Table 1
For the watersheds considered, Övre Abiskojokk, Kaalasjärvi, Tängvattnet
and Niavve are the steepest watersheds (Table 1). Övre Abiskojokk,
Kaalasjärvi, Killingi and Gauträsk have higher than average elevations
of the watersheds, which makes Övre Abiskojokk and Kaalasjärvi the two
most mountainous watersheds in this study, both residing next to each
other (Table 1). Karesuando, Mertajärvi, Lannavaara and Junosuando have
the lowest average elevations, with Mertajärvi being the lowest.
Karesuando, Mertajärvi, Lannavaara and Karats have the slightest slopes
of the watersheds, with Mertajärvi again being the flattest. Övre
Abiskojokk rests in the pass between the mountains that border Norway
and Sweden, which brings warm winds from the Baltic Sea. Mertajärvi and
Karesuando are near the Finnish border and are the most northern
watersheds. Tärendö and Junosuando stretch into the eastern part of the
country, characterized with lower elevations than other watersheds
(Figure 2). Tärendö is the largest watershed, stretching close to the
Gulf of Bothnia. (Sjöberg et al., 2013), followed by Karesuando and
Junosuando. The smallest watersheds are Tängvattnet, Skirknäs,
Mertajärvi and Övre Abiskojokk (Table 1).
Data availability statement
The discharge data that supports the findings of this study were
obtained from the Swedish Meteorological and Hydrological Institute
(SMHI), at Vattenwebb. URL:
(https://vattenwebb.smhi.se/station/#),
the list of catchment used can be found in the supplement.
Meteorological data were taken from (SMHI), from the URL:
(https://opendata-download-metobs.smhi.se/explore/?parameter=0#).
These data were derived from resources available in the public domain.
Daily measurements of precipitation, maximum and minimum temperatures
and snow depth were used. Evapotranspiration is complex in arctic
conditions and studies have shown its importance and the changing
influence as climate conditions change (Hinzman & Kane, 1992;
Young-Robertson et al., 2018). Maximum and minimum temperatures were
used to roughly estimate daily potential evapotranspiration using a
Priestley-Taylor approach (Priestley & Taylor, 1972). For watersheds
where discharge data is collected separately from meteorological data,
the closest geographic meteorological station was used, the name of
sites used for each watersheds is in the supplement.
Figure 2
Study Approach
To determine the degree of non-linearity of the storage-discharge
relationship (β ), we selected hydrograph observations when Q was
larger than 0.5 mm/d in order to focus on the wetter periods when we
expect a larger effect of frozen soil layers and exclude low flows with
deviating recession patterns. In addition, we selected periods when both
precipitation and evapotranspiration were less than half of Q. Data was
excluded during the first 3 days after a precipitation event to avoid
errors in precipitation and timing thereof to affect the estimation of
watershed response.
To identify and quantify the potential effects climate warming may have
on river recessions, we propose the following four analyses and explain
in detail the approach for each.
Temporal trends in recession slopes over time for each catchment. We
expect to find increasingly non-linear storage-discharge relationships
(i.e. positive trends in β) in a warming climate.
Trends in recession slope are determined from non-overlapping moving
time windows of three years, encompassing the entire three years. A too
long time window means less recession slopes points and large
uncertainty in trends; too short of a time window yields large variance
in the data which obscures trends. Both the Mann Kendall and Theil Sen
test were used to determine the trend characteristics. The Mann Kendall
test assesses the monotonic trend significance, and Theil Sen test
examines the robustness of the linear trends. In addition to the Mann
Kendall test, the Theil Sen test was used because it is relatively
insensitive to outliers and gives a magnitude of the trend (Figure 3b).
Recessions are grouped into spring and summer periods (Figure 3a).
During spring periods, we expect that the hydrologic effects of frozen
grounds are more influential than during summer periods. Therefore, we
expect spring periods have a lower recession slope (more linear
storage-discharge relationship) than summer periods.
The start of spring is marked by snowmelt. During this time the ground
will often still be frozen. The thawing soils may either be dry and
absorb part of the snowmelt or be fully saturated, which will induce
overland flow (Kane & Stein, 1983). Start of spring was defined based
on a degree day methodology approach proposed by Ploum et al. (2019).
Mean daily temperatures are summed but cumulative sums below zero were
set to zero. The first day the cumulative sum exceeded 15-degree days
was defined as the onset of spring.
Snow depth was irregularly documented in the catchments by SMHI, but
when it was not available or very sparse, the depth was roughly
approximated by using the recorded data in combination with summing the
winter precipitation during freezing conditions (0˚C and below).
\(\frac{\text{dSWE}}{\text{dt}}=precipitation\ \) for Temperature ≤
0˚C (3)
\(\frac{\text{dSWE}}{\text{dt}}=-1.8*temperature-0.8*precipitation\ \)for
Temperature > 0˚C (4)
The snowmelt rate is 1.8 [mm SWE/day/C] above 0˚C with an added rate
of 0.8 times precipitation if any precipitation was recorded (Kustas,
Rango, & Uijlenhoet, 1994). When the snow was melted, a 31-day lag was
added to allow snowmelt water to leave the watershed. The end of summer
was determined by three consecutive days with equal or below 0˚C average
temperatures. We did not define perfect spring and summer periods but
rather divided the runoff season into two contrasting periods each with
enough recession events to allow for statistical comparison. Our results
are not sensitive to small changes in the end of spring/begin of summer
definitions. The lag time could vary between 21 and 31 days and the
results did not change significantly. Therefore, we choose a larger lag
time to increase spring periods and increase the number of recessions
during spring. An accurate onset of spring excluding low winter flows
before the spring melt was important.
Spring and summer recessions were compared to determine if the recession
slopes were significantly different between periods (Figure 3a and 3d).
For this comparison we used the two-sample z-test (Cohen, Cohen, West,
& Aiken, 2002).
We analyzed temporal trends for spring and summer recession slopes to
identify which season contributed most to the observed yearly trends
from analysis 1.
This analysis followed the same method as for the trend over time
analysis described in step 1 (above) but was applied separately for
spring and summer periods. For each 3-year window, spring and summer
recession slopes were determined, if the recessions were significantly
different, then it became essential to understand if this difference
changed over time as the hydrology in the catchment evolves or if the
seasonal recessions have always been constantly different (Figure 3b).
We examined winter temperature influence on recession slopes. We
expect recessions following winters with deeply frozen soil behave
more linearly than after warm winters. For this analysis into the
potential effect of frozen soils, we look at average winter
temperature, winter snow depth and a combination of both.
The depth of seasonal freezing depends on winter air temperatures and
the thickness of the insulating snow cover (Tingjun Zhang, 2005). The
soil potentially freezes deeper during cold winter. Therefore, it
potentially takes longer for frozen soil to thaw during spring as
compared to warmer winters (Lawrence & Slater, 2010). Hence, recession
during springs following cold winters are expected to behave more
linearly compared to the warm winters. We split winters into two equally
sized groups, half are cold winters and half are warm winters, based on
the average winter temperatures (Figure 3a).
Snow depth is another important control on frozen soil depth (Hardy et
al., 2001). The snow depth was determined as described in step 2
(Equation 3 and 4) as a proxy for snow cover insulation to approximate
frozen soil depth. This method was not intended to quantify depth of
snow, but rather qualitatively distinguish between deep and shallow snow
depth. The assumption is that snow insulates the ground from freezing
temperatures. If seasonally frozen soil depth influences the flow paths
and impacts the watershed’s recession, then a significant change of
recession slope between the snow depth groups would be expected. The
final winter indicator of frozen soil depth and extent was a combination
of shallow snow depth and winter temperature. The insulation effect of
snow peaks at about 40 cm (Tingjun Zhang, 2005), therefore to obtain the
shallow snow depth effect, we took a little more than half of 40 cm (25
cm → SWE of 2.5 cm). Using the previously calculated snow depth, we took
only the periods with SWE less than 2.5 cm. The temperatures during
these annual periods were summed, and then the years were divided into
two groups to create shallow snow cold winters and shallow snow warm
winters (Figure 3a). For all three indicators of frozen soil conditions,
we used the two-sample z-test to determine significant differences
between the selected periods (Figure 3a).
Figure 3
Figure 3d shows a z-test graph with confidence lines and a shaded purple
area, which correlates to the purple arrow in Figure 3e. The arrow is a
representation of the area between the recession lines: the larger the
arrow, the greater the difference. The direction of the arrows indicates
findings, upwards implies a result anticipated by our hypothesis and
downwards implies a result divergent from our hypothesis.
Results
Trend over time
Our analyses demonstrated a widespread increase in non-linearity of
recessions in Northern Sweden for all seasons combined (Figure 4). Ten
of sixteen watersheds show a significant trend of increasing
non-linearity in the storage-discharge relationship (i.e. increasing β)
for both trend tests (Mann Kendall and Theil Sen) and no watershed had a
significant negative trend. Overall, fourteen of the sixteen watersheds
had a positive trend, and twelve watersheds had a significant positive
trend over time for the Theil Sen test (Table 2). On average, recession
slopes increased 0.006 Y-1 with mostly linear
recessions (β = 0.8-1.3) in the period 1950-1970 increasing toβ = 1.3-2.2 during 2010-2018. Following our hypothesis, a trend
of increasing non-linearity in recession slopes suggests increased
diversity of flow routes contributing to stream discharge.
Table 2
It was not until the 1980’s and 1990’s when recession slopes started to
increase (Figure 4). Mertajärvi, Tärendö, Niavve, Karats, Stenudden,
Laisvall, Gauträsk, Tängvattnet and Solberg had statistically
significant recession slope increases for both statistical tests. Övre
Abiskojokk, Junosuando, and Skirknäs were significant for only the Theil
Sen test.
Figure 4
Stenudden, Niavve and Karats are geographically close and had similar
recession slopes and trends. Of these, Karats showed the strongest
increase in non-linearity during the last decade (Figure 4). Solberg,
Skirknäs, Gauträsk and Tängvattnet are also geographically close (Figure
2). Tängvattnet was the lowest of the four with more linear recession
slopes, it had an increase in trend over time, but Tängvattnet starts
with a recession slope under 1.0 and it did not increase above 1.0 until
1995. Junosuando’s recession slope was the only significant negative
trend slope of the watersheds, having significance only for the Theil
Sen test. Junosuando’s gauging site was originally situated upstream of
a river bifurcation, and now the bifurcation is currently upstream of
the site, we suspect this to be the cause of the negative slope.
Spring and summer analysis
Our hypothesis that spring recessions are more linear than summer
recessions was substantiated. Twelve out of sixteen watersheds had more
linear spring recession slopes and more non-linear summer recession
slopes (Table 2). The watersheds with significant results were
designated with thick purple arrows (Figure 5). Mertajärvi, Junosuando,
Stenudden and Tängvattnet were the watersheds without a significant
difference between summer and spring periods. Junosuando has the
bifurcation, which might explain the insignificance.
The Mann Kendall test yielded six watersheds with a significant summer
trend, Mertajärvi, Kaalasjärvi, Stenudden, Killingi, Niavve and
Tängvattnet (Table 2). Thirteen summer recession trends are significant
for the Theil Sen estimator with only Junosuando having a negative slope
for summer trends. The Mann Kendall test yielded only one watershed with
a significant spring trend, Tärendö. The Theil Sen estimator determined
nine spring recession trends were significant. For the significant
spring trends, seven watersheds had a positive slope (Figure 5a).
Figure 5
When comparing the two seasonal periods, summer had the greater number
of significant recession slopes, but increases in recession slopes (i.e.
trends) were generally larger during spring (Figure 5a). In Tärendö,
Gauträsk, Tängvattnet, Solberg, (Figure 5a) yearly trends in recessions
slopes seem dominated by spring changes, only Mertajärvi and Niavve have
dominant summer changes. For all other watersheds, a significant trend
in recession slope could not clearly attributed to either spring or
summer but was driven by both periods.
Attribution to winter conditions
Table 3
Twelve of sixteen watersheds had recession slopes closer to linearity
for colder winters than for warmer winters as shown in Table 3. Ten of
the twelve watersheds had a significant difference. None show
significant higher non-linearity in cold winter compared to warm winter.
The first step to analyze deep and shallow snow depth was to separate
deeply frozen soils from shallow frozen soils. Without available
continuous frozen soil data for the watersheds, determining when frozen
soil depth was at its greatest depth was done by assuming shallow snow
depth does not insulate as much as deep snow depth. Our results were
inconclusive. There was geographical clustering to the significant
watersheds (Table 3, Figure 5b), Övre Abiskojokk, Kaalasjärvi, Killingi
and Tärendö had a higher recession slope when there was deep snow packs.
The four are part of the same mountain range. From our results, we
cannot conclude how a watershed’s recession slope will react to a change
in snow depth.
While looking at Figure 3a, for deep and shallow snow depth, shallow
snow depth for Niavve happens more in recent years, leading us to
believe that there has been a change in snow depths over time. As soil
frost is controlled by both winter temperature and snow insulation, our
final soil frost indicator is winter temperature while snow depth is
shallow. As shallow snow signified poor insulation, cold winters during
shallow snow would result in a deeper frozen layer than warm winters
during shallow snow. This had consistent results. Six watersheds had
significant differences between the cold and warm winters during shallow
snow periods. While it is fewer watersheds than expected, the
significant watersheds had increased non-linearity in recession slopes
during warm winters than cold winters, echoing our anticipated results
for warm and cold winters.
Because many of the warmer winters are within recent years, we wanted to
check if the significant difference in recession slopes between warm and
cold winters was due to winter temperature differences between
individual years or rather related to a general increase in temperatures
during the recent warm decades. Additionally, we wanted to establish if
winter temperature characteristics impacted the recession slopes during
the periods before clear changes in overall recession slope were visible
(the period up to 1990) similarly as during the entire period. We
selected two watersheds with the longest datasets, Karats and Niavve,
split the datasets between when their recession slopes were constant and
when their recession slopes started to increase and performed z-test for
all of the winter characteristics we had examined previously. For this
period (1951-1990) we found there to be no significant difference
between warm and cold winters or shallow snow depth and deep snow depth
for either catchment. During 1951-1990, for shallow snow in cold and
warm winters, Karats is significantly different. However, for the
entirety of Karats’ dataset (1951-2018), it was not significant (Table
3). We should note there is less data if we limit the analyses to
pre-1990, which increases uncertainty and decreases the probability of
finding significant differences.
Discussion
Recession slope trends over time
Watersheds with data starting in the 1950’s had yearly average recession
slopes around 1.0, which have significantly increased to values around
1.5 through the present. We find recession slopes as low as 0.7 and as
high as 2.6 enveloping the entire range of slopes found by theoretical
consideration (Troch et al., 2013). (Brauer et al., 2013; Karlsen et
al., 2019; Kirchner, 2009; Ploum et al., 2019; Sjöberg et al., 2013;
Troch et al., 2013; Van Der Velde, Lyon, & Destouni, 2013) indicated
that watersheds analyzed with different regression methods will result
in different recession slope values. Therefore, comparison of recession
slopes between studies using different methods has to be done with care.
In general, the direction of differences between watersheds and between
periods of the same watershed using the same method are expected to be
comparable (Shaw & Riha, 2012). Within our datasets, there has been no
known physiographic changes, such as topography, artificial lakes, or
changes in discharge measuring method; thus, the changes in recession
slopes come from other aspects, such as land cover change, or thawing
permafrost. If soil frost thaw is truly causing a change in recession
slope, it is mainly by the increasing depth of active layer as the soil
thaws, which contributes to a larger diversity in flow paths
contributing to stream discharge. The recession slope has been shown to
change with changes in land cover and soil type (Bogaart et al., 2016).
In addition to significant differences in seasonal recessions found by
(Karlsen et al., 2019; Ploum et al., 2019), our research contributes
with showing significant increasing non-linearity of recession behavior
since 1950.
Ten recession slope trends over time are positive and significant for
both statistical tests, while no recession slope trends are negative and
are significant for both statistical tests. Sjöberg et al. (2013) found
for the same catchments that late summer recession intercept had
increasing trends, along with increasing minimum winter discharge
trends. While these two characteristics are affected by thawing
permafrost, the extent and agreement between late summer recession
intercept and minimum winter discharge trends varied thought the
watersheds. Lyon & Destouni (2010) and Sjöberg et al. (2013) focused on
late summer for recession analysis and winter for minimum discharge
analyses, during winter when river discharge originates from sub-frost
groundwater, a recession slope of 1 can be assumed. They found
increasing intercepts of the recession is related to changes in the
active layer. We show that for spring and summer conditions during
recent decades linear reservoir behavior (β = 1) cannot be
assumed. Moreover, we confirmed the effects of a warming arctic on river
discharge would be more pronounced during spring conditions, than during
summer conditions by showing the omnipresent trend of increasing
non-linearity in storage-discharge relationships (increasing slopes in
the recession plots) and that these observed changes are dominantly
caused by changes in spring.
Groundwater flow though thawing soils is a complex process. Arctic
watersheds have been documented to be changing over a long period of
time, with streamflow and precipitation increasing, land cover shifts,
changes in soils, decline in permafrost and seasonally frozen soil depth
and changes in the snow cover extent and timing (Bring et al., 2016;
Hirota et al., 2006; Prowse et al., 2015; Wrona et al., 2016). We
provide overwhelming evidence that discharge recession slopes are
increasing in Northern Sweden. This means that storage-discharge
relationships are becoming increasingly non-linear; under low to
intermediate wetness conditions, water is stored more effectively within
the watersheds, while under wet conditions water is released faster,
making river flows more unpredictable and uncertain. It is clear from
these results that hydrological models which aim to predict changes in
artic river discharges caused by climate change cannot rely on constant
storage-discharge relationships, but need to account for climate warming
effects on the physical watershed properties that underlie
storage-discharge relationships.
Seasonal recession slopes
Ploum et al., (2019) explored storage-discharge relationships for Övre
Abiskojokk, a watershed included in our study, and concluded recessions
slopes were likely influenced by soil frost. Ploum et al (2019)
hypothesized that summer recessions slopes would be larger than spring
recession slopes due to the winter soil frost being thawed further in
summer than in spring. Additionally, Ploum et al (2019) showed that the
observed direction of the difference between spring and summer could not
be explained by summer evapotranspiration. Similarly, Karlsen et al.
(2019) consistently found higher summer recessions slopes than in spring
in the boreal catchment Krycklan. With 15 more watersheds, we also find
storage-discharge relationships with consistently higher slopes, (higher
non-linearity), in summer than in spring (Table 2). We interpret this
robust finding in terms of flow paths and storage. The subsurface volume
available for water storage within a watershed increases from spring to
summer. When conditions are wet and storages full, a more immediate and
strong response of discharge to additional rainfall can be expected in
summer compared to spring, although due to snowmelt, discharges in
spring tend to be higher. In line with findings of Karlsen et al.
(2019), it is apparent that spring and summer recession are two separate
recessions and therefore the difference between the seasons is to be
expected.
Spring and summer recession slope trends
We find spring recession slopes have had greater changes compared to
summer. However, our results for spring and summer trends are not
completely straightforward. Although trend analysis with year-round data
shows clear increases in non-linearity of the storage-discharge
relationship, separate trend analyses for spring and summer recessions
do not clearly yield one dominant period that controls the observed
yearly trend in recessions. For twelve watersheds, summer has the
greater recession slope (i.e. more non-linear) (Table 2). Spring has
lower recession slopes when compared to summer, but over time, we see
spring as the season undergoing the greatest change. Frampton, et al.,
(2011) suggested that thawing soils lead to increasing flow pathway
diversity, which in turn will decrease seasonal variability in water
flow. This can explain why the summer recessions observed in this study
do not have strong trends as this is the period when thawing soils are
at their greatest depths. However, when permafrost is omnipresent within
the watershed and permafrost is disappearing this will also strongly
affect summer discharge recessions. Potentially, a stronger effect could
be expected in summer rather than during spring when soils are still
primarily frozen from the previous winter.
Attribution to winter conditions
Ploum et al. (2019) suggested winter air temperatures have an influence
over recession slopes. Following this logic, if winter air temperatures
have an effect on recession slopes, we would see impact on recession
analysis when our dataset is separated by years following cold and warm
winters. For the region considered, there has been increasing winter
temperatures since the start of 1900 (Luterbacher, Dietrich, Xoplaki,
Grosjean, & Wanner, 2004). With no exceptions, all ten significantly
different watersheds have a higher recession slope following warm
winters than cold winters. Therefore, it can be concluded that there is
a significant increase in non-linear recession slopes during warmer
winters than colder winters for these sub-arctic watersheds (Figure 5b).
Our hypothesis was that this pattern is because warmer winters have a
shallower frozen soil layer, which thaws quicker than after winters with
a deeper frozen soil layer. However, we could not confirm this pattern
when examining data before 1990 for the two watershed with the longest
records. St. Jacques & Sauchyn (2009) also found that winter air
temperatures affecting streamflow can be seen; they suggested that these
changes are caused by the thawing soil that increases infiltration and
flow paths.
Payn et al. (2012) stated that correlation between flow paths and
surface attributes decreased during recession leading to subsurface
structures gaining influence on the flow paths. Subsurface structures
can be many things, including permafrost, geology of the watershed and
seasonally frozen soil. Snow is an important feature for frost depth as
it insulates the ground from heat loss. Hirota et al. (2006) found
correlation between increasing snow depth and decreasing seasonally
frozen soil depth. Seasonally frozen soil is a complex process that
cannot be simplified with just a snow depth assumption. Eleven of the
watersheds do show significant different recession slopes between deep
and shallow winter snow packs, but there is no clear pattern. Out of
eleven significant watersheds, six watersheds show the expected higher
recession slope during deep snow depths. It is also meaningful to
recognize that recent years are having smaller snow depths during winter
in this region (Figure 3a). We can maintain that there is a difference
in recession slopes between shallow and deep snow depth, but with deeper
snow depth becoming less common while winters temperatures are
increasing, the link between deep snow depth and soil frost might be
diminishing.
Shallow snow depth (below 25cm) periods and winter air temperatures were
combined to identify years with more and less than average frozen soil
depth. We expected soils to freeze deeper when cold temperature occur
during periods with little snow (Hardy et al., 2001; Osterkamp, 2007).
Six watersheds had significant differences, with higher recession slopes
for shallow snow warm winters (Table 2) showing shallow frozen ground
does have an effect on recession slopes. When shallow snow depth was
separated by winter temperatures, watersheds had significantly lower
recessions slope during cold temperatures compared to warm temperatures,
indicating the lesser extent of soil frost during warm winters. Using
snow depth and winter air temperatures in combination to determine the
extremes of frozen soil depth yielded more unambiguous results than just
snow depth showing that six out of sixteen catchments have significantly
more non-linear recession slopes during shallow snow warm winters. The
majority of watersheds did not have significance, but none of the
catchments were significantly more non-linear recession slopes during
shallow snow cold winters. Kohler et al., (2006) modeled the snow depth
in Abisko and concluded increasing snow depth averages over the last
century. Naivve’s snow depth trend and Abisko’s snow depth trend
indicate different watersheds have different snow depth responses to
climate change. The increasing or decreasing of snow depth has been seen
in multiple catchments in Sweden, Åkerman & Johansson (2008) found five
of their nine watersheds to having increasing snow depths. Because our
catchments are undergoing unique snow depth changes over time, the
climatic shift towards an increase or decrease in snow depth in various
watersheds could explain why some watersheds have significance during
shallow snow depths between cold and warm winters and others do not.
Warm winters cause increased non-linear recession slopes, and shallow
snow depths lead to greater frozen soils depths, these two winter
conditions are becoming regular in the arctic and counterbalance each
other’s effect on the recession slope.
We provide evidence that recession slopes depend on preceding winter
conditions, which controls when flow paths start to flow and how many
are available. As recent winters globally have been some of the hottest
on record (LeComte, 2020), we wanted to determine if similar significant
results can be found in the first half of our datasets, when recession
slopes were not increasing so quickly. For warm and cold winters, there
was no significance difference, nor was there significance between
shallow and deep snow depths. Karats before 1990, did show a significant
difference between cold and warm winters during shallow snow periods,
implying the sudden increase in recession slope from 1990 onwards
potentially masked the influence of snow depth on recession slopes.
Discovering the point when frozen soil thaws can lead to recognizing
changes in the watershed recession through a transitional point of
permafrost thaw rather than the results of the arctic adapting to
climatic changes during the last decades.
Effects of frozen soil and permafrost on river recessions: A
conceptual model.
Based on our results and results of previous studies we summarized our
finding into a conceptual model (Figure 6). We found approximately
linear storage-discharge relationships during spring for all catchments.
Moving into summer, these storage-discharge relationships become more
non-linear, a similar transition we find with the storage-discharge
relationship trend over time. When permafrost thaws or the extent and
depth of seasonally frozen soils reduces both the spring and summer
storage-discharge relationship become more non-linear. We even tend to
see that spring recessions change more than summer recessions. Although
conceptually straightforward, it remains a challenge to confirm this
conceptual model with a physically based model. First steps have been
made in this direction by (Frampton et al., 2011; Sjöberg et al., 2016;
Walvoord, Voss, & Wellman, 2012). These studies looked at a numerical
model for non-isothermal, three-phase flow of air and water or recession
intercepts and annual minimum discharge determined by the interactions
between groundwater and permafrost or a numerical simulation of flow
paths and water budget with winter base flows. Such a dynamic modelling
effort is out of the scope of this research but is crucial in making
progress in the prediction of changing water flows and dynamics of river
discharges within the warming arctic.
Figure 6
Seasonally frozen soils along with decreasing amounts of permafrost have
an effect on groundwater flow in arctic catchments (Figure 6). Recession
slopes in this study’s catchments are becoming increasingly non-linear,
though the degree of change depends on the watershed’s topography,
differences in bedrock and surficial geology and current continuity of
permafrost presence. We hypothesized that recession slopes are partly
controlled by frozen ground and in turn, react to thawing ground. Our
results support this hypothesis, but we cannot exclude that other
changes within the watersheds could have similar effects on recessions.
This study did not examine the potential effect of soil moisture on the
recession slope. Other studies are suggesting arctic catchments are
becoming wetter, which may also change the storage/discharge
relationship, and consecutively, the recession slope (Raynolds &
Walker, 2016; Rowland et al., 2010). The recession slope change could
also be caused by land cover change, as vegetation migrates further
north to the Arctic, different flora would have different water needs
influencing the hydrological response, along with different
evapotranspiration rates, and therefore there would be a change in water
yield flowing to the rivers (Costa et al., 2003).
While we wanted to answer how permafrost and seasonally frozen soil thaw
affect recession slopes, the extent of permafrost in Sweden is mostly
discontinuous or sporadic. Although continuous permafrost has been
present in Sweden, the extent and timing appears uncertain as permafrost
detection methods have been limited in the past. (Gisnås et al., 2017;
Kullman, 1989; Lagerbäck & Rodhe, 1985; Sjöberg et al., 2013). The
remaining permafrost in Sweden is in mountainous regions. Gruber &
Haeberli (2009) states for mountainous regions, permafrost is impacted
by the slope of a watershed along with topography, elevation and strong
winds dictating snow cover conditions. If the lingering permafrost is in
alpine regions, next steps should include permafrost in flat regions to
see if we find similar changing patterns.
Conclusion
We show watersheds in Northern Sweden have been undergoing a significant
change in the recession behavior since 1950 and especially since 1990.
The storage-discharge relationships of thirteen out of sixteen
investigated watersheds has become more non-linear, with in-general a
larger change in spring than summer. This means these watersheds are
better able to store their water under low to medium wetness conditions
but release their water more instantaneously under wet conditions,
making river discharge in the Arctic more unpredictable. We hypothesized
that these changes are primarily caused by disappearing permafrost and a
reduced depth of seasonal soil frost. Although several of our
data-analysis tests confirm parts of this hypothesis, without solid
knowledge of the extent of permafrost, depth of winter frozen soils and
summer active layer depth, this link remains speculative. Moreover, it
is likely other landscape changes such as vegetation change, soil
organic matter and soil biota changes also strongly affect water flow
paths thusly contributing to the observed changes in storage-discharge
relationships (Figure 6). Our results clearly demonstrate that
predicting arctic river discharges in a warming climate cannot rely on
models that assume fixed storage-discharge relationships, but requires
models that describe how warming affects the physical properties of
watersheds that underlie the storage-discharge relationships.
The main contribution of this study is that we established hydrological
change trajectories of the storage-discharge relationships for
terrestrial Northern Sweden. Because of the complexity of the changing
Artic in which climate, vegetation, soils, ice, and landscape form (e.g.
river systems) interact, the future of the Artic is very difficult to
predict. Understanding and predicting the effect of further Arctic
warming starts with establishing such ongoing change trajectories and
use process-based models to reproduce and extrapolate into the future.
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Supplement
Table 4