Death tolls and economic losses from natural hazards continue to rise in many parts of the world. Only in 2018 they caused almost 12000 deaths across the world and over 130 US billion dollars of economic losses (CRED, 2018). European states are experiencing a continuous and significant burden from multiple natural disasters (Wolfgang et al., 2019): in 2016 Germany, Belgium, and Switzerland have been hit by a series of flash floods and storms causing over $2.2 billion in losses; in 2013 Storm Xaver caused to northern Europe at least 15 fatalities, dozens of injured, and more than €800 million total economic losses (e.g. Rucińska, D., 2019); the July-August 2003 European heat wave that caused a total of 70,000 deaths (e.g. Russo et al., 2019; Bouchama, 2004).
A combination of several factors contribute to explain the increasing social and economic toll caused by natural hazards: increase in exposed assets, i.e. rising population and capital at risk (e.g. Visser et al., 2014), effects of anthropogenic climate change on climatic extremes (e.g. Donat et al., 2016; Bouwer, 2011), better impact reporting procedures (e.g. Doktycz and Abkowitz, 2019).
International agreements on disaster loss reduction (Sendai Framework for Disaster Risk Reduction 2015–2030) explicitly recognizes the benefits of multi-hazard early warning and forecasting systems (MHEW&F-S)”. In 2017 Member States of the United Nations stated the deemed need of MHEW&F-S and agreed on its the definition as integrated system that “address several hazards and/or impacts of similar or different type in contexts where hazardous events may occur alone, simultaneously, cascadingly or cumulatively over time, and taking into account the potential interrelated effects” (UNISDR, 2017). Here the term early warning (EW) is extend with the term forecasting (&F) to explicitly acknowledge that each hazard have a specific forecast lead time which can varies from minutes/hours for flash-floods, days for pluvial floods or heat/cold waves, to months for drought hazard. The scientific community also agree in the need of novel approaches and local scale models for assessing impacts caused by climate change (e.g. Schewe et al., 2019). In order to answer to this call and to move towards a rigorous framework for multi-hazard risk assessment in this project I propose to implement a novel local scale multi-hazard impact-centered forecasting system. It aims to:
· Quantify the three fundamental components of the risk, i.e. hazard, exposure, and vulnerability, and to combine them in a multi-hazard framework, exploiting the most recent dataset and the more appropriate models;
· Provide timely effective warnings (not just of the hazards but also) of the most probable sectorial impacts that may be triggered by multiple hazard conditions.
The system will be unique and novel because it will be the first operative system for multi-risk quantification including:
· a local high-resolution meteorological forecasting system with operationally runs at 1 km resolution capable to explicitly model convective phenomena;
· a detailed and component-based open-source framework for multi-hazard quantification, locally and automatically calibrated for estimating the probability of occurrence of floods, droughts, shallow-landslides/debris-flow, heatwave/coldwaves and windstorms;
· a new set of exposure and vulnerability layers variable in space and time to account for accounting of socio-economic changes in the risk analysis;
· an innovative framework based on a probabilistic graphical model (Bayesian Network) dynamic in time and variable in space, which consider all the risk components (hazards, exposure, and vulnerability as in Formetta and Feyen, 2019) as stochastic variables and models all their possible interactions using probabilistic expressions. The latter will be inferred using a Bayesian learning process involving: 1) a database of reported impacts (fatalities and economic losses) occurred in the past (1980-2018) in the study area specifically organized in the project and 2) the corresponding hazard probabilities, exposure, and vulnerability at the time of the reported event (computed by using points ii and iii).
The project study area is the Trentino Alto-Adige region located in the eastern Italian Alps. The choice of the area is motivated by different reasons: i) there is no such a system currently running (this is also valid for all the others Italian regions); ii) in the near future mountain regions will be even more exposed to the occurrence of climatic extremes due to climate warning. The selected geographical domain is only a test-bed where the framework will be implemented, set up, tested, and verified against observed data for each single component, i.e. meteorological forecasting skills (against rainfall or air temperature measurements), hydrological calibration and validation (against historical measured river discharge), hydrological forecasting skills (against observed river-discharge using forecasted meteorological forcing data), historical and forecasted impacts (against reported fatalities and economic losses
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Bouchama, A. (2004). The 2003 European heat wave. Intensive care medicine, 30(1), 1-3.
Bouwer, L. M. (2011). Have disaster losses increased due to anthropogenic climate change?. Bulletin of the American Meteorological Society, 92(1), 39-46.
Donat, M. G., Alexander, L. V., Herold, N., & Dittus, A. J. (2016). Temperature and precipitation extremes in century‐long gridded observations, reanalyses, and atmospheric model simulations. Journal of Geophysical Research: Atmospheres, 121(19), 11-174.
Doktycz, C., & Abkowitz, M. (2019). Loss and Damage Estimation for Extreme Weather Events: State of the Practice. Sustainability, 11(15), 4243.
Formetta, G., & Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920.
Rucińska, D. (2019). Describing Storm Xaver in disaster terms. International journal of disaster risk reduction, 34, 147-153.
Russo, S., Sillmann, J., Sippel, S., Barcikowska, M. J., Ghisetti, C., Smid, M., & O’Neill, B. (2019). Half a degree and rapid socioeconomic development matter for heatwave risk. Nature communications, 10(1), 19.
Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J., ... & Van Vliet, M. T. (2019). State-ofthe-art global models underestimate impacts from climate extremes. Nature communications, 10(1), 1-14