Introduction
The hydrology of cold regions is characterized by long winters dominated
by snowfall and rapid spring melting, which is the main cause of the
high spring streamflow (Pomeroy et al., 2016). In the province of
Quebec, Canada, the amount of accumulated snow is very important, with a
mean annual maximum of 200 to 300 mm of snow water equivalent (SWE)
(Brown, 2010). As a result, the streamflow regime is nival to
nivo-pluvial and strongly influenced by the snowmelt contribution, which
occurs between April and June depending on the basin geographic location
and the year. In the southern part of the province, snow begins to
accumulate in November and melting occurs between March and May. Peak
flow typically occurs in the spring following snowmelt while a second
flood peak typically occurs in summer in response to convective
rainstorms, or in the fall caused by the advection of moist air masses
with above-freezing temperatures. In northern Quebec, snow accumulation
begins earlier in October and melting occurs later, in June and July,
with a single streamflow peak mainly driven by snowmelt (Assani et al.,
2010a; Buttle et al., 2016; Saint-Laurent et al., 2009).
Knowledge of the SWE stored in the winter snowpack and of ablation
dynamics in the spring is key for accurate streamflow predictions and
operational management of reservoirs in Quebec. As such, a reliable
seasonal forecast of spring freshet based on winter and early spring
conditions is essential for reservoir operators to optimize two
conflicting objectives, namely flood protection and hydropower
production (Turcotte et al., 2010), as well as for governmental agencies
to prepare flood mitigation and disaster relief measures. Nevertheless,
the relation between snowpack conditions and the inter-annual variations
in the magnitude and timing of the spring peak flow is not
straightforward, due to the complexity of spring runoff generation
mechanisms (Merz and Blöschl, 2003; Tarasova et al., 2019). In fact, the
same annual snow accumulation can induce more or less severe floods
because of the multiplicity of antecedent hydrological conditions that
can control runoff in addition to snowpacks, such as meteorological
conditions during the melt period, the occurrence of rain-on-snow
events, soil moisture and soil freezing. Therefore, a good understanding
of the flood generation mechanisms and of the relative contribution of
the key driving factors involved is essential to explain the interannual
variability of the spring peak flow characteristics and guide future
forecasting efforts.
The variability in flood characteristics in North America has been
linked with large-scale climatic
indices, and several previous studies have studied
how
these indices influence extreme floods (Assani et al., 2010a; Assani et
al., 2010b). Mazouz et al. (2012) studied the relationship between the
interannual variations of high spring flow characteristics in southern
Quebec (magnitude, duration, period of occurrence, frequency, and
variability) and several global climatic indices using canonical
correlation analysis.
A
significant correlation between the Atlantic Multi-Decadal Oscillation
(AMO) and North Atlantic Oscillation (NAO) indices and four flood
characteristics (duration, period of occurrence, frequency, and
variability) was found, while no relationship was found between these
indices and the flow magnitude. This correlation was explained by the
low temperature during the negative phases of the AMO and the positive
phases of the NAO, which causes a later date of occurrence, a higher
frequency, a longer duration and lower variability of heavy spring
floods (Mazouz et al., 2012).
Heavy rainfall events during spring can also contribute significantly to
runoff while also accelerating snowmelt, causing more devastating floods
(Fang and Pomeroy, 2016; Pomeroy et al., 2016; Sui and Koehler, 2001)
depending on the antecedent conditions of the snowpack (Garvelmann et
al., 2015). The relative contribution of melting and rainfall to runoff
and floods becomes more complicated during rain-on-snow events and
affects the results of forecasting studies. Rain-on-snow events in
Canada have been addressed by several studies (Dyer, 2008; Mccabe et
al., 2007; Pomeroy et al., 2016; Wayand et al., 2015). In Quebec, many
devastating spring floods have been caused by a combination of heavy
rainfall during melting and a deep accumulated snowpack, such as for the
Richelieu river floods in 2011 (Saad et al., 2015). Teufel et al. (2018)
studied the devastating spring floods that occurred in Montreal during
May 2017, showing that heavy rainfall events during April and May
combined with snowmelt were the culprit of these extreme events.
Likewise, antecedent moisture conditions in catchments plays a key role
in runoff generation during melt; the degree of soil saturation below
the snowpack determines the infiltration and runoff of snowmelt water in
snow-covered basins (Koster et al., 2010; Mahanama et al., 2012). These
two studies quantified the contributions of snow accumulated on January
1st and soil moisture to the skill of seasonal forecasts of spring
snowmelt in 23 basins of the eastern United States. They demonstrated
that despite the important role of snow, the contribution of soil
moisture to the skill of streamflow forecast was significant. Several
studies showed also the importance of ’soil memory’, i.e. soil moisture
conditions before soil freezing (Curry and Zwiers, 2018; Mahanama et
al., 2012; Webb et al., 2018; Wever et al., 2017) so that understanding
the relationship between floods, soil moisture and snow cover in these
basins is necessary to understand the spring streamflow generation.
The main challenges in studying how antecedent hydrological variables
control spring floods are the choice of predictors, the possible
interaction between them, the period over which these factors are
calculated and the unavailability of observations for some variable such
as soil moisture and SWE (Coles et al., 2016; Curry and Zwiers, 2018;
Fang and Pomeroy, 2016; Nied et al., 2013; Nied et al., 2014). In
western Canada, Curry and Zwiers (2018) investigated the influence of
hydroclimatic conditions on the variability of annual maximum daily flow
magnitude using multivariate linear regression models in a
snow-dominated basin. Potential predictors were ranked according to
their degree of control on the maximum basin peak flow. The maximum
annual snowpack (SWEmax) ranked first, followed by the
snowpack melting rate calculated between the date of
SWEmax and that of peak flow, the Pacific Decadal
Oscillation (PDO) and El Niño-Southern Oscillation (ENSO), and finally
the rate of air temperature warming between April 1stand the date of peak flow. Some of the variables used were measured
while others were simulated by a hydrological model, such as the
snowmelt rate and soil moisture. Coles et al. (2016) studied snowmelt
runoff generation in the Canadian prairies hillslopes using a decision
tree learning approach to rank the processes responsible for the
generation of runoff. The impact of antecedent hydroclimate conditions
on flow peaks were, in order of importance, total snowfall, snow cover,
fall soil surface water content, snowpack melt rate, melt season length,
and fall soil profile water content. A salient result was the importance
of the degree of soil saturation during the fall before the frost
period, or ‘soil memory’, in controlling runoff. Fang and Pomeroy (2016)
studied the sensitivity of the June 2013 flood in Calgary to pre-flood
conditions as simulated by the physically-based Cold Region Hydrological
Model (CRHM). They studied streamflow generation processes by varying
the amount of precipitation, the land cover and the soil storage
capacity during the pre-flood period. They showed that runoff increases
rapidly in response to prior accumulation of snow and soil moisture and
that antecedent soil moisture in the basin is a better indicator of
flood magnitude than the antecedent snowpack in this basin. Using
multiple linear regression, Maurer and Lettenmaier (2003) found that
soil moisture dominates runoff predictability in the Mississipi River
basin for lead times of 1.5 months, except in summer in the western part
of the basin where snow dominates. In western Canada, Dibike et al.
(2021) found that basin average maximum SWE, April 1stSWE and spring precipitation were the most important predictors of both
annual maximum and mean springtime flow, with the proportion of
explained variance averaging 51.7%, 44.0% and 33.5%, respectively.
Outside North America, Zhang et al. (2014) used path analysis to
identify influential climatic factors on spring floods in an alpine
catchment in Xinjiang, China. They found that winter snowfall and mean
thawing degree days in spring had the most direct influence on flood
peaks, while accumulated freezing degree days in winter had an indirect
influence on floods.
In Quebec basins, the hydroclimatic drivers, or ‘predictors’ of
interannual variations in the magnitude and timing of spring flow peaks
are not well identified and have not been studied except in relation
with global climatic indices (Assani et al., 2010a; Assani et al.,
2010b; Mazouz et al., 2012). Hence, the main objective of this study is
to identify and better understand the factors that control the
interannual variability of spring freshet characteristics in the
tributary catchments of the St. Lawrence River in view of improving
seasonal flood forecasts. The limited availability of snow depth, SWE
and soil moisture observations has always been an obstacle when
analyzing historical hydrological datasets. In this study, we use
outputs of simplified conceptual models to simulate snow accumulation
and melt as well as soil moisture storage in the basins. We seek to
answer the following questions: (i) is the inter-annual variability of
the spring freshet magnitude mainly dependent on the antecedent
snowpack, with higher flow peak occurring in years with deep snowpack?
(ii) Does the quantity and intensity of rainfall during the pre-flood
period have a strong influence on the characteristics of the spring
freshet? (iii) How do the preconditioning factors vary between basins,
according to their latitude, physiographic region and predominant land
cover?