Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System
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Fig. 2.2: Installation work on the observation station. The Kyagar Glacier is visible in the background.

With the glacier observation station a possible outburst can be recognized at an early stage. The lead time of an outburst observation till the impact of the flood wave at the floodplain is from the glacier station appox. 34 - 36 hours, the one from Cha Hekou 22 h and from Kuluklangan 7.5 h. GLOFs typically show an obvious peak with rapidly rising and declining discharge. If a GLOF is detected, an automatically generated alarm-signal will immediately be sent to mobile phones of the Chinese authorities. Thus, emergency actions can be initiated. The station at Kuluklangan covers a large catchment area and is therefore suitable to monitor GLOFs, meltwater and rainfall floods. In case of a GLOF it serves as a redundant alarm station of the one at Cha Hekou.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System
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Figure

Image /page/3/Picture/2 description: A diagram illustrating a monitoring and alarm system for a river basin, likely for Glacial Lake Outburst Floods (GLOFs). The diagram features a map of the Yarkant River and Keleqin River with several key locations marked. Data is collected by three observation stations, shown in inset photographs: one at Kyagar Glacier Lake, one at Cha Hekou, and one at Kuluklangan. A Satellite using SAR remote sensing also collects data. Information on 'hazard potential' is sent from the satellite and the Kyagar Glacier Lake station to a central server. 'Monitoring' data flows from the observation stations along the rivers to the server. The server then disseminates information to 'End-users' depicted as people working on computers in the towns of Markit, Shache, and Zepu. A separate 'alarm' pathway, indicated by a red arrow, travels from the observation stations along the river to two smartphones displaying 'GLOF' alerts. An alarm clock icon indicates a time of '22 h' along this path. The map includes a scale from 0 to 60 km and a north arrow.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.2 The Early Warning System
Type: text

Fig. 2.3: Concept of the early warning system, combining satellite remote sensing and automatic terrestrial observation stations.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge
Type: text

The daily images from the automatic camera installed at Kyagar Lake in September 2012 provide an exceptional possibility to assess geometrical changes close to the glacier terminus in a qualitative way at high temporal resolution.

The repeated observations of glacier flow speed show that ice flow was very slow (10-30 m yr-1) on the glacier tongue until 2010. Over the last years, however, a considerable speed-up of the glacier was observed, first in the upper reaches of Kyagar Glacier, later extending towards the glacier terminus. In some places the speed has nearly doubled between 2011 and 2012 (Haemmig et al. 2014). The changes in surface velocity have been interpreted as the result of a glacier surge. In fact, the surge activity announced by a DEM comparison and by the analysis of surface velocities has arrived in the region of the ice dam in 2014/2015. It has been leading to a dramatic acceleration of flow speed and a considerable thickening of the glacier (Fig. 2.4).


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge
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Figure

Image /page/3/Figure/7 description: A photograph of a glacier in front of a snow-covered mountain range, with a glacial lake in the foreground. The image is annotated with five colored lines that trace the surface level of the glacier at different points in time, showing its thickening. A legend in the top right corner indicates the date for each colored line: the red line represents February 2014, the yellow line represents June 2014, the pink line represents October 2014, the orange line represents February 2015, and the green line represents June 2015. The lines show a progressive increase in the glacier's height from the earliest date to the latest.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.3 Observation of Glacier Surge
Type: text

Fig. 2.4: Direct comparison of the glacier surface between February 2014 and June 2015. The thickening is most pronounced in the upstream region and propagates towards the glacier terminus. The ice-dam at the lake thickened by about 1/3 within one year, which corresponds to an elevation increase of the ice surface of approx. 30 - 50 meters (Haemmig et al. 2014).


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015
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Since 2014 a dangerous lake has been impounding behind the ice-dam (Fig. 2.5). Based on camera images acquired by the Kyagar observation station, the lake volume has been estimated using terrestrial markers on the mountain flanks. Hence, it was possible to directly identify the hazard potential on photographs taken by the automatic camera.

An accurate forecast of an outburst event is extremely complex due to the continuous processes at the ice-dam (i.e. surge-activity, ablation, availability of subglacial discharge channels) and the highly variable water-influx into the lake basin. Sophisticated monitoring technologies, such as terrestrial observation stations and satellite remote sensing are the base of a robust GLOF hazard assessment. The GLOF hazard level was periodically estimated based on terrestrial and satellite images.

In summer 2015, due to the thickening of the ice-dam, the onset of the flotation equilibrium was estimated at a lake level of approx. 4'840 m a.s.l. with a corresponding lake volume of approx. 40 million m3 (see Fig. 2.6). On June 29th 2015, the lake reached a volume of approx. 17.2 million m3.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015
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Figure

Image /page/4/Picture/6 description: A side-by-side comparison of two photographs, labeled 'a' and 'b', showing a glacial lake from different perspectives. Image 'a' is a wide shot of the lake with a large glacier and snow-capped mountains in the background under a cloudy sky. The glacier's icy front meets the dark, rippling water. Image 'b' is a fisheye lens view from a low angle, showing the lake with a brown, rocky mountain and a portion of the glacier in the distance. In the immediate foreground of image 'b', a solar panel and some chunks of ice are visible at the water's edge.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015
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Fig. 2.5: Glacial lake, observed by automatic observation station at Kyagar Glacier (June 29th, 2015).

Since June 30th, 2015, after almost three years of successful monitoring, the automatic observation at Kyagar Glacier Lake has been out of operation. The station was submerged due to the rapid impoundment of the glacial lake, triggered by the surge-activity of the glacier tongue.

Afterwards the ongoing rise of the lake level and the hazard potential have been estimated based on very high-resolution SAR data of the TerraSAR-X satellites. Based on SAR backscatter, lake surfaces for every image could be delineated. Lake volumes were calculated using lake shorelines and the detailed reference DEM of the lake basin established in 2011. Hazard levels were estimated as a result of the lake size.

The glacial lake with an estimated volume of approx. 50 million m3 drained completely at the end of July 2015. Based on satellite remote sensing, the timing and volume of the outburst could be reliably predicted. The implemented fully automatic GLOF early warning system successfully warned the Chinese decision-makers.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015
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Figure

Image /page/5/Figure/2 description: A line graph showing the change in lake volume and elevation over time during the year 2015. The x-axis represents the months from January to October. The left y-axis shows 'Lake volume [million m³]' from 0 to 60, and the right y-axis shows 'Lake elevation [m a.s.l.]' from 4'765 to 4'848. There are two data lines on the graph. A black line, labeled 'terrestrial monitoring', shows a slow increase in lake volume from near 0 in January to about 18 million m³ in late May. A horizontal line at this level is labeled 'terrestrial observation station submerged'. A green line, labeled 'satellite remote sensing', starts in late May and shows a rapid, steep increase in lake volume, peaking at approximately 43 million m³. A vertical red line marks the date of a 'Spontaneous Glacial Lake Outburst, July 28 2015', which corresponds to the peak of the green line. A light blue shaded area at the top of the graph, between approximately 40 and 60 million m³, is labeled 'level of flotation equilibrium'.


Document: 1. Introduction
Section: 2. Case Study China – Glacier Lake Outburst Flood > 2.4 GLOF Event July 2015
Type: text

Fig. 2.6: Based on terrestrial and satellite based monitoring techniques an accurate forecast of the GLOF was possible. Terrestrial monitoring was based on the original DEM 2011, whereas volume estimates by satellite remote sensing (sentinel-1) is based on a modified DEM 2011.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.1 Intruduction
Type: text

The Greater Caucasus is a large mountain range between Georgia and Russia with Mt. Elbrus as its highest peak. The main road connecting Georgia with Russia runs along Mt. Kazbek, a 5'048 m high volcano and one of the highest summits of the Caucasus range (Fig. 3.1).


Document: 1. Introduction
Section: 3. Case study Georgia > 3.1 Intruduction
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Figure

Image /page/5/Figure/7 description: The image displays three maps related to the Kazbegi area in Georgia. On the left, there are two maps for location context. The top-left is a physical map of the Caucasus region, showing Georgia and its neighboring countries (Russia, Turkey, Armenia, Azerbaijan), with the Kazbegi Area circled. The bottom-left map is a globe showing the location of Georgia in the world. The main, larger map on the right is a detailed topographic map of the Kazbegi area. This map shows the border between Russia and Georgia, the Amali River, the Tergi river, and the Georgian military road. It specifically highlights a "Glacier collapse area; May 17th 2014" near Mt. Kazbeg (5'033 m a.s.l.). A dashed purple line indicates the "Debris flow path" originating from the collapse and following the Devdoraki-glacier and Amali River. Other labeled features include a gas-pipeline, a "Border Control to Russia" point, and elevation points such as Pt. 3'284 and Pt. 2'967. The map includes a scale in meters and coordinate grids.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.1 Intruduction
Type: text

Fig. 3.1: Situation of Devdoraki area within the Mt.Kazbeg region in Georgia.

In May 2014 a huge debris flow consisting of rock and ice blocked the main road just south of the Russian border (Fig. 3.2). The source of the debris flow was high up at the headwall of Mt. Kazbeg, where part of the glacier had collapsed (Fig. 3.3). The debris slid down the Devdoraki glacier, covering the road as well as damaging an

important gas pipeline in the main valley. Further events in August 2014 affected the infrastructure of the border control, the customs service and a hydro power plant.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.1 Intruduction
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Figure

Image /page/6/Picture/3 description: A figure labeled "Fig. 2.2" displays two images side-by-side. The left image is a topographic map overlaid on an aerial photograph of a mountainous region. The map includes contour lines with elevations like 3000 and 4000, and grid lines with coordinates such as 4'730'000 and 462'500. A large, reddish-brown area, possibly a landslide or debris flow, is outlined with a red dotted line. The terrain shows snow-covered peaks and some cloud cover. The right image is an aerial photograph of a steep valley, showing the aftermath of what appears to be the same event. A large, dark debris field has flowed down the mountainside, covering a significant portion of the valley floor and reaching a body of water at the bottom. Some industrial-looking buildings are visible at the top of the debris field.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.1 Intruduction
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Fig. 3.2: Pleiades satellite image; acquired on May 18th 2014 with outlined process area of the May 17th 2014 event (red dotted line, left) and debris flow deposition in the main valley (right).

Based on a detailed hazard assessment local authorities decided to implement an early warning system for a) monitoring the glacier activities in the upper part of Mt. Kazbeg and b) alarming affected people and infrastructures in the valley from devastating debris flow processes. The concept of the EWS is identical with the one implemented in China (monitoring and alarm stations, data transfer and processing). It is foreseen to install the whole system in April 2016.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
Type: text

The reason of the event in May 2014 was a combination of a gravitational slope failure along pre-existing discontinuities, longterm degradation of permafrost and glacier melting and probably also volcanic activity expressed in low seismicity and thermal activities. The seismic institute of Illia Stat University, Tbilisi, registered a small tremor prior to the May  $17^{th}$  2014 event. Volcanic activity (tremor / temperature) may eventually also have a negative impact on the slope stability situation on a very short term and trigger catastrophic events depending on the intensity of the activity.

3. Case study Georgia
3.2 Hazard Assessment – Scenarios


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
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Figure

Image /page/6/Picture/8 description: An aerial, eye-level shot of a majestic, snow-covered mountain peak against a clear, deep blue sky. A large, reddish-brown landslide or rockfall path cuts through the snow on the mountain's face, extending from near the summit down the slope. The surrounding terrain is also rugged and covered in snow and ice.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
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Figure

Image /page/6/Picture/9 description: An aerial photograph of a vast, rugged mountain range under a partly cloudy sky. In the background, a large, snow-covered peak is partially obscured by clouds. In the foreground, a rocky ridge with patches of snow and sparse green vegetation slopes down. To the right, a deep valley features a prominent, steep slope with a distinct reddish-brown color, contrasting with the surrounding gray and brown rock faces.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
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Fig. 3.3: Main scarp of the glacier collapse in the summit area of Mt. Kazbeg (left) and silent witnesses of the debris flow event in the Devdoraki valley (right).

Rising mean annual air temperatures are causing enduring progressive permafrost degradation (Noetzli and Gruber, 2009) while percolation of melted water can advectively penetrate into bedrock along joints and therefore lead to thermal perturbation and fast modification of the mechanical conditions at depth. Such observations give evidence that the observed increase in frequency and magnitude of mass movements in glacial environments have a relation to climate change (Huggel et al., 2010). The global permafrost zonation index map shows that the glaciated part of Mt. Kazbeg lies within a zone of highly probable permafrost occurrence.

In the lower part of the eastern slope of Mt. Kazbeg the main Devdoraki glacier divides into several glacier tongues. One of the tributary glaciers just ends on a steep slope several dozens of meters above the main glacier. The tongue is mainly detached from the main glacier by huge cracks. Signs of strain and deformation along the limits of the tongue (lose rock material, silent witnesses of actual icefall events, flowing water) have been detected in autumn 2015.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
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Figure

Image /page/7/Figure/4 description: A side-by-side comparison of two images of a mountainous region. The image on the left is a topographical map with a color overlay indicating "Permafrost Zonation". A legend in the top left corner explains the color coding: blue for "Permafrost in nearly all conditions (cold, deep)" and a pink-to-yellow gradient for "Permafrost only in very favorable conditions (warm, shallow)". The map is credited to the University of Zurich and has a red oval circling an area on the upper mountain. The image on the right is a photograph of a snow-covered mountain peak and a large glacier, which is labeled "Devdoraki glacier".


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
Type: text

Fig. 3.4: Global permafrost zonation index map (Noetzli and Gruber, 2009) on Google Earth showing the detachment area of the ice collapse within the main permafrost area (left). View of Mt. Kazbeg from the east with active tributary glacier tongue in the lower part of the picture.

According to the hazard assessment in the detachment zone and along the Devdoraki valley, the general structural setting as well as the rough glacier stability analysis the scenarios for future events have been defined:

- Rock-ice slope failures similar to the May 17th 2014 event, with volumes of several millions m3, can happen again in the next 10 30 years. Such big slope failures are often preceded by smaller events in the detachment area possibly announcing an imminent bigger event.
- At the tongue of the tributary glacier, signs of strong stress have been detected in the ice. Experiences from hanging glacier collapses in the Alps indicate that a complete detachment of this part of the tributary glacier cannot be excluded. The instable glacier mass has a volume of approx. 1 Mio. m3

Evens and Claque (1988) stated that glacial environments can significantly enhance the runout distance of rapid mass movements by travelling on low-friction surfaces such as on glaciers, by melting of ice and snow due to frictional heating that causes pore pressure effects at the base of the moving mass or fluidizes the entire flow body, and by channeling or air-launching the debris by moraines. Schneider et al. (2011) as well as Bottino et al. (2002) quantified by means of numerical simulations the observed runout distances of rock-ice avalanches in glacial environments to exceed predicted ones for pure rock avalanches by up to 30%. The event of May 2014 confirms these conclusions. Thus we know that both scenarios end in huge debris flows reaching down to the main valley endangering the main road, a hydropower plant, a gas pipeline, border police as well as all people using the road. In order to predict the flow velocity of the different events as well as flow heights numerical simulations for the event itself and the defined scenarios have been carried out with the model RAMMS (Christen et al., 2012, Fig. 3.5). According to the simulations the warning time for a debris flow detection system installed at location A (Fig. 3.1 and Fig. 3.6) will be around 5 minutes, which is an absolute minimum to evacuate the whole traffic on the critical road section.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
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Figure

Image /page/8/Figure/2 description: A topographical map displaying the maximal velocity of a flow, likely a debris flow, in a mountainous region. The map features a color-coded overlay along a river valley, with a legend in the top-left corner that correlates colors to velocity ranges in meters per second (m/s). The legend is as follows: white for 'no value', light purple for '< 7 m/s', darker purple for '7-14 m/s', blue for '14-21 m/s', cyan for '21-28 m/s', light green for '28-35 m/s', yellow-green for '35-42 m/s', yellow for '42-49 m/s', orange for '49-56 m/s', and dark red for '56-74 m/s'. The flow path originates in the southwest, where it is widest and shows the highest velocities (dark red, orange, yellow), and proceeds northeast, narrowing as it follows the course of the 'Devdoraki' and 'Amali River' towards the 'Tergi river' on the eastern side of the map. The velocity generally decreases downstream, with lower speeds indicated by greens, blues, and purples. The map includes coordinate markings, such as 44°32'0"E and 470'000.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.2 Hazard Assessment – Scenarios
Type: text

Fig. 3.5: Debris flow simulation with RAMMS to define flow velocities of the main event in 2014. The maximum warning time interval with an early warning system positioned at location A (fig. 3.1 and fig 3.7) can be deviated from the velocities.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System
Type: text

The early warning system consists of a glacier observation station and a debris flow alarm system. While the glacier observation point is situated on the eastern ridge of Mt. Kazbeg at 3000 m a. s. l., the debris flow detection is located along the main channel down in the valley (Fig. 3.1).

3. Case study Georgia
3.3 The Early Warning System


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras
Type: text

Glaciers terminating in steep terrain are potentially hazardous. This has been shown in different historical events, where devastating rock-ice avalanches have buried towns or entire cities (e.g. Mattmark, Switzerland 1965 (McCall et al. 1992) or Huascaran, Peru 1962 and 1970 (Evans et al., 2009)). In order to detect such an ice collapse in the topmost area of Mt. Kazbek as well as on the tongue of the tributary glacier a monitoring station will be implemented (Fig. 3.6).

3. Case study Georgia
3.3 The Early Warning System
Glacier monitoring cameras


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras
Type: figure
Figure

Image /page/9/Figure/2 description: A figure displaying a topographic map and corresponding photographs from webcams. On the left is a "Map of northeastern slope of Mt. Kazbeg headwall" with a scale of 1:35000. The map shows contour lines, latitude and longitude coordinates, and a scale bar in meters. A point labeled "EWS location C" is marked, from which two fields of view are projected. A purple cone represents the "view of webcam no. 1," and a green dashed cone represents the "view of webcam no. 2." Several areas are labeled on the map, including the "tongue of tributary glacier," the "tongue of Devdoraki glacier," and the "detachment area May 2014 event." On the right, two photographs are shown. The top photo, labeled "view webcam no. 1," shows a snowy mountain peak circled in purple. The bottom photo, labeled "view webcam no. 2," shows a wider view of a glacier and mountainside, outlined with a green dashed line. A legend at the top right explains that the purple cone is the "view of webcam no. 1 from EWS location C to Mt. Kazbeg" and the green dashed cone is the "view of webcam no. 2 from EWS location C to tongue of tributary glacier and tongue of Devdoraki glacier."


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras
Type: text

Fig. 3.6: Situation of the glacier monitoring station at location C on 3000 m a.s.l. The critical ice masses will be monitored by two automatic cameras transmitting the taken pictures directly on a password protected data portal.

Two webcams will shoot images of the critical glacier areas at regular intervals, allowing local authorities to determine the glacier movement based on feature tracking within the images (example see fig. 3.7). All webcam images can be accessed on a password protected data portal, providing additional information of all implemented devices.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras
Type: figure
Figure

Image /page/9/Figure/5 description: A scientific figure, labeled "Figure 3.7: Displacement monitoring based on image analysis (example from Trift...)", displays a photograph of a rocky and icy mountainside overlaid with a dense field of colorful vectors. The vectors, represented by arrows of varying colors and lengths, illustrate surface displacement. A prominent feature, likely a glacier or rockslide, runs diagonally from the upper left to the lower right, characterized by a high concentration of arrows colored green, yellow, orange, and red, indicating significant movement downwards along the slope. The surrounding stable rock areas are marked with smaller, predominantly dark blue arrows, signifying minimal displacement. In the upper left corner, a snow-covered area is marked with numerous magenta 'x's and some arrows.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Glacier monitoring cameras
Type: figure
Figure: Figure 3.7: Displacement monitoring based on image analysis (example from Trift glacier, Switzerland). Green: small displacement, red: large displacement, purple crosses: stable

Figure 3.7: Displacement monitoring based on image analysis (example from Trift glacier, Switzerland). Green: small displacement, red: large displacement, purple crosses: stable


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
Type: text

For the automated closure of the road and the alerting of local authorities, a large scale monitoring system will be installed in the lower part of the debris channel approximately 3 km away from the damage potential (Fig. 3.8). The alarm system is equipped with the following components:

- three gauge radars hanging on lines spanned across the channel to estimate flow height and debris flow magnitude (Fig. 3.8, right).
- Two webcams with live-access and infrared floodlights allow day and night alarm verification.
- One geophone positioned at the channel border to record seismic data for a better understanding the physical processes of the debris flows

3. Case study Georgia
3.3 The Early Warning System
Alarm installations for debris flow


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
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Figure

Image /page/10/Figure/2 description: A two-part figure displaying a topographical map and a corresponding elevation profile graph. The left panel is a topographical map showing a cross-section marked by a green line between points A1 and A2 across the Amali River. The right panel is a graph plotting the elevation profile from East to West. The vertical axis is labeled "Elevation [m.a.s.l.]" and ranges from 1750 to 1850. The horizontal axis is unlabeled but has markers from 0 to 240. A green line labeled "topography channel" shows the ground profile, dipping into a valley. Two red squares, "fundament A1" and "fundament A2", are shown at the ends of the profile. A red line connects these two fundaments, passing over the valley. Along this red line, three symbols indicate the position of "level radars and webcams". A vertical bracket shows the maximum height difference between the red line and the bottom of the channel, labeled "ΔH\_max: 35 m - 40 m".


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
Type: figure
Figure: Figure 3.8: Debris flow detection installation at position A with level radars and cameras. The channel width of 250 to 270 m is a challenge for system.

Figure 3.8: Debris flow detection installation at position A with level radars and cameras. The channel width of 250 to 270 m is a challenge for system.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
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The dimensions of the channel as well as the rough climate during winter time will be the major challenges for the system. At the installation point the channel is about 40 m deep and 250 m wide. Influence of wind and icebuilding on the cables can hardly be calculated in a proper way.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
Type: figure
Figure

Image /page/10/Picture/5 description: A two-panel image. The left panel shows a wide, high-angle view of a mountain valley with a river running through it. The slopes are a mix of green and brown vegetation and exposed reddish and grey rock. A black line is drawn across the valley, labeled "270 m". The right panel shows a close-up of a piece of scientific equipment, consisting of a metal frame with electronic boxes, a yellow sensor, and four flat panels, suspended by a network of cables over a steep, rocky slope.


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
Type: figure
Figure: Figure 3.9: Location A (left) where gauge radars for the alarm system will be positioned on wire ropes (right).

Figure 3.9: Location A (left) where gauge radars for the alarm system will be positioned on wire ropes (right).


Document: 1. Introduction
Section: 3. Case study Georgia > 3.3 The Early Warning System > Alarm installations for debris flow
Type: text

Local authorities developed an action plan in case of an alarm (debris flow) as well as an upcoming glacier collapse. Today this document is still under construction and strictly confidential.


Document: 1. Introduction
Section: 4. Conclusions
Type: text

As mountainous countries China, Georgia and Switzerland are affected by more frequent extreme flood events and increased glacier melt due to climate change. Switzerland has a lot of experience and knowledge regarding natural hazards and risks. In both project the main target is to improve international knowledge in the assessment of climate impacts and risks, and to develop practical approaches to climate change adaptation and emergency response.

In remote areas, a continuous glacier monitoring proved to be important for the hazard assessment. Early warning system based on satellite data and terrestrial stations is a reliable and efficient tool for local communities / authorities. Expert know-how and human resources on local level are limited. Pragmatic planning and proactive support is necessary. Cooperation and know-how exchange on local level with a strong governmental backing/support is helpful.

The strategy to avoid the unmanageable (mitigation – land-use planning) and to manage the unavoidable (adaptation to climate change – early warning system) is valid around the globe. The implemented methodology in the Karakoram Mountains as well as in the Caukasus (monitoring, early warning system, risk management) can be transferred to other regions.


Document: 1. Introduction
Section: 4. Conclusions
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Figure

Image /page/11/Picture/1 description: The image displays the logos and names of several organizations, separated into two columns by a vertical dotted line. On the left is the UNESCO logo, which resembles a classical temple, with the text "United Nations Educational, Scientific and Cultural Organization" below it. On the right, at the top, are the logos for "uniTwin" and "EPFL ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE". Below these logos is a bulleted list stating "UNESCO Chair in technologies for development" and "Lausanne (Switzerland)".


Document: 1. Introduction
Section: 4. Conclusions
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The working partnership between specialized private companies, authorities and universities is the backbone of a sustainable project management. Adaptation strategies in those projects mean anticipating the effects of climate change and taking appropriate actions to reduce the damage they can cause.


Document: Extreme Precipitation in a Warming Climate Climate Central
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Climate Matters • May 1, 2024

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Document: Extreme Precipitation in a Warming Climate
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Image /page/0/Picture/4 description: An infographic on a dark blue background titled "WARMER AIR HOLDS MORE MOISTURE". A text box states, "1°F increase = 4% more water vapor". A graph shows "Temperature" on the x-axis and "Moisture" on the y-axis, with a blue line curving upwards to illustrate that as temperature rises, the air holds more moisture. To the right, a cloud is shown above a temperature bar that is blue on the left and transitions to yellow on the right, with "32°F" marked at the transition point. The cloud is shown producing "Heavier snow" on the colder side and "Heavier rain" on the warmer side. The Climate Central logo is in the bottom right corner.


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Click the downloadable graphic: Warmer air holds more moisture


Document: Extreme Precipitation in a Warming Climate
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Figure

Image /page/0/Picture/6 description: A map of the United States titled 'HEAVIER DOWNPOURS' with the subtitle 'Change in precipitation on heaviest 1% of days'. The map is color-coded to show the percentage change in precipitation from 1958 to 2021. A color key on the right shows a scale from 0% to 40%, with colors ranging from light green to dark blue. The map displays the following regional percentage changes: Northeast +60%, Midwest +45%, Southeast +37%, a southern region +21%, a northern plains region +24%, Southwest +17%, Northwest +1%, Alaska +21%, Hawaii -19%, and the Caribbean -24%. The source is cited at the bottom as 'USGCRP, 2023: Fifth National Climate Assessment'. The Climate Central logo is in the bottom right corner.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate
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Click the downloadable graphic: Extreme Precipitation Change 1958 to 2021


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate
Type: figure
Figure

Image /page/0/Picture/8 description: A map of the contiguous United States titled "WARMER FUTURE, MORE EXTREME PRECIPITATION," which shows the "Projected change in precipitation on heaviest 1% of days." The map is color-coded by county to represent the "% change at 2°C (3.6°F) of global warming." A vertical color bar legend on the right indicates the percentage change, ranging from 0% (light beige) to 40% (dark green) in increments of 10%. Most of the country is shaded in green, indicating an increase in extreme precipitation, with the darkest shades concentrated in the Northeast, Great Lakes region, and parts of the Southeast. A small area in the Southwest, including parts of Texas and New Mexico, is shaded in light beige, indicating little to no change. The source is cited at the bottom as "USGCRP, 2023: Fifth National Climate Assessment," and the map is branded with the "CLIMATE CENTRAL" logo.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate
Type: text

Click the downloadable graphic: Future Extreme Precipitation 2024


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > KFY CONCEPTS
Type: text

Climate change is supercharging the water cycle, bringing heavier precipitation extremes — and related flood risks — across the U.S.

As the climate has warmed from 1958 to 2021, the most extreme precipitation days have intensified in every major U.S. region, led by the Northeast (+60%) and Midwest (+45%).

This hazardous intensification is expected to continue with future warming.

With 2°C (3.6°F) of warming, 85% of 3,111 total U.S. counties are likely to experience a 10% or higher increase in precipitation falling on the heaviest 1% of days.

High future levels of extreme precipitation intensification are concentrated in: Alaska, Hawaii, Tennessee, Alabama, Georgia, Mississippi, Maine, North Carolina, and Kentucky.

People, ecosystems, and infrastructure in both wet and dry locations are facing the risks that come with short bursts of extreme rainfall.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Download KML map and XLSX
Type: text

**data file:** projected change in precipitation extremes under 2°C of warming for over 3,000 U.S. counties


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle
Type: text

Climate change is bringing heavier rainfall extremes and increased, inequitable flood risk to many parts of the U.S.

For every 1°F of warming the air can hold an extra 4% of moisture, increasing the chances of heavier downpours that contribute to the risk of flash floods.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle
Type: figure
Figure

Image /page/2/Picture/7 description: An infographic on a dark blue background titled "WARMER AIR HOLDS MORE MOISTURE". The infographic explains the relationship between air temperature and moisture content. A graph shows "Temperature" on the x-axis, increasing from left to right, and "Moisture" on the y-axis. A blue line curves upward, indicating that as temperature rises, the air's capacity to hold moisture increases. A text box states, "1°F increase = 4% more water vapor". An arrow points from the upward-curving line to a large grey cloud. Below the cloud, precipitation is shown. On the left, under colder conditions, snowflakes fall, labeled "Heavier snow". On the right, under warmer conditions, raindrops fall, labeled "Heavier rain". A temperature bar below the precipitation is blue on the left and transitions to yellow on the right, with a marker at "32°F" indicating the freezing point. The logo for Climate Central is in the bottom right corner.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle
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Click the downloadable graphic: Warmer air holds more moisture

Heavy downpours bring more rain, faster — causing flash flooding and landslides that can displace families, drown crops, damage infrastructure, and expose people to hazardous debris, contaminants, and waterborne disease.

The rapid onset of flash floods limits time to get people out of harm's way. In the U.S., racial minorities and those living in mobile homes are disproportionately exposed to flood risk, especially in the South and in rural areas.

In the U.S., extreme daily rainfall has become more frequent since the 1980s. Hourly rainfall intensity has also increased since 1970 — by 13% on average across 150 U.S. locations analyzed by Climate Central.

Risky rainfall extremes are the focus of analysis of past and projected future change in extreme rainfall across the U.S. featured in the Fifth National Climate Assessment.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier
Type: text

As the climate has warmed over recent decades, the most extreme precipitation days have become more intense across the U.S.

As precipitation extremes intensify, the wettest days each year bring increasing flood hazards. And this intensification trend has been widespread.

In the Northeast and Midwest, the amount of precipitation falling on the heaviest 1% of days has increased 60% and 45%, respectively, from 1958 to 2021 according to the Fifth National Climate Assessment.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier
Type: figure
Figure

Image /page/4/Picture/2 description: A map of the United States titled "HEAVIER DOWNPOURS" shows the "Change in precipitation on heaviest 1% of days" from 1958 to 2021. The map is color-coded to represent the percentage change, with a legend on the right showing a scale from 0% (light yellow-green) to 40% (dark blue). The eastern half of the country is shaded in shades of blue, indicating a significant increase in precipitation, while the western half is shaded in greens, indicating a smaller increase. Specific percentage changes are noted for different regions: Northeast +60%, Midwest +45%, Southeast +37%, Northern Plains +24%, Southern Plains +21%, Southwest +17%, and Northwest +1%. Hawaii shows a decrease of -19%, and a shape representing Puerto Rico/U.S. Virgin Islands shows a decrease of -24%. The source is cited as "USGCRP, 2023: Fifth National Climate Assessment," and the Climate Central logo is in the bottom right corner.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Heaviest precipitation days have become heavier
Type: text

Click the downloadable graphic: Extreme Precipitation Change 1958 to 2021

The heaviest rainfall events have become wetter across all other major regions of the continental U.S. from 1958 to 2021 as well, led by: the Southeast (+37%); the Northern Rockies and Plains (+24%); and the South (+21%).


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes
Type: text

With continued warming, precipitation extremes are likely to increase globally — even in regions with decreasing average precipitation, according to the latest reports from the Intergovernmental Panel on Climate Change.

Most of the U.S. is projected to see increases in precipitation extremes with 2°C (3.6°F) of global warming, according to analysis in the Fifth National Climate Assessment. The planet has already warmed more than 1.1°C (2°F). Only by rapidly expanding and accelerating efforts to reduce heat-trapping emissions can we ensure that clobal warming levels remain below 2°C.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes
Type: figure
Figure

Image /page/5/Picture/2 description: A choropleth map of the contiguous United States titled "WARMER FUTURE, MORE EXTREME PRECIPITATION," which shows the "Projected change in precipitation on heaviest 1% of days." The map is color-coded by county to indicate the percentage change at 2°C (3.6°F) of global warming. A legend on the right shows a color scale from light tan to dark green, representing a range from 0% to 40% change. The darkest green, indicating the highest increase in precipitation (30-40%), is concentrated in the Northeast, the Upper Midwest, and parts of the Pacific Northwest. Most of the rest of the country is shaded in lighter greens, indicating increases of 10-30%. The Southwest, particularly Arizona and parts of California and New Mexico, shows the lowest change, with colors in the light tan and light green range (0-10%). The source is cited as "USGCRP, 2023 Fifth National Climate Assessment," and the logo for "CLIMATE CENTRAL" is in the bottom right corner.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Climate change is supercharging the water cycle > Warmer future, heavier precipitation extremes
Type: text

Click the downloadable graphic: Future Extreme Precipitation 2024

With 2°C (3.6°F) of warming, the majority (85% or 2,645) of the 3,111 total U.S. counties are likely to experience a 10% or higher increase in precipitation falling on the heaviest 1% of days.

On average, U.S. counties are likely to experience a 17% increase in precipitation falling on the heaviest 1% of days.

Counties likely to experience at least a 30% increase in extreme precipitation are concentrated in: Tennessee, Alabama, Georgia, Mississippi, Maine, North Carolina, and Kentucky.

Alaska and Hawaii are likely to experience some of the highest levels of extreme precipitation intensification (43% and 30%, respectively) at 2°C (3.6°F) of global warming.

A recent study suggests that, with high future levels of heat-trapping emissions (RCP8 5), U.S. flash floods could also intensify — especially in

the Southwest — underscoring that the risks posed by short bursts of extreme rainfall affect both wet and dry regions.

These rising rainfall intensity risks are unlikely to be equally shared. A recent study suggests that the burdens of an estimated 26% increase in overall U.S. flood risk by 2050 could disproportionately impact Black communities along the Atlantic and Gulf coasts.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate
Type: text

1. For every 1°F of warming, the air can hold an extra 4% of moisture.

This relationship between air temperature and water vapor pressure is governed by the laws of thermodynamics and represented in the Clausius-Clapeyron equation.

The U.S. has already warmed 2.6°F since 1970 — meaning our atmosphere can already hold about 10% more moisture.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > 2. More moisture in warmer air increases heavier downpours.
Type: text

Theory suggests that rainfall becomes more intense with warming. Both rain gauge data and modeling experiments support this theory.

Globally, the frequency and intensity of heavy rainfall events have increased since the 1950s, largely due to humancaused climate change, according to the latest reports from the Intergovernmental Panel on Climate Change.

In the U.S., extreme daily rainfall events have been on the rise since the 1980s.

3. Intensifying rainfall due to climate change has cost the U.S. billions in inland flood damages over the last three decades.

Inland flooding in the U.S. caused \$230 billion in damages from 1988 to 2021. Over one-third (37%) of those damages are attributed to precipitation changes due to climate warming.

That's \$84 billion in past flood damage due to warming-induced rainfall intensification. And the most intense downpours have caused the largest damages.

Learn more: Extreme Weather
Toolkit: Heavy Rain and Flooding

LOCAL STORY ANGLES


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Three key points: extreme precipitation in a warming climate > How vulnerable is your area to flooding?
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Factors like local topography, age of infrastructure, watershed health, and

precipitation trends influence flood vulnerability. See current flood risks in your neighborhood with FloodFactor's ZIP code-level risk identifier. A 2021 report found that one-fourth of critical infrastructure is put at risk of failure by flooding. The American Society of Civil Engineers grades the integrity of infrastructures by state and category.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > Who is most vulnerable to heavy rain-related hazards?
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Racial minorities and mobile homes are disproportionately exposed to flooding, especially in rural areas and in the southern U.S. The American Flood Coalition has resources on adaptation, equitable disaster recovery, and funding opportunities for flood-resilient communities.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > What are the health and safety risks from flooding?
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Climate Central's report, *After the Storm: Health risks from damp, moldy homes*, summarizes the potential health risks from poor indoor air quality following heavy precipitation and flooding. Mold is just one of many potential hazards in a storm-damaged home. Other safety hazards include debris, structural damage, or downed electrical wires. The CDC provides tips to protect health and safety following a storm or flooding.


Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > What can you do to protect against flooding and extreme precipitation?
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Firstly, know your own risk using the Federal Emergency Management Agency's flood maps. Individual homeowners can insure their homes, maintain rainwater systems, reduce impervious surfaces on their property, elevate important utilities and equipment, and take on low-cost indoor projects recommended by FEMA. Communities can invest in flood management systems, green infrastructure and watershed restoration.


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Section: Extreme Precipitation in a Warming Climate > CONTACT EXPERTS > Greg Carbin
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Branch Chief, Forecast Operations National Weather Service

**Contact:** gregory.carbin@noaa.gov **Related expertise:** short-term forecasting of extreme rainfall


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Submit a request to SciLine from the American Association for the Advancement of Science or to the Climate Data Concierge from Columbia University. These free services rapidly connect journalists to relevant scientific experts.

**Browse maps** of climate experts and services at regional NOAA, USDA,

and Department of the Interior offices.

**Explore databases** such as 500 Women Scientists, BIPOC Climate and Energy Justice PhDs, and Diverse Sources to find and amplify diverse expert voices.

**Reach out** to your State Climate
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Document: Extreme Precipitation in a Warming Climate
Section: Extreme Precipitation in a Warming Climate > METHODOLOGY
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Data on past (1958–2021) and projected future (under 2°C of global warming) change in total precipitation (including rain and snow) falling on the heaviest 1% of days across the U.S. are from: USGCRP, 2023: *Fifth National Climate Assessment*. Crimmins, A.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, B.C. Stewart, and T.K. Maycock, Eds. U.S. Global Change Research Program, Washington, DC, USA.

https://doi.org/10.7930/NCA5.2023. Data were accessed via Figure 2.8a and Figure 2.12a metadata and the Fifth National Climate Assessment Interactive Atlas.

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Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: ABSTRACT
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Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three stateof-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

© 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

data/('Flood susceptibility modelling using advanced ensemble machine learning models', '.pdf')_extraction.md


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 1. Introduction
Type: text

Among various hydro-meteorological hazards, floods are identified as the most devastating hazards in the world (Prăvălie and Costache, 2013; Mishra and Sinha, 2020; Sarkar and Mondal, 2020). Flood often causes severe loss of human lives and socio-economic damage (Hirabayashi et al., 2013; Costache, 2019). As per the United Nations Office for Disaster Risk Reduction (UNISDR) statistical data, about 150,061 flood events happened worldwide, and around 157,000 people died by

E-mail address: nguyentthuylinh58@duytan.edu.vn (N.T.T. Linh).

floods from 1995 to 2015, which were liable for 11.1% of the global disaster casualties (Wahlstrom and Guha-Sapir, 2015; Hong et al., 2018a). Several investigations report that floods affected around 200 million people each year on a global scale (Tien Bui et al., 2019a). Moreover, considering climate change predictions, land use pattern changes and the increase of population also, it is anticipated that the occurrence rates and intensity of floods are likely to be exacerbated by 2050 which may cause huge loss ( $\approx$ US\$ 1 trillion) (Bubeck and Thieken, 2018; Ali et al., 2019; Alexander et al., 2019; Huang et al., 2019; Jadandideh-Tehrani et al., 2019; Xu et al., 2019; Serraj and Pingali, 2019). Thus, studies on flood modelling spatially occurring at basin-scale at the regional level are urgently needed to alleviate or take adaptation and mitigation measures against the devastating flood's effects (Cao et al., 2019; Su et al., 2019; Costache et al., 2020).

$<sup>^{\</sup>ast}\,$  Corresponding author at: Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam.

Generally, based on their occurrences, floods are of four types, i.e., flash floods, riverine floods, coastal floods, and urban floods (Tien Bui et al., 2019b; Costache et al., 2020). Flash floods are most devastating among these types of floods, which may cause huge losses of life and properties because of its rapid onset attributes along with grate velocity of flow (Bui et al., 2020a). Moreover, a massive amount of debris has moved along the river basin; thus, floods bring severe destruction for the infrastructure and fatalities (Siegel, 2020; Brinkmann, 2020; Luino et al., 2020; Kuriqi et al., 2016). In the under-developing and developed countries, also the severe destruction by the effect of flash floods are reported (Kuriqi and Ardiçlioğlu, 2018; Bui et al., 2020b; Munir et al., 2020; Okaka, 2020). On the other side, the developing countries are more affected by flash floods compared to developed countries due to the lack of proper infrastructures, essential financial assets, and technological advancement to predict the flood events or to mitigate the consequences of the floods. It is, thus, of immense importance to build up a high-performance prediction model for flood events likelihood and the susceptibility mapping of inundation regions.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 1. Introduction
Type: text

- developing and developed countries , also the severe destruction by the effect of flash floods are reported ( Kuriqi and Ardiçlioğlu , 2018 ; Bui et al . , 2020b ; Munir et al . , 2020 ; Okaka , 2020 ) . On the other side , the developing countries are more affected by flash floods compared to developed countries due to the lack of proper infrastructures , essential financial assets , and technological advancement to predict the flood events or to mitigate the consequences of the floods . It is , thus , of immense importance to build up a high - performance prediction model for flood events likelihood and the susceptibility mapping of inundation regions .

Bangladesh, a highly flood-prone country, is positioned in the junction of three mighty Ganges-Brahmaputra-Meghna river systems in the Southeast Asian region (Rahman, 2019). Because of its unique hydro-geographic setting and low-lying flood plain area, Bangladesh experiences floods every year from June to September during the rainy season. Flash floods cause tremendous economic loss, which may be exceeded by US\$20 million and a significant number of fatalities, damage to housing, and infrastructure, including roads and bridges (Fao and Unicef, 2017). Flash floods are frequent events in northern Bangladesh that often occur in the downstream regions, especially in the lower Teesta River basin. For example, in August 2017, a recent flash flood occurred in the lower Teesta River basin, especially in the districts of Kurigram, Lalmanirhat, and Nilphamari, resulting in a significant landmass being submerged and five individuals swept away. According to the Network for Information, Response and Preparedness Activities on disaster (NIRAPAD), a flash flood adversely affected about 6.8 million people and more than 560,000 hectares of cropland in August 2017; the total damage is up to US\$ 10 million (Fao and Unicef, 2017). Flood Susceptibility Modeling (FSM) should be undertaken in the flood danger areas to support decision makers and emergency management in mitigating floods and maintaining risk-prone natural resources. Therefore, FSM is a valuable method to classify at-risk regions and to secure these high-risk regions and natural resources (Masks et al., 2019).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 1. Introduction
Type: text

Information , Response and Preparedness Activities on disaster ( NIRAPAD ) , a flash flood adversely affected about 6 . 8 million people and more than 560 , 000 hectares of cropland in August 2017 ; the total damage is up to US \ $ 10 million ( Fao and Unicef , 2017 ) . Flood Susceptibility Modeling ( FSM ) should be undertaken in the flood danger areas to support decision makers and emergency management in mitigating floods and maintaining risk - prone natural resources . Therefore , FSM is a valuable method to classify at - risk regions and to secure these high - risk regions and natural resources ( Masks et al . , 2019 ) .

Prediction and development of flood's risk maps are challenging due to several factors, such as roughness heterogeneity (Ardıclıoğlu and Kuriqi, 2019) and the complexity of the river geomorphology (Kuriqi et al., 2019). Nevertheless, in recent decades, regional data were obtained from the satellite by using Remote Sensing (RS) methods coupled with Synthetic Aperture Radar (SAR) data (Li et al., 2019; Nikolaos et al., 2019; Pourghasemi et al., 2020a). Also, optical sensor images (Arora et al., 2019; Talha et al., 2019; Bui et al., 2020a) have been applied to predict flood-prone susceptible regions. The RS data, along with GIS technology, are utilized in a combined fashion to build the list of flood occurrence and several affecting parameters. Remote sensing databases combined with several statistical and mathematical techniques have been used in different research works on basin-scale FSM and flood hazard appraisals (Pradhan, 2010; Youssef et al., 2011; Pourghasemi et al., 2020b). These state-of-art techniques can handle spatial datasets and produce high resolution and prediction performances (Ma et al., 2020; Abba et al., 2020; Uthayakumar et al., 2020).

Many scholars have applied several kinds of methods for developing flood susceptible models (Sahana et al., 2020; Costache et al., 2020; Talukdar et al., 2020). Therefore, many forms of flood-susceptible models can be (Chen et al., 2019a; Bui et al., 2019a; Mahmood et al., 2019; Hong et al. 2018a; Termeh et al., 2018; Siahkamari et al., 2018), like (1) analytical hierarchy process (expert based model) (Souissi et al., 2019; Dano et al., 2020; Nachappa et al., 2020), (2) statistical and bi-variate based models like frequency ratio (FR) (Chen et al.,


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 1. Introduction
Type: text

; Talukdar et al . , 2020 ) . Therefore , many forms of flood - susceptible models can be ( Chen et al . , 2019a ; Bui et al . , 2019a ; Mahmood et al . , 2019 ; Hong et al . 2018a ; Termeh et al . , 2018 ; Siahkamari et al . , 2018 ) , like ( 1 ) analytical hierarchy process ( expert based model ) ( Souissi et al . , 2019 ; Dano et al . , 2020 ; Nachappa et al . , 2020 ) , ( 2 ) statistical and bi - variate based models like frequency ratio ( FR ) ( Chen et al . ,

2020a; Moghaddam et al., 2019; Khosravi et al., 2019a, Sahana et al., 2020), information value (IV model) (Xu, 2013; Chen et al., 2014), certainty factor, logistic regression (LR) (Tien Bui et al., 2019a; Shafapour Tehrany et al., 2019; Pham et al., 2020a; Ali et al., 2020), weights-of-evidence (WoE model) (Chen et al., 2019b; Paul et al., 2019), fuzzy logic (FL) (Wang et al., 2019a; Sahana and Patel, 2019), neuro fuzzy logic (ANFIS) (Termeh et al., 2018; Hong et al., 2018b) (3) machine learning algorithms (MLAs) (Bui et al., 2019b; Wang et al., 2019b; Wang et al., 2020; Shahabi et al., 2020; Dodangeh et al., 2020), and (4) hydrological models like Soil Water Assessment Tool (SWAT) (Oeurng et al., 2011) and Hydraulic Engineering Centre-River Analysis System among others (Getahun and Gebre, 2015). Machine learning methods, which many researchers use in FSMs, have recently gained more and more interest (Bui et al., 2020a; Chen et al., 2019a; Hong et al., 2018a; Wang et al., 2020). The most popular machine learning techniques are random forest (RF) (Avand et al., 2019; Paul et al., 2019; Chen et al., 2020b; Vafakhah et al., 2020; Nhu et al., 2020), decision trees (DT) (Choubin et al., 2019; Moghaddam et al., 2019; Chen et al., 2020c), support vector machine (SVM) (Termeh et al., 2018; Khosravi et al., 2019a), artificial neural networks (ANN) (Falah et al., 2019; Moghaddam et al., 2019; Pham et al., 2020b; Bui et al., 2020b), and that can forecast flash flooding in the areas at risk, FSM, however, faces numerous obstacles, such as choosing the best modeling methods from a large range of methods, and each approach yields distinct outcomes (Shafizadeh-Moghadam et al., 2018). Also, there are several drawbacks to modeling or forecasting the FSMs for any of these approaches. Consequently, these drawbacks were solved by hybrid ensemble machine learning algorithms, which demonstrated greater results than standard and single models (Pham et al., 2016, 2017a). Of late, researchers have implemented algorithms of ensemble machine learning and techniques of data mining quite extensively, such as Reptree (Chen et al., 2019a; Ghasemain et al., 2020), naive Bayes (Ali et al., 2020; Pham et al., 2020c; Tang et al., 2020), bagging (Shahabi et al., 2020; Chen et al., 2019b; Yariyan et al., 2020), random subspace (Pham et al., 2020b; Chen et al., 2019a) for high precision flood susceptibility models. The excellent results of hybrid machine learning algorithms inspire the implementation and creation of hybrid machine learning algorithms for many natural hazard models. There was, however, no universal consensus on the selection of the best tool for various forms of simulation of natural hazards, such as landslides or flood susceptibility (Chen et al., 2019b).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 1. Introduction
Type: text

; Tang et al . , 2020 ) , bagging ( Shahabi et al . , 2020 ; Chen et al . , 2019b ; Yariyan et al . , 2020 ) , random subspace ( Pham et al . , 2020b ; Chen et al . , 2019a ) for high precision flood susceptibility models . The excellent results of hybrid machine learning algorithms inspire the implementation and creation of hybrid machine learning algorithms for many natural hazard models . There was , however , no universal consensus on the selection of the best tool for various forms of simulation of natural hazards , such as landslides or flood susceptibility ( Chen et al . , 2019b ) .

Researchers advocate designing and evaluating new methods for mapping flood susceptibility and other forms of simulation of natural hazards (Chen et al., 2019a). Therefore, in the present study, we also developed the ensemble machine learning algorithms like alternating decision tree-based random subspace and Dagging models for flood susceptibility modelling in the Teesta river basin. However, Dagging ensemble machine learning algorithms have been applied very rarely in different branches of environmental science and disaster management. Thus, we attempted to compare the performance of conventional machine learning and ensemble machine learning algorithms for building flood susceptibility models. The hybrid ensembles of the Dagging, and the RS, coupled with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) models have been applied scarcely for natural hazards modelling.

Thus based on concerns mentioned above, the main aim of the present study is (i) to investigate and compare the ANN, SVM, RF, RS, and Dagging model's ability to predict flood susceptibility maps, and (ii) to assess flood influencing factors for generating flood susceptibility maps in the Teesta River basin, Bangladesh. In this study, for generating the flash flood susceptibility map, the Teesta River basin, which is prone to regular flash floods was chosen as a study case. The novelty of this study lies on (i) for the first time, the mapping of flood susceptibility in the Teesta River basin of northern Bangladesh was generated more rigorously; and (ii) for the first time a new hybrid ensemble Dagging model that to best of the author's knowledge has never been utilized before was employed in modelling flood susceptibility maps. The goal of

this contribution is to apply four state-of-art models and ensemble models (RF, ANN, SVM, and RS) along with the proposed Dagging hybrid model to map flood susceptibility and distinguish flood hazard regions in the studied basin. The outcome of the study will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.


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Section: 2. Material and methods > 2.1. Study area description and materials
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The basin considered in this study is positioned in the Teesta subcatchment of the northern region of Bangladesh, occupying about 2284 km² (Fig. 1), spanning over the five major districts, including Nilphamary, Lalmanirhat, Kurigram, Rangpur, and Gaibandha districts. In Bangladesh part, this basin is situated between the geographical locations of latitudes of 25°30′02′′N–26°18′37′′N, and 88°52′58′′E–89°45′ 34′′E longitudes. From the geomorphologic point of view, the floodplain region is the highest geomorphic unit in Bangladesh, and the drainage system comprises several small rivers, which are spread over elevation varying from 5 m to 110 m. For the occurrence of floods, the general slope of the river ranges of 0.47–0.55 m/km (Rahman et al., 2011), which indicates a quite flat terrain. Morphologically, the depression of the basin is shallow and in the moribund river valley, which generates changes of long and morphologically in the pathways of the river basin. This basin is susceptible to flooding. Every year, the flash floods

are a common occurrence; the situation of the basin in the rainy season is worst concerning flood conditions. The hydrological characteristics of the study area are relatively complex, where the distribution of the river network is very dense in association with six rivers, including Buri-Teesta, Naotora, Ghagot, Dharla, Jamuna, Old-Brahmaputra.

In terms of geology, the Teesta basin is situated under the Bengal basin, on the Rangpur saddle of the Indian platform. The new floodplain deposits, including silt, clay, fine to medium sand, are associated with this region (Islam et al., 2014).

The subtropical monsoonal climate in association with two predominant seasons, namely, monsoon (June to September) and dry season (October to May), are the characteristics of this basin. The average annual rainfall of this basin is higher than in 1900 mm. (Akter et al., 2019), and accounts for more than 80% of the total annual rainfall mostly occurred in the monsoon season. In August 2017, the basin area was recorded to have heavy and unusual rainfall. The tropical depression occurred from 6 to 10 August 2017, which was considered as the most destructive phenomenon that happened in the study area.

Consequently, the flash flood occurred, which leads to destroying a tremendous amount of farmland and more than 20,000 houses (Akter et al., 2019). The average temperature in this basin during monsoon and dry seasons is around 15°C and 35°C, respectively. Evapotranspiration has reached its maxima in April, when other climatic parameters including sunshine, wind speed are close to their maximum as well (Zaharia et al., 2020; Hampf et al., 2020). As per the population census 2011 (BBS, 2016), approximately 10.42 million inhabitants are living in this study area. The study area comprised 14% of the


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Figure

Image /page/2/Figure/10 description: A map of the Teesta River Basin, showing its elevation and the location of training and validation points. The map is titled "Teesta River Basin" and includes a scale bar from 0 to 20 km and a compass rose. The main map displays the river basin with elevations ranging from a low of 18 meters (light green) to a high of 69 meters (dark brown), as indicated by the legend. A river, represented by a light green line, meanders through the basin. Scattered across the map are numerous black triangles representing "Training Points" and white pentagons representing "Validation Points". The map is gridded with longitude lines from 88°53'E to 89°44'E and latitude lines from 25°36'N to 26°10'N. In the bottom left corner, an inset map shows the location of the study area, highlighted by a red box, within the context of surrounding countries including India, Bangladesh, Nepal, Bhutan, China, and Myanmar, with the Bay of Bengal to the south. The caption below the map reads: "Fig. 1. The location of the study area having the training and validation flood points."


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.1. Study area description and materials
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Fig. 1. The location of the study area having the training and validation flood points.

agricultural land to the total agricultural land of the Northern part of Bangladesh. Thus, this basin has become a lifetime for Bangladesh. The basin area has been covered with sub-urban types of settlements which are dominated by poor people. Thus, the present study area has been exposed to frequent flooding because of inadequate resources and lack of awareness.

As the research region has undergone regular flooding each year, the historical flooding inventories were also prepared based on the field survey and understanding of local residents. For flood susceptibility modeling, we used multiple data forms in the current analysis. To prepare land use land cover maps, we used Landsat and Operational Land Imager (OLI) (Path/Row: 138/42, Spatial resolution: 30 m., date:19/03/2019), obtained from the website of the United States Geological Survey (USGS). We used ASTER GDEM (Version 2) to extract topographical variables and hydrological variables (Spatial Resolution: 30 m). We collected rainfall from Bangladesh Meteorological Department (BMC), Dhaka, Bangladesh. We utilized a soil taxonomy map from the Natural Resources Conservation Service (NRCS) of the Department of Agriculture of the United States (USDA). The drainage map was generated using topographic maps (scale-1:250,00) collected from the Bangladesh Water Development Board.


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Section: 2. Material and methods > 2.2. Methodology
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The methodological flow chart was prepared to show the summary of the whole work that includes flood inventory map, generation of flood conditioning factors, evaluation of the flood conditioning factors using information gain ratio and multi-collinearity test and flood susceptibility models using machine learning methods (ANN, SVM, RF, RS, and Dagging) were utilized for the present study (Fig. 2).

2. Material and methods
2.2. Methodology


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map
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The preparation of flood inventory map of the research area is the preliminary stage for creating the flood susceptibility map (Bui et al.,

2020a; Sarkar and Mondal, 2020). The accessible technologies of GIS was used to achieve and investigate the normal state at the flooded sites. Since flood prediction relies on predictive analysis, where the near-future flood likelihood would be influenced by the recent past factor affecting flood, and that is why this is a critical step. 2.283 km2 of the Teesta River Basin for the preparation of the flood inventory map, where 167 flood points (Fig. 1) of the Teesta River Basin were collected in this report (Plate 1). Hence, the flood points were checked from historical data sources, fieldwork, perception of local residents, and Google Earth ®. The obtained 167 flood points were then randomly divided into 80 percent (134 points) and 20 percent (33 points) categories to train and test flood-susceptible models. Flood vulnerability mapping, however, is known to be a binary classification in which the flood inventory has been categorized into two groups, such as flood points and nonflood points. Therefore, binary values such as 1 as flood points and 0 as non-flood points are needed to prepare the training flood inventory, which is considered to be the dependent variable for prediction. The flood points were the exact points where recurrent floods have been observed, while the non-flood points were the points where floods have not been registered in the last 12 years. We are expected to obtain negative samples or non-flood points, close to flood points. Several researchers suggested choosing the same number of non-flood points as the positive or flood samples to circumvent bias (Tang et al., 2020). Therefore, based on the topographic map, historical flood info, field survey and NDWI 12-year maps, we randomly selected 167 non-flood points. Non-flood points have been allocated to 0. Subsequently, we split the non-flood points arbitrarily into 80% (134 points) and 20% (33 points) categories. Thus, as training data sets, we prepared a dependent factor that included 134 flood points as 1 and 134 non-flood points as 0.

Similarly, validation datasets were also designed to test the final models, including both 33 flood and non-flood points as 1 and 0. Both the datasets for training and validation are seen in Fig. 1. Depend on the training datasets, by using the 'extract values to point' tools in

2. Material and methods
2.2. Methodology
2.2.1. Flood inventory map


Document: Flood susceptibility modelling using advanced ensemble machine learning models
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Figure

Image /page/3/Figure/10 description: A flowchart illustrating a methodological approach for flood susceptibility analysis. The process is divided into three main sections: Data collection and analysis, Statistical test, and Flood susceptibility modeling.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
Section: 2. Material and methods > 2.2. Methodology > 2.2.1. Flood inventory map
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1. \*\*Data collection and analysis\*\*: This section starts with identifying 'Flood influencing factors'. Data is collected from four sources: DEM, Satellite image, Meteorological data, and River. These sources are used to derive several factors:
 \* From DEM: Slope, Aspect, Soil type, TRI, Curvature, TWI, SPI, STI.
 \* From Satellite image: LULC.
 \* From Meteorological data: Rainfall.
 \* From River: Distance to road.

2. \*\*Statistical test\*\*: This section, labeled 'Selection of Flood conditioning factors', involves the 'Selection of influencing factors' using three methods: Information gain ratio, Multicollinearity diagnostics tests, and Pearson's correlation.

Both the data from the analysis and the selected factors from the statistical tests feed into a central 'Training and validation data set'.

3. \*\*Flood susceptibility modeling\*\*: This section uses the training and validation data set for two parallel processes:
 \* \*\*Building of flood models\*\*: The models listed are Artificial Neural Network, Support Vector Machine, Random forest, Random subspace, and Dagging.
 \* \*\*Validation of flood susceptibility models\*\*: The validation metrics are ROC curves, RMSE, MAE, and R-square.

The outputs from both the model building and validation processes contribute to the final step, which is 'Flood Susceptibility Mapping'.

Fig. 2. Methodological approach applied for flood susceptibility analysis.

ArcGIS 10.5 software, we derived data from twelve flood conditioning parameters (spatial datasets). We then imported these datasets into the WEKA package (version 3.9.3), and all the modeling was performed over there.

In plate 1. field photographs of the flooding situation in the Teesta river basin representing (a,c,d) flooded road, (b) damaged houses due to flooding, (e) flooded village, (f, h) destroyed road due to devastating flooding, (g) camping on the national road by the people affected by flooding, and (i) over flow on the culvert.


Document: Flood susceptibility modelling using advanced ensemble machine learning models
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The construction of the susceptible spatial flood model is typically very complex and precise, as many geospatial topographical and hydrological variables are needed. Recognition of variables driving the flood is thus a critical activity, and the scientifically selected parameters will validate the accuracy of the flood susceptibility maps. Based on the available literature on flood susceptibility, in the current study area, the twelve flood influencing parameters were selected, such as elevation, curvature, aspect, slope, topographic roughness index (TRI), topographic wetness index (TWI), stream power index (SPI), sediment

transport index (STI), land use/land cover (LULC), distance to the river, soil type, and rainfall (Janizadeh et al., 2019; Paul et al., 2019; Bui et al., 2019b; Chen et al., 2019b; Sturzenegger et al., 2019; Moghaddam et al., 2019; Arabameri et al., 2019). The all influencing factors were transformed into the format of raster with 30m spatial resolution.

Topographic factors are of paramount importance for modelling flood studies, which directly and indirectly influence the hydrological characteristics of the study area (Bui et al., 2020b; Arabameri et al., 2020; Lei et al., 2020). At first, a Digital Elevation Model (DEM) was prepared from the ASTER GDEM (Version 2) with 30m spatial resolution for the study basin in ArcGIS 10.2 environment. We have derived topographic factors from DEM, such as slope, curvature, aspect, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), and topographic roughness index (TRI) in ArcGIS environment.

*2.2.2.1. Elevation.* The elevation is the most vital factor affecting modelling floods (Dodangeh et al., 2020; Bui et al., 2020b). The flooding is inversely related to the elevation, Higher the elevation, lower the chances


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Image /page/4/Figure/9 description: A figure, labeled Fig. 3, displays four maps of the same geographical region, illustrating data layers for flood susceptibility mapping. The maps are labeled a, b, c, and d.


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Map (a) shows Elevation in meters (m). The color scale ranges from red for high elevation (69 m) to green for low elevation (18 m). A legend also indicates that a black dot represents a flooding point.

Map (b) shows Curvature. The color scale ranges from green for high curvature (0.824) to red for low curvature (0.327).

Map (c) shows Aspect (Direction). A detailed legend provides color codes for different directions: Flat (-1) is grey, North (0-22.5 and 337.5-360) is red, Northeast (22.5-67.5) is orange, East (67.5-112.5) is yellow, Southeast (112.5-157.5) is light green, South (157.5-202.5) is light blue, Southwest (202.5-247.5) is medium blue, West (247.5-292.5) is purple, and Northwest (292.5-337.5) is dark purple.

Map (d) shows Slope in degrees (°). The color scale ranges from blue for high slope (5.75°) to red for low slope (0°).

A scale bar at the bottom of the figure indicates a length of 80 kilometers.

Fig. 3. Data layers for flood susceptibility mapping (a) elevation, (b) curvature, (c) aspect and (d) slope

of occurrences of flooding, and vice versa (Chen et al., 2020d). Hence, the Teesta river basin is situated in a flat and lower elevated region. Consequently, the chances of flooding are frequent in the entire region (Basin, 2017) (Fig. 3a).

2.2.2.2. Curvature. As the curvature affects the flooding water budget, it is thus, used for the modelling of flood susceptibility (Ahmadlou et al., 2019). Consequently, The curvature distinguishes the divergent and convergent runoff regions (Torcivia and López, 2020). The runoff convergence operation is related to the negative value regions (Costache and Bui, 2020; Rau et al., 2019). These areas are highly vulnerable to flash-flooding (Fig. 3b).

2.2.2.3. Aspect. Aspect, another factor, affects the directions of flooded water flows, along with which maintains the soil humidity (Chu et al., 2020). The aspect is, thus, indirectly affecting the flooding. For example, the shaded slope area, where the humidity of soil is relatively high and with extreme runoff (Fig. 3c).

2.2.2.4. Slope. The other important factor for influencing the flood is the slope, which controls the flowing water's (flooded) speed (Stevaux et al., 2020). The higher the slope angle, the lower the chances of water stagnation, a lower rate of infiltration, and the higher the flow

velocity. Therefore, the lower and flat regions, like the Teesta river basin, will have more chances of flooding (Fig. 3d).

2.2.2.5. Topographic roughness index. Topographic roughness index (TRI) is one of the major influencing factors for flood events. It depends on the local topographic of the study basin. The occurrence of higher flood probability is associated with the lower TRI value. In the present study, the TRI map was kept in stretch format, and the value ranges between 0-27 (Fig. 4a).


Document: Flood susceptibility modelling using advanced ensemble machine learning models
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lower rate of infiltration , and the higher the flow velocity . Therefore , the lower and flat regions , like the Teesta river basin , will have more chances of flooding ( Fig . 3d ) . 2 . 2 . 2 . 5 . Topographic roughness index . Topographic roughness index ( TRI ) is one of the major influencing factors for flood events . It depends on the local topographic of the study basin . The occurrence of higher flood probability is associated with the lower TRI value . In the present study , the TRI map was kept in stretch format , and the value ranges between 0 - 27 ( Fig . 4a ) .

2.2.2.6. Topographic wetness index. For the occurrence of flood, Topographic Wetness Index (TWI), is an effective factor (Abdel Hamid et al., 2020), by which the difference in the wetness of a basin was spatially expressed (Meles et al., 2020). This index shows the amount of water contained in every pixel size of the region. (Zhang et al., 2020), and that is computed using the Eq. (1):

$$TWI = \frac{\ln{(A_s)}}{\tan{\beta}} \tag{1}$$

The specific catchment area  $(m^2m^{-1})$  and the slope gradient (in degrees) are indicated by  $A_s$  and  $\beta$ , respectively. In general, the high TWI values and flooding are strongly correlated with each other (Shit et al.,


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Image /page/5/Figure/11 description: A figure displaying four maps of the same geographical region, labeled a, b, c, and d, each illustrating a different topographical index. A scale bar at the bottom indicates distances from 0 to 100 km.


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Map (a) shows the Topographic Roughness Index (TRI). It is a pixelated map with colors ranging from green for low values to red for high values. The legend indicates a range from a low of 0 to a high of 27.

Map (b) shows the Topographic Wetness Index (TWI). This is a higher resolution map with colors from green (low) to red (high). The legend shows a range from a low of -1.54 to a high of 7.72.

Map (c) shows the Stream Power Index (SPI). The entire region is colored a uniform brownish-yellow. The legend has a color gradient from green to yellow, labeled 'Low' and 'High' respectively, without numerical values.

Map (d) shows the Stream Transport Index (STI). This map highlights a network of streams in shades of yellow and red against a dark green background. The legend indicates a range from a low of 0 to a high of 140.64.

Fig. 4. Data layers for flood susceptibility mapping (e) topographic roughness index, (f) topographic wetness index, (g) Stream power index, (h) stream transport index.

2020). The TWI value in the study area ranges from -1.54 to 7.72 (Fig. 4b).

2.2.2.7. Stream power index. Stream power index (SPI) has an essential influence on the fluvial system (Knighton, 1999). The SPI is computed using Eq. (2).

$$SPI = A_s tan\beta \tag{2}$$

Where,  $A_s$  and the slope gradient represent the specific catchment area is indicated by  $\beta$  (radians) (Wu et al., 2020a). The sediment transport capacity and the erodibility of its bed is referred to as total SPI (Chen et al., 2020c).

2.2.2.8. Sediment transport index. Sediment transport index (STI), another factor for causing flooding, can increase the frequency of flood, which upshots in the damage of foundation (Wang et al., 2019c). The STI is derived from DEM using Eq. (3).

$$STI = \left(\frac{A_s}{22.13}\right)^{0.6} \left(\frac{sin\beta}{0.0896}\right)^{1.3} \tag{3}$$

where, each pixel of the slope is represented by  $\beta$ , and the area of upstream is indicated as  $A_s$  (Wang et al., 2019c). Based on hydro-climatic and geomorphologic attributes of the basin area, the STI is calculated

(Shukla and Gedam, 2019). The channel's bed changes with the deposition of sediment, which reduced the capability of storing water within the channel and caused flooding (Antoniazza et al., 2019). In the present study, the STI value ranges from 0 to 140.64 (Fig. 4d).


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{ 3 } $ $ where , each pixel of the slope is represented by $ \ beta $ , and the area of upstream is indicated as $ A_s $ ( Wang et al . , 2019c ) . Based on hydro - climatic and geomorphologic attributes of the basin area , the STI is calculated ( Shukla and Gedam , 2019 ) . The channel ' s bed changes with the deposition of sediment , which reduced the capability of storing water within the channel and caused flooding ( Antoniazza et al . , 2019 ) . In the present study , the STI value ranges from 0 to 140 . 64 ( Fig . 4d ) .

2.2.2.9. Land use/Land cover. LULC influences the runoff of the surface and sediment transportation and, consequently, directly affects the frequency of floods (Benito et al., 2010). Because the LULC directly controls the surface runoff generation and infiltration. The flooding is more frequent in the built-up area as these regions do not allow to infiltrate water and generate surface water. In contrast, the forest area allows infiltrating water that causes less flooding (Abdel Hamid et al., 2020). The relationship between flood events and the density of vegetation is inverse when it compares hydrological responses at various temporal scales (García-Ruiz et al., 2008; Dodangeh et al., 2020). In the present study, we have prepared a LULC map from Landsat and OLI image (path/row: 138/43; spatial resolution: 30 m) using an Artificial neural network in ENVI software (version 5.3). We classified six classes in the LULC map, such as vegetation, bare land, built-up area, sand bar, agricultural land, and water body (Fig. 5a).

*2.2.2.10. Distance to the river.* Most affected flood-inundation areas are usually located adjacent to the river. The distance from the river is an essential conditioning factor for identifying the regions which are


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Image /page/6/Figure/12 description: The image displays four maps of the same geographical region, labeled a, b, c, and d, each illustrating a different environmental factor. A scale bar at the bottom indicates a range from 0 to 80 km.


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Map (a) is titled "Land use land cover" and shows the distribution of different land types. The legend includes: Vegetation (dark green), Bare land (light blue), Built up (red), Sand bar (gray), Agricultural land (orange), and Water body (dark blue). The map shows a river system running through the region, surrounded by agricultural land, sand bars, and built-up areas, with large expanses of vegetation further away.

Map (b) is titled "Distance to river (m)" and uses a grayscale gradient to show proximity to the river. Black represents a low distance of 0 m, while white represents a high distance of 1503 m. The river channels are clearly delineated in black.

Map (c) is titled "Soil types (USDA)" and uses a color-coded system to display different soil classifications. The legend includes: Water (dark green), Usterts (medium green), Aquults (light green), Humults (lighter green), Udults (even lighter green), Ustults (yellow-green), Aqualfs (yellow), Ustalfs (light orange), Ochrepts (medium orange), Aquepts (darker orange), and Psamments (darkest orange).

Map (d) is titled "Rainfall (mm)" and shows rainfall distribution using a color gradient. The legend indicates that red represents high rainfall (550.411 mm) and green represents low rainfall (361.066 mm). The map shows higher rainfall in the northwest, gradually decreasing towards the southeast.

Fig. 5. Data layers for flood susceptibility mapping (a) land use, land cover, (b) distance to river, (c) soil types, and (d) rainfall.

vulnerable to flooding of a basin (Tehrany et al., 2015) because the distance controls the incident of flooding and the river flow to river factor (Gupta, 2020). The higher the distance to the river is, the lower the likelihood of flood events will be (Binh et al., 2020). At the basin scale, the regional level flooding is related to the storage of terrestrial water (Deng et al., 2020; Frappart et al., 2019). In the present study, we have prepared the distance to the river map from the topographic map having a scale of 1: 50,000 and Google Earth (Fig. 5b).

2.2.2.11. Soil types. One of the vital influencing factors which control the mechanism of rainfall-runoff is soil types (Xie et al., 2019). The soil properties directly control the water infiltration that influences the rainfall-runoff generation, although the other factors like local weather conditions, erosion process control rainfall-runoff (Flügel, 1995; Phillips et al., 2019). The higher the rate of infiltration, the lower the chances of flooding occurrences. In the present study, we have used the United States Geological Survey (USDA) soil map and classified it into 12 classes based on USDA soil taxonomy (Fig. 5c).


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One of the vital influencing factors which control the mechanism of rainfall - runoff is soil types ( Xie et al . , 2019 ) . The soil properties directly control the water infiltration that influences the rainfall - runoff generation , although the other factors like local weather conditions , erosion process control rainfall - runoff ( Flügel , 1995 ; Phillips et al . , 2019 ) . The higher the rate of infiltration , the lower the chances of flooding occurrences . In the present study , we have used the United States Geological Survey ( USDA ) soil map and classified it into 12 classes based on USDA soil taxonomy ( Fig . 5c ) .

2.2.2.12. Rainfall. Rainfall has been chosen as one of the major influencing factors for the occurrence of flooding (Pourghasemi et al., 2020a). Because the high-intensity rainfall within a short period can cause flooding (Aristizábal et al., 2020; Lu et al., 2020; Peptenatu et al., 2020). We have obtained rainfall data from four meteorological stations of Bangladesh and employed the kriging interpolation method for preparing rainfall maps in ArcGIS 10.3 environment. We used the kriging method for interpolation because we have data only for four points, and this method is highly suggested when the amount of data is very less (Kourgialas and Karatzas, 2011). However, the annual rainfall ranges from 361 to 550mm in the study area (Fig. 5d).