ALBERT

Description: The evaluation of potential landslides in mountain areas is a very complex process. Currently, event understanding is scarce due to information limitations. Identifying the whole chain of events is not a straightforward task, and the impacts of mass-wasting processes depend on the conditions downstream of the origin. In this paper, we present an example that illustrates the complexities in the evaluation of the chain of events that may lead to a natural disaster. On 16 December 2017, a landslide occurred in the Yelcho mountain range (southern Chile). In that event, 7 million m3 of rocks and soil fell on the Yelcho glacier, depositing 2 million m3 on the glacier terminal, and the rest continued downstream, triggering a mudflow that hit Villa Santa Lucía in Chilean Patagonia and killing 22 people. The complex event was anticipated in the region by the National Geological and Mining Survey (Sernageomin in Spanish). However, the effects of the terrain characteristics along the run-out area were more significant than anticipated. In this work, we evaluate the conditions that enabled the mudflow that hit Villa Santa Lucía. We used the information generated by Sernageomin's professionals after the mudflow. We carried out geotechnical tests to characterize the soil. We simulated the mudflow using two hydrodynamic programs (r.avaflow and Flo-2D) that can handle the rheology of the water–soil mixture. Our results indicate that the soil is classified as volcanic pumices. This type of soil can be susceptible to the collapse of the structure when subjected to shearing (molding), flowing as a viscous liquid. From the numerical modeling, we concluded that r.avaflow performs better than Flo-2D. The mudflow was satisfactorily simulated using a water content in the mixture ranging from 30 % to 40 %, which would have required a source of about 3 million m3 of water. Coupling the simulations and the soil tests that we performed, we estimated that in the area scoured by the mudflow, there were probably around 2 800 000 m3 of water within the soil. Therefore, the conditions of the valley were crucial to enhancing the impacts of the landslide. This result is relevant because it highlights the importance of evaluating the complete chain of events to map hazards. We suggest that in future hazard mapping, geotechnical studies in combination with hydrodynamic simulation should be included, in particular when human lives are at risk.

Description: One of the consequences of recent glacier recession in the Cordillera Blanca, Peru, is the risk of glacial lake outburst floods (GLOFs) from lakes that have formed at the base of retreating glaciers. GLOFs are often triggered by avalanches falling into glacial lakes, initiating a chain of processes that may culminate in significant inundation and destruction downstream. This paper presents simulations of all of the processes involved in a potential GLOF originating from Lake Palcacocha, the source of a previously catastrophic GLOF on 13 December 1941, killing about 1800 people in the city of Huaraz, Peru. The chain of processes simulated here includes (1) avalanches above the lake; (2) lake dynamics resulting from the avalanche impact, including wave generation, propagation, and run-up across lakes; (3) terminal moraine overtopping and dynamic moraine erosion simulations to determine the possibility of breaching; (4) flood propagation along downstream valleys; and (5) inundation of populated areas. The results of each process feed into simulations of subsequent processes in the chain, finally resulting in estimates of inundation in the city of Huaraz. The results of the inundation simulations were converted into flood intensity and preliminary hazard maps (based on an intensity-likelihood matrix) that may be useful for city planning and regulation. Three avalanche events with volumes ranging from 0.5 to 3  ×  106 m3 were simulated, and two scenarios of 15 and 30 m lake lowering were simulated to assess the potential of mitigating the hazard level in Huaraz. For all three avalanche events, three-dimensional hydrodynamic models show large waves generated in the lake from the impact resulting in overtopping of the damming moraine. Despite very high discharge rates (up to 63.4  ×  103 m3 s−1), the erosion from the overtopping wave did not result in failure of the damming moraine when simulated with a hydro-morphodynamic model using excessively conservative soil characteristics that provide very little erosion resistance. With the current lake level, all three avalanche events result in inundation in Huaraz due to wave overtopping, and the resulting preliminary hazard map shows a total affected area of 2.01 km2, most of which is in the high hazard category. Lowering the lake has the potential to reduce the affected area by up to 35 %, resulting in a smaller portion of the inundated area in the high hazard category.

Description: Current methods to estimate evacuation time during a natural disaster do not consider the socioeconomic and demographic characteristics of the population. This article develops the Response Time by Social Vulnerability Index (ReTSVI). ReTSVI combines a series of modules that are pieces of information that interact during an evacuation, such as evacuation rate curves, mobilization, inundation models, and social vulnerability indexes, to create an integrated map of the evacuation rate in a given location. We provide an example of the application of ReTSVI in a potential case of a severe flood event in Huaraz, Peru. The results show that during the first 5 min of the evacuation, the population that lives in neighborhoods with a high social vulnerability evacuates 15 % and 22 % fewer people than the blocks with medium and low social vulnerability. These differences gradually decrease over time after the evacuation warning, and social vulnerability becomes less relevant after 30 min. The results of the application example have no statistical significance, which should be considered in a real case of application. Using a methodology such as ReTSVI could make it possible to combine social and physical vulnerability in a qualitative framework for evacuation, although more research is needed to understand the socioeconomic variables that explain the differences in evacuation rate.

Description: Current methods used to estimate people's evacuation times during a natural disaster assume that human responses across different social groups are similar. However, individuals respond differently based on their socioeconomic and demographic characteristics and previous knowledge. This article develops the Response Time by Social Vulnerability Index (ReTSVI), which is a methodology to estimate how human response time to evacuation warnings during a natural hazard is affected by considering characteristics related to both physical and social vulnerability. ReTSVI is a three-step methodology: first we calculate a population's evacuation curves considering social vulnerability level, certain demographic information and a model that describes an inundation hazard. Then, we use a mobilization model to generate evacuation maps per level of vulnerability and we also estimate the social vulnerability index for the area of study. In the third step, we combine the results from the second step to generate a map that indicates the percentage of people that could evacuate a hazard zone according to their social vulnerability level. Finally, we provide an example of the application of ReTSVI in a potential case of a severe flood event in Huaraz, Peru. The results show that during the first 5 minutes of the evacuation, the population that lives in neighborhoods with high social vulnerability evacuate 15 % and 22 % fewer people than the neighborhoods with medium and low social vulnerability. These differences gradually decrease over time after the evacuation warning and social vulnerability becomes less relevant after 30 minutes. Using a methodology such as ReTSVI allows first responders to identify areas where the same level of physical vulnerability affects distinct groups differently, providing them with a tool to quantify the differences in time to evacuate and where the resources before and during an evacuation should be preferentially allocated.