📄 earth_science

An Operative Framework for Risk to People Mapping of Landslide-Triggered Tsunami in Lakes

This paper presents a physically based, operative framework for quantifying landslide-triggered lake tsunami risks to shoreline populations using progressive levels of approximation, illustrated through a case study of Lake Iseo in northern Italy to bridge the gap between scientific research and practical decision-making.

Original authors: Riccardo Bonomelli, Marco Pilotti, Gabriele Farina

Published 2026-08-12

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CC BY 4.0

Original authors: Riccardo Bonomelli, Marco Pilotti, Gabriele Farina

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine standing on the edge of a deep, mountain lake. It's beautiful, calm, and surrounded by towns where people live, work, and play. But hidden beneath the surface of this tranquility is a geological ticking clock: steep slopes that could suddenly give way. This is the world of Landslide-Triggered Tsunamis (LTT). Unlike the giant ocean tsunamis caused by earthquakes, which give us hours to run, a lake tsunami is a "sneak attack." It happens when a massive chunk of mountain crashes into the water, sending a wall of water racing across the lake in seconds. Because the lake is enclosed, the water bounces off the opposite shore and can focus its energy, making the waves even taller and more destructive right where people live.

The big challenge for scientists and emergency planners is that these events are incredibly hard to predict. We don't know exactly when a mountain will slide, how big the chunk will be, or how fast it will move. Traditional computer models that try to simulate every single drop of water and every grain of dirt are like trying to count every raindrop in a storm while also tracking the wind; they are so slow and complex that by the time they finish, the emergency might already be over. So, the question becomes: How do we quickly figure out which towns are in the most danger, so we can save lives, without waiting weeks for a super-computer to crunch the numbers?

This paper, written by researchers Riccardo Bonomelli, Marco Pilotti, and Gabriele Farina, proposes a clever, two-step "smart shortcut" to solve this problem. Think of it like a detective first scanning a whole city with a drone to spot the most suspicious neighborhoods, and then sending a team of investigators with flashlights to those specific spots to look for clues.

First, the team built a computer model that treats the sliding mountain and the water it hits as two separate fluids moving on the same grid. They tested this "shortcut" model against real-world data from a past landslide in Iceland and found it worked just as well as the super-slow, complex models, but it finished the job in just 6 hours instead of 25 days. Using this fast model, they ran a massive "ensemble" of 54 simulations for Lake Iseo in Italy, a place where a huge landslide is currently moving and could crash into the lake at any moment. In these simulations, they varied the size of the landslide (from 1.2 million cubic meters to 2 million cubic meters) and how "slippery" the rocks were (friction angles between 18° and 23°) to see what could possibly happen.

In this first round, they didn't model the houses or the streets; they just looked at the lake and the shoreline as if the towns were invisible walls. This allowed them to quickly map out the "envelope" of the highest waves that could hit every part of the coast. They then combined this wave data with population maps to create a "Risk Index." This index didn't just look at how big the wave was or how many people lived there; it multiplied the two together. This revealed a surprising truth: the town with the biggest wave wasn't necessarily the most dangerous, nor was the town with the most people. The most dangerous spots were where a high wave and a lot of people happened to overlap.

Once they identified the most at-risk towns, they moved to the second, more detailed step. They zoomed in on the town of Marone, which came out high on their risk list. Here, they rebuilt the computer model to include the actual shapes of buildings, treating them like holes in the water flow. They ran two specific scenarios: an "average" bad day and the "worst-case" nightmare (a 2 million cubic meter landslide with low friction). The results showed that while the buildings acted like a shield, slowing down the water and lowering the wave height in some spots, the danger remained extreme. In the worst-case scenario, waves could reach 10 meters high, and the water could rush at speeds of 7 to 9 meters per second.

The paper concludes that this two-step framework is a vital tool for emergency planners. It suggests that by using fast, physics-based simulations to rank towns by risk, officials can quickly decide where to focus their limited time and resources. Instead of trying to model the entire lake in perfect detail immediately, they can first find the "hotspots" and then dive deep into those specific areas to create detailed evacuation maps. The authors emphasize that while their model simplifies the messy splash where the rock hits the water, it provides a reliable, fast, and physically consistent way to protect people when time is running out.

Technical Summary: An Operative Framework for Risk to People Mapping of Landslide-Triggered Tsunami in Lakes

Problem Statement
Lake shores in mountainous regions often host densely populated areas and valuable assets, yet they remain vulnerable to Landslide-Triggered Tsunamis (LTT). Unlike earthquake-generated tsunamis, LTT events are intrinsically impulsive and difficult to predict due to the uncertainty surrounding slope-failure timing and location. The time between landslide occurrence and wave arrival is often too short for effective evacuation, making early warning systems feasible only if instability is detected prior to failure. Consequently, decision-makers require modeling approaches capable of providing a first-order, physically consistent characterization of risk within limited computational times.

A significant gap exists between scientific advances and the availability of practical, standardized procedures for LTT risk-informed decision-making. Traditional Probabilistic Tsunami Hazard Assessment (PTHA) is challenging for LTTs because landslide recurrence statistics are poorly constrained, and the interaction between a landslide and a water body involves complex processes requiring high spatial resolution. Furthermore, existing scenario-based methods often rely on empirical relationships that may not capture the specific dynamics of complex lake geometries or the coupling between landslide motion and wave generation.

Methodology
The authors propose a physics-based, ensemble-driven framework designed to quantify the impacts of lake tsunamis on shoreline communities using population exposure as a primary risk metric. The methodology employs a unified Shallow Water Equation (SWE) approach for three distinct phases: landslide movement, wave propagation, and onshore inundation.

Unified Numerical Model: The framework utilizes a second-order accurate SWE solver on unstructured grids. It models the landslide as a granular flow using the Voellmy rheology and the resulting wave as a Newtonian fluid using Manning's equation. The two phases are coupled via a momentum transfer method (based on Franz et al.) that assumes elastic collision between landslide particles and water, conserving kinetic energy and momentum. This allows the simulation of the landslide impact, wave generation, and propagation using two instances of a single-phase SWE model on the same grid.
Two-Stage Operational Workflow:
- Stage 1 (Preliminary Screening): To maximize efficiency and handle parametric uncertainty, a large ensemble of simulations (54 runs in the case study) is performed on a confined computational domain that excludes the detailed shoreline and islands (treated as fixed walls, except near the landslide). This stage uses a Monte Carlo approach to vary landslide volume and internal friction angle. The output is a maximum wave elevation envelope along the shoreline.
- Stage 2 (Risk Quantification): A GIS-based exposure assessment identifies areas where the population is potentially exposed (defined as a 100m buffer from the coast). A simplified risk index is calculated as the product of normalized maximum wave elevation and normalized exposed population. This index ranks municipalities to prioritize areas for detailed analysis.
- Refined Simulation: For prioritized towns, a refined computational mesh is generated that includes building footprints (using a "building-hole" approach). Detailed simulations are run for representative scenarios (average and worst-case) to generate high-resolution inundation maps.
Human Vulnerability Modeling: The framework integrates a physically based human vulnerability model (Milanesi et al.) that evaluates the stability of individuals (sliding and toppling) and vehicles based on flow depth and velocity. This divides the hazard space into five vulnerability zones (V1–V4), ranging from safe for all to life-threatening for adults.

Case Study: Lake Iseo, Italy
The framework was applied to Lake Iseo, focusing on the Mount Saresano landslide, a 2 million $m^3$ granular mass that accelerated in 2021. The study considered a volume range of $1.2 \times 10^6$ to $2 \times 10^6$ $m^3$ and friction angles between 18° and 23°.

Results: The preliminary ensemble identified that over 50% of the potentially exposed population (1,955 people) is concentrated in the municipalities of Iseo and Marone. However, the risk ranking, which combines hazard intensity and exposure, highlighted Marone as a high-priority area due to its combination of significant wave heights and population density, despite Iseo having a larger total population.
Detailed Simulation: Refined simulations for Marone (worst-case: $2 \times 10^6$ $m^3$ , 18°; average: $1.5 \times 10^6$ $m^3$ , 21°) demonstrated that buildings significantly alter flow paths and provide a sheltering effect, reducing local wave heights and velocities. The resulting vulnerability maps indicated that most impacted areas in the worst-case scenario would be unsafe for adults (V4 zone).

Key Contributions and Claims
The paper claims to bridge the gap between advanced numerical modeling and rapid, decision-oriented risk mapping for LTTs. Key contributions include:

Operational Efficiency: The proposed SWE-based unified approach provides results comparable to fully 3D multiphase models (validated against the Lake Askja event) but at a fraction of the computational cost (6 hours vs. 600 hours for a full event simulation). This enables the execution of large ensembles necessary for uncertainty quantification within a realistic emergency timeframe.
Risk-Centric Prioritization: Unlike studies focusing solely on inundation extent, this framework explicitly integrates population exposure and human vulnerability. It argues that risk cannot be represented by hazard or exposure alone; the proposed normalized product metric effectively prioritizes municipalities for emergency planning.
Progressive Refinement: The methodology supports a "progressive levels of approximation" strategy, allowing decision-makers to move from a rapid, broad overview to detailed, building-resolved risk assessments as time and data permit.

Significance
The authors assert that this framework offers a practical tool for civil protection prioritization under time and data constraints. By avoiding the need for multiple software tools and complex coupling issues, the unified SWE approach simplifies the operational workflow. While acknowledging that the SWE approximation cannot fully resolve near-field splash-zone mixing processes, the authors argue that the ensemble-based approach provides physically consistent and actionable results for emergency management, particularly in complex lacustrine environments where warning times are minimal. The work aims to support community decision-makers in prioritizing mitigation actions and evacuation planning for landslide-prone lake shores.

Technical Summary: An Operative Framework for Risk to People Mapping of Landslide-Triggered Tsunami in Lakes

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