UNIVERSITY OF GOTHENBURG
Department of Earth Sciences
Geovetarcentrum/Earth Science Centre
Dendrochronological
Analysis of
Flooding Signals
Isabelle Andersson
Sofia Sjögren
ISSN 1400-3821
Mailing address
Geovetarcentrum
S 405 30 Göteborg
Address
Geovetarcentrum
Guldhedsgatan 5A
B765
Bachelor of Science thesis
Göteborg 2014
Telephone
031-786 19 56
Telefax
031-786 19 86
Geovetarcentrum
Göteborg University
S-405 30 Göteborg
SWEDEN
Abstract
Precipitation intensity and mean precipitation are projected to increase across the globe,
which in turn affects the water level and streamflow. Arvika is a town located in western
Sweden and is a place vulnerable to high water levels, and is therefore in need of a risk
assessment of the hydrological variance. In Scandinavia few investigations have been carried
out with tree-ring dating and hydrological parameters. This study explore if dendrochronology
can be used to reconstruct flooding history. By selecting places vulnerable to water levels in
the area around Arvika, it was investigated if flooding signals could be found in Scots pine
(Pinus Sylvestris), and if it is possible to use tree-ring dating for flooding reconstructions.
Tree-ring samples were collected from low and high elevations to investigate if water level,
streamflow and precipitation could be noticed in trees affected by flooding, and if the trees
from different elevations showed differing patterns. The trees at lower elevation, close to the
waterside, showed highest correlation with all climatic factors during winter and early spring,
while the trees at higher elevation showed highest correlation with precipitation during early
summer, water level during summer and streamflow during winter. The low-growing trees
correlated better with temperature than with precipitation, whereas it was the opposite for the
elevated trees. The study concludes that since trees in the studied location showed weak
correlations, they are affected by many different climatic factors at different times of the year.
This made it difficult to point out one signal. However, indications of hydrological signals
were visible, and flooded trees showed a different pattern than the high-growing trees which
implies the possibilities to reconstruct flooding with the use of tree-rings. It is therefore of
importance to evaluate the sampling location and strategy. It should preferably be a site
undisturbed by humans which have only one main limiting factor for the tree-growth.
Keywords: Dendrochronology, Tree-rings, Arvika, Glafsfjorden, Flooding, Water level,
Streamflow, Precipitation.
i
Sammanfattning
Intensitet av nederbörd och genomsnittlig nederbörd förväntas öka över hela världen, vilket i
sin tur påverkar vattennivån och vattenföring. Arvika är en stad belägen i västra Sverige som
är känslig för höga vattennivåer och är därför i behov av riskanalyser av den hydrologiska
variationen. I Skandinavien har få undersökningar gjorts med trädringsdatering och
hydrologiska parametrar. I den här studien undersöks om dendrokronologi kan användas för
att rekonstruera översvämningshistorik. Genom att välja ut platser där träd varit utsatta för
höga vattennivåer i området kring Arvika undersöktes det om det går att hitta
översvämningssignaler i tall (Pinus sylvestris) samt om datering av trädringar är ett möjligt
sätt att rekonstruera översvämningar. Trädborrskärnor samlades in från låga och höga höjder
för att undersöka om vattennivå, vattenföring och nederbörd var märkbara i träd som drabbats
av översvämningar och om träden från de olika höjderna visade på någon skillnad. Träden på
lägre höjd, som växer nära vattnet, visade högst korrelation med alla klimatfaktorer under
vinter och tidig vår medan träden på högre höjd visade högst korrelation med nederbörd under
försommaren, vattennivån under sommaren och vattenföringen under vintern. De lågt
växande träden korrelerade bättre med temperatur än med nederbörd, medan det var tvärtom
för de högt belägna träden. I studien dras slutsatsen att då träden i studieområdet visar svaga
korrelationer överlag påverkas de av många olika faktorer vid olika tider på året. Det gör det
svårt att peka ut endast en påverkande faktor som visar på en översvämningssignal. Dock
visar träden på indikationer av spår från hydrologiska signaler där träden på de olika höjderna
visar upp olika mönster. Detta indikerar på att det är möjligt att använda dendrokronologi till
att rekonstruera översvämningssignaler. För att kunna använda denna metod är därför valet av
studieområde och hur provtagningen utförs av stor betydelse. Studieområdet bör lämpligtvis
inte vara påverkat av mänsklig aktivitet och har till fördel om endast en faktor begränsar
trädens tillväxt.
Nyckelord: Dendrokronologi, Trädringar, Arvika, Glafsfjorden, Översvämning, Vattennivå,
Vattenföring, Nederbörd.
ii
Preface
This study is a Bachelor thesis in Earth Sciences with specialization in climatology,
performed at Gothenburg University. The project is carried out by us, Isabelle Andersson and
Sofia Sjögren, who did this work together. Two different fields of responsibilities can
therefore be seen in the results and discussion parts. Isabelle mainly concentrated on the lowgrowing trees, while Sofia focused on the high-growing trees.
There are some people we would like to thank for their contribution to the final results of this
report. First of all we would like to say thanks to PhD student Jesper Björklund who helped us
in the dendrolab at the department of Earth Sciences, and guided us through the methods used
in this investigation. Elin Alsterhag at Arvika Teknik AB was also very helpful giving us
access to the climate data, while Petter Stridbeck from the dendrolab helped us sorting it.
Finally, we would like to thank our supervisor Professor Hans Linderholm who made this task
possible and lead us through the project.
Gothenburg University, June 2013
Isabelle Andersson & Sofia Sjögren
iii
Contents
Abstract ....................................................................................................................................... i
Sammanfattning ......................................................................................................................... ii
Preface ....................................................................................................................................... iii
1.
Introduction ........................................................................................................................ 1
2.
Dendrochronology .............................................................................................................. 2
3.
4.
5.
6.
2.1
Dendroclimatology ...................................................................................................... 2
2.2
Scots Pine .................................................................................................................... 3
Study Area .......................................................................................................................... 3
3.1
Arvika .......................................................................................................................... 3
3.2
Glafsfjorden ................................................................................................................. 4
Method ............................................................................................................................... 5
4.1
Fieldwork ..................................................................................................................... 5
4.2
Preparation, Measurements and Dating ....................................................................... 6
4.3
Standardization ............................................................................................................ 6
4.4
Climatic Data ............................................................................................................... 7
4.5
Analysis ....................................................................................................................... 7
Results ................................................................................................................................ 8
5.1
Hydrological Relationships ......................................................................................... 8
5.2
Low-growing Tree-ring width Chronology ................................................................. 9
5.3
High-growing Tree-ring width Chronology .............................................................. 11
5.4
Comparison of the Chronologies ............................................................................... 12
Discussion ........................................................................................................................ 13
6.1
Low-growing Trees ................................................................................................... 13
6.2
High-growing Trees ................................................................................................... 14
6.3
Comparison of the Chronologies ............................................................................... 16
6.3.1
Temperature and Precipitation ........................................................................... 16
6.3.2
Streamflow and Water level ............................................................................... 16
6.3.3
Flooding Signals ................................................................................................. 17
6.3.4
Uncertainties ....................................................................................................... 17
7.
Conclusions ...................................................................................................................... 18
8.
Bibliography ..................................................................................................................... 19
1. Introduction
According to the Intergovernmental Panel on Climate Change (IPCC, 2012) the climate is
expected to be warmer in the future with the consequence of increased climate variability.
Precipitation intensity and mean precipitation is projected to increase across the globe but
particularly at mid- and high latitudes. This has a direct effect on the risks of flooding (IPCC,
2012). Arvika is a town located in western Sweden and is a place vulnerable to high water
levels (Arvika Kommun, 2012a). The site is therefore used in this study to analyze the
possibility to find flooding signals in trees affected by high water levels. The town is located
at the northern shore of bay Kyrkviken, which is connected to the larger lake Glafsfjorden.
When persistent or intense rainfall affects the area, Glafsfjorden can flood the adjacent
communities (Arvika Kommun, 2012a). According to the risk and vulnerability analysis of
Arvika municipality, the probability of flooding to occur more than one time of the year is
very high with the consequence of large damage to property and societal functions (Arvika
Kommun, 2012b). In year 2000 were the town suffering from one of the most severe flooding
events in the modern history of Sweden (Blumenthal et al, 2010). The water level increased
above normal levels by 3 m due to a very high amount of precipitation during the autumn
months (Svensson et al., 2002). As a result of the flooding, bay Kyrkviken expanded and large
parts of the city were inundated by water. The flooding led to huge economic losses for the
society, public transport in and around the town was cancelled and the road network was
largely affected (Blumenthal et al, 2010). Therefore, there is a need for a risk assessment of
hydrological variance for the area around Glafsfjorden which is something that started to
develop after the flooding in year 2000 (Svensson et al., 2002).
One of the best known and best developed ways to study paleoclimate is to use tree-ring
dating. Trees alter the growth rate in response to changing climate and their rings can refer to
past temperature and moisture changes (Jansen et al., 2007). The tree-ring records can be
accurate to year or season (Jansen et al., 2007) and due to the annual formation of tree-rings
each ring can be associated with a calendar year (Fritts, 1976). Many different environmental
factors influence the tree growth. However, only one factor is dominating in regulating the
growth which could be either temperature or precipitation (Stokes & Smiley, 1968). Since
most of previous studies in the Scandinavian countries focus on the relationship between treerings and temperature (e.g., Briffa et al., 1992; Kirchhefer, 2001; Gunnarsson & Lidnerholm,
2002; Helama et al., 2002; McCarroll et al., 2013) this study will explore the relationship
between tree-rings, precipitation, water levels and streamflow. In other parts of the world,
studies of tree-rings and hydrology parameters have been performed. They showed that it is
possible to reconstruct streamflow, the amount of water flowing into the lake (Poff et al.,
1997), with the use of tree-rings (eg., Cleaveland, 2000; Akkemik et al., 2008; Gou et al.,
2010; D’Arrigo et al., 2011; Sun et al., 2013).
The base of an efficient water resources planning program is made by the understanding of
long term trends and patterns of hydrological variability (Boucher et al, 2011). More
knowledge about precipitation patterns visible in tree-rings can contribute to the
understanding of climate history. However, there is much uncertainties related to evaluations
1
of precipitation change. There is a lack of reliable precipitation data in many places since just
a few meteorological stations were available before 1900 and the quality of the data from the
20th century could be questioned (Linderholm et al., 2010). The study of tree-rings and
precipitation in Scandinavia are few and the potential of using tree-rings as a proxy for
precipitation has yet not been fully investigated (Linderholm et al., 2004). However,
Linderholm et al. (2004) concluded in their study from east central Sweden that 30% of the
tree growth could be explained by precipitation in early summer. A study from mideast
Sweden by Jönsson and Nilsson (2009) emphasizes the results of Linderholm et al. (2004) and
also found that variations can be detected in trees growing where they generally not are
limited by water.
What has yet not been explored is the trees response to direct inundation of water and
changing water levels. By studying tree-rings around the water sensitive Arvika in the region
of Glafsfjorden, this study will aim to investigate the possibility to reconstruct flooding
signals, water levels and streamflow, with the use of tree-ring dating.
The following questions are addressed in the report:
 Is it possible to see any signs of changing water levels in trees affected by flooding?
 Are there any difference between trees that are direct affected by flooding and trees
growing at higher altitudes?
 Is the method used suitable for reconstruction of water levels and flooding?
2. Dendrochronology
Dendrochronology is the study of annual ring-growth in trees that can be dated in time. Treering dating is possible since trees form annual rings with characteristic patterns (Stokes &
Smiley, 1968). In temperate climates the tree-ring growth is beginning between April and
June and ceases in August or September. In these regions the ring is consisting of two parts,
earlywood and latewood. Earlywood is light in color and consist of wide cells with thin walls
which make the earlywood porous and low in density. The second part of the ring, called the
latewood, is dark in color, consists of denser cells with thicker walls and is less porous than
the earlywood. The earlywood is produced during spring and early summer while latewood
usually is produced in late summer. The transition between earlywood and latewood is
gradual, while the latewood has an abrupt transition to next year’s earlywood. This abrupt
transition makes it possible to determine the growth-year of each ring. Occasionally trees can
have missing rings. This happens when the tree does not form a ring throughout the whole
stem due to extreme climate. At other times a change in cell structure within an annual growth
ring might appear as a false ring due to a change in climate during the growing season (Fritts,
1976).
2.1 Dendroclimatology
The knowledge of dendrochronology can be applied to a wide range of fields where
dendroclimatology is one. Tree-rings can provide information about past climate variability
when comparing the qualities of the rings with climate parameters, most commonly
2
temperature or precipitation. If sampled trees from the same region are limited by climate the
tree-rings show about the same variability and ring-width. The tree-rings can then serve as a
proxy and be used in reconstructions of paleoclimate. Dendrohydrology is a related subfield
where water levels and streamflow are reconstructed (Speer, 2010). It is of importance to
notice that the tree-rings do not only record climate. It is a result of several growth-controlling
factors such as change in biological competition, soil cover, nutrient availability, the
biological age trend where the tree produces larger rings when it is young and thinner at an
older age, and other disturbances from outside the tree population (Cook et al., 1989).
2.2 Scots Pine
Scots pine (Pinus Sylvestris) is, apart from Norway spruce (Picea Abies), the most common
tree in the Swedish forest. It is resistant to both cold and wind but sensitive to air pollution
and requires light. The best settling conditions are on argillaceous moraines but it grows on
dry and rocky grounds and peat lands as well. The pine is usually cut down at an age of 90150 years but can reach an age of 800 years (SkogsSverige, 2012).
3. Study Area
3.1 Arvika
The study is focused on the area around Arvika, a city in the county of Värmland in western
Sweden (N 59°39′15″ E 12°35′29″) (figure 1). The city has about 26 000 inhabitants, is
traversed by the main railway between Stockholm and Oslo and is the innermost harbor of
Sweden. The area around Arvika is characterized by small lakes and the dominating rock type
is grey gneiss. 85 % of the land is covered by forest, mostly Scots pine and Norway spruce. In
valleys and close to the lakes the landscape is dominated by deciduous forests, meadows,
agriculture and built up areas (Söderman, 2013).
Figure 1: Map of the study area, Arvika and the lake Glafsfjorden with its bay Kyrkviken, located in Westcentral Sweden.
3
According to Köppen’s Climate Classification the climate in Arvika is classified as a moist
continental climate with warm summers (Dfb), although, it is not far from the border to a
subartic continental climate with cool summers (Dfc) (Rubel & Kottek, 2010). As seen in the
climograph (figure 2) December to February are the coldest months with a mean temperature
of about -4°C. During the summer months the temperatures are increasing and there is a peak
in July which has a mean temperature of 16.5°C. February to April are the driest months
while the largest amount of precipitation falls in July and August.
3.2 Glafsfjorden
100
20
70
45,8
15
60
45,6
70
10
60
50
5
40
0
30
20
Temperature [°C]
Precipitation [mm]
80
Streamflow [m³/s]
90
45,4
40
45,2
30
45
20
-5
10
-10
0
10
0
50
44,8
Water level [m a.s.l.]
Glafsfjorden with its bay Kyrkviken is the neighboring lake to Arvika. It is connected to the
drainage area of Byälven which covers an area of 4785 km2. A lot of smaller lakes are
connected to the river system where Glafsfjorden is the largest one with an area of 100 km 2.
In the northern parts of the system three different subrivers flow into Glafsfjorden and another
one flows into the lake from the west. Glafsfjorden has its outflow in Byälven, and the water
continues from Byälven through the town Säffle and ends in lake Vänern. The highest part in
the drainage area is 519 m a.s.l. while the lowest part is 45 m a.s.l., and a large area around
Glafsfjorden and Byälven are lower than 50 m a.s.l. The distance between Glafsfjorden and
Vänern is 32 km with a height difference of only 1 m (Svensson et al., 2002). The normal
water level in Glafsfjorden is about 45.5 m a.s.l. but is times to times exceeded causing
flooding and damages to Arvika (Arvika Kommun, 2013). As seen in figure 3, the water level
in Glafsfjorden is low in the beginning of the year and has its highest peak in May. During the
summer months the water level is low but increases at the end of the summer for another peak
in November and December. As the water level, the streamflow into Glafsfjorden is low in the
beginning of the year (figure 3). The peak in streamflow precedes the water level with one
month and occurs in April. During the following months of the year the streamflow shows the
same pattern as the water level, but one month in advance.
44,6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Water level
Streamflow
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precipitation
Temperature
Figure 2: Climograph of mean temperatures and precipitation in
Arvika (1961-2011), data retrieved from SMHI (2012a).
4
Figure 3: Mean water level (1934-2000) and mean streamflow (19902011) in Glafsfjorden, data retrieved from SMHI (2012b) and Arvika
Teknik AB.
4. Method
Different steps are involved in the process of gathering information from tree-rings; from
fieldwork, sample preparations, dating and measurements of tree-ring width, standardization
to the final analysis.
4.1 Fieldwork
To identify trees that have been flooded a
map was made in ArcGIS 10.1
(Geographical Information System) to
locate potential flood plains around
Arvika. Elevation and terrain data for the
map were taken from Lantmäteriet
(figure 4). With information from the
map possible sampling areas could be
determined for both high and low
elevations. The collection of tree-cores
was made in April 2013 in connection to
Glafsfjorden. Samples were taken from
the tree species Scots pine using an
increment borer. In total 47 pines were
sampled which are shown in figure 5. Figure 4: Flooded areas with increased water level with 0.5 m, then in 1
For each pine two cores were taken with m steps, and to the maximum water level in year 2000 in Glafsfjorden,
modeled in ArcGIS 10.1.
at least 90 degrees between them at the
height of approximately 1.3 m. The coordinates of each pine was marked in a GPS and the
quality and surroundings of the pines were noted.
26 of the pines were growing in close proximity to the lake at a low elevation (33-47 m a.s.l.).
Half of these low-growing pines were
sampled inside Kyrkviken and were
growing on small capes and cliffs
covered by mosses and heather. They
were surrounded by common reed,
brushwood
and
sprawl
growing
deciduous trees at different sizes, but
also pines. The other half of the lowgrowing pines was sampled in the
northern parts of Glafsfjorden, very
close to the waterside. These pines were
growing on rocky grounds together with
common reed, brushwood and smaller
Figure 5: Map of sample locations of the trees and the climate stations
birches towards the waterside, and with
used in the study. Black triangles indicate low-growing trees and red
spruces and pines away from the
circles indicate high-growing trees.
waterside (figure 6).
5
At a higher elevation
(58-186 m a.s.l.) 21
pines were sampled at
four different locations
around the lake. They
were all growing in
slopes or at the top of
hills surrounded by
mosses,
heather,
smaller rocks, pines
and other conifers
(figure 7). In total, 9
of the pines were
sampled in a nature
Figure 6: Habitat for a low-growing Scots pine Figure 7: Habitat for a high-growing Scots
reserve while the by Glafsfjorden.
pine around Arvika.
others
could
be
disturbed by forestry and human activities since the study location are in close proximity to a
town.
4.2 Preparation, Measurements and Dating
The tree-cores from each tree were glued in pairs on wooden sticks for easier handling of the
samples and the cell structure was put in a vertical direction. To be able to see the rings and
the cell structure the samples were sanded using sandpaper with gradually finer material. In
the lab, the tree cores were measured and dated for analysis. This was done using the software
Tsap-WinTM together with the measuring board LintabTM and a stereo microscope with the
precision 1/1000 mm. The length between every ring was measured following the cell
structure and all rings were dated to the year it was formed starting from last growing year,
which in this case was 2012. After measuring a few samples from the same location the treering series were run in the software Cofecha which can validate the reliability of the
measurements through statistical quality control of the data. A low correlation between the
measured series would mean that there is a failure in the dating and that the samples possibly
have missing or false rings. This was the reason for using cross-dating before continuing with
the measurements. Cross-dating is a process where false or missing rings are identified by
finding pointer years. Pointer years can be found by recognizing reoccurring rings in many
tree-cores that differ from the surrounding rings. Every ten year was marked on the core and
differing years noted. This led in turn to the detection of pointer years. With the knowledge of
pointer years, measurement and dating of the tree-cores could be continued in Tsap-WinTM. 18
samples were damaged or too difficult to date, and were therefore removed. The measured
and dated series were validated in Cofecha again to be certain that each ring was dated to the
right year. The trees from high and low elevation were validated separately.
4.3 Standardization
The tree-ring width series are influenced by a biological age-trend where they commonly
decline exponentially with age. The decline in ring-width is related to the increase in radial
6
size of the tree each year. This can disrupt the common signal in the series when compared
with each other, but also obstruct the analysis with climatic data. By detrending and
standardize the series the desired signal could be enhanced, the biological age-trend removed
and samples with large difference in growth rate combined (Gunnarsson et al, 2011). This was
made in the software Arstan (Cook & Holmes, 1986), for trees at both high and low elevation.
In the standardization process a curve was fitted to the tree-ring width series and each value in
the series was divided by the value of the curve, which creates a series of tree-ring width
indices. These indices of tree-ring width series were then averaged into one site chronology.
In Arstan, three different types of chronologies were produced. In this study the residual
chronology was used and was based on the residuals of the standard chronology (Speer,
2010). As the residual had the largest year to year variance it is most suitable for this study.
4.4 Climatic Data
Data of temperature and precipitation were provided by the Swedish Metrological and
Hydrological Institute (SMHI, 2012a), measured at their metrological station in Arvika, which
is shown in the map (figure 5). The data was supplied as monthly means for the period 19612011. Streamflow data was also provided by SMHI (2012b), measured in the inflow to
Glafsfjorden at a station called Jössefors (figure 5) and was supplied as monthly means
between the years 1990 to 2011. Data of water level in Glafsfjorden was provided by Arvika
Teknik AB, measured in Kyrkviken (figure 5). This data was available for the period 19342000 as daily values but sporadically measured, with a gap in the data between the years 1962
and 1965.The values were averaged into monthly means for the following analysis of the data.
At the time when the trees were sampled the water level was measured to 45.6 m a.s.l. in
Kyrkviken.
4.5 Analysis
The chronologies were analyzed in Microsoft Excel 2010, and the residuals of the
chronologies were plotted and correlated with each other to explore the difference between
them. Both chronologies were then correlated to monthly climate data. One year’s ring-width
was compared to the same year’s climate as well as to the previous year’s climate, starting
from the previous growing season until the end of the growing season the current year. This
was made to see if previous year’s climate could have an effect on the coming year’s ringgrowth. The climate information were also plotted and correlated with each other to find the
relationships between them. The data were organized by month or year and the residuals and
climate data were arranged in columns next to a column with dates, this to get graphs over
averaged or correlated values for months or years. A significance level of 0.05 was also
plotted in graphs were a correlation was made to ensure the reliability of the values.
7
5. Results
5.1 Hydrological Relationships
The result of the analysis between the different hydrological parameters used in the study
shows a delayed response to
precipitation, the main driver of the
1.Precipitation
2. Streamflow
3. Water level
system. As seen in figure 8,
precipitation
leads
to
increased Figure 8: The hydrological relationship shown in a flowchart.
streamflow which in turn raises the Precipitation increases the streamflow which raises the water
level.
water level.
Correlation coefficient
When analyzing the relationship between streamflow and water level in Glafsfjorden a
correlation was found between streamflow and the following month’s water level (figure 8)
with correlations coefficients of 0.61 - 0.97. The correlation between streamflow and the
same month’s water level are
1
also quite high, except from
0,9
January, February, April and
0,8
October.
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Streamflow
Water level same month
Water level following month
Correlation coefficient
Figure 9: Monthly correlation 1990-2011 of streamflow and water level the same
month (blue bars), and correlation of water level the following month of the
streamflow (violet bars) in Glafsfjorden.
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precipitation
Water level same month
Water level following month
Figure 10: Monthly correlation 1961-2000 of precipitation and water level the
same month as the precipitation (blue bars), and correlation of water level the
following month of the precipitation (green bars) in Glafsfjorden.
8
The correlation analysis of
precipitation and water level
shows that the water level in the
lake responds to the previous
month’s precipitation. Water
level the following month, after
the precipitation, is better
correlated than the water level
the same month as the
precipitation (figure 10). This is
true for all months of the year
apart from December which has
approximately
the
same
correlation the same and the
following month.
There is a difference when it
comes to winter precipitation.
Precipitation that falls as snow
accumulates during the winter
months and melts in spring are
increasing the water level in May
(figure 11). The same passes for
November, where the water level
depends on precipitation that
falls during summer and fall,
700
700
600
600
Precipitation May-Oct [mm]
Precipitation Dec-Apr [mm]
shown in figure 12. Since the correlation is high between water level and precipitation, 0.73
and 0.70 respectively for the two periods, this means that water level with some certainty is
dependent on earlier precipitation. With abundant rainfall comes higher water levels, and vice
versa.
500
400
300
200
100
y = 96,08x - 4165,6
R² = 0,5311
0
44
45
46
47
500
400
300
200
100
y = 94,418x - 3870,5
R² = 0,4942
0
44
48
45
46
47
48
Water level November [m a.s.l.]
Water level May [m a.s.l.]
Figure 11: Correlation of the sum of the precipitation in Figure 12: Correlation of the sum of the precipitation in MayDecember-April and the water level in May in Glafsfjorden.
October and the water level in November in Glafsfjorden.
5.2 Low-growing Tree-ring width Chronology
The chronology of low lying trees consists of 38 samples with time series dated from 2012
back to 1836 (figure 13). Most samples are from 2011 and decreases with sample depth back
in time, where fewer time series extends. In the figure the residual of the chronology is shown
in green.
2
Inter-correlation: 0.47
Tree-ring index
1,8
160
1,6
140
1,4
120
1,2
100
1
80
0,8
60
0,6
40
0,4
20
0,2
0
1836
1844
1852
1860
1868
1876
1884
1892
1900
1908
1916
1924
1932
1940
1948
1956
1964
1972
1980
1988
1996
2004
2012
0
Number of cores
Residual
Figure 13: Standardized tree-ring width chronology and the number of tree-cores used for low-growing trees
around Glafsfjorden between the years 1836-2012 with a series inter-correlation of 0.47.
9
When
correlated
to
temperature
and
precipitation the values
0,4
of the chronology are
0,2
most consistent with the
winter
months
and
0,0
reasonably
consistent
-0,2
with late summer and fall
(figure
14).
The
Previous year
year
Previous
Current year
-0,4
temperature
shows
-0,6
significant values for
Temperature
Precipitation
December, February and
Temperature (Dec/Feb/Mar)
Significance level
March,
while
the
Figure 14: Monthly correlation between the low-growing tree-ring width chronology
precipitation
show
around Glafsfjorden, total precipitation and mean temperature 1961-2011 from previous
year’s April to current October. The line is the 0.05 significance level. Dec/Jan/Feb show
strongest correlation in
the mean of temperatures in December, January and February correlated with the
December and February,
chronology.
but do not have any values high enough to become significant. To strengthen the significance
level for the temperature an average temperature is shown for December, February and March
correlated to the chronology. As the number of years for both temperature and precipitation
are 50 years the significance level, the probability that climate data correlate with the
chronology, is 0.32.
Correlation coefficient
0,6
Correlation coefficient
The highest correlation between streamflow and the chronology, and water level and the
chronology are found, as well as the temperature and precipitation, in the winter months
(figure
15).
The
0,6
streamflow
correlation
does not reach the
0,4
significance level in any
month, but has the highest
0,2
values in February and
0,0
March. The water level
correlation on the other
-0,2
hand has two significant
Previous year
Current year
-0,4
values, in February and
March. What separate the
-0,6
Streamflow
Waterlevel
two correlations is that the
Water
level
(Feb/Mar)
Significance level (water level)
water level has positive
Significance level (streamflow)
values during fall, while
15: Monthly correlation between the low-growing tree-ring width chronology
streamflow has negative. Figure
around Glafsfjorden, mean streamflow (1990-2011) and mean water level (1934-2000) from
year’s April to current October. The dotted line is the 0.05 significance level for
The significance level for previous
the streamflow correlation and the solid line is the 0.05 significance level for the water
streamflow is 0.3, while it level.
is 0.5 for the water level.
10
5.3 High-growing Tree-ring width Chronology
The chronology for the high-growing trees goes back to year 1767 (figure 16). 38 cores are
used in the chronology but the number is decreasing further back in time which makes the
chronology unreliable before the late 19th century.
2
Inter-correlation: 0.57
Tree-ring index
1,8
160
1,6
140
1,4
120
1,2
100
1
80
0,8
60
0,6
40
0,4
20
0,2
0
1767
1775
1783
1791
1799
1807
1815
1823
1831
1839
1847
1855
1863
1871
1879
1887
1895
1903
1911
1919
1927
1935
1943
1951
1959
1967
1975
1983
1991
1999
2007
0
Number of cores
Residual
Figure 16: Standardized tree-ring width chronology and the number of tree-cores used for high-growing trees
around Arvika between the years 1767-2012 with a series inter-correlation of 0.57.
Correlation coefficient
Precipitation shows the strongest correlation with the chronology during May and June which
together have a positive
0,6
correlation of 0.55 and is
largely exceeding the
0,4
significant level (figure
0,2
17). There is a weak
precipitation signal in
0
October and previous
August.
Temperatures
Previous year
-0,2
show a weak positive
Current year
Previous year
-0,4
correlation
in
the
beginning of the year and
-0,6
a
weak
negative
Precipitation
Temperature
Precipitation (May+Jun)
Significance Level
correlation in July and
August but none of them
Figure 17: Monthly correlation between the high-growing tree-ring width chronology
around Arvika, total precipitation and mean temperature 1961-2011 from previous year’s
are significant.
April to current October. The line is the 0.05 significance level. May+Jun show the sum of
precipitation in May and June correlated with the chronology.
11
As seen in figure 18, the
correlation analysis of water
level in Glafsfjorden and the
0,4
high-growing chronology is
0,2
weak throughout the year,
except from the late summer
0
and autumn months. There is
-0,2
a
positive
significant
correlation in July to
Previous year
Current year
-0,4
October, together with a
strongest correlation of 0.47.
-0,6
Water level
Streamflow
The correlation analysis of
Water level (Jul/Aug/Sep/Oct)
Streamflow (Feb+Mar)
Significance level (water level)
Significance level (streamflow)
streamflow and the highFigure 18: Monthly correlation between the high-growing tree-ring width chronology growing chronology shows
around Arvika, mean streamflow (1990-2011) and mean water level (1934-2000) in
the strongest correlation in
Glafsfjorden, from previous year’s April to current October. The dashed bars are the sum
of February and March streamflow and the average water level from July to October February and March, together
correlated with the chronology. The solid line is the 0.05 significance level for the
with a significant correlation
streamflow correlation.
coefficient of 0.5. In April and May a negative correlation are visible, the correlation is
slightly positive in June to August, and negative again in October.
Correlation coefficient
0,6
5.4 Comparison of the Chronologies
The tree-ring series differ from each other and the correlation between them is 0.44 (figure
19). The chronology for the high-growing trees reaches further back in time and has a greater
variation than the low-growing chronology. When analyzing the relationship between
streamflow and water level in Glafsfjorden a correlation was found between streamflow and
the following month’s water level (figure 8) with correlations coefficients of 0.61 - 0.97. The
correlation between streamflow and same month’s water level are also quite high, except from
January, February, April and October.
Correlation: 0.44
Tree-ring index
2
1,5
1
0,5
Residual high-growing trees
Tree-ring index
0
2
1,5
1
0,5
Residual low-growing trees
0
1767 1782 1797 1812 1827 1842 1857 1872 1887 1902 1917 1932 1947 1962 1977 1992 2007
Figure 19: Tree-ring index for high-growing trees and low-growing trees around Glafsfjorden in Arvika. The correlation
between the chronologies is 0.44.
12
6. Discussion
6.1 Low-growing Trees
The lowland trees have a better correlation with temperature than with precipitation (figure
14) which probably means that these trees are not dependent on precipitation. Since the
maximum elevation of the sampled trees was close to the lake level, and the distance to the
shoreline not exceeded 20 m is it possible to think that the ground where the trees were
standing contained much water. Water table is a suitable water uptake for pines (Vincke &
Thiry, 2008) and since the roots may have reached the groundwater, they were well-satisfied
without influences from precipitation.
However, the highest correlations with the low-growing trees are found during winter for both
temperature and precipitation. Normally there is no tree growth during winter, but possibly
the growth in the beginning of the growing season depends on how much snow and how deep
ground frost it is in an earlier stage of the year. According to Comerford et al. (2013), less
snow depth means lower soil temperatures, which obstruct the tree growth. Snow and soil
frost depths are related to stem density of the trees (Ottosson Löfvenius et al., 2003).
Water level [m a.s.l]
Temperature [ºC]
Tree-ring index
The water level has significant values
2
when correlated to the chronology in
February and March (figure 15). This
1
means that the water level have
impacts on the trees, and most during
Residual
this early period of the year when the
0
10
water stage is at its lowest. It could
be an indication that the trees have a
limited access to water and are most
0
sensitive to water during this period.
When looking closer at the water
Temperature
-10
level in March, it is noted that the
47
tree growth follows the water level
46
(figure 20). Since the chronology also
45
follows the temperature it is likely
44
that the starting signal of the trees
Water level
growing season is controlled by the
43
1961 1966 1971 1976 1981 1986 1991 1996
water level and temperature in the
beginning of the year, especially in Figure 20: Tree-ring index for the low-growing tree-ring width
temperature and water level in Glafsfjorden in March. The
March. Higher temperatures lead to chronology,
vertical dashed lines are year’s width temperatures above zero, and
melting of snow and ice from the above normal water level and tree-ring growth.
winter season, and when the melting occurs early in the season the availability of water for the
trees increases early in the season as well, which could be seen in the growth rate. The
increased growth could also be a release of nutrients from the previously frozen soil or ice
(Poff et al., 1997), while at the same time temperature improves the tree growth (Grace &
Norton, 1990).
13
The correlation between streamflow and the chronology is strongest in February (figure 15).
The correlation is not significant, but since the streamflow is shifted one month from the
water level (figure 3), it is reasonable to assume that the streamflow indirect have impacts on
the tree growth. Figure 11 reinforces this theory, since the correlation between winter
precipitation and water level in May has a high correlation, and the fact that the temperature
slowly begins to rise during this period (figure 2). The high correlation therefore means that
winter precipitation that melts in spring and provides maximum streamflow in April will be
seen in the water level in May, when the level of water is highest. As early increased water
level, early streamflow is related to early warming and therefore early growth.
Despite the assumption that water level affects the tree growth, the correlation between water
level in May and the chronology are negative. Probably is this because of the circumstances
that the trees are not limited by water anymore, which not make them sensitive. What also
could be assumed is that, since water level is at its lowest during winter and summer, the
correlation with the chronology would look the same for these periods. This is not the case.
Probably are the trees not water stressed during summer, as they are during winter. The lower
correlation during summer could, even though a low water level, be explained by more
precipitation (figure 2). Although streamflow and water level correlates well with each other
the chronology correlation during summer are negative with streamflow and positive with
water level. At a guess this is because higher air temperatures increases evaporation, which
reduces the streamflow, and in the same time increases the water temperature, which makes
the water expand and reaches higher water levels.
The water balance shows a signal in the trees during winter and early spring, but to
reconstruct trustworthily water levels and streamflow the correlations should preferably be
higher. There are several factors why the correlation is not as high. Since the samples were
taken from trees at low levels there is a chance that the trees get flooded often, maybe even
every season when the water level rises. This leads to that the trees do not know any
difference between extremes and normal high water levels. Some of the sampled trees may
have been flooded more often than others, which means that the tree-ring series could look
different and explains the low correlation of the low elevated chronology. However, if all
trees were flooded during the same years, there is still a possibility that they were not affected
by this high water level. Since the growth is better with higher water levels the flooding
period is, as a suggestion, too short to make the trees affected by it (Glenz et al., 2006). In
studies from China where flood periods can be noticed in trees and streamflow can be
reconstructed (Gou et al., 2010), the climate is drier and the growth is presumably more
sensitive to water than these trees, which are growing in a more humid climate.
6.2 High-growing Trees
In accordance with previous findings in this part of the world, the high-growing trees seem to
be both precipitation and temperature dependent (Linderholm et al., 2010). The correlation
analysis of the high-growing pines and precipitation shows that the trees are favored by
precipitation in May and June (figure 17). This is in line with past research in the Nordic
countries, and could be used to describe parts of the precipitation falling these months as seen
in figure 21 (Helama & Lindholm, 2003; Linderholm et al., 2004). The dependence on early
14
2
Tree-ring index
1,5
1
0,5
Residual
0
300
Precipitation [mm]
summer precipitation is reasonable
since it is the time when the growth
of earlywood occurs (Speer, 2010).
In May and June the water level is
at its highest (figure 3) which
contradicts the water limitation of
the trees. The water level though, is
mainly the result of the spring
flood event (figure 11), and would
not have an extensive effect on the
trees at high elevation.
200
100
Water level [m a.s.l.]
May+June Precipitation
There is a strong signal of water
0
46
level during the summer and
45,5
autumn which indicates that these
45
44,5
months’ tree-growth can describe
44
parts of changing water levels in
43,5
July-October Water level
Glafsfjorden (figure 21). This
43
result appears to be strange since
the trees grow at such a height
Figure 21: Tree-ring index for the high-growing tree-ring width chronology,
difference from the lake. The water precipitation in May and June and water level in Glafsfjorden in June to
for the period 1935-2012. The vertical dashed lines are year’s width
level data contains gaps and October
above normal precipitation and water level along with increased tree-ring
irregular measurements and should growth.
therefore be taken with some cautiousness, but what has been found is the delayed response of
the water level to precipitation (figure 10). Rain in spring and early summer raises the water
level during the following months and late summer. It could possibly be this rain that favors a
continuous prospering tree-growth throughout the summer. As seen in figure 21, years with
above normal precipitation in May and June have also above normal water levels in July to
October, and these years show an especially positive tree-growth. The common time-period is
short but gives an indication of where the late summer water level signal originates.
The climatic conditions in the initial stage of the growing season seem to be of importance to
the growth, as explained by Linderholm and Chen (2005). In February and March, streamflow
and precipitation is at their lowest in the area studied, and the mean temperature is below zero
(figure 2). These months show a positive correlation of streamflow and the high-growing trees
(figure 18). The spring flood occurs most often in April (figure 3), and if the temperature
increases earlier than usual, it would mean that water is available earlier than normal for the
pines, and nutrients is released which is necessary for the tree-growth (Fritts, 1976). Because
of this, the trees are favored by high streamflow and temperatures during this time of year
since it sets the growing conditions earlier than usual. By contrast, there is a negative
correlation of streamflow in April and May indicating that the trees are disfavored by this.
When streamflow is high in April and May, lots of snow has fallen during the winter. As
described by Linderholm and Chen (2005) the soil might be saturated by a high amount of
15
melting water and this surplus of water could lead to anoxic-conditions for the trees and
depress them.
All in all, a flooding reconstruction of the elevated trees is found challenging and uncertain.
The most sensitive times of the year, when it comes to flooding, are when the peaks in water
level occur. The precipitation that falls in May and June can to some extent be reconstructed,
but this precipitation affects the water level the following months (figure 10) which would be
June and July, and these months are normally not suffering from extreme water levels due to
high temperatures. On the other hand, the negative correlation of streamflow in April and
May are weak but could possibly be a sign of extremely high streamflow since it even affects
the elevated trees.
6.3 Comparison of the Chronologies
Figure 19 shows that there is a difference in the growth of the flooded trees and those growing
at higher altitudes, although the geographically close location of the trees. That would indicate
that the close connection to the lake is affecting the trees in some way.
6.3.1 Temperature and Precipitation
There is a noticeable difference in the correlation analysis of the low and the high-growing
chronologies. The high chronology shows a strong precipitation signal and a weak
temperature signal while the low chronology shows the opposite. This would be the result of a
difference in water availability at the two locations. The two chronologies are correlated with
the same temperature data, while at the same time the temperature could differ at the two
locations. The elevated trees are growing in a forest environment with other trees surrounding
them, resulting in a shading effect that the low-growing trees to some extent lack due to the
adjacent open waterside. Also, higher elevation generates lower temperatures (Oke, 1987).
These differences could possibly be an explanation to the delayed correlation with
temperature and the elevated trees compared to the low-growing trees. Another thing to keep
in mind is that the meteorological station is located in the city of Arvika, where there possibly
could be a small urban heat island effect (Taha, 1997), and does not necessary need to
correspond to the temperature and precipitation of any of the sampling locations.
6.3.2 Streamflow and Water level
A shift in the correlation between streamflow, water level and the two chronologies are
observed, where positive correlations are found at an earlier time of the year in the lowgrowing chronology than the high-growing chronology. This is presumably another indication
of the altitudinal, and in turn temperature differences, between the two locations. Early
melting of the snow cover correlates with the low-growing chronology in January and
February, and it is the same for the high-growing chronology but one month later. The
following month does the water level respond to increased streamflow (figure 9), and a
correlation are found in February and March between the water level and the low-growing
chronology. This correlation is absent when it comes to water level and the high-growing
chronology, with the possible explanation that the water flows downhill and accumulates in
the lake, and does not affect the elevated trees.
16
6.3.3 Flooding Signals
In general, the correlations are very weak and only significant in a few cases. The importance
of the conditions at the initial phase of the growing season is found in both chronologies. In
the same months, February and March, the high-growing trees show a strong correlation with
streamflow and the low-growing trees show a strong correlation with water level. This is also
the driest time of the year which indicate that to find signals of flooding a water limitation is
required, as the case is for previous reconstructions of streamflow in e.g. Turkey and China
(Akkemik et al., 2008; Gou et al., 2010; Sun et al., 2013). What can be seen is that all climatic
parameters are related to each other, and since more than one factor is regulating the treegrowth, to different extents and at different time of the year, it is difficult to point out only
one factor. Since floods are more common in Sweden than droughts, a dry location with low
water holding capacity of the ground could be an approach to enhance the flooding signal in
this region. Another possible improvement could be to find and investigate trees that are even
more sensitive to water than the Scots pine.
However, if the trees would be dependent on only one growth-controlling factor, still flooding
signals could be difficult to find in the trees. This since the timing and the duration of the
flood might have an impact on the signal. There would be reasonable to believe that it is
possible to reconstruct the spring flood, since it occurs during the growing season, but more
difficult to find signs of the autumn flood, like the one that occurred in Arvika year 2000. The
use of more than one proxy for changing water levels could possibly give a more robust
result. For example, flooding was reconstructed in the Northern Great Plains, USA, where
tree-rings, lake sediments and ancient shorelines showed a similar response to changing water
levels (Shapely et al., 2005). Then the potential time of the year to find flooding signals would
not be limited to the trees growing-season.
An indication of that too much water could restrict the tree-growth is shown in the negative
correlation of streamflow and the elevated trees, at the time of year when the streamflow has
its peak. This could imply that trees higher elevated, not in direct proximity to a lake, would
be more affected by higher than normal water flow and more suitable for flooding
reconstructions.
6.3.4 Uncertainties
In the area studied it was difficult to find suitable trees to sample, especially trees growing in
close proximity to the lake which would not be affected by human activities and for the trees
growing higher up to be affected by forestry. The water flow is also affected by human
activities and this reduces the climate signal. These possible disturbances of the trees could
have resulted in a larger noise of the chronologies. A study area further away from a city with
less disturbance from humans could give a stronger climate signal, but since it was a local
study where both metrological and hydrological data were essential, proximity to a city was
necessary.
All climatic data used in the study are few in observation years, especially when it comes to
the streamflow data. The short timespan used in the correlations make them unreliable and
weak. Also, the fact that the values for water level were not homogeneous might have had an
17
effect on the correlations. In addition, the chronologies could be more reliable using more and
older trees. The cross-dating of the trees was found to be tricky since a lot of false rings
appeared in the tree-ring series, and since the low elevated chronology showed little variance
it was difficult to find pointer years.
7. Conclusions
The investigated trees show weak correlations with most of the climatic parameters studied.
This since many factors are regulating the tree-growth and these factors varies throughout the
year. The trees are not depressed by high water levels, rather the other way around, especially
in the initial stage of the tree-growth at an early time of the year. However, there is a notable
difference in the tree-growth between trees growing at high- and low altitudes. They are also
correlated differently to the climatic data. This, and the fact that some responses to the
hydrological parameters can be seen, would imply the possibility that flooding signals are
recorded in the trees. The signals could be enhanced with the use of trees that are more
sensitive to water and not flooded regularly, at an undisturbed site with low water holding
capacity. With the knowledge from this study, a better sampling strategy could be used along
with a carefully selected sampling location, and flooding reconstructions could be possible.
18
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