Document: 1 Introduction
Section: 3 Materials and methods > 3.2 Estimating geometries of glacial lakes
Type: text

Estimating the potential flood volume of a glacial lake is critical for determining the magnitude of a GLOF. Bathymetry data are necessary for calculating lake volume as well as potential flood volume, but bathymetry for glacial lakes is scarce due to the challenging and inaccessible environments in which glacial lakes are often located, including for Thorthomi lake. Although several reports have estimated Thorthomi lake volume (Karma, 2013; Singh, 2009), no details on how volumes were estimated have been documented.

To address data scarcity for glacial lake geometries, various studies have proposed methods for estimating glacial lake depth and volume based on other more accessible pa-

rameters such as lake area (Cook and Quincey, 2015; Huggel et al., 2002; O'Connor et al., 2001; Sakai, 2012), as well as depression angle from the lakeshore (Fujita et al., 2013) and surrounding topography (Heathcote et al., 2015). Empirical relationships such as area–volume and area–depth are useful for estimating a lake's depth and potential flood volume. Cook and Quincey (2015) refined the area–volume relationship proposed by Huggel et al. (2002) by increasing sample size and removing duplicated samples. They also classified the predictability of lake volume and depth based on regions and lake types and determined that predictability is influenced by a lake's origin and evolution. The relationship proposed by Cook and Quincey (2015) takes the following form:

$$D_{\text{mean}} = 0.1697 A^{0.3778},\tag{1}$$

where  $D_{\text{mean}}$  is the mean depth (in m) and A is the area (in m2). The volume–area relationship (V, volume in m3) can simply be derived by multiplying the area of both sides, as follows:

$$V = 0.1697A^{1.3778}. (2)$$

Sakai (2012) used a similar approach and proposed a model for estimating maximum depth instead of mean depth. The bathymetric measurement data of 17 glacial lakes (15 moraine-dammed glacial lakes and 2 thermokarst lakes) from Bhutan, Nepal, and Tibet were used to derive an area—maximum depth—volume relationship, so estimations of depth and volume from the area of glacial lakes could be determined (Sakai, 2012). The regression equation took the following form:

$$D_{\text{max}} = 95.665 A^{0.489},\tag{3}$$

where  $D_{\text{max}}$  is maximum depth (in m) and A is area (in km2). The volume–area relationship (V, volume in  $10^6 \text{ m}^3$ ) takes the following form:

$$V = 43.24A^{1.5307}. (4)$$

For predicting the moraine dam breach process explained in the following section, lake geometries, especially the maximum depth, are crucial, so we employed the equations proposed by Sakai (2012). The equations allow the independent calculation of maximum depth and volume. As conceptualised by Cook and Quincey (2015), bathymetry of the lake was estimated based on idealised geometric shape. The lake bottom was also assumed to follow an elliptical shape, as commonly observed in most moraine-dammed glacial lakes in Bhutan.


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies
Type: text

GLOFs are triggered by a breach of a moraine dam that holds the lake in place and are caused by an external triggering

data/('A glacial lake outburst flood risk assessment for the Phochhu river basin', '.pdf')_extraction.md


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies
Type: figure
Figure

Image /page/5/Figure/2 description: A flowchart illustrating a methodology for creating an inundation map and hazard zonation. The chart uses a legend to define its components: gray rounded rectangles for 'Input Data', blue parallelograms for 'Model', and green rounded rectangles for 'Result'. The process starts with input data including 'Digital Elevation Model', 'Lake bathymetry data', and 'Lake moraine data'. These inputs, along with processed data like 'Field bathymetry data for GLOF reconstruction' and 'Regression analysis data for GLOF prediction', are fed into a 'Dam Breach Model'. The output of this model is a 'Breach flow hydrograph'. This result, along with the 'Digital Elevation Model' and other inputs like 'Land Use Land Cover Data' and 'Satellite imagery', goes into a 'Hydrodynamic Model'. The 'Hydrodynamic Model' produces 'Simulated flow Hydraulics'. This result, combined with 'Land Use Land Cover Data', 'Satellite imagery', and 'Settlement data', is processed by a 'GIS' model. The final output of the entire process is an 'Inundation Map Hazard Zonation'.


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies
Type: figure
Figure: Figure 4. A schematic diagram of the methodology employed in our study.

Figure 4. A schematic diagram of the methodology employed in our study.


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.1 Previous studies
Type: text

event. While the structure of the dam itself is an important factor, destabilisation of a dam due to a trigger event is the primary cause of a breach. Since the overtopping of lake water is the major failure mode (Awal et al., 2010; Begam et al., 2018; Neupane et al., 2019), our study assumed that GLOFs are triggered by the overtopping of lake water.

To estimate flood flow and associated hazards resulting from a dam breach, several studies have simulated dam breach floods using dam breach models (Bajracharya et al., 2007; Hagg et al., 2021; Huggel et al., 2002; Koike and Takenaka, 2012; Maskey et al., 2020; Meyer et al., 2006; Shahrim and Ros, 2020; Wang et al., 2008; Worni et al., 2014). BREACH is a numerical model describing the dam breach process and the resulting outflow hydrograph. The model is based on fundamental principles of hydraulics, sediment transport, soil mechanics, and the physical properties of dam materials and the reservoir. The model is physically based and was designed to predict the size, shape, and time of dam breach development, as well as the resulting flow rate and the volume of water released. Unlike parametric models, physically based breach models, including BREACH, consider the geotechnical aspects of dam materials, as well as hydraulic and sediment transport (Fread, 1988; Maskey et al., 2020; Worni et al., 2014), which increases the predictive accuracy of future GLOF processes. Due to this, the BREACH model has been widely used in studies of dam breach flood hazards and risk assessments (Fread, 1988).

Koike and Takenaka (2012) used the BREACH model coupled with the flood flow model, FLO-2D, to perform a sce-

nario analysis on the risks of a GLOF on the Mangdechhu river basin, due to an outburst flood of the Metatshota glacial lake in Bhutan. The study concluded that although the breaching potential of the lake is low due to the wide crest and gentle slope of the moraine dam, a GLOF would affect several houses and farmland located on the flood plain (Koike and Takenaka, 2012). Hagg et al. (2021) performed a GLOF hazard assessment within the Mochhu basin in Bhutan using the HEC-RAS dam break module, simulating a dam breach of the Shintaphu glacial lake, and concluded that risk is comparably small.

Our study used BREACH for describing the dam breach process for target lakes due to its better predictive accuracy for a future extraordinary Thorthomi GLOF event. The dam is assumed to breach due to overtopping flow resulting from a trigger event, such as an ice calving/avalanche or a rock avalanche. Most of the geotechnical properties of dam materials required as an input parameter are available in the report published by the National Center for Hydrology and Meteorology (NCHM, 2020). A few properties were published by Koike and Takenaka (2012). Some unavailable data were estimated by referring to previous studies.


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach
Type: text

For reconstruction of the 1994 Luggye GLOF, the dam breach outflow hydrograph was estimated using BREACH (Fread, 1988). The bathymetry of Luggye lake (Fig. 5) and


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach
Type: figure
Figure

Image /page/6/Figure/2 description: A bathymetric map showing the depth elevation of a lake. The lake is elongated and slightly curved. A north arrow is in the top left corner. The legend in the bottom left indicates the lake boundary with a thin black line and the lake outlet with a red-brown circle, which is located at the westernmost tip of the lake. A scale bar at the bottom shows a total length of 1.3 kilometers, with marks at 0, 0.325, and 0.65 kilometers. On the right, a vertical color scale labeled "Bathymetry Depth elevation (m)" ranges from 4353.5 (dark purple, deeper) to 4465.8 (dark green, shallower). The map shows the lake is shallowest around the edges and has its deepest point in the central-eastern area. The easternmost part of the lake is a white area labeled "No Data".


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach
Type: figure
Figure: Figure 5. The bathymetry of Luggye lake (data from NCHM).

Figure 5. The bathymetry of Luggye lake (data from NCHM).


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.2 Reconstruction of the 1994 Luggye GLOF dam breach
Type: text

the material properties of the moraine dam (the middle row of Table 1) required for the model were based on various reports (NCHM, 2019, 2020). Topographic data of the moraine dam were derived from the digital surface model (DSM). Since wave overtopping is a more common failure mode for moraine-dammed glacial lakes as compared to a piping failure (Neupane et al., 2019), to estimate breach outflow from Luggye lake, overtopping failure of the moraine dam was assumed. The properties of moraine dam material have a significant effect on the growth of a breach (Maskey et al., 2020; Westoby et al., 2014). The mechanism in which the formation of a breach largely occurs determines the shape of the breach outflow hydrograph (Westoby et al., 2014). Therefore, gathering accurate in situ data for reliable breach process reproduction is essential. Based on the estimation by Fujita et al. (2008), deduced from a combination of field measurements and remote-sensing observations, the level of lake water was reduced 19 m during the event.


Document: 1 Introduction
Section: 3 Materials and methods > 3.3 The moraine dam breach and its modelling > 3.3.3 The Thorthomi GLOF prediction
Type: text

Breach initiation is assumed to occur due to an overtopping wave at the existing outlet (Fig. 3) induced by any probable triggering event. Moraine material properties and topographic data for the Thorthomi lake BREACH model were either estimated from available terrain data or adopted from available reports and research documents, and this is also listed in the right row of Table 1.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model
Type: text

A hydrodynamic model is essential for understanding the characteristics of a flood wave caused by a GLOF propagating downstream, as well as for quantitatively evaluating the potential risks caused by a flood. To simulate the propagation of outflow from glacial lake breaches in Nepal, numerous studies, such as HEC-RAS used in Maskey et al. (2020), have employed various hydrodynamic models. Similar approaches that couple dam breach models to hydrodynamic models (e.g. Bajracharya et al., 2007; Koike and Takenaka, 2012; Westoby et al., 2015; Worni et al., 2014) have been

performed for modelling the GLOF process chain in various regions.

Worni et al. (2014) provided a summary of various hydrodynamic models that have been used to model GLOFs. Discussed models include HEC-RAS, FLO-2D, BASEMENT, and Delft3D. The choice of a hydrodynamic model depends on factors such as the end objective, data availability, and the available budget. Each model has its own level of accuracy; however, the accuracy of results is primarily dependent on the precision of the elevation model, including channel geometry and floodplain topography. Errors in the elevation model can lead to inaccuracies in results (Casas et al., 2006; Xu et al., 2021).

HEC-RAS is a commonly used hydrodynamic model that allows users to perform 1D and 2D steady/unsteady flow simulations (Brunner, 2016b). We used the HEC-RAS to perform a 2D unsteady flow simulation for floods caused by a glacial lake dam breach. Since they represent spatially varied flood hydraulics (Horritt and Bates, 2001), the twodimensional models employed are standard in flood modelling. In a 2D unsteady simulation, flow varies in time, along two spatial dimensions, and processes are predicted by the laws of conservation of mass (continuity) and the conservation of momentum for two horizontal directions. We used a full set of momentum equations (the shallow water equations) to simulate flooding as clear water flow. Although high-viscosity and hyper-concentrated (sediment entrained) flows are inherent to the GLOF phenomenon (Clague and Evans, 2000; Vuichard and Zimmermann, 1987), to simplify modelling complexity and data requirements, most studies (Hagg et al., 2021; Koike and Takenaka, 2012; Maskey et al., 2020; Rinzin et al., 2023) have simulated GLOFs as clear water flow.

Other important considerations in hydrodynamic modelling are Manning's roughness coefficient and channel geometry. Both have significant impacts in predicting inundation extent and flow characteristics (Mosquera-Machado and Ahmad, 2007; Ye et al., 2018; Zhu et al., 2019). Hagg et al. (2021) demonstrated the influence of Manning's roughness coefficient for glacial lake outburst floods from the Shintaphu glacial lake in Mochhu basin, Bhutan, and concluded that channel roughness is not essential for inundation extent but exerts a significant effect on flood velocity and flood arrival time.

3 Materials and methods
3.4 Flood routing
3.4.1 The hydrodynamic model


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model
Type: text

al . , 2023 ) have simulated GLOFs as clear water flow . Other important considerations in hydrodynamic modelling are Manning ' s roughness coefficient and channel geometry . Both have significant impacts in predicting inundation extent and flow characteristics ( Mosquera - Machado and Ahmad , 2007 ; Ye et al . , 2018 ; Zhu et al . , 2019 ) . Hagg et al . ( 2021 ) demonstrated the influence of Manning ' s roughness coefficient for glacial lake outburst floods from the Shintaphu glacial lake in Mochhu basin , Bhutan , and concluded that channel roughness is not essential for inundation extent but exerts a significant effect on flood velocity and flood arrival time .

Since such information is needed to estimate the area needed for evacuation and the lead time for evacuation, flood travel time and peak flow are essential parameters for early warning purposes. In this study, flood travel time was calculated based on timing of the breach outflow hydrograph and the flow hydrograph at the point of interest, when there was significant inundation depth and extent. Peak flow is the maximum simulated flow resulting from a dam breach.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model
Type: table
Table

| Moraine dam data                                 | 1994 Luggye GLOF | Thorthomi GLOF |
|--------------------------------------------------|------------------|----------------|
| Surface area of the lake (km2) (RSA)             | 1.46b            | 4.3b           |
| Volume of water in the lake (106 m3), Eq. (4)    | 65.19b           | 400a           |
| Maximum depth of the lake (m), Eq. (3)           | 96.93b           | 161a           |
| Top elevation of the dam (m) (HU)                | 4465a            | 4446a          |
| Toe elevation of the dam (m) (HL)                | 4370a            | 4345a          |
| Slope of the upstream face of the dam (1 : ZU)   | 1 : 4.8a         | 1 : 6.2a       |
| Slope of the downstream face of the dam (1 : ZU) | 1 : 6.5a         | 1 : 6.3a       |
| Dam material properties                          |                  |                |
| Grain size ( <i>D</i> 50) (mm)                   | 1.362b           | 2.01b          |
| Porosity (%)                                     | 36.5c            | 36.5c          |
| Cohesive strength (kN m-2)                       | 1.5b             | 1.5b           |
| Internal friction (°)                            | 41b              | 39b            |
| Unit weight (kN m-3)                             | 22.92b           | 22.43b         |
| Manning's coefficient (s m-1/3)                  | 0.07b            | 0.07b          |

Moraine dam data
1994 Luggye GLOF
Thorthomi GLOF


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.1 The hydrodynamic model
Type: text

Table 1. Input parameters for the 1994 Luggye GLOF reconstruction and the Thorthomi GLOF prediction.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning
Type: text

The accuracy of hydrodynamic model results is heavily influenced by the quality of the elevation model used (Gyasi-Agyei et al., 1995; Yamazaki et al., 2014, 2017). Casas et al. (2006) demonstrated the effects of a topographic data source and resolution on flood peak discharge and the extent of inundation and then concluded that laser-based elevation data are a suitable source for hydraulic modelling. Similarly, the influence of grid size on inundation propagation and water depth under varied topographical settings in 2D modelling has been analysed (Tsubaki and Kawahara, 2013). Both fine grid size representing main topographic features of the floodplain and accurate elevations at each grid point are essential for simulating flood flow with less uncertainty. Therefore, using the best available elevation model for the hydrodynamic simulation of floods is essential.

Since an accurate elevation model is essential for accurate hydrodynamic simulations, various methods for correcting generic noise errors and biases originating from topography measurements have been proposed and have been used in elevation models prior to running hydrodynamic/hydrological analyses.

Below, we compare three different elevation models covering the study area. The first model, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro DEM (Fig. 6a), was developed based on SRTM and AW3D DEM. Water layer data at a 3 arcsec resolution ( $\sim 90\,\mathrm{m}$ ) were developed for river hydrology analyses at global, as well as at local, scales (Yamazaki et al., 2017, 2019). Other bias-corrected elevation data are the Forest And Buildings removed Copernicus DEM (FABDEM) (Fig. 6b), developed from Copernicus DEM (COPDEM), where the height of trees and buildings

are removed using machine learning, and are also a preferable source for terrain data (Hawker et al., 2022). The AW3D digital surface model (DSM) (Fig. 6c) was jointly developed by the Remote Sensing Technology Centre (RESTEC) and the NTT DATA Corporation, utilising PRISM data acquired by the Advanced Land Observing Satellite (ALOS) of the Japan Aerospace Exploration Agency (JAXA). Roughresolution DSMs are distributed by several organisations free of charge; however, for our study, we used the finer commercial DSM product, AW3D 2.5 m, as the primary source of topography. The cell size of the DSM for the focus area was approximately  $2.2 \,\mathrm{m}$  for both the X and Y directions and was projected to WGS84 UTM zone 45N. The AW3D 2.5 m DSM represents details of topography, especially in the river valley and in the developed flood plain, much better than other models.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning
Type: text

ALOS ) of the Japan Aerospace Exploration Agency ( JAXA ) . Roughresolution DSMs are distributed by several organisations free of charge ; however , for our study , we used the finer commercial DSM product , AW3D 2 . 5 m , as the primary source of topography . The cell size of the DSM for the focus area was approximately $ 2 . 2 \ , \ mathrm { m } $ for both the X and Y directions and was projected to WGS84 UTM zone 45N . The AW3D 2 . 5 m DSM represents details of topography , especially in the river valley and in the developed flood plain , much better than other models .

Since the AW3D DSM was obtained using satellite photogrammetry, representations of the river bottom, especially in forested and deep gorge areas, are sometimes inaccurate. If we directly used topography in hydrodynamic modelling, the DSM covered structures along the river, as well as bridges crossing the river, disturbing flood water flow. To avoid such anomalies in the elevation model and in the hydrodynamic simulation results, a river channel delineation was performed. The presence of spikes within the DSM, along the river's path, can obstruct flood water flow, resulting in the formation of non-existent deep pools. One way to improve topography surrounding a river is the use of bathymetric survey data. However, no such survey has been conducted within the study area. To improve representation for the river channel, our study utilised a rule-based correction method.

The Agriculture Conservation Planning Framework (ACPF) is a GIS-based tool developed by the United States

<sup>a Estimated in this study. b NCHM (2020). c Koike and Takenaka (2012).


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning
Type: figure
Figure

Image /page/8/Figure/2 description: A figure with three panels labeled (a), (b), and (c), showing topographic maps of the same area at different resolutions. The area shown is around Punakha Dzong. Panel (a) is a low-resolution, pixelated map with a scale bar from 0 to 1.2 kilometers. Panel (b) shows the same area at a higher resolution, with less prominent pixelation. Panel (c) is a high-resolution, smooth map that includes a legend for Punakha Dzong and a north arrow in the upper right corner. All three maps use a color gradient from brown for higher elevations to blue-green for lower elevations and show the location of Punakha Dzong with a black outline.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning
Type: figure
Figure: Figure 6. The Punakha Dzong region as represented by three different terrain models: (a) the MERIT HydroDEM 90 m, (b) the FABDEM-30 m, and (c) the AW3D 2.5 m DSM.

Figure 6. The Punakha Dzong region as represented by three different terrain models: (a) the MERIT HydroDEM 90 m, (b) the FABDEM-30 m, and (c) the AW3D 2.5 m DSM.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.2 Ground elevation models and pre-conditioning
Type: text

Department of Agriculture (USDA) to identify areas with impeded water flow and to improve hydrologic flow using flow direction and an accumulation analysis (Porter et al., 2016). While the ACPF is a valuable tool for hydrologic flow and watershed planning, it has limited applicability for terrain correction in hydrodynamic modelling because the ACPF does not allow users to define the bathymetry of a river channel.

Another widely used channel modification method is the inbuilt function of HEC-RAS. The Channel Design/Modification editor tool is a module used to modify an unrealistic cross-section or to introduce a user-defined channel crosssection (Brunner, 2016a). The tool effectively removes spikes in the elevation model along a river channel while maintaining the natural slope of the represented topography. The modified channel TIN (triangulated irregular network) can be overlain on the original DSM and exported as a single raster file with modified features. Rinzin et al. (2023) applied the method to modify terrain and to delineate river flow paths for a GLOF simulation. For our study, we used this tool to condition the DSM in the middle region of the model domain, a forested deep gorge, where huge spikes were included within the elevation model. The modification was only applied to the channel section, and the remaining portion was left as it was.


Document: 1 Introduction
Section: 3 Materials and methods > 3.4 Flood routing > 3.4.3 Implementation for GLOF reconstruction and prediction
Type: text

Downstream propagation of the flood was simulated using the HEC-RAS model for the 1994 Luggye GLOF reconstruction. The calculation domain was defined by the 2D flow area. The overall size of the flow area was  $64 \,\mathrm{km^2}$ . The domain was modelled using a 20 m resolution computational grid, consisting of 157 188 computational cells, and solved with a time step of 1 s. The elevation of each grid cell was specified based on a 2.2 m, hydro-conditioned digital surface model, and Manning's n was set to 0.035, in the range provided by the HEC-RAS manual (Brunner, 2016a). The dam breach outflow hydrograph obtained from the BREACH

model was used as the upstream boundary; and normal depth, calculated based on downstream slopes derived from the DSM, was used as the downstream boundary condition.

For the Thorthomi GLOF prediction, we used a hydrodynamic model similar to the model used for the 1994 Luggye GLOF reconstruction. The domain was a bit shortened because the Thorthomi glacial lake is located 2 km downstream of the Luggye glacial lake. The overall size of the flow area was 62 km2. The domain was modelled using a 20 m resolution computational grid consisting of 153 790 computational cells with a temporal resolution of 1 s.


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.1 Dam breach processes
Type: text

Simulated peak flow  $(Q_p)$  of the dam breach outflow hydrograph was  $6030\,\mathrm{m}^3\,\mathrm{s}^{-1}$ , and  $1.9\,\mathrm{h}$  ( $\sim 114\,\mathrm{min}$ ) was required to reach peak flow (the time to peak,  $T_p$ ) (Fig. 7a). The volume of the GLOF and the reduction of lake water level due to the event were  $21\times10^6\,\mathrm{m}^3$  and  $19\,\mathrm{m}$ , respectively, in agreement with the findings of Fujita et al. (2008), which was based on a combination of in situ observations and remotesensing data. Dimensions for the estimated breach of the dam are provided in Fig. 7b.

4 Results
4.1 The 1994 Luggye GLOF reconstruction
4.1.1 Dam breach processes


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: text

Flow hydrographs at various locations along the flow path are provided in Fig. 8. Conforming to the findings of Meyer et al. (2006), after approximately 6 h, the GLOF had a peak discharge of 2897 m3 s-1 as it reached Punakha Dzong, located 93 km downstream of the lake. Peak flow at the WangdiRapid station (shown in Fig. 2c), 15 km downstream of Punakha Dzong, was 2455 m3 s-1, close to the recorded value of 2539 m3 s-1. Here, the recorded flow rate included the contribution of normal flow from tributaries, which was not ac-


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: figure
Figure

Image /page/9/Figure/2 description: The image contains two diagrams labeled (a) and (b). Diagram (a) is a line graph titled "Flow vs. Time". The x-axis is labeled "Time (h)" and ranges from 0 to 24. The y-axis is labeled "Flow (m³ s⁻¹)" and ranges from 0 to 6000. The graph shows a sharp peak in flow, reaching approximately 6000 m³ s⁻¹ at around 2 hours, and then gradually decreasing over the 24-hour period. Diagram (b) is a 3D representation of a trapezoidal channel cross-section. The top width of the channel is labeled as 102 m. There are two vertical height measurements indicated: one is 19 m from the bottom of the channel to an intermediate level, and the other is the total height of 22 m from the bottom to the top.


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: figure
Figure: Figure 7. (a) A breach outflow hydrograph from the BREACH model and (b) an illustration of breach parameters, breach width $(\leftrightarrow)$ , breach depth $(\updownarrow)$ , and the change in water surface elevation $(\updownarrow)$ .

Figure 7. (a) A breach outflow hydrograph from the BREACH model and (b) an illustration of breach parameters, breach width  $(\leftrightarrow)$ , breach depth  $(\updownarrow)$ , and the change in water surface elevation  $(\updownarrow)$ .


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: figure
Figure

Image /page/9/Figure/4 description: A line graph showing Flow (m³ s⁻¹) versus Time (h). The y-axis, labeled "Flow (m³ s⁻¹)", ranges from 0 to 4000. The x-axis, labeled "Time (h)", ranges from 0 to 24. There are two curves on the graph. The black curve, labeled "Punakha Dzong", starts rising sharply around 5.5 hours, peaks at approximately 2900 m³/s at 6 hours, and then gradually decreases. The blue curve, labeled "WangdiRapid Station", starts rising around 7 hours, peaks at about 2500 m³/s around 8 hours, and then gradually decreases. A red horizontal arrow labeled "~ 6 h" and identified in the legend as "Flood Travel Time" extends from time 0 to the peak of the Punakha Dzong curve at 6 hours.


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: figure
Figure: Figure 8. A simulated GLOF hydrograph at different locations along the flow path.

**Figure 8.** A simulated GLOF hydrograph at different locations along the flow path.


Document: 1 Introduction
Section: 4 Results > 4.1 The 1994 Luggye GLOF reconstruction > 4.1.2 Downstream peak flow and flood travel time
Type: text

counted for in our analysis. Good agreement of results for simulated flow and flood travel time with observed data, as well as previous studies, indicated that the performance of the employed models and the modelling approach were adequate and capable of yielding satisfactory results for predictive modelling of the target lake. The total inundated area along the basin was approximately 13.1 km2.


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry
Type: text

The estimated volume and maximum depth of Thorthomi lake based on Eqs. (3) and (4) were  $400 \times 10^6$  m3 and 161 m, respectively (the top right of Table 1). The estimated volume and maximum depth of Thorthomi lake falls within the predicted band, considering a 95% confidence level. The utilised equations showed a good relationship between area and volume and between and area and maximum depth, with the prediction of  $400 \times 10^6$  m3 for the mean and  $281 \times 10^6$  m3 and  $560 \times 10^6$  m3 for the lower bound and the upper bound, respectively. The prediction range for maximum depth was 161 m for the mean prediction (130 m and 270 m for the lower bound and the upper bound, respectively). Compared to other glacial lakes in Bhutan, the estimated parameters indicate that the Thorthomi glacial lake is one of the largest and deepest lakes. The bathymetry of Thorthomi lake, estimated based on the above parameters, is provided in Fig. 9.

A recent study from Nepal proposed a glacial lake volume estimation equation by considering the width and length ratio

4 Results
4.2 Future Thorthomi GLOF prediction
4.2.1 Lake bathymetry


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry
Type: figure
Figure

Image /page/9/Figure/11 description: A bathymetric map of the Thorthomi Lake Area. The map includes a legend, a north arrow, a scale bar, and a color scale for elevation. The legend indicates symbols for Outlets (red circle), Outflow (wavy blue line), Thorthomi Lake Area (solid outline), PDGL area (dashed outline), and Glaciers (grey shaded area). The map shows the lake with a single outlet and outflow on its western side. Glaciers are located at the northern end of the lake, and a PDGL area is situated along the eastern side. The bathymetry, representing depth and elevation in meters, is shown with a color gradient from green (4446 m) to brown (4285 m), indicating the lake is deepest in its central and southern parts. A scale bar at the bottom is marked in increments up to 1.8 kilometers.


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry
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Figure: Figure 9. The estimated bathymetry of Thorthomi lake.

**Figure 9.** The estimated bathymetry of Thorthomi lake.


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.1 Lake bathymetry
Type: text

of the lake (Qi et al., 2022). Based on the equation provided in Qi et al. (2022), the water volume of Thorthomi lake can be estimated as  $227 \times 10^6 \, \text{m}^3$ . This volume is substantially small compared to the volume estimated by Eq. (4). The discrepancy may be related to the dataset used for each study, namely Eq. (4) is based on lakes in Bhutan, Nepal, and Tibet; and Qi et al. (2022) is based on lakes in the Peruvian Andes and other areas, including non-moraine-dammed lakes.


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes
Type: text

Different dam breach scenarios for maximum breach and a partial breach for a half breach width and depth (50% of maximum width and depth) were simulated to ascertain the potential risk under various breaching possibilities, including a partial dam breach, which occurred in 1994 (e.g. the 1994 Luggye GLOF). Simulated peak flow  $(Q_p)$  resulting from the Thorthomi dam breach under different breach scenarios ranged from  $9700 \,\mathrm{m}^3 \,\mathrm{s}^{-1}$  (for a 50 % breach depth) to 16 360 m3 s-1 (for maximum breach width and depth), with a time to peak  $(T_p)$  of 3.4 to 4 h, respectively (Fig. 10a). The bathymetry of the lake and the topography of the moraine dam dictate the total lake drawdown depth and the volume of the outburst flood. In this study, we estimated that 100 m of lake water depth will be lowered before the breach outflow channel becomes sufficiently stable, after sending  $283 \times 10^6 \,\mathrm{m}^3$  (approximately 70 % of estimated lake water) of flood water downstream (Fig. 10b). The breach outflow channel was assumed to be stable when its bottom elevation reached the natural bed level of the downstream channel and down-cutting ceased.

4 Results
4.2 Future Thorthomi GLOF prediction
4.2.2 Dam breach processes


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes
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Figure

Image /page/10/Figure/2 description: The image contains two panels, labeled (a) and (b). Panel (a) is a line graph that plots Flow in cubic meters per second (m³ s⁻¹) against Time in hours (h). The x-axis ranges from 0 to 24 hours, and the y-axis ranges from 0 to 15,000 m³ s⁻¹. There are three curves on the graph, each representing a different scenario: 'Maximum Breach' (dark red line), 'Half Breach Width' (blue line), and 'Half Breach Depth' (black line). The 'Maximum Breach' curve peaks at the highest flow, over 15,000 m³ s⁻¹ at approximately 4 hours. The 'Half Breach Depth' curve peaks at around 13,500 m³ s⁻¹ at about 3.5 hours. The 'Half Breach Width' curve has the lowest peak, just under 10,000 m³ s⁻¹ at about 4.5 hours. Panel (b) is a 3D diagram of a trapezoidal channel, illustrating its dimensions. The top width is indicated as 225 m, and the depth is shown as 100 m.


Document: 1 Introduction
Section: 4 Results > 4.2 Future Thorthomi GLOF prediction > 4.2.2 Dam breach processes
Type: figure
Figure: Figure 10. (a) A dam breach outflow hydrograph obtained from the BREACH model for three different scenarios and (b) breach parameters, breach width $(\leftarrow)$ , and breach depth $(\cline{1})$ in metres for the maximum breach scenario.

Figure 10. (a) A dam breach outflow hydrograph obtained from the BREACH model for three different scenarios and (b) breach parameters, breach width  $(\leftarrow)$ , and breach depth  $(\cline{1})$  in metres for the maximum breach scenario.


Document: 1 Introduction
Section: 4 Results > 4.3 Downstream peak flow and flood travel time
Type: text

The simulated flow hydrographs for three different scenarios at eight major settlement areas are provided in Fig. 11. Peak flow of the GLOF gradually attenuated as it propagated downstream. Peak flow at Punakha Dzong ranged from 8900 to  $14\,130\,\mathrm{m}^3\,\mathrm{s}^{-1}$  and decreased from 8200 to  $11\,500\,\mathrm{m}^3\,\mathrm{s}^{-1}$  when it arrived at the hydropower plant (PHPA-I).

A schematic representation of an approximate distance, peak flow, averaged channel slope, and the estimated flood travel time for a maximum breach condition is provided in Fig. 12 (refer to Sect. 3.4.1 for the definition of the peak flow and flood travel time in this study). The estimated peak flow at Punakha Dzong,  $14\,130\,\mathrm{m}^3\,\mathrm{s}^{-1}$ , is expected to be over 5 times higher than the 1994 Luggye GLOF (the recorded value is  $2539\,\mathrm{m}^3\,\mathrm{s}^{-1}$ , and the value estimated in this study is  $2455\,\mathrm{m}^3\,\mathrm{s}^{-1}$ ).


Document: 1 Introduction
Section: 5 Discussion > 5.1 Inundation hazard in five vulnerable areas under the maximum breach scenario
Type: text

The flood depth distribution, highlighting five vulnerable areas for a maximum breach scenario, is provided in Fig. 13. The villages of Thanza, Toncho, and Lhedi, located in the northernmost part of the study area (Fig. 2), are expected to be inundated under a Thorthomi GLOF scenario. The 1994 Luggye GLOF also caused major damage to these settlement areas, but a Thorthomi GLOF is expected to cause more severe damage due to larger flood volume and shorter lead time. Major settlements along the river basin lie in the lower valleys of the Punakha and Wangdue districts, where large areas are expected to be flooded. Major towns and settlements, such as Samdingkha, Khuruthang, and Bajo, are expected to be inundated. The Mochhu river converges with the Phochhu river at the top left of Fig. 13c. Substantial overflow surrounding the Mochhu river, around the confluence, has been predicted. This result is due to backwater flow from the Phochhu river. Water flow from the Mochhu river is not easy to accurately estimate in advance and was not accounted for in this study, so inundation surrounding Punakha Dzong may be underestimated. However, the contribution of water from the Mochhu river can be negligible because the base flow of the Mochhu river is approximately  $100\,\mathrm{m}^3\,\mathrm{s}^{-1}$ , which is substantially small compared to estimated peak flow in this area,  $14\,130\,\mathrm{m}^3\,\mathrm{s}^{-1}$ . Total inundated area due to a Thorthomi GLOF, with a maximum breach, was estimated to be approximately  $22\,\mathrm{km}^2$ , which is almost twice the area inundated under the Luggye GLOF simulation ( $13.1\,\mathrm{km}^2$ , estimated in this study).


Document: 1 Introduction
Section: 5 Discussion > 5.2 A comparison between three scenarios
Type: figure
Figure: Figure 14 compares the maximum inundation depth and extent for three different scenarios for the town of Khuruthang. Simulation results for the three scenarios considered in this study revealed that the overall inundation extent and flood depths were higher for the maximum breach scenario. However, the depth and flood extent for the two other scenarios were comparable to the maximum breach scenario. The results indicate that even for a partial breach of the moraine dam, substantial damage within the downstream settlement areas (Punakha and Wangdue) is expected. The results imply that the difference in glacial lake bathymetry may also affect the maximum inundation in downstream areas but is not very sensitive because of the nature of the GLOF event (consisting of a rapid dam breach process and flood routing in steep valleys).

Figure 14 compares the maximum inundation depth and extent for three different scenarios for the town of Khuruthang. Simulation results for the three scenarios considered in this study revealed that the overall inundation extent and flood depths were higher for the maximum breach scenario. However, the depth and flood extent for the two other scenarios were comparable to the maximum breach scenario. The results indicate that even for a partial breach of the moraine dam, substantial damage within the downstream settlement areas (Punakha and Wangdue) is expected. The results imply that the difference in glacial lake bathymetry may also affect the maximum inundation in downstream areas but is not very sensitive because of the nature of the GLOF event (consisting of a rapid dam breach process and flood routing in steep valleys).


Document: 1 Introduction
Section: 5 Discussion > 5.3 Time series change of flood depth distribution surrounding Punakha Dzong
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The spatial distribution of flood depth for a maximum breach scenario, at different time steps, for Punakha Dzong and Khuruthang town are provided in Fig. 15. Due to higher peak flow and a longer flood duration, overall flood hazard potential for the inhabited area caused by the Thorthomi lake GLOF, as compared to damages during the 1994 Luggye GLOF, was significantly higher. Most of the flood path lies in the narrow V-shaped valley, where there are few to no settlements or infrastructure. We estimated that over 1277 houses, most in the lower region of the study area, will be inundated in a GLOF. Aside from this, infrastructure such as roads, bridges, and sand dredging equipment will be damaged.

Notable damage during the 1994 GLOF occurred in Punakha Dzong. The area near the Punakha Dzong was completely inundated in the 1994 Luggye GLOF. The simulated future GLOF indicates that the Punakha Dzong area will be completely flooded, with a maximum depth of over 10 m (Figs. 13 and 15).


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Type: text

The Punakha (the middle downstream of the domain; see Fig. 13) and the Wangdue districts (consisting of the Bajo and Jagathang settlements, as well as the downstream do-


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Figure

Image /page/11/Figure/2 description: An image displaying eight line graphs in a 2x4 grid, each showing simulated flood hydrographs for different locations. The locations are Lhedi, Samdingkha, Punakha Dzong, and Khuruthang in the top row, and Jagathang, Bajo, WangdiRapid station, and Hydropower plant in the bottom row. Each graph plots Flow in cubic meters per second (m³ s⁻¹) on the y-axis against Time in hours (h) on the x-axis, which ranges from 0 to 24. A legend in the top-left graph indicates three scenarios: 'Max' (red line), 'BRD-50%' (black line), and 'BRW-50%' (blue line). In all graphs, the 'Max' scenario shows the highest peak flow, followed by 'BRD-50%', and then 'BRW-50%'. The peak flows generally decrease and occur later in time for locations further down the grid. For example, in the 'Lhedi' graph, the 'Max' flow peaks at approximately 16,000 m³/s around 5 hours, while in the 'Hydropower plant' graph, the 'Max' flow peaks at approximately 11,000 m³/s around 9 hours.


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Figure: Figure 11. A simulated flow hydrograph at important locations, derived from the HEC-RAS; result for each scenario (max: maximum breach; BRD-50 %: half of maximum breach depth; BRW-50 %: half of maximum breach width).

**Figure 11.** A simulated flow hydrograph at important locations, derived from the HEC-RAS; result for each scenario (max: maximum breach; BRD-50 %: half of maximum breach depth; BRW-50 %: half of maximum breach width).


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Figure

Image /page/11/Figure/4 description: A schematic diagram illustrating the characteristics of a flood along a river path, starting from Thorthomi Lake. The top part of the diagram shows a profile view of the river's elevation, which decreases as the distance from the lake increases. Key locations along the path are marked with their distances from the lake: Lhedi (19 km), Samdingkha (83 km), Punakha Dzong (90 km), Khuruthang (94 km), Bajo (103 km), and a Hydropower Plant (115 km). Below this profile, three horizontal bars provide quantitative data for different segments of the river. The first bar, labeled "Peak Flow (m³ s⁻¹)", shows a decreasing flow rate from 16,300 at Lhedi to 11,550 at the Hydropower Plant. The second bar, "Average Slope (dy/dx)", shows the slope for each segment, with values ranging from 0.001 to 0.038. The third bar, "Flood Travel Time (h)", indicates the time taken for the flood to traverse each segment, with values such as 2.6 hours for the first segment and 7.1 hours for the last.


Document: 1 Introduction
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Figure: Figure 12. A schematic representation of flood parameters at six important locations along the flow path for the maximum breach scenario.

Figure 12. A schematic representation of flood parameters at six important locations along the flow path for the maximum breach scenario.


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main; see Figs. 2 and 13) are leading producers of rice, an essential crop for the country's GDP and food security. Any damage to agricultural land would have a devastating impact on farmers and the nation. Aside from potential damage to buildings and infrastructure, such as roads and bridges, agricultural land would also become submerged and destroyed by a flood. We estimated that approximately 193 to 245 ha of agricultural land will be inundated under different scenarios

in a Thorthomi GLOF event. Figure 16 shows the potential extent of floods for different land use classes and highlights probable damage to agricultural land, particularly in the areas of Samdingkha and Jagathang.

The overall hazard potential of a GLOF from Thorthomi lake under different scenarios is summarised in Table 2. Although the peak flow rate of each scenario is different (29 % to 37 % between the maximum and minimum for the result


Document: 1 Introduction
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Figure

Image /page/12/Figure/2 description: A figure displaying a series of maps illustrating flood inundation in a study area. The figure is composed of a central legend, a study area map, and five detailed maps labeled (a) through (e).


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The legend defines symbols for Bridges, Education Centres, Health Care Centres, Historical Places, Road Network, Settlements, River, and Thorthomi\_Lake.

The study area map shows a river originating from Thorthomi\_Lake with five locations marked along its path: (a), (b), (c), (d), and (e).

Each of the five detailed maps shows a satellite view of one of these locations with a flood inundation overlay, a scale bar, and a legend for flood depth in meters (m).

- Map (a) Thanza: Shows a wide flooded area. The flood depth legend is: 0 - 5 m, 5.1 - 10 m, and 10.1 - 15.6 m. The scale bar goes up to 50 kilometers.
- Map (b) Samdingkha: Shows a flooded river channel. The flood depth legend is: 0 - 6 m, 6.1 - 12 m, and 12.1 - 18.7 m. The scale bar goes up to 1.6 kilometers.
- Map (c) Punakha Dzong: Shows a flooded area at a river confluence. The flood depth legend is: 0 - 7 m, 7.1 - 14 m, and 14.1 - 23.2 m. The scale bar goes up to 2 kilometers.
- Map (d) Khuruthang: Shows a flooded river bend. The flood depth legend is: 0 - 8 m, 8.1 - 15 m, and 15.1 - 25.8 m. The scale bar goes up to 3 kilometers.
- Map (e) Jagathang & Bajo: Shows a flooded winding river. The flood depth legend is: 0 - 7 m, 7.1 - 15 m, and 15.1 - 22.6 m. The scale bar goes up to 3 kilometers.

Each map uses shades of blue to represent flood depth, with darker shades indicating deeper water. Icons from the legend are placed on the maps to show the locations of infrastructure and settlements.


Document: 1 Introduction
Section: 5 Discussion > 5.4 Socio-economic impact
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Figure: Figure 13. A maximum GLOF inundation map of the study area under the maximum breach scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.

Figure 13. A maximum GLOF inundation map of the study area under the maximum breach scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.


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depicted in Fig. 11), the total inundation area, the number of submerged buildings, and the area of impacted cultivated land are not much different (12%, 22%, and 21%, respectively), implying that the estimated flood is significant even for the most minor flood scenario (BRW-50% scenario) for the Thorthomi GLOF. The scenarios indicate that most of the damage will occur for river properties and that farmland will be substantially damaged, even when a dam breach is not drastic. The soil in farmlands will also be eroded and covered by debris. Damage to irrigation is expected and may affect agriculture in farmland located behind flooded areas. Over the long term, damage to soil and irrigation would extensively reduce farmers' production. In advance, careful evacuation planning and business continuity planning (e.g. JICA,

2015), including a plan for agriculture, are essential for mitigating damage caused by a future Thorthomi GLOF.


Document: 1 Introduction
Section: 5 Discussion > 5.5 Limitations of this study
Type: text

Due to a lack of actual surveyed data, volume and maximum depth were estimated based on the statistical relationships established by past studies; an uncertainty for the estimated bathymetry of Thorthomi lake is a major limitation of our study. As compared to the bathymetry presented, the use of actual, surveyed bathymetric data may yield a more accurate prediction. An additional limitation of our study is the clear water assumption. Compared to clear water, hyperconcentrated water has different dynamic properties. Debris in flood water may cause substantial damage to farmland, in-

5 Discussion
5.5 Limitations of this study


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Section: 5 Discussion > 5.5 Limitations of this study
Type: figure
Figure

Image /page/13/Figure/2 description: A figure composed of four panels, labeled (a), (b), (c), and (d), illustrating a flood model for a river system. Panel (a) is a map showing the overall area. It includes a north arrow and a scale bar from 0 to 40 kilometers. The legend in panel (a) identifies symbols for Khuruthang Town, a River, the Model Domain, Thorthomi Lake, and the Maximum breach inundation extent. The map displays Thorthomi Lake at the top, feeding a river that flows downwards. A small rectangular area on the map is magnified in the other panels. Panels (b), (c), and (d) are three similar close-up aerial views of a town next to a river, showing flood inundation. Each of these panels includes a legend for water depth in meters (m), with three color-coded ranges: light gray for 0 - 10 m, medium blue for 10.1 - 15 m, and dark blue for 15.1 - 25.9 m. The blue overlay on the aerial images shows the extent and depth of the floodwaters, with the deepest water in the main river channel.


Document: 1 Introduction
Section: 5 Discussion > 5.5 Limitations of this study
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Figure: Figure 14. A comparison of inundation depth and extent for three breach scenarios within the Khuruthang study area. (a) A model domain highlighting Khuruthang town. (b) The maximum breach scenario. (c) A 50 % breach depth scenario. (d) A 50 % breach width scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.

**Figure 14.** A comparison of inundation depth and extent for three breach scenarios within the Khuruthang study area. (a) A model domain highlighting Khuruthang town. (b) The maximum breach scenario. (c) A 50 % breach depth scenario. (d) A 50 % breach width scenario. Map data: © Google Earth 2023, CNES/Airbus, Maxar Technologies.


Document: 1 Introduction
Section: 5 Discussion > 5.5 Limitations of this study
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Figure

Image /page/13/Figure/4 description: A multi-panel figure, labeled Figure 15, illustrating the temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. Panel (a) is a map showing the model domain, which includes Thorthomi Lake in the north and a river flowing south. Two locations are marked: Punakha Dzong and Khuruthang Town. The legend indicates symbols for the river, model domain, and Thorthomi Lake. A scale bar shows distances up to 20 kilometers. Panels (b) through (g) show a time-series of the flood's progression in a zoomed-in area around Punakha Dzong and Khuruthang Town. Each of these panels has a legend for water depth in meters, categorized into three levels: 0-10 m (light blue), 10.1-15 m (medium blue), and 15.1-25.9 m (dark blue). The panels show the increasing extent and depth of the flood at different time steps: (b) 05:54, (c) 06:06, (d) 06:12, (e) 06:24, (f) 06:30, and (g) 08:00. The sequence shows the flood wave arriving, peaking in depth and extent around time step 06:30, and then beginning to recede by 08:00, with significant inundation affecting Khuruthang Town.


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Section: 5 Discussion > 5.5 Limitations of this study
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Figure: Figure 15. The temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. (a) The hydrodynamic model domain. (b-g) The inundation depth at six time steps.

Figure 15. The temporal change of the spatial extent of flood depth at Punakha Dzong and Khuruthang. (a) The hydrodynamic model domain. (b-g) The inundation depth at six time steps.


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Section: 5 Discussion > 5.5 Limitations of this study
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frastructure, and human life. Research on glacial lakes and their outburst floods is an emerging field (e.g. Qi et al., 2022; Taylor et al., 2023). To obtain more accurate damage predictions, data and methods should be revised following research progress.

The close proximity of glacial lakes within the Lunana region, especially the Thorthomi and Rapstreng lakes (Fig. 3), poses an even greater potential risk due to a possible cascad-

ing GLOF event. Failure of the lateral moraine of Thorthomi lake would lead to lake water breaching into Rapstreng lake, which would consequently cause the failure of its moraine dam. Since our study considered failure of the terminal moraine in the direction of the existing outlet, it is highly unlikely for such an event to occur under the current scenario. Accordingly, a cascading GLOF was not assessed in our study, but such possibilities should also be explored to


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Figure

Image /page/14/Figure/2 description: A figure displaying three land use maps of a mountainous region, with a comprehensive legend. The main map on the left shows a large area with a scale bar from 0 to 20 kilometers. It depicts a valley with various land use classes, including large areas of green (Alpine Scrubs; Forests; Meadows; Shrubs) and light blue (Moraines; Snow and Glacier). A white line delineates the 'Simulation Domain' along the valley. Two smaller, zoomed-in maps are on the right, labeled 'Samdingkha' and 'Jagathang', each with a scale bar from 0 to 1.2 kilometers. These maps show a more detailed, pixelated view of sections of the river, with a red line indicating the 'Inundation Boundary'. The legend provides a key for the 'Land use class' colors: Black for 'Built up', Blue for 'Water Bodies', Brown for 'Cultivated Agriculture', Green for 'Alpine Scrubs; Forests; Meadows; Shrubs', Light Blue for 'Moraines; Snow and Glacier', and Grey for 'Non Built up; Rocky Outcrops'. All maps include a north arrow.


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Section: 5 Discussion > 5.5 Limitations of this study
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Figure: Figure 16. The probable GLOF inundation extent on land use classes. (Land use data source: National Land Commission Secretariat, Bhutan.)

Figure 16. The probable GLOF inundation extent on land use classes. (Land use data source: National Land Commission Secretariat, Bhutan.)


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Section: 5 Discussion > 5.5 Limitations of this study
Type: table
Table: Table 2. The damage potential of a GLOF from Thorthomi lake.

| Hazards → Scenarios↓                 | Total inundation area (km2) | Number of buildings inundated | Total cultivated agricultural land impacted (ha) |
|-----------------------------------------|-----------------------------|-------------------------------|--------------------------------------------------|
| Maximum breach                          | 22.7                        | 1277                          | 245.6                                            |
| Half of maximum breach depth (BRD-50 %) | 20.8                        | 1044                          | 206.4                                            |
| Half of maximum breach width (BRW-50 %) | 19.9                        | 1000                          | 193.4                                            |


Document: 1 Introduction
Section: 5 Discussion > 5.5 Limitations of this study
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better understand the potential risk of cascading events which may cause more severe damage to society.


Document: 1 Introduction
Section: 6 Conclusion
Type: text

We explored future hazards and damages arising from a GLOF from Thorthomi lake, one of the potentially dangerous glacial lakes in Bhutan but not well investigated within the scientific literature to date. To validate the approach used in our study and to calibrate the model, we reconstructed the 1994 Luggye lake GLOF prior to assessing the hazards of a Thorthomi GLOF. The BREACH model was used to es-

timate the outflow hydrograph emanating from a failure of moraine dams due to overtopping flow. Moraine materials and soil parameters used to parameterise the model were obtained from a report published by the National Center for Hydrology and Meteorology (NCHM), Bhutan. Propagation of the GLOF was simulated using a 2D routing module in HEC-RAS for modelling unsteady flow, which is an inherent characteristic of a GLOF where there is a sharp rise in the flow hydrograph.

The bathymetry of Thorthomi lake was estimated based on a regression equation derived from the relationship between lake area—depth—volume found within moraine lakes. We estimated that the total volume of the lake is approximately  $400 \times 10^6 \,\mathrm{m}^3$ , with a maximum depth of 161 m. According to the maximum breach scenario, the Thorthomi GLOF may release  $283 \times 10^6 \,\mathrm{m}^3$  of water in under 12 h, with a peak flow rate of  $16\,360 \,\mathrm{m}^3 \,\mathrm{s}^{-1}$ , occurring approximately 4 h following initiation of the breaching process. Outflow hydrographs estimated by the model were used as the upstream boundary condition in hydrodynamic modelling.

Flood routing was performed to reach a length of approximately 115 km, and then peak discharge, flood travel time, and flood depths at major downstream settlements were estimated. According to the maximum breach scenario, Punakha Dzong, which lies 90 km downstream of Thorthomi lake and at the beginning of major settlements, would witness a peak discharge of  $14\,128\,\mathrm{m}^3\,\mathrm{s}^{-1}$ , approximately 6 h following breach initiation. A potential GLOF from Thorthomi lake would cause extensive agricultural and infrastructural damage to 245 ha of agricultural lands, and, for a maximum breach scenario, 1277 buildings are expected to be inundated. Comparable damage is also expected for two minor flood scenarios, implying that such damage is inevitable for a future Thorthomi GLOF.

A hazard assessment for a GLOF plays a crucial role for understanding and mitigating risks associated with these devastating natural events. Our study quantified the potential danger a GLOF from Thorthomi lake would pose to the downstream settlements and infrastructure. Such assessments will enable policymakers, local communities, and relevant stakeholders to make informed decisions regarding land use planning, disaster preparedness, and early warning systems.


Document: 1 Introduction
Section: 6 Conclusion
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and infrastructural damage to 245 ha of agricultural lands , and , for a maximum breach scenario , 1277 buildings are expected to be inundated . Comparable damage is also expected for two minor flood scenarios , implying that such damage is inevitable for a future Thorthomi GLOF . A hazard assessment for a GLOF plays a crucial role for understanding and mitigating risks associated with these devastating natural events . Our study quantified the potential danger a GLOF from Thorthomi lake would pose to the downstream settlements and infrastructure . Such assessments will enable policymakers , local communities , and relevant stakeholders to make informed decisions regarding land use planning , disaster preparedness , and early warning systems .

Since glacial environments are dynamic and subject to change due to climate variations, GLOF hazard assessments are not static. Continuous monitoring and regular reassessments of glacial lakes and associated hazards are essential to account for environmental shifts and to ensure the effectiveness of mitigation strategies. Furthermore, a multidisciplinary approach in GLOF hazard assessments is necessary. Collaborations between researchers, policymakers, local communities, and other stakeholders are essential for effective decision-making, disaster preparedness, and the implementation of mitigation measures. To essentially reduce GLOF risk, the development of methods to safely release dammed water to downstream areas is important.

*Data availability.* Our study used open and commercial data (NSB, 2021, 2018; NCHM, 2021). Commercial data may be distributed under licence terms and conditions.

Author contributions. TW: conceptualisation, data curation, method, and writing original draft; RT: conceptualisation, data curation, draft writing, and reviewing and editing.

Competing interests. The contact author has declared that neither 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. Tandin Wangchuk acknowledges a Human Resource Development Scholarship from the Japan International Cooperation Agency (JICA). We acknowledge Yuji Toda and Takashi Tashiro, as well as other lab members of the Hydraulic Research Laboratory at Nagoya University. We also express our gratitude to Shigeo Suizu, Tomoyuki Wada, and Toru Koike at Earth System Science Co., Ltd. We sincerely thank members of the National Center for Hydrology and Meteorology for their continued support of our study.

Review statement. This paper was edited by Pascal Haegeli and reviewed by Stefan Ram and Rayees Ahmed.


Document: An ontology for Emergency Managing and
Type: text

Abstract—In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/.

Index Terms—Ontology, Vocabularies, Crisis Management, Hazard Domain, Emergency, Ontology Quality, Knowledge Reuse, Disaster Management.

data/('An ontology for Emergency Managing and', '.pdf')_extraction.md


Document: I. INTRODUCTION
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We can not change the nature. However, we can promote our planning, preparation, and response strategies about crises happening in the three phases of hazards, i.e. (i) pre-hazard, (ii) in-hazard and (iii) post-hazard. Currently, a substantial body of situational information collected from sources such as satellite images, sensors, social media content generated by people who are involved in crisis reporting and response, etc. Indeed, harnessing and exploiting this hazard-related Big Data is an essential means towards situational awareness which helps to manage crises, threads and risks of hazards in each phase. Despite the availability of such data, still there is a significant deficiency in representing, annotating and more importantly integrating this heterogeneous hazardrelated big data. This deficiency can be relieved by providing an ontology which conceptualizes and organizes situational and environmental awareness data (events, activities) subjected to hazards of any kind. Our investigation in the state-of-the-art hazard-related conceptualization (i.e., taxonomy, vocabulary and ontology) revealed existing of a few works which mainly conceptualized either hazard domain or crisis management domain from a limited perspective or for a particular type. For example, Humanitarian eXchange Language (HXL) [1] and EDXL-RESCUER Ontology [2] are mainly concerned with help and rescue aspect of crisis management domain. However, the broader and more diverse nature of this domain requires a comprehensive and abstract modeling and representation. Another deficiency is related to lack of incorporating relations into the conceptualization. Thus, they indeed should be called vocabulary or taxonomy rather than ontology (e.g., Management of Crisis vocabulary (MOAC)). These shortages motivated us to introduce an ontology that takes into consideration the prior conceptualizations (taxonomies and vocabularies) while relying on a promoted representation. Also, it brings new concepts and relations which were ignored previously although playing an essential role in capturing situational awareness (e.g., surveillance information, human sensing report, humanitarian event: prayer, concepts synonyms and instances). This paper presents our contribution in providing an ontology for Emergency Managing and PlAnning abouT Hazard crIses (empathi).

The rest of the paper is organized as follows: Section II presents the relevant state-of-the-art hazard or crisis vocabularies and compare their main features. Section III lists the external vocabularies which are integrated empathi. Thereafter, Section IV introduces empathi along with its major top concepts. Section VII reviews the related work. We close with the conclusion and the future work.


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In this section, we present an overview of the state-of-the-art vocabularies concerned with hazard domain as well as crisis management domain. With this respect, Table I represents a succinct comparative study of these vocabularies. The first column (i.e., Vocabulary) states the name of the state-of-theart vocabulary or ontology and empathi is included in the last row (based on recency). The second column (i.e., **Domain** Coverage) mentions the particular areas of the hazard-related domain or crisis management domain which it covers. The third column (i.e., URI) checks whether or not the URI of the vocabulary is dereferenceable. The fourth column (i.e., F for File) specifies the available format of the vocabulary (i.e., OWL, RDF, TTL). The fifth column (i.e., **D** for **Documenta**tion) shows whether the vocabulary has online documentation or not. Then, we list the significant publications utilizing this vocabulary within the sixth column (i.e., MC for Major Citations). The seventh and eight columns represent the number of classes #C) and relations #R) specified within the vocabulary. The last column represents the external resources (i.e., IV for Imported Vocabularies) imported by their respective vocabulary in the first column. In the following, we shortly describe each vocabulary.


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| Vocabulary | Domain Coverage                                                                          | U | F           | D | MC                                                | #C  | #R  | IV                                                                    |
|------------|------------------------------------------------------------------------------------------|---|-------------|---|---------------------------------------------------|-----|-----|-----------------------------------------------------------------------|
| HXL        | Disaster,Geography,Damage, Organization, Humanitarian Response                        | ✓ | TTL         | ✓ | [3], [1], [4], [5] [9], [10], [11], [12], [13] | 50  | 66  | [6], [7], [8]                                                         |
| MOAC       | Impact of Crisis, Recovery and Response Activities, Geo-locations                  | ✗ | RDF         | ✓ | [3], [14] [10], [16] [17], [18]             | 70  | 30  | [7], [15]                                                             |
| SMEM       | Social Media and Emergency Management                                                 | ✗ | ✗           | ✓ | -                                                 | -   | -   | [19], [20], [21], [22], [7], [8] [23], [24], [1], [25], [26], [27] |
| DO         | Temporal and Spatial Concepts, Impact, Rehabilitation and Facilities Facilities | ✓ | Web- App | ✗ | -                                                 | 97  | -   | -                                                                     |
| ERO        | Report Specification                                                                     | ✗ | ✗           | ✓ | [28], [29], [30], [31], [32]                      | -   | -   | [33], [34], [35]                                                      |
| DoRES      | Events and Reports Specification                                                      | ✓ | RDF         | ✓ | -                                                 | 96  | 261 | [7], [8] [23], [36]                                                |
| EF         | Fire Disaster Specification, Protocol Design and Planning                             | ✗ | ✗           | ✓ | -                                                 | 37  | 90  | -                                                                     |
| empathi    | Hazard Situational Awareness, Crisis Management, Hazard Events                        | ✓ | OWL         | ✓ | -                                                 | 423 | 338 | [37], [19], [20], [8], [7] [36], [21], [23], [38], [39]            |

Vocabulary
Domain Coverage
U
F
D
MC
#C
#R
IV


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Table I: Comparison of the state-of-the-art Hazard-related Vocabularies, Taxonomy, and Ontologies. U, F, D respectively stand for referenceability of URI, availability of File and Documentation. Furthermore, MC, C, R and IV respectively stand for Major Citations, number of classes, number of relations, and imported vocabularies. ERO: EDXL-RESCUER Ontology, DO: Disaster Ontology, EF: Emergency Fire.

- a) HXL: HXL stands for Humanitarian eXchange Language. HXL1 is a standard aiming at information sharing during humanitarian calamity by overcoming the burden of interoperability. HXL ontology has a total of 50 classes and 66 relations. Main concepts contained in HXL are Place, Survey and assessment, Operation, Cash and Finance, Crisis. Furthermore, HXL provides links to UN OCHA vocabularies such as Global Coordination Groups2, Disaster Types3, Organization Types4, Vulnerable groups and Humanitarian themes5. Furthermore, HXL provides a hashtag schema containing related social media tags such as #channel, #crisis, #impact, #event, etc. [1].
- b) **MOAC**: MOAC6 which is concerned with management of crisis is a vocabulary [40] providing concepts mainly related to crisis management. It was created by the Inter-Agency Standing Committee (IASC)7, Emergency Shelter Cluster in Haiti8, UN-OCHA 3W Who What Where Contact Database9 and Ushahidi Platform10.
- c) **DoRES**: DoRES stands for DOcument-Report-Event-Situation Ontology. DoRES11 is an ontology sharing information between individuals and organizations using situational reports for describing the situation [13]. It helps humanitarian organizations to structure their plans.
- d) EDXL-RESCUER Ontology: EDXL-RESCUER12 stands for Emergency Data Exchange Language Reliable and Smart Crowdsourcing Solution for Emergency and Crisis


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Image /page/1/Figure/18 description: A diagram illustrating the components that feed into a central concept called "empathi". The central concept is enclosed in a yellow oval. There are two main categories of components, each with its own label and color coding. On the left, under a blue rectangular label "Crisis/Hazard Related Taxonomy and Lexicon", there are six blue ovals: FEMA, MA-Ont, HXL, MOAC, EM-DAT, and Geo-Names. On the right, under a green rectangular label "Imported Vocabularies", there are six green ovals: DC-Terms, SKOS, LODE, SIOC, iContact, and FOAF. Arrows point from each of these twelve ovals towards the central "empathi" oval, indicating that it integrates information from all these sources.


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Figure: Figure 1: Importing existing ontologies in empathi

Figure 1: Importing existing ontologies in empathi


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Management. An ontology-based on EDXL developed for coordinating and interchanging information with the legacy system [32], [2].

- e) Emergency Fire (EF): It is an ontology explicitly designed for fires in the building. It comprises of 131 terms along with definitions created after subjective research. It serves as a protocol for information sharing, analysis, evaluation and comprehension by an organization in the situation of disaster caused by fire [41].
- f) Social Media Emergency Management (SMEM): During an unprecedented onset of a natural hazard, social media overflows with textual content about situational information, prayers, weather information, the impact of crisis and events. Of all the available information on the social media,

<sup>1https://github.com/hxl-team/HXL-Vocab/blob/master/Tools/hxl.ttl

<sup>2https://goo.gl/CD6vHY

<sup>3https://reliefweb.int/taxonomy-descriptions#disastertype

4https://goo.gl/Uzy9UA

<sup>5http://vocabulary.unocha.org

<sup>6http://observedchange.com/moac/ns

<sup>7https://goo.gl/ESn99F

8https://goo.gl/nDoa9F

9https://goo.gl/jQLnYh

10https://goo.gl/XPSnyG

11https://goo.gl/Sw4XGt

12http://www.rescuer-project.org

which portion is contextually relevant and action-oriented to personnel in charge of a relief-giver organization. SMEM ontology provides concepts in a hierarchical structure which transforms high-volume of messy content to low-volume of action-oriented information [42].

g) Disaster Ontology: It is one of the ontologies listed in Finnish Ontology Library Service ONKI13. Disaster Ontology (DO)14 is comprised of 97 concepts (classes) concerning man-made and natural hazard. This ontology is useful for managing disaster situations but disregards concepts related to social media (e.g., news reports, modality of data, surveillance, prayer and monitoring the status of the services provided by organizations).


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In this section, we list the external vocabularies which partially integrated into empathi. Not all of them are necessarily related to the domain of hazard or crisis management (we reuse generic concepts from well-known vocabularies, e.g., FOAF). Figure 1 concisely represents an integration aims at reusing the existing vocabularies following ontology design methodologies (Methontology [33] and NeOn [43]) or interlinking *empathi* to other vocabularies which enhance its visibility.

- Federal Emergency Management Agency (FEMA)15 provides a glossary of terms related to disaster preparation and management [37].
- **Emergency Disasters Database** (**EM-DAT**)16 provides a precise definition of concepts and furthermore a categorization of disturbance-related events [19].
- MA-Ont17 supports detailed properties describing media files and appropriate metadata mapping [20].
- **iContact**18 provides conceptual classes for defining international addresses. It is relevant for using GeoNames [36], [21] ontology for describing places.
- **Friend Of A Friend (FOAF)**19 was created for describing people, relations, and associated events. Coupled with SIOC [23] and disaster domain model [39], [38], it can describe social media communities formed during the disaster scenarios [7].
- **GeoNames**20 is a part of GeoNames Database providing information about 11 million places (toponyms) covering all the countries. Integration of GeoNames ontology to our ontology adds geospatial semantic information which is critical for actionable hazard response. The ontology contains 150 classes and two relations forming 758 axioms on location dereferencing. Mapping syntax provided by this ontology is compatible with schema.org, DBpedia

- Linked Open Descriptions of Events (LODE)21 defines event as an action which takes place at a certain time and has a specific location. It can be a historical action as well as a scheduled action. Thus, it provides the generic concept of Event along with locational (i.e., atPlace), temporal (i.e., atTime) aspects and people who play a role (i.e., involvedAgent).
- **Simple Knowledge Organization System** (**SKOS**)22. We utilized this data model to describe the concepts of our domain. It provides a better organization of domain knowledge (i.e., Hazard Crisis) [8].
- Semantically-Interlinked Online Communities (SIOC)23 is a W3C ontological standard to describe information from online communities. It can support a volunteer or caregiver with actionable information in the realm of social media [23].


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Image /page/2/Figure/15 description: A concept map or ontology diagram showing relationships between different entities. At the top, an oval labeled 'empathi' has a 'has subclass' relationship pointing to 'Impact'. 'Impact' in turn has a 'has subclass' relationship with 'Affected Population'. 'Affected Population' is a central node with several outgoing relationships: 'has subclass' to 'Trapped People' and 'Missing People'; 'requireHumanSupport' to 'Volunteer Support'; 'needHelp' to 'Service'; and 'contactInfo' to 'iContact Thing'. Both 'Trapped People' and 'Missing People' have an 'ageRange' relationship pointing to 'Age Group'. The 'Service' node has three relationships: 'isLocatedAt' pointing to 'Place', 'currentServiceStatus' pointing to 'Status', and 'contactInfo' pointing to 'iContact Thing'. The arrows representing relationships are color-coded and have different styles (solid or dashed).


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Figure: Figure 2: Partial representation of the concept Affected Population in empathi

Figure 2: Partial representation of the concept Affected Population in *empathi*


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As Table I shows, *empathi* contains 423 classes and 338 relations. In figure 2, concepts linked to "Affected Population" via solid lines, forms structural concepts ("is-a"/ "subclass"), while the concepts linked via colored dotted line are semantically related concepts to "Affected Population". Furthermore, in figure 2, the concept "iContactThing" is taken from the vocabulary *iContact* shown in figure 1. In the following, we present only the super-classes which imply the generic coverage.

- **Age Group:** this class groups people based on their age similarity by providing the following sub-classes (i) Adolescent, (ii) Adult, (iii) Child and (iv) Infant.
- **Event:** this concept defines events along with spatial and temporal constraints happening in any phase of hazard. This concept embodies the following sub-classes

ontology, LinkedGeoData ontology and INSEE ontology [36].

13 https://onki.fi/en/

14http://onki.fi/en/browser/overview/disaster

15https://goo.gl/QtKzev

<sup>16http://www.emdat.be/Glossary.Itisadatabase

<sup>17https://www.w3.org/ns/ma-ont

18 http://ontology.eil.utoronto.ca/icontact.html

19http://xmlns.com/foaf/spec/

<sup>20http://www.geonames.org/ontology/documentation.html

<sup>21http://linkedevents.org/ontology/

<sup>22https://www.w3.org/2004/02/skos/

<sup>23http://rdfs.org/sioc/spec/


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Table

| Hazard Type | Tweet                                                                                | Hazard Concept              |
|-------------|----------------------------------------------------------------------------------------------------------------------------|-----------------------------|
| Flood       | 1. 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume.                       | Impact (AP, ID)             |
|             | 2. @? Hope people get adequate relief and no one is left out.Nation stands with Chennai.                                   | Event (HP)                  |
|             | 3. @? in who can offer places to stay, pls fill the form for volunteers collating info #chennairains                       | Event (VS)                  |
|             | 4. @? Chennai has been declared disaster zone. Army has been deployed. Army Helpline - +XXX XXX XXXX #ChennaiRains         | Service (H)                 |
| Hurricane   | 5. Surveillance video captures several people looting a Houston store #Harvey in the wake of Hurricane                     | Event (CA)                  |
|             | 6. The aftermath. 10,000 people now homeless because of Hurricane Irma. #WednesdayWisdom #climatechange                    | Impact (AP), Event(CC)      |
|             | 7. #HurricaneIrmaRecovery Drive for #Homestead & #FloridaKeys today! Drop off supplies at @? #DJLMS #dontgivebackjustgive. | Service (S)                 |
|             | 8. At least 56 of Florida's 639 nursing homes still have *no* electricity this morning, five days after #HurricaneIrma     | Impact (ID)                 |
| Blizzard    | 9. @? "2-3 days, it could take before airlines begin to clear the backlog? @? at on #blizzard2016.                         | Impact (ID)                 |
|             | 10. @?: I-75 in Kentucky closed due to large number of accidents, state patrol says #blizzard2016                          | Impact (AP,ID)              |
|             | 11. @?: The baton is passed. Buoy 50 miles south of Wilmington #Jonas #blizzard2016                                        | Place (L)                   |
|             | 12. @?: Blizzard with "life and death implications" hits Washington, Mid-Atlantic #blizzard2016                            | Place (L), Impact (AP)      |
| Landslide   | 13. @?: Be sure to follow @BGSLandslides for lots of up to date information on landslides across the UK #StormFrank        | Report (ER)                 |
|             | 14. #StormFrank landslide at Glasscarraig Norman Motte & Bailey in Co Wexford — @? #archaeology #floods                    | Place (L)                   |
|             | 15. SRI LANKA: At least 73 dead after week of flooding, landslides; 243,000 in temp shelters — TorStar #ExtremeWeather     | Service (SH), Impact (AP)   |
|             | 16. @?: #EcuadorEarthquake - landslides closing down roads & making it challenging for help to reach hardest hit towns     | Impact (ID), Hazard Type(*) |


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Table II: Sample of hazard-related tweets from different hazard types. AP: Affected Population, ID: Infrastructure Damage, HP: Human Prayer, VS: Volunteer Support, H: Helpline, CA: Criminal Activity, S: Supply, L: Location, ER: Expert Report, SH: Shelter, Hazard Type (\*): One hazard (Earthquake) causing another hazard (Landslide).

- (i) Climate Change (ii) Criminal Activity (iii) Emergency Exercises (iv) Evacuation Plan (v) Humanitarian Event (vi) Recovery Plan (vii) Rescue Operation (viii) State Mitigation Plan (ix) Volunteer Support and (x) Early Warning.
- **Facility:** defines an amenity made accessible for a specific purpose. It attributes to following sub-classes (i) Communication (ii) Electricity (iii) Gas Facility (iv) Water Facility and (v) Education Resource.
- Hazard Type: lists different types of hazards that can affect human community. It is an entity type that embodies sub-classes (i) Airburst (ii) Coastal erosion (iii) Drought (iv) Earthquake (v) Explosion (vi) Fire (vii) Flood (viii) Hurricane (ix) Landslide (x) Sandstorm (xi) Storm (xii) Tornado (xiii) Toxic Radioactivity (xiv) Tsunami (xv) Volcano and (xvi) Winterstorm.
- **Hazard Phase:** categorizes different activities carried out by various organizations before, during and after a catastrophic event into three sub-classes (i) During Hazard (ii) Pre-Hazard and (iii) Post-Hazard.
- **Impact:** a forceful negative affect on someone or something in an unprecedented manner. This concept embodies sub-classes (i) Affected Population (ii) Animal Loss (iii) Health Issues (iv) Food Shortage (v) Financial Crisis (vi) Contamination (vii) Infrastructure Damage (viii) Severity.
- **Involved Actors:** People or Organisation associated (negative or positive) with any catastrophic event. Sub-classes of this concept are (i) Organisation (ii) People.
- Modality of Data: Information (raw, structured or semi-


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Post - Hazard . - * * Impact : * * a forceful negative affect on someone or something in an unprecedented manner . This concept embodies sub - classes ( i ) Affected Population ( ii ) Animal Loss ( iii ) Health Issues ( iv ) Food Shortage ( v ) Financial Crisis ( vi ) Contamination ( vii ) Infrastructure Damage ( viii ) Severity . - * * Involved Actors : * * People or Organisation associated ( negative or positive ) with any catastrophic event . Sub - classes of this concept are ( i ) Organisation ( ii ) People . - Modality of Data : Information ( raw , structured or semi -

- structured) conveyed or represented by a particular arrangement or sequence of text, audio, video or photos. Sub-classes included by this concept are (i) Audio (ii) Photo (iii) Text and (iv) Video.
- **Place:** a physical surrounding defined by longitude, latitude, and area, providing a relative position of the someone or something during a hazard situation. One sub-class of the Place is Location, described by longitude, latitude, and the area of the affected place.
- Report: documents evidence of the destruction caused by the natural disaster. All the activities carried out by various governmental and non-governmental organizations (NGOs) are stated in the report. The report is a way to keep people vigilant. Sub-classes encompassed under the concept: Report is (i) Expert Report (ii) Human Sensing Report and (iii) Media Report.
- **Service:** is an act of providing support to someone in a situation of distressing incidents. Core sub-classes of this concept are (i) Financial Care (ii) Healthcare Service (iii) Helpline (iv) Human Remains Management (v) Resource and Information Centre (vi) Supply (vii) Transportation and (viii) Prayer Location.
- **Status:** defines the state of services that are planned during the pre/in/post hazard phases. Associated subclasses are (i) Available (ii) Offered (iii) Requested and (iv) Unavailable.
- **Surveillance Information:** A systematic, ongoing collection, collation, and analysis of data and the timely dissemination of information to those who need to know


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Image /page/4/Figure/0 description: A diagram illustrating a hierarchical classification system, mapping concepts to a text example. At the top, a central oval labeled 'empathi' is the root of a tree structure. Solid arrows point downwards to create the hierarchy. From 'empathi', branches lead to 'Impact', 'Hazard phase', 'Place', and 'Hazard Type'. The 'Impact' node further branches into 'Population Affected' and 'Damage to Infrastructure'. 'Population Affected' leads to 'Dead and Toll', and 'Damage to Infrastructure' leads to 'Airport Damage'. The 'Hazard phase' node leads to 'Post Hazard', 'Place' leads to 'Location', and 'Hazard Type' leads to 'Flood'. At the bottom of the diagram, a sentence within a dashed box reads: 'Chennai Floods: 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume'. Dashed arrows connect some of the lower-level ovals to highlighted phrases in the sentence: 'Location' points to 'Chennai' (highlighted in blue), 'Flood' points to 'Floods' (highlighted in orange), 'Dead and Toll' points to '188 killed' (highlighted in orange), and 'Airport Damage' points to 'Airport closed' (highlighted in green). An arrow from 'Post Hazard' also points towards the beginning of the sentence.


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Figure: Figure 3: Mapping tweet and its words to empathi concepts.

Figure 3: Mapping tweet and its words to empathi concepts.


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so that action can be taken. The surveillance concept in the setting of natural disasters can help to identify the resulting health-related needs which in turn, will lead to the more rational and effective deployment of resources to affected populations.

V. CASE STUDY

A substantial impact of empathi ontology is empowering us to annotate text semantically (e.g., tweets posted during hazard). Figure 3 shows mapping of segments within a given tweet to empathi concepts. Thus, from an abstract level, this tweet is related to the flood occurred in Chennai and reports two associated impacts. With this respect, in a first experiment, we compiled 53 million tweets from the 30 significant hazards happened in the past. Such as Hurricane Sandy in 2012 and Irma in 2017, Oklahoma Wildfires in 2017, Chennai Flood in 2005, Alaska Earthquake in 2018, Florida Rains in 2000 and 2016, Houston Floods in 2017, New-Zealand Earthquake in 2016, Typhoon Haima in 2016, Winter Storm Kayla in 2016, and many more. After that, we identified the tweets related to the six central concepts of empathi i.e., (i) impact, (ii) modality of data, (iii) hazard type, (iv) place, (v) transportation and (vi) surveillance. Table III and IV show the statistics of identified tweets related to each of the chosen concepts and sub-concepts. Furthermore, Table II represents samples of these tweets (i.e., column two) along with the mapped *empathi* concepts (i.e., column three) from various hazard type (i.e., column one).


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| Sub-Concepts of empathi      | #Tweets |
|------------------------------|---------|
| Water Facility (Fac.)        | 218,968 |
| Gas Facility (Fac.)          | 33,047  |
| Involved Organization (Inv.) | 4,249   |
| Severity (Imp.)              | 1,344   |


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Extensive coverage by *empathi* provides the capability of extracting structured information from unstructured and sparse content (e.g., Twitter) [44]. For identifying relevant information from unstructured social media text, it is essential to map


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Table: Table III: Mapping sub-concepts of empathi to tweets of hazards. Fac.: Facility, Inv.: Involved, and Imp.: Impact are concepts

| Concepts of empathi | #Tweets   |
|---------------------|-----------|
| Hazard Type         | 3,034,257 |
| Impact              | 618,446   |
| Modality of Data    | 509,645   |
| Facility            | 258,117   |
| Place               | 16,397    |
| Transportation      | 6,694     |
| Surveillance        | 1,588     |


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Table IV: Mapping concepts of *empathi* to tweets of hazards.

the words to ontology classes enable efficient classification of tweets as relevant and irrelevant to crisis domain. For instance in figure 2, the tweet: Chennai Floods: 188 killed, Airport closed they say that the runway came peeling off. it may take time to resume is identified as a post-hazard tweet. Chennai links to concept Place, Floods links to concept Hazard type, 188 killed links to concept Affected population, and Airport closed links to concept Infrastructure Damage. Moreover, such a procedure is termed as semantic annotation using expanded concepts (a.k.a. hypernyms). It can improve understanding of social media messages which pose challenges like ill-formed sentences, ambiguous word senses, poor syntactic structure, and implicit referencing. Semantic features formed using empathi can enhance supervised and unsupervised learning in crisis domain [45].


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To build up a quality ontology, we followed the principles of ontology methodologies such as NeON [43] and Methontology [33] which encourage the reuse of existing ontologies. However, to quantitatively measure the quality of *empathi*, we designed a user evaluation survey. This survey contained 17 questions concerning with hierarchical, relational and lexical aspects of *empathi* inspired by [46]. Precisely, the participants in the survey have to evaluate the following criteria: (1) the correctness of structure (hierarchy) (2) the correctness of relations between concepts, and (3) lexical evaluation, i.e., quality of annotations associated with both concepts and relations. In the following, we elaborate on these criteria.

- a) Structural evaluation: In this evaluation, the hierarchical structure is assessed concerning the correctness of "is-a" relationship as of whether the given concept A is-a particular type of the given concept B. For instance, "Animal Loss" is-a subclass of "Impact" in *empathi*. Such evaluation is necessary to confirm the utility of ontology for classification task [47]. We presented parts of hierarchy in the survey, and asked the participants, how far this hierarchy makes sense to them. They have to rate in the range 1 (fully disagree) -5 (fully agree).
- b) Semantic relational evaluation: Ontology is evaluated for holding semantically correct relations between concepts. For instance, there is a relation between the concept "Affected Population" and the concept "Service" referring "need for help". Thus, Affected Population is the domain and Service is the range. Concerning w.r.t. the prior survey [47], having quality relations, higher capability for summarization, subgraph extraction, and contextualization tasks. We represented a number of relations of empathi to the participants


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Image /page/5/Figure/0 description: The image displays three bar charts stacked vertically. The top chart, labeled (a), is titled '7 Structure-related Questions (SQ)'. It has a y-axis for 'Agreement Percentage' from 0 to 100. The blue bars show the following percentages for each question: SQ1 is 92.3%, SQ2 is 92.3%, SQ3 is 100%, SQ4 is 92.3%, SQ5 is 53.8%, SQ6 is 84.6%, and SQ7 is 76.4%. The middle chart, labeled (b), is titled '7 Semantic Relations-related Questions (SRQ)'. It also has a y-axis for 'Agreement Percentage'. The purple bars show the following percentages: SRQ1 is 76.9%, SRQ2 is 92.3%, SRQ3 is 66.7%, SRQ4 is 84.6%, SRQ5 is 84.6%, SRQ6 is 84.6%, and SRQ7 is 53.8%. The bottom chart is a partially cropped grouped bar chart with a y-axis for 'Agreement Percentage'. It shows groups of three bars colored blue, orange, and green. The visible data points for the first group are 84.6%, 46.2%, and 76.9%. For the second group, they are 92.3%, 76.9%, and 92.3%. For the third and fourth groups, the values are identical: 84.6%, 69.2%, and 84.6%.


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Figure: Figure 4: Agreement of 13 evaluators on (a). 7 Questions that evaluate Structure of empathi , (b). 7 Questions that evaluate Semantic Relations of empathi , and (c). 3 Questions that evaluate Lexical characteristic of empathi for 4 concepts: Animal Loss, Health Issues, Food Shortage, Human Prayer. Agreement percentage is calculated as the percentage of evaluators responded "agree" or "yes" in the ontology evaluation form.

Figure 4: Agreement of 13 evaluators on (a). 7 Questions that evaluate Structure of *empathi*, (b). 7 Questions that evaluate Semantic Relations of *empathi*, and (c). 3 Questions that evaluate Lexical characteristic of *empathi* for 4 concepts: Animal Loss, Health Issues, Food Shortage, Human Prayer. Agreement percentage is calculated as the percentage of evaluators responded "agree" or "yes" in the ontology evaluation form.