# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
## 4 Results
### Timing and Inundation  
There are significant differences in the arrival times (Table 2). In the \"worst case\" scenario (90_0.04), the flood arrives in Punakha 3:45 h after the breach. Both decreasing outburst volume and increasing channel roughness result in a delay of the flood by up to almost 3 h in the \"best case\" scenario (40_0.08).  
Table 2 also shows that total inundation areas are sensitive to the outburst volume, rather than to channel roughness. Within the same outburst volume, areas differ within a range of only 2-4%, while between the 40% and 90% outburst volumes, scenarios with corresponding \(n\)-values have a variation of inundation areas of around 25%.  
The flood depths also depend on the selected outburst volume rather than on channel roughness. Here, increasing \(n\)-values cause higher water stages. If the flood is retarded in a rougher channel, water levels rise. This effect is in the order of few decimeters, while there is a difference in the mean inundation depth of more than 2 m between the 40% and 90% outburst scenario.  
Figure 4: GLOF hydrographs for the 9 scenarios (see text for explanations) at Punakha and Punstsangchu I.  
HEC-RAS calculates the maximum velocity for each raster cell. Table 2 shows the average of these maximum velocities in the flow section from the dam to Punakha. Along the flow path, the values generally decrease downstream due to the decreasing slope, but they also have local variations. For the worst case scenario (90_0.04), they increase in narrow valley sections to a maximum of more than 20 m/s and slowing to less than 1 m/s in wide floodplains (Figure 5).  
Some 45 km below the glacial lake, the flood reaches the five bath houses of Gasa Hot Springs (Figure 6), which is a popular site for tourists. These buildings are affected under every scenario, with flood depths between 1 m (best case) and 1.60 m (worst case).  
Figure 7 shows an inundation map for the most populated Punakha region. Here, more buildings are affected under the worst case and under the average GLOF scenario, but not under the best case scenario, including a small commercial or industrial complex of buildings on the outskirts of Punakha (a in Figure 7), two buildings at the Punakha riverfront (b in Figure 7), and one religious place (Thangzona Choelkhang) located 700 m upstream of Punakha D  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Arrival Time** & **Arrival Time** & **Mean Max Velocity** & **Inundation Area** & **Mean Flood Depth** \\  & **Punakha (h:mm)** & **Punakangchu I** & **Dam-Punakha** & **Dam-Punakha** \\  & **(h:mm)** & **(m/s)** & **(km\({}^{2}\))** & **(m)** \\ \hline
90\_004 & 3:54 & 6:10 & 5.6 & 13.38 & 5.72 \\
90\_006 & 4:30 & 7:50 & 4.7 & 13.57 & 5.79 \\
90\_008 & 5:12 & 8:46 & 4 & 13.65 & 5.88 \\
70\_004 & 4:16 & 6:50 & 5 & 12.35 & 4.69 \\
70\_006 & 5:00 & 8:36 & 4.1 & 12.49 & 4.79 \\
70\_008 & 5:46 & 9:48 & 3.5 & 12.63 & 4.90 \\
40\_004 & 5:06 & 8:12 & 3.9 & 10.56 & 3.35 \\
40\_006 & 6:04 & 10:24 & 3.1 & 10.77 & 3.44 \\
40\_008 & 7:00 & 12:50 & 2.6 & 10.97 & 3.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Arrival times, inundation areas, mean inundation depths, and mean maximum velocities of the GLOF scenarios.  
Figure 5: Maximum flow velocity of the worst case scenario in the uppermost section of the GLOF and lake bathymetry (taken from [13], slightly modified). (Source: Esri, Maxar, Earthquake Geographics, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community).