Pricing Flood Insurance with a Hierarchical Physics-Based Model - Boudreault, Mathieu; Grenier, Patrick; Pigeon, Mathieu; Potvin, Jean-Mathieu; Turcotte, Richard (North American Actuarial Journal, 2020)
Floods account for a large part of global economic losses from natural disasters. As a result, the private insurance sector is increasingly participating in the financial risk sharing, thus expanding the role of actuaries to flood risk management. In this article, we investigate pricing and spatial segmentation of flood risk in the context of private insurance, meaning that individual risk assessment should minimize adverse selection. As such, we design a hierarchical flood risk model that allows an assessment at the individual level. Our model relies on a chain of physics-based climate, hydrological, and hydraulics modules combined with civil engineering methods to map the distribution of individual flood losses at high resolution. Building on such approach, we design pricing and segmentation methods tailored for flood risk management. We then apply the methods to study flood risk in a small city in the province of Quebec. We calculate premiums, analyze the impacts of risk sharing, set pricing territories consistent with the spatial flood risk, and finally, quantify the impact of greenhouse gas emission scenarios on individual and aggregate losses, premiums, and tail risk measures.
Publisher: Routledge _eprint: https://doi.org/10.1080/10920277.2019.1667830
Keywords: Site_internet_RIISQ

Mapping SWE in near real time across a large territory using a particle filter - Odry, Jean; Boucher, Marie-Amélie; Lachance Cloutier, Simon; Turcotte, Richard; Saint-Louis, Pierre-Yves (2020)
In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites. The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered. In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework. The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys. References: Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20. Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)