Statistical properties of water level extremes along the St. Lawrence fluvial estuary

The governmental Flood Hazard Identification and Mapping Program (FHIMP) seeks to update standards for flood mapping and risk area definition in Canada. Within this initiative, Environment and Climate Change Canada (ECCC) has been mandated to provide 2D simulations of water levels in the St. Lawrence fluvial estuary to estimate return periods of extreme water levels under historical and future conditions. Long-term fine-scale hydrodynamic simulations are necessary to reproduce accurately the complex interplay of hydrological, meteorological and tidal processes responsible for extreme water levels in this system. However, the substantial computational resources and time needed to run the hydrodynamic numerical models constrain the feasibility of producing numerous long-term simulations with a wide range of potential flood-generating conditions. Consequently, this study considers a complementary statistical framework to assess the extreme characteristics and drivers from historical data to prepare input scenarios for climatic projections. 

Event-based analyses of water level records are conducted at 18 stations across the St. Lawrence system using univariate and multivariate techniques to characterize the observed extreme dynamics and flood events. Specifically, univariate frequency analysis is applied at each station to quantify local flood risk based on approximately 400 extreme events observed in the Estuary between 1972 and 2022. Multivariate investigations based on a non-stationary tidal harmonic regression tool (NS Tide) are then used to study the system dynamics involved in major observed events and reconstruct the extreme water level series using a set of hydrological, meteorological, and astronomical covariates. Finally, multivariate spatial analyses are performed on the identified extreme events and NS Tide continuous reconstructions. The goal is to assess the characteristics of high water-level events (e.g., duration, seasonality, and probability distribution) and extreme drivers at the local and regional scales.