Conference Agenda

The landslide of Cadegliano Viconago in the province of Varese (IT) has been for over twenty years a danger to the population and infrastructures located along the course of the Tresa river, at the border between Italy and Switzerland. The landslide leads to the accumulation of debris which has caused several events, involving the road passing at the foot of the landslide. The slope even shows traces of deep damage which manifests with the exposure of scarps and traction fractures, with even centimeter displacements during intense and continuous rainfall events. A catastrophic collapse would lead to the partial or total interruption of the road system, a barrage of the Tresa river with the possible formation of a basin and the destruction of wells field for drinking water use in Switzerland.

Even though in recent years of monitoring it was observed a trend towards smaller displacements, intense and prolonged rains could drastically influence the dynamic behavior of the landslide and cause bigger displacements that could even lead to a catastrophic failure.

For this reason, thanks to the financing of an Interreg project with the involvement of the Interregional Agency for the Po River and the Canton Ticino Land Department, two DMS (Differential Monitoring of Stability) columns (Lovisolo,et al. 2003) were installed in March 2021 to investigate the landslide activity and to define alert thresholds. DMS multiparametrical columns provide continuous data with detailed displacement, accelerometric, and piezometric measurements. The deep monitoring data joined by spatially distributed geodetic measurements since 2006, provide information on the landslide’s dynamics. The monitoring, the cores from the drilling, and the in-situ surveys provide a basis to set a numerical stability model to understand the slope kinematics and the response to the water table changes.

The displacement data ranged between 1 cm/year in the first three years, up to 1 cm/week in an intense event in late March 2024 connected to a remarkable lifting of the water table. Similarly, intense rain events recorded in autumn 2002 and 2014, led to small-scale debris flows, partial destruction of the road and development of decimeters-width tensile fractures, making a catastrophic collapse plausible.

The definition of the humidity status identified by the Standard Precipitation Index (SPI), showed a very high value in correspondence of the landslide maximum activity, which makes it a possible factor for setting alert status.

The observation of recorded displacement by DMS and the stratigraphic data, suggests a slip surface at a depth of about thirty meters, which can plausibly lead to the detachment of a volume mass ranging from 70’000 to over 2’000’000 m³.

The numerical stability model was followed by expansion models of the unstable mass based on the observation of previous collapses. The modelled scenarios remarked the hazard for the infrastructures in both Italian and Swiss territories.

The World Meteorological Organization strongly encourages the development and use of impact-based early warning systems (IBEWS), along with recent advancements in research, e.g., Merz et al. (2020). As Potter et al. (2021) note, IBEWS improve public understanding of warnings, enhance interagency communication, and reduce "false alarms". This
the study aims to quantify the added value of an IBEWS specifically designed for flash flood anticipation by focusing on detecting impacted sectors.
In France, local flood forecasting services assess flood risk on major rivers, coordinated by the Central Service of Hydrometeorology and Flood Forecasting (SCHAPI), which provides a national risk map with four levels (green, yellow, orange, red) that incorporate both hazard intensity and potential impacts. However, small ungauged rivers are not covered by this service. An automated warning system based solely on the flood return period exceedance has been implemented for municipalities along these rivers.

We extended this hazard-based system to an impact-based approach for ≃20,000 km of river network in the French Mediterranean area, which is particularly vulnerable to flash floods (Gaume et al., 2016). The model structure aligns with existing European methods (Dottori et al., 2017), using hydrological and hydraulics approaches developed in France
(Piotte et al., 2020, Davy et al., 2017). The real added value of this work lies in the quality and diversity of observed impact data sources for evaluating the benefits of impact-based versus hazard-based warnings and the validation methods employed. [...]

In the impact model literature, predictive models often focus on a country or a region. This is the case of the 510 model (Teklesadik et al., 2023) or the grid-based model by Kooshki Forooshani et al. (2024), both statistical models for predicting impact on buildings in the Philippines or the Hybrid model (Hou et al., 2020) which also predicts impact on buildings in China. However, resources exist at the global level, including the Advanced Disaster Analysis and Mapping (ADAM) system which provides estimates of exposed population to different windspeed and accumulated rainfall ranges (World Food Programme, 2016) at the administrative 2 level or the DisasterAWARE platform (Sharma et al., 2020) by the Pacific Disaster Center which estimates not only population exposed but also infrastructure (Hospitals, schools, etc) and capital exposed at a grid level (30m×30m tiles). In this work, we propose a statistical model (Extreme Gradient Boosting or XGBoost) that predicts the affected population in cases of Tropical Cyclones (TC) at a grid level (1 km2 tile) in 60 countries. This model not only considers weather-related variables like wind speed and accumulated rainfall but also considers country-specific features such as topographical measures and poverty indexes. Furthermore, by employing XGBoost, we can handle non-linear relationships between variables.

Addressing data scarcity in the Global South is increasingly critical as climate change is expected to result in more frequent and severe flood events in highly vulnerable regions. A novel risk assessment framework tailored to these challenges is demonstrated on a use case based in Angwan Iku, a flood prone community in Gwagwalada, Nigeria. The community experienced a high magnitude flood event in 2020, which sustained adverse consequences for built structures, local residents, and their livelihoods. Key inputs include local context provided through community engagement, high resolution data acquired conducting household-level field surveys, and freely available medium resolution (30 m) topography data.

The study first identifies key local flood risk drivers, including flood mechanisms and building vulnerability classes. A multivariate analysis is performed on field data to identify building features that contribute most to flood damage. Flood risk zones are further delineated by performing contour analysis on identified flood threshold levels on local river banks. These zones are then validated against a 2020 flood extent map derived from field surveys, which served as a point of reference or ground truth. Finally, adaptation strategies are proposed for at-risk buildings, informed by the risk zones and building vulnerability classes.

The approaches within this framework can generate custom, baseline flood risk profiles for data-scarce communities, which can also be further refined and validated over time. Integration of community insights ensures that local risk factors are accurately captured and that proposed adaptation measures directly address community defined priorities and needs and are sustainable. Moreover, the methods make use of data that can be acquired for any data-scarce region, and are therefore transferrable to comparable communities and scalable with available resources. Most notably, we demonstrate how the framework extends beyond working with existing data gaps, towards the translation of evidence-based insights into actionable solutions specific to the needs of each community, so that flood risk is effectively minimised on the ground.

Groundwater is among the most important sources of freshwater for drinking water production and irrigation purposes across Switzerland. Recent drought events have shown that these groundwater resources, traditionally considered a safe source of water supply, are affected by prolonged periods with limited or no rainfall and high evaporation. Early warning systems may help inform groundwater resource managers about the possible future state of groundwater levels in different aquifers across Switzerland. Such a system could provide crucial support by enabling timely responses to mitigate the potential consequences of droughts on groundwater resources.

This study aims to investigate the feasibility of forecasting groundwater levels in Switzerland up to 32 days into the future using sub-seasonal meteorological forecasts provided by MeteoSwiss. This study employs lumped-parameter models from the Pastas software (Collenteur et al., 2019) to simulate groundwater levels at seven strategically selected monitoring wells across Switzerland. These models are driven by ensembles of daily meteorological forecasts to generate ensemble daily groundwater level predictions. A noise model is applied to enhance forecast accuracy, a step that was found critical to improving forecast quality. The forecast quality is verified using weekly re-forecasts from 2012-2023 and compared against two naive benchmarks: persistence and climatology-based forecasts. The results indicate that skilful forecasts of groundwater levels can be made for the entire forecast period. Whether the ensemble groundwater predictions outperform, the naive forecasts are related to the response time of the groundwater system.