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Hazard driven debris flow simulation and risk evaluation in the Karakorum Mountain ranges, Northern Pakistan
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Abstract: Northern Pakistan is highly susceptible to debris flows due to its complex geomorphic structure, steep topography, climate change, glaciation, monsoonal rainfall, active seismicity, deforestation, and human activities. Despite this, high-resolution, quantitative assessments of debris flow hazards are limited, constraining effective disaster risk management. This study aims to model a representative debris flow event in the Ghizer District to predict potential deposition areas and support emergency preparedness and sustainable land-use planning in this hazard-prone region. High-resolution Unmanned Aerial Vehicle (UAV)-derived topographic data were integrated with the Rapid Mass Movement Simulation (RAMMS-DF) model to simulate debris flow runout for three release scenarios, representing distinct and combined initiation zones. Satellite images and field validation were used to delineate release and erosion zones, while vulnerability and risk were assessed by mapping exposed elements including 210 buildings and a population of 1,500 and applying spatial multi-criteria analysis within a Geographic Information System (GIS) framework. The numerical simulation for the most critical event, Scenario 3 (representing simultaneous dual-source initiation), yielded the highest magnitude results with a total flow volume of 193,717 m3, a peak flow height of 12.96 m, and a maximum impact pressure of 992.28 kPa. Vulnerability mapping identified infrastructure and agricultural land as the most exposed. Risk assessment showed that the combined scenario posed the greatest threat to local assets and communities. The study demonstrates that debris flow dynamics in high-mountain environments are non-linearly sensitive to initial release volumes and the interaction between multiple flow sources. Topographic controls such as channel confinement and slope variations are the primary drivers of flow intensity and energy dissipation. This research establishes a replicable, data-driven framework for quantitative risk assessment in data-limited mountainous regions. The resulting high-resolution hazard and risk maps provide a scientific basis for defining land-use restrictions, prioritizing slope stabilization, and guiding the placement of emergency infrastructure to support disaster-resilient development in northern Pakistan.
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Key words:
- Debris flow /
- Runout simulation /
- GIS /
- Vulnerability
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Figure 1. Location map of the selected watershed (Khalti Village) for the debris flow modelling
Notes: (a) Elevation of the district, (b) Khalti catchment with streams, and the inset shows a zoomed-in view of UAV imagery of the fan
Figure 3. Field photographs showing the nature of the material and erosion in the catchment
Figure 4. Flowchart of the methods for the hazard scenarios, vulnerability and risk assessments of all scenarios for debris flow
Figure 5. Simulated debris flow height using RAMMS-DF for the calibrated values (μ = 0.07, and ξ = 550 m/s2), showing the simulation
Notes: The purple gradient represents the modeled debris flow height (DF_Height) along the flow path. The insets highlight key deposition zones for calibration and validation.
Figure 6. Release areas and erosion material for debris flow
Notes: Red colored polygons indicate release area 1, blue colored polygons indicate release area 2, and yellow-colored polygons show the erosion area.
Figure 7. Simulation output from the source zone to the depositional zone for Scenario 1: (a) height, (b) pressure, and c) velocity
Figure 8. Debris flow dynamics over time for Scenario 1
Notes: (a) Shows the moving percent (green line, left Y-axis), which indicates the percentage of total debris mass in motion, and moving momentum (red dashed line), which reflects the energy of the moving flow. (b) Shows the flow volume (blue line, Y-axis). The x-axis in both figures represents time
Figure 9. Simulation output from the source zone to the depositional zone for Scenario 2: (a) height, (b) pressure, and (c) velocity
Figure 10. Debris flow dynamics over time for Scenario 2
Notes: (a) Shows the moving percent (green line, left Y-axis), which indicates the percentage of total debris mass in motion, and moving momentum (red dashed line), which reflects the energy of the moving flow. (b) Shows the flow volume (blue line, y-axis). The x-axis in both figures represents time
Figure 11. Simulation output from the source zone to the depositional zone for Scenario 3: a) height, b) pressure, and c) velocity
Figure 12. Debris flow dynamics over time for Scenario 3
Notes: (a) Shows the moving percent (green line, left y-axis), which indicates the percentage of total debris mass in motion, and moving momentum (red dashed line), which reflects the energy of the moving flow. (b) Shows the flow volume (blue line, y-axis). The x-axis in both figures represents time
Figure 13. Hazard maps of debris flow for all three scenarios based on height: (a) Scenario 1. (b) Scenario 2. (c) Scenario 3. The insets show zoomed-in views of low to very high hazard classes for each scenario
Figure 14. Debris flow vulnerability maps for all three scenarios. (a) Scenario 1; (b) Scenario 2; (c) Scenario 3. The insets show zoomed-in views of low to very highly vulnerable zones for each class
Figure 15. Debris flow risk maps for all three scenarios: (a) Scenario 1, (b) Scenario 2, (c) Scenario 3, the insets show zoomed-in views of the risk maps
Table 1. Parameters for RAMMS-DF software in scenario-based analysis following (Gardezi et al. 2021)
Analysis Friction
Coefficient, µTurbulent coefficient, ξ (m/s2) DEM (m) Scenario-1 0.07 550 2 Scenario-2 0.07 550 2 Scenario-3 0.07 550 2 
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Table 2. Values used for hazard assessment zoning following (Tang et al. 1993)
S. No Debris flow hazard zoning Flow velocity (m/s) Flow height
(m)01. Extremely high-hazard area > 5 > 3 02. High hazard area 2–5 1–3 03. Medium hazard area 1–2 0.5–1 04. Low hazard area < 1 < 0.5
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Table 3. Shows assigned weights to elements at risk for vulnerability assessment based on AHP
Element at risk Assigned weights Buildings 0.40 Roads 0.25 Agriculture land 0.15 Orchards 0.06 Bridge 0.10 Lake 0.04
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Table 4. Summary of simulation output values
Scenario Max Flow Height (m) Peak Impact Pressure (kPa) Max Velocity (m/s) Scenario 1 10.21 771.27 19.64 Scenario 2 11.08 1012.53 22.50 Scenario 3 12.96 992.28 22.27
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Table 5. Spatial distribution of vulnerability zones for each scenario
Scenario Low vulnerability/m2 Moderate vulnerability/m2 High vulnerability/m2 Very high vulnerability/m2 Scenario 1 1,377,100 71,739 6,498 486 Scenario 2 1,702,140 51,021 37,152 3,141 Scenario 3 2,818,670 42,255 32,499 3,951
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Table 6. Shows the number of houses and persons in risk classes for all three scenarios
Scenarios Low risk Medium risk High risk Very high risk Houses Person Houses Person Houses Person Houses Person 1 165 1378 21 166 9 72 3 24 2 149 1192 32 248 12 96 5 40 3 134 1072 40 320 16 128 8 64
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