In this representation, the red bands are defined by the range of n' values recorded just before each GLOF event was initiated. The blue bands represent lakes with volumes below the normalized lake depth of 0.60. (b) Normalized critical lake depths and the high-risk and critical zones for GLOFs, with an inferred gradation for risk within the safe zone, shown by shades of blue. (c) The relationship between the reference volume ( $V_{\text{REF}}$ ) and the measured volume ( $V_{\text{PEN}}$ ) obtained through a geometric approach. The locations of these glaciers are presented in Fig. S1 in the Supplement.


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tion policies to minimize losses. Herein, it has been shown tentatively that glacier surge speed may correlate negatively with ice-dammed lake volumes such that glacier dynamics control lake volumes. Consequently, in those cases where glacier surge speed is monitored, trends in surge speed can provide a timely indication that a lake might form, allowing the risk of a potential GLOF to be considered and mitigation measures to be reviewed. Identifying the critical depths, lake volumes, and pressures of ice-dammed lakes worldwide associated with GLOFs has indicated that GLOFs may be imminent when the normalized depth (n') for lakes exceeds a critical value (n'=0.60) with a typical water pressure on the dam face exceeding 510 kPa. Identifying a critical depth

that might lead to a GLOF is relatively straightforward and, thus, is a useful measure that provides a timely warning for downstream communities. Comparing published surveyed lake volumes with geometric volume estimates for 23 GLOF events from the Karakoram and 27 events from around the world, linear least-squares regression ( $R^2 = 0.972$ ) demonstrated that geometric estimates can be robust in the absence of detailed field or remote sensing surveys. Such an approach to determining lake volumes is useful on two accounts. Firstly, the approach can provide a quick estimation of lake volume. Then, if the lake volume is considered to be of concern, more detailed survey work can be commissioned to obtain a more accurate estimate of the volume. Sec-

ondly, in those situations where sufficient resources may not be available to conduct detailed volumetric surveys, the geometric approach provides a ready tool to obtain a reasonable volume estimate. Taken together, these findings suggest that future exploration should concentrate on specific volume and depth parameters to determine critical thresholds associated with normalized depth and the associated lake volume for future predictive purposes. In this respect, it should be noted that the water pressure recommended herein as potentially of concern (510 kPa) pertains to lake depths of ca. 50 m, whereas deeper lakes in other regions might drain at different values of pressure.

Data availability. Data and/or further information regarding the data used can be obtained from the corresponding authors upon request.

Supplement. Supplementary information for this paper is available in the Supplement and at https://doi.org/10.1016/j.earscirev.2020. 103432 (Bazai et al., 2021) and https://doi.org/10.1016/j.gloplacha. 2021.103710 (Bazai et al., 2022). The supplement related to this article is available online at: https://doi.org/10.5194/tc-18-5921-2024-supplement.

Author contributions. NAB conceptualized and designed the study and methodology, generated and compiled field data, processed data visualization, and drafted the manuscript. PAC contributed to conceptualization, methodology, interpretation, discussion, review, and editing. PC supervised funding acquisition and commented on the paper, and WH contributed to compiling the field data. NAB, ZG, LD, and JH contributed to remote sensing data analysis.

Competing interests. The contact author has declared that none of the authors has any competing interests.


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online at : https : / / doi . org / 10 . 5194 / tc - 18 - 5921 - 2024 - supplement . Author contributions . NAB conceptualized and designed the study and methodology , generated and compiled field data , processed data visualization , and drafted the manuscript . PAC contributed to conceptualization , methodology , interpretation , discussion , review , and editing . PC supervised funding acquisition and commented on the paper , and WH contributed to compiling the field data . NAB , ZG , LD , and JH contributed to remote sensing data analysis . Competing interests . The contact author has declared that none of the authors has any competing interests .

*Disclaimer.* Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Acknowledgements. Special thanks go to the monitoring team of the Gilgit-Baltistan Disaster Management Authority (GBDMA), Quaid-i-Azam University, and Karakoram International University for their support, data sharing, and technical assistance. We are also grateful to Iqtidar Hussain for sharing his expertise and to the Special Research Assistant Program of the Chinese Academy of Sciences. Additionally, we deeply thank the editor and the three reviewers for their valuable and constructive comments.

Financial support. This study was supported by the National Natural Science Foundation of China (grant no. 42350410445) and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (grant no. 2019QZKK0906).


Document: remotesensing-13-02511-v2
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Abstract: With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level's ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.

Keywords: knowledge graph; surveying; remote sensing; knowledge visualization


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Image /page/0/Picture/12 description: A graphic on a white background features a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in black. The words 'check for' are on the top line, and the word 'updates' is on the bottom line in a bold font.


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Citation: Hao, X.; Ji, Z.; Li, X.; Yin, L.; Liu, L.; Sun, M.; Liu, Q.; Yang, R. Construction and Application of a Knowledge Graph. *Remote Sens.* **2021**, *13*, 2511. https://doi.org/10.3390/rs13132511

Academic Editor: Frédérique Seyler

Received: 5 June 2021 Accepted: 24 June 2021 Published: 26 June 2021

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Image /page/0/Picture/17 description: The image shows the Creative Commons Attribution (CC BY) license icon. It is a horizontal rectangle with a grey top section and a black bottom section. On the left side of the grey section, there is a white circle with the letters 'CC' in black. On the right side, there is a white circle with a black stick figure icon, representing attribution. The black bottom section has the letters 'BY' in white.


Document: remotesensing-13-02511-v2
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Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).


Document: 1. Introduction
Section: 1. Introduction
Type: text

In 2012, Google officially proposed the concept of the knowledge graph, which aims to assist intelligent search engines [1]. After the knowledge graph was formally proposed, it was quickly popularized in academia and industry and was widely used in intelligent search, personalized recommendation, intelligence analysis, anti-fraud and other fields. Essentially, a knowledge graph is a semantic network and knowledge base with a directed graph structure that describes entities (concepts) and their relationships in the physical world in symbolic form. The knowledge graph is represented in the form of triples (Entity1-Relation-Entity2), where the nodes of the graph represent entities or concepts, and the edges represent the relationships between entities or concepts [2].

Knowledge graphs are a new method of knowledge representation. In essence, the semantic web is an early form of the knowledge graph, which is an abstract concept that describes entities and relationships between entities in the objective world and is also a networked knowledge base composed of entities, properties, and relationships. A knowledge graph is a collection of concepts, entities, and their relationships in the abstract physical world [3]. The knowledge graph has changed the traditional method of information retrieval. On the one hand, knowledge graphs describe the semantic and attribute

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relationship between concepts to reason about concepts through fuzzy string matching. Conversely, knowledge graphs display the structured knowledge of classification and arrangement to users through the grid graphic information display interface. Concurrently, knowledge graphs solve the problem of manual filtering of useless information, which has practical significance for an intelligent society [4].

Knowledge graphs can be divided into general knowledge graphs and domain knowledge graphs. Knowledge graphs used in surveying and remote sensing are domain knowledge graphs. To date, few studies have investigated knowledge graphs for surveying and remote sensing. Wang and others proposed a framework for remote-sensing interpretation of knowledge graphs [5]. Xie and others designed a framework for the construction of a large knowledge graph in the field of remote-sensing satellites [6]. Geoscience knowledge graphs are also used and have been studied in detail. Xu and others proposed the concept, framework, theory, and characteristics of geoscience graphs based on geoscience graphs and geoscience information graphs. Jiang proposed the process of constructing knowledge graphs and explored the key technology of geographic knowledge graphs [7]. Lu and others systematically reviewed the research progress on topics related to geographic knowledge graphs and analysed the key issues of current geoscience knowledge graph construction [8]. However, these studies all discuss the construction of knowledge graphs in theory and do not provide any real examples of constructing knowledge graphs. Based on previous research experience, this paper describes an example of constructing knowledge graphs for surveying and remote sensing.


Document: 1. Introduction
Section: 1. Introduction
Type: text

others proposed the concept , framework , theory , and characteristics of geoscience graphs based on geoscience graphs and geoscience information graphs . Jiang proposed the process of constructing knowledge graphs and explored the key technology of geographic knowledge graphs [ 7 ] . Lu and others systematically reviewed the research progress on topics related to geographic knowledge graphs and analysed the key issues of current geoscience knowledge graph construction [ 8 ] . However , these studies all discuss the construction of knowledge graphs in theory and do not provide any real examples of constructing knowledge graphs . Based on previous research experience , this paper describes an example of constructing knowledge graphs for surveying and remote sensing .

The construction of this knowledge graph can provide services for users of surveying and remote sensing, surveying and remote-sensing experts, and developers of surveying and remote-sensing software. Users can visualize knowledge through the knowledge graph and discover the relationship between knowledge more easily. Searches conducted through the knowledge graph improve the user's search efficiency. Remote-sensing experts gain insights and discover new rules in the field of surveying and remote sensing through the inference function of the knowledge graph. Software developers can integrate the knowledge graph into the remote-sensing product e-commerce platform, which can not only improve search efficiency, but also accurately recommend products for users. The professional field of surveying and remote sensing is the ladder of social progress. With the development of social intelligence, it is significant to study professional development technology for intelligent surveying and remote sensing.


Document: 1. Introduction
Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs
Type: text

The system framework of a knowledge graph in the fields of surveying and remote sensing primarily refers to its construction mode structure, which describes the process of constructing a knowledge graph in the fields of surveying and remote sensing (Figure 1). The process of knowledge graphing in the field of surveying remote sensing can be divided into two parts: mode level construction and data layer construction.

The mode level is built on the data layer, and the factual expression in the data layer is standardized through the ontology library. The ontology is the conceptual template of the structured knowledge base. The knowledge base constructed through the ontology library has the advantages of a strong hierarchical structure and low redundancy. The data layer is composed of a series of knowledge entities or concepts. Knowledge is stored in units of facts, and the data layer expresses knowledge in the form of triples (Entity 1-Relation-Entity 2) or (Entity-Attribute-Attribute Value). The logical structure of the knowledge graph in the field of surveying and remote sensing is shown in Figure 2.

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Figure

Image /page/2/Figure/1 description: A flowchart illustrating the process of creating a knowledge graph in the field of surveying and remote sensing. The flowchart is divided into two main parallel processes: "Mode level construction" and "Data layer construction".


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Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs
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Under "Mode level construction", the steps are sequential: "Application areas of knowledge graph", followed by "Determine the scope of ontology knowledge", then "Ontology construction", and finally "Ontology storage".

Under "Data layer construction", the process begins with "Surveying and remote sensing data". This feeds into a sub-process labeled "Knowledge acquisition", which includes "Concept and entity acquisition" followed by "Relationship acquisition". The final step in this section is "Data storage".

An arrow connects the "Ontology construction" step from the mode level to the knowledge acquisition sub-process in the data layer.

The outputs of both main processes converge. An arrow from "Ontology storage" is labeled "Ontology editing tool", and an arrow from "Data storage" is labeled "Relational database". Both arrows point to a database symbol at the bottom, which represents the final product: "Knowledge graph in the field of surveying and remote sensing".


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Figure: Figure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.

Figure 1. The framework of the knowledge graph in the fields of surveying and remote sensing.


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Image /page/2/Figure/3 description: A diagram illustrating a two-tiered structure, divided into an "Ontology layer" and a "Data layer". The Ontology layer at the top shows a hierarchical structure starting with "Owl:Thing". Below "Owl:Thing", connected by lines labeled "Subclass", are two boxes: "Genre" and "Knowledge", with an ellipsis between them. The Data layer at the bottom contains several boxes. From the "Genre" box, lines labeled "Entity" point to "Surveying" and "Remote sensing". From the "Knowledge" box, lines labeled "Entity" point to "Collinear equation" and "Photogrammetry". An ellipsis is shown between "Remote sensing" and "Collinear equation". Within the Data layer, two curved arrows labeled "relation" point from "Surveying" and "Remote sensing" to "Photogrammetry".


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Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs
Type: figure
Figure: Figure 2. The logical structure of the knowledge graph in the fields of surveying and remote sensing.

Figure 2. The logical structure of the knowledge graph in the fields of surveying and remote sensing.


Document: 1. Introduction
Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.1. Design of Disciplinary Knowledge Graph Mode in the Field of Surveying and Remote Sensing
Type: text

The mode level construction process begins from the application domain of the knowledge graph, determines the knowledge scope of the ontology, and then constructs the ontology. The construction of the ontology is the key link in the construction of the pattern layer. The ontology is an explicit description of the shared conceptual mode and is used to add semantics to semantic web pages and describe the relationship between concepts [9]. The ontology is the pattern layer, conceptual mode, and logical basis of the knowledge graph. The mode level of the knowledge graph in the field of surveying and remote sensing uses ontology to realize the storage and management of knowledge. When constructing an ontology, we mainly construct a collection of concepts from the subject words of remote-sensing teaching materials. The relationship is mainly a hierarchical relationship between the upper and lower positions. Entity filling is mainly obtained from structured data sources, and the ontology database is filled in a bottom-up manner.

A domain ontology is a specialized ontology that describes concepts in a specific domain (such as remote sensing, meteorology, environment, etc.) and the relationship between concepts. The domain ontology of surveying and remote sensing, as a kind of unique ontology, can clearly describe the relationship between concepts belonging to and concepts in this domain. The basic principles of ontology construction are clarity, objectivity, consistency, minimum coding deviation, and minimum ontology constraints.

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Ontology construction uses a seven-step method. The steps of this method are as follows: (1) determine the professional field and category of the ontology; (2) examine the possibility of reusing existing ontology; (3) list the important terms in the ontology; (4) define the hierarchical relationship between classes; (5) define the properties of the class; (6) define the constraints between the properties; and (7) create an instance [10].


Document: 1. Introduction
Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design
Type: text

In this article, the data extraction method is used to construct the data layer. When extracting related entities, the relationship between the entities in the field of surveying and remote sensing is first defined. The relationship between entities is mainly the upper-lower relationship and the non-upper-lower relationship. Research on non-subordinate relationships in the knowledge graphs focuses on two aspects: entity attributes and entity relationships. The non-subordinate relationship of entity attributes is mainly used for triples: entity-attribute-attribute value, where the attribute depends on the corresponding entity. Each attribute will have its corresponding attribute value. In the definition of entity relationships, there is always a direct or indirect relationship between entities. Through the relationship analysis between entities, the general relationship between the entities used is defined. The relationship between entities mainly includes the relationship of belonging, containing, etc. These relationships are common relationships among entities in the field of surveying and remote sensing.


Document: 1. Introduction
Section: 2. The Theoretical Basis for the Construction of Knowledge Graphs > 2.2. The Data Layer of the Knowledge Graph in the Field of Surveying and Remote Sensing 2.2.1. Data Layer Relational Structure Design > 2.2.2. Data Layer Construction
Type: text

The data layer construction process is based on unstructured data and uses manual, automatic, or semiautomatic techniques to extract knowledge from the data and store it in the database. Data acquisition is a key step in the construction of the data layer. The core of knowledge acquisition is how to automatically obtain knowledge elements of structured information, such as entities, relationships, and properties, from unstructured and semi-structured data sources. Typically, automatic or semi-automatic machine learning technology is used to extract entities, relationships, attributes, and other information about the knowledge graph from open multisource data [11]. Knowledge acquisition includes the extraction of entities, relationships, properties, etc. Entity acquisition is the process of automatically identifying called entities (knowledge points, type names, etc.) from text data sets. Relation extraction is the process of discovering semantic relationships between entities from data sources using methods such as machine learning. Attribute extraction is the process of extracting attribute information about entities from data sources. The difference between attribute extraction and relation extraction is not only to identify the attribute name of the entity but also to identify the attribute value of the entity. Therefore, most studies are based on rules for extraction. [12].


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs
Type: text

Construction of knowledge graphs is the core content of this article. It includes five parts: data acquisition and storage, ontology construction and storage, ontology and database mapping, query and reasoning of knowledge graphs, and visualizing the knowledge graph on Neo4j.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
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The data used in this article are from surveying and remote sensing. The data source is a textbook related to the field of surveying [13] and remote sensing [14]. DeepDive (http://deepdive.stanford.edu/, accessed on 6 May 2021) was used to extract semi-structured table data from unstructured text data [15]. DeepDive can extract structured data from unstructured data and perform a series of data processing steps to build a knowledge base and extract relationships. It is very good at handling data sources in different formats.

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DeepDive has good database support, supports PubMed and other database data sources, and is a data source that has been processed in natural language. DeepDive has established a framework to standardize the construction process of the knowledge base system and allows users to design their own extractors and markers according to the knowledge base that needs to be constructed. The relationship extraction process of DeepDive is shown in Figure 3. The DeepDive based domain text knowledge extraction method includes the following steps [16]:

- (1) Data processing. First, the original corpus will be loaded. The natural language processing (NLP) tag is added. A set of candidate relationships and the sparse feature representation of each candidate relationship are extracted.
- (2) Remote supervision of data and rules, and then various strategies will be used to supervise the data set so that we can use machine learning to learn the weight of the mode.
- (3) Learning and inference: mode specification. Then, the advanced configuration of the mode will be specified.
- (4) Error analysis and debugging. Finally, we will show how to use DeepDive's tags, error analysis and debugging tools.

3. Construction of Knowledge Graphs
3.1. Data Acquisition and Storage
3.1.1. Data Acquisition


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
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Figure

Image /page/4/Figure/6 description: A flowchart illustrating a data processing and machine learning pipeline, likely for relationship extraction. The main flow on the right begins with 'Data preparation', followed by 'Import the database', 'Text data table', and 'NLP processing'. This leads to 'POS/NER/dependency syntax analysis of text data, etc.', which then proceeds sequentially through 'Mention extraction', 'Candidate entity pair', 'Entity-pair feature extraction', and culminates in 'Data set: entity pair + feature + Label'. The final step is 'Training factor, get variable confidence'. A parallel branch on the left starts from 'Import the database' and goes to 'Entity pair data table with known relationship', which is then used to 'Matches the known relationship in the candidate entity pair'. Both the 'POS/NER...' step and the 'Matches the known relationship...' step feed into a box labeled 'Mark some data based on rules'. This box then leads to a 'Known variable table'. Finally, the 'Known variable table' feeds into the 'Data set: entity pair + feature + Label' step, merging the two branches of the flowchart.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
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Figure: Figure 3. The relationship extraction process of DeepDive [15].

Figure 3. The relationship extraction process of DeepDive [15].


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
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The extraction process of domain knowledge based on DeepDive:

- (1) Experiment preparation (preparation before knowledge extraction). First, DeepDive stores the involved input, intermediate and output data in a relational database. DeepDive supports many databases, such as Postgres, Greenplum and MySQL. The database used in this experiment is Postgres.
- (2) Data processing. This part is divided into four steps: ① Loading the original input data. First, we convert the target data into an electronic text format. Only the two fields of document id and document content are reserved. We store the cleaned data in a comma-separated values (CSV) format file. Then import the original text data into the associated database. We set the data format of the document storage in the app. ddlog file. There are two text fields in the articles table, articles (id text, content text). Then, we put the compressed file of the original data into the specified input folder and run the "DeepDive compile" command. Then, we execute the "DeepDive do articles" command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. ② Adding NLP markups. We use the CoreNLP

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natural language processing system to add annotations to the original data. The steps of NLP are: first, the input original article is divided into sentences. The sentence is divided into words, and the part-of-speech tags, standard forms, dependency analysis and entity recognition tags of the words in the sentence are obtained. After NLP, some commonly used entities (person names, place names, etc.) can be marked. It is also necessary to ensure further entity identification of the data processed by NLP. The input are the data in the sentences table. The output are the marked data. Finally, we import the final marked data into the sentences\_new table in the database. ③ Extracting candidate relation mentions. DeepDive proposes corresponding input and output interfaces, allowing users to design their entity or relationship extractors. Generally speaking, the SQL(Structured Query Language) statement is used as the input interface to extract data from the database; The output is the corresponding table in the database. We perform entity extraction on the data after entity recognition, and then establish the corresponding database table structure. ④ Extracting features for each candidate. First, we extract the feature description and store the feature in the func\_feature table. The purpose is to use certain attributes or characteristics to represent each candidate pair. There is a library DDlib that can automatically generate features in DeepDive, which defines features that are not dependent on the domain. There are also many dictionaries in the DDlib library. These dictionaries contain words related to the correct classification of descriptions and relationships and are usually combined with domains and specific applications. We declare the extract\_func\_features function in app.ddlog. The input of this function includes the information of the two entities in the entity mention table and the NLP results in the sentence where the two entities are located. The output is the two entities and their characteristics.


Document: 1. Introduction
Section: 3. Construction of Knowledge Graphs > 3.1. Data Acquisition and Storage > 3.1.1. Data Acquisition
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or characteristics to represent each candidate pair . There is a library DDlib that can automatically generate features in DeepDive , which defines features that are not dependent on the domain . There are also many dictionaries in the DDlib library . These dictionaries contain words related to the correct classification of descriptions and relationships and are usually combined with domains and specific applications . We declare the extract \ _func \ _features function in app . ddlog . The input of this function includes the information of the two entities in the entity mention table and the NLP results in the sentence where the two entities are located . The output is the two entities and their characteristics .

- (3) Distant supervision with data and rules. We will use remote supervision to provide noisy label sets for candidate relationships to train machine learning models. Generally speaking, we divide the description method into two basic categories: mapping from secondary data for distant supervision and using heuristic rules for distant supervision [17]. However, we will use a simple majority voting method to solve the problem of multiple labels in each example. This method can be implemented in ddlog. In this method, first, we sum the labels (all -1, 0, or 1). Then, we simply threshold and add these labels to the decision variable table has\_spouse. In addition, we also need to make sure that all the spouse candidates who are not marked with rules are not included in this table. Once again, we execute all the above.
- (4) Learning and inference: model specification. We need to specify the actual model that DeepDive will perform learning and inference. DeepDive will learn the parameters of the model (the weights of features and the potential connections between variables). Then we perform statistical inferences on the learned model to determine that the probability of each variable of interest is true. ① Specifying prediction variables. In our experiment, we have a variable to predict the mention of each spouse candidate. In other words, we want DeepDive to predict the value of a Boolean variable for each mentioned spouse candidate to indicate whether the value is correct. DeepDive cannot only predict the value of these variables but also predict the marginal probability, that is, DeepDive's confidence in each prediction. ② Specifying features. We need to define the following: each has\_spouse variable will be connected to the elements of the corresponding spouse\_candidate row; We hope that DeepDive understands the weights of these elements from the data we remotely monitor; those weights of the element should be the same for the specific function of all instances. ③ Specifying connections between variables. We can use learning weights or given weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another person mentions p2 as a spouse relationship in the sentence, then it should also consider the opposite.

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The model should be strongly biased towards everyone mentioning a sign of marriage. Instead, we use negative weights for this operation. ④ Finally, we want to perform learning and inference using the specified model. This will build a model based on the data in the database, learn the weights, infer the expected or marginal probabilities of the variables in the model and then load it back into the database. In this way, we can see the probability of the has\_spouse variable inferred by DeepDive.