Introduction

Over recent years interest in large-scale flood modelling has grown due to the increase in computational capacity and availability of remotely-sensed terrain data sets (Alfieri et al., 2013; Dottori et al., 2016; Sampson et al. 2015; Wing et al. 2017; Winsemius et al., 2013). Historically, the vertical accuracy of large-scale terrain data sets has proven to be one of the most significant obstacles to obtaining accurate flood projections (Schumann 2014). Recent improvements to the wider accessibility of high-quality terrain data sets at large scales, such as the LiDAR-rich US National Elevation Dataset or the rapidly improving LiDAR coverage in Quebec with 1-m Digital Elevation Models (DEMs) freely available, have permitted the development of such models at national scales (Wing et al., 2017; Choné et al., in review). When built with high quality input data, national scale flood models have been shown to demonstrate levels of skill approaching those of local scale models (Wing et al., 2017), and even where input data are less detailed they remain a useful starting point for the scoping of more detailed strategic and local-scale flood risk assessments. Due to the lack of accessible information on lakes and reservoirs and the complexity and heterogeneity of the physical processes involved, these models do not usually consider the effect of lakes during flood events and their skill in such areas remains poorly understood (Sampson et al., 2015).
With nearly 900,000 lakes covering more than 10 hectares, Canada accounts for 62% of the world’s lakes, a legacy of glaciers’ scouring action and their subsequent melting (Loïc et al. 2016). Recent flood events, such as the spring floods of 2017 and 2019 caused not only rivers but also lakes to overflow in the province of Quebec. In 2019, these inundations caused major flood stage to be recorded at 6 locations and middle flood stage at 12 locations, including the Lake of the Two Mountains (Lac des Deux Montagnes) and Lake Louise, damaging 2,341 homes and forcing around 1,200 residents to evacuate (Floodlist.com 2019). It is therefore unsurprising that the need for a more thorough understanding of lake water levels at large-scale has emerged in this context.
The literature currently provides various approaches to tackle the challenge of modelling water level stages in lakes. Previous studies focused on modelling the hydrological water balance of water basins including lakes (Setegn et al. 2008) or on identifying trends in the water level variability in a specific lake (Jöhnk et al. 2004). Other studies focused on the long-term prediction of changes in the water level using artificial intelligence methods (Altunkaynak 2006; Buyukyildiz et al. 2014; Khan & Coulibaly 2006; Piaseck et al. 2018) or on real time monitoring via satellite observations (Crétaux et al. 2011). Detailed hydrological models of lakes were developed in data-rich areas, considering riverine inflow, precipitation on the lake surface, evaporation and riverine outflow (Gibson et al. 2006). In other cases, spatially distributed hydrologic models were used for flood event simulation over basins with a complex system of reservoirs (Montaldo et al. 2004) and flood routing methods were applied to evaluate the effect of large artificial reservoirs (Gioia et al. 2016; Lee et al. 2001). However, no studies focused on analysing the impact of extreme flows on the increase of water level in both natural lakes and reservoirs and the consequential flood that could occur on the lakeshore.
This study sought to address this knowledge gap and derive a methodology that could approximately define the water level increase in lakes and reservoirs due to an extreme event with a specific probability of occurrence, and thus delineate the flood prone area in the surroundings. Ideally this method should be applicable to different types of water bodies, including natural lakes and artificial reservoirs. Since the final purpose of such a methodology is to be applied in the framework of large-scale flood simulations, the information required for each lake cannot be extensive and has to be easily available in a semi-automated way at national scales.