Discussion
The results found in this study
using measured and simulated pre-flood hydroclimatic factors contribute
to improve our understanding of interannual variations in spring flood
characteristics. Overall, the ranking of preconditioning factors based
on their frequency of appearance as significant predictors in the linear
models of Qmax across the twelve basins is as follow:
(i) snowmelt intensity (mean and max), (ii) rainfall intensity (mean and
max), (iii) snowmelt volume, (iv) rainfall amount, (v) peak SWE and (vi)
soil moisture. One note of caution to be mentioned is that the stepwise
approach finds the best combination of factors explaining the most
variations in Qmax, and as such can remove predictors
that are still important on their own, but that are redundant
(collinear) in a multivariate context. Still, snowmelt intensity also
appears as the most important univariate predictor of
Qmax as shown by the correlation analysis (Fig. 5), but
peak SWE (SWEmax), which is the second-best univariate
predictor of Qmax (Fig. 5) was often excluded from the
multivariate models. A thicker snowpack is more likely to survive later
into the spring season and be subjected to faster melt rates (e.g. Aygün
et al., 2022; Musselman et al., 2017), which could explain the redundant
predictive power of these two variables, along with
Meltsum, in the multivariate models. Still,
SWEmax by itself could only explain 10 to 28% of the
variability in Qmax in 8 out of 12 basins as shown by
the bivariate correlation analysis (Fig. 5). This shows that the memory
of the snowpack in early spring is not sufficient to accurately forecast
springtime flood magnitude in southern Quebec.
The pre-flood mean melt intensity (Meltint) emerged as a
good predictor of the peakflow magnitude in seven basins (Acadie,
Famine, Beaurivage, Etchemin, Matawin, Bras du Nord and York) and was
the most skillful predictor for four of these basins (Acadie, Famine,
Beaurivage, Etchemin). The maximum melt intensity
(Meltintmax) was also a good predictor but only in the
Nicolet and Ouelle basins. So, overall, snowmelt intensity appears to be
the dominant control on peakflow magnitude for 9 of the 12 basins
studied. SWEmax on the other hand was only retained as a
significant predictor of Qmax in four basins (Nicolet,
Bécancour, Famine and Etchemin).
The sensitivity of peakflow magnitude to antecedent hydroclimatic
conditions also varied according to land cover and use. Overall, the
interannual variability in peakflow magnitude was primarily controlled
by the snowmelt dynamics (initial snowpack SWE, snowmelt amount and
intensity) in the more southerly and/or more agricultural basins, while
in the northern, snowier and more forested basins rainfall conditions,
especially the rainfall intensity, were more important (Fig. 7). This
can at first appears counter intuitive, that spring floods in southerly
basins with less snowfall and thinner snowpacks are controlled by
snowmelt dynamics, while rain events are the main trigger of floods in
colder, snowier northern basins. However, snowmelt in forested basins is
slower due to shading by the canopy (Ellis et al., 2011; Gelfan et al.,
2004), so that Qmax variability is less dependant on
SWEmax. Also, the more porous forested soils and reduced
soil freezing under thick snow covers have been found to favor snowmelt
infiltration, which attenuates flood peaks (Aygün et al., 2022). On the
other hand, SWEmax is more variable interannually in
agricultural basins (Table 1); melting in open fields is fast, and
infiltration is restricted in the often clay-rich and compacted soils
(Aygün et al., 2020), which all boost the influence of snowpacks and
snowmelt rates on peakflow interannual variability.
Soil moisture was considered to be a key factor in controlling runoff in
snow-dominated basin in previous studies (Wever et al., 2017). In this
study, no coherent correlation was found between the simulated degree of
soil saturation and peakflow variations. Even within the multivariate
regression analysis this factor was found to be a significant predictor
of peakflow magnitude and timing only for three basins, and a
counterintuitive negative effect was found for two of these basins
(Bécancour and York). The negative effect of soil moisture on
Qmax could be an indirect effect reflecting the
depletion of the soil reservoir during cold winters with limited
snowmelt and rainfall, which could then be associated with thicker
snowpacks and higher flows in the following spring. However, the lumped
GR4J model does not consider soil freezing processes, which could be
important especially in agricultural basins with thinner snowpacks
(Aygün et al., 2020). Therefore, further research is needed in Quebec
basins using models that explicitly represents soil freezing and fall
moisture ‘soil memory’ in order to better simulate pre-melt soil
moisture and its effect on runoff partitioning. Soil freezing is often
assumed to play an important role on infiltration, but deep snowpacks
can also inhibit soil freezing and cancel its impact on infiltration
(Aygün et al., 2021; Aygün et al., 2022).
As for the peakflow timing, a pattern emerged in which an increasing
amount of low-intensity rainfall combined with increased snowmelt
amounts delayed flood peaks in the more northerly basins, while in the
more southerly basins, slower snowmelt and increased rainfall amounts to
a lesser extent, led to later flood peaks (Table 7).
Our results are different than those reported by Curry and Zwiers (2018)
in the Fraser River basin in western Canada, between the Coast Mountains
and the Continental Divide, where the generation of spring runoff was
found to be controlled mainly by the maximum accumulated SWE and
secondly by the melt rate, with rainfall and soil moisture playing
lesser roles. On the other hand, Coles et al. (2016) found that the
processes responsible for the generation of runoff in the Canadian
prairies hillslopes were, in order of importance, the total snowfall,
snow cover amount, fall soil surface water content (0-15 cm) and melt
rate. The more humid climate of southern Quebec compared to the Canadian
Prairies, and the lower elevation compared to the mountainous basins of
western Canada, could explain the fact that interannual variations in
accumulated SWE are generally less important than the melt rate and
the quantity and intensity of
rainfall events during snowmelt. Our results showed that interannual
variations in snowmelt volumes were either the prime driver, or as
equally important as rainfall, in controlling flood volume variability
(Table 5 and Fig. 4). However, our regression analysis showed that
snowmelt variables were the most important drivers of peakflow
interannual variability in the more agricultural southern basins, even
in the southernmost Acadie basin where snowmelt contributes less water
than rainfall to flood volumes. Conversely, in the more northerly, snowy
and forested basins flood volumes were primarily controlled by snowmelt
volumes, whereas rainfall was more important in controlling interannual
variations in peakflow.
Initial basin conditions (snow storage, soil moisture) and their
forecasting skill are very important for the seasonal prediction of
streamflow (Foster et al., 2018; Koster et al., 2010; Li et al., 2009;
Mahanama et al., 2012) but these variables are not well measured in most
basins. Turcotte et al. (2010) discussed the difficulties encountered by
the prediction systems developed for Quebec basins due to errors in the
snow observation methods. Therefore, using satellite products of snow
cover in conjunction with physically-based models might be a good way
forward to improve our understanding of the spring freshet generation
mechanisms and the independent role of snow cover, rain on snow events
and the soil moisture status in future snow hydrology studies in Quebec.
Still, this study showed that knowledge of snow storage in early spring
(SWEmax) gives only limited forecasting capability for
flood magnitude and timing in southern Quebec, and that synoptic scale
weather variability plays an important role in defining rainfall and
snowmelt intensity, which contribute largely to runoff processes and
ensuing flood characteristics.