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.