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This is the published version of a paper published in Environmental Monitoring & Assessment.
Citation for the original published paper (version of record):
Löfgren, S., Fröberg, M., Yu, J., Nisell, J., Ranneby, B. (2014)
Water chemistry in 179 randomly selected Swedish headwaterstreams related to forest
production, clear-felling and climate.
Environmental Monitoring & Assessment, : 1-22
http://dx.doi.org/10.1007/s10661-014-4054-5
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Environ Monit Assess
DOI 10.1007/s10661-014-4054-5
Water chemistry in 179 randomly selected Swedish headwater
streams related to forest production, clear-felling and climate
Stefan Löfgren & Mats Fröberg & Jun Yu &
Jakob Nisell & Bo Ranneby
Received: 7 March 2014 / Accepted: 11 September 2014
# The Author(s) 2014. This article is published with open access at Springerlink.com
Abstract From a policy perspective, it is important to
understand forestry effects on surface waters from a
landscape perspective. The EU Water Framework
Directive demands remedial actions if not achieving
good ecological status. In Sweden, 44 % of the surface
water bodies have moderate ecological status or worse.
Many of these drain catchments with a mosaic of managed forests. It is important for the forestry sector and
water authorities to be able to identify where, in the
forested landscape, special precautions are necessary.
Highlights • Water chemical data from a random subsample of
hemiboreal headwaters
• Forest status classified based on satellite and forest inventory data
• Stream concentrations of N, P and TOC related to forest
production
• Climate, affecting both forest production and elemental losses,
causal factor
• Clear-felling affects the concentrations of N, P and TOC
S. Löfgren (*) : M. Fröberg
Department of Aquatic Sciences and Assessment, Swedish
University of Agricultural Sciences, Box 7050, SE-750
07 Uppsala, Sweden
e-mail: [email protected]
J. Yu
Department of Mathematics and Mathematical Statistics,
Umeå University, SE-901 87 Umeå, Sweden
The aim of this study was to quantify the relations
between forestry parameters and headwater stream concentrations of nutrients, organic matter and acid-base
chemistry. The results are put into the context of regional
climate, sulphur and nitrogen deposition, as well as
marine influences. Water chemistry was measured in
179 randomly selected headwater streams from two regions in southwest and central Sweden, corresponding to
10 % of the Swedish land area. Forest status was determined from satellite images and Swedish National Forest
Inventory data using the probabilistic classifier method,
which was used to model stream water chemistry with
Bayesian model averaging. The results indicate that concentrations of e.g. nitrogen, phosphorus and organic
matter are related to factors associated with forest production but that it is not forestry per se that causes the
excess losses. Instead, factors simultaneously affecting
forest production and stream water chemistry, such as
climate, extensive soil pools and nitrogen deposition, are
the most likely candidates The relationships with clearfelled and wetland areas are likely to be direct effects.
Keywords Water chemistry . Headwater streams .
Boreal landscape . Forestry . Representative sampling .
Probabilistic classifying
J. Nisell
Geological Survey of Sweden, P.O. Box 670, SE-751
28 Uppsala, Sweden
Introduction
B. Ranneby
Centre of Biostochastics, Swedish University of Agricultural
Sciences, SE-901 83 Umeå, Sweden
Effects of forest management on water quality are typically studied using plot- or catchment-scale experiments at sites as homogenous as possible and where
Environ Monit Assess
most or all of the study area is treated. However, the
Swedish boreal landscape is a mosaic of different land
cover, and the spatial distribution of forestry operations
on productive forestland is heterogeneous due to a multitude of factors. Productive forestland is the dominant
(55 %) land cover in Sweden (land area 40.8 million
hectares), while impediments such as rock surfaces, bog
and marsh land, and mountains and alpine coniferous
forest constitute another 2, 11 and 9 %, respectively.
National parks, nature reserves and other protected areas
cover an additional 10 % of Sweden and generally
represent these types of land cover (Swedish Forest
Agency 2012). Forest management activities are the
main human impact on the productive forests, while
the most important human impacts on impediment
lands and protected areas are atmospheric deposition
and presumably climate change.
While all forest management activities can impact
surface water quality, the effects of final felling and
subsequent site preparation are the most dramatic.
Elevated nitrogen (N), phosphorus (P), dissolved organic carbon (DOC, Ahtiainen and Huttunen 1999;
Akselsson et al. 2004; Löfgren 2007; Piirainen et al.
2007; Schelker et al. 2012) and base cation (Löfgren
et al. 2009b; Piirainen et al. 2009; Rosén et al. 1996)
fluxes to small headwater streams have been observed,
sometimes up to more than a decade after final felling in
the boreal zone of Fennoscandia and North America
(Jerabkova et al. 2011; Kreutzweiser et al. 2008;
Lamontagne et al. 2000). Nitrogen and phosphorus fertilization (Binkley et al. 1999; Lundin and Bergquist
1985) and drainage including ditch maintenance
(Ahtiainen and Huttunen 1999; Joensuu et al. 2002;
Lundin and Bergquist 1990; Nilsson and Lundin 1996)
have effects on water quality for more than 5 years after
treatment. Thinning and retention harvest seem to have
less aquatic impact (Akselsson et al. 2004; Jerabkova
et al. 2011), while differences in effects between tree
species are largely unknown. Scots pine (Pinus
sylvestris L.), Norway spruce (Picea abies (L.) Karst.)
and birch (Betula spp.) are the three dominant tree
species in Sweden making up more than 90 % of
the standing biomass (Swedish Forest Agency
2012). Of these, Scots pine and Norway spruce
are the most common species for regeneration
and plantations, while planting exotic tree species
is rare (Gustafsson et al. 2010).
Almost 90 million m3sk (stem volume over bark
from stump to tip) is felled annually in Sweden. Final
felling is annually performed on approximately
225,000 ha (ca 1 % of the productive forestland), of
which ca 85 % is soil scarified and 75 % planted for
regeneration. Pre-commercial thinning occurs on approximately 1.5 % of the productive forestland, while
2 % is commercially thinned and 0.3 % fertilized
( S w e d i s h F o r e s t A g e n c y 2 0 1 2 ) w i th u p t o
200 kg N ha−1 (Swedish Forest Agency 2007). More
than 1 million ha forestland on peat or wet mineral soils
has been drained (Hånell and Magnusson 2005), and
protective ditching is performed annually in around
1,000 ha (Swedish Forest Agency 2012). Except for
harvesting, these measures are taken in order to stimulate forest production. Since the 1920s, the total standing
volume of Swedish forests has increased by over 80 %
(Swedish Forest Agency 2012).
At the landscape level, historical management determines current tree species composition and age structure
of each stand. At the stand level, tree species composition and age are important parameters for identifying
when a certain forestry operation should be undertaken.
However, the timing and location of forest management
activities are controlled by landowner decisions. These
decisions are influenced by the market economy, legislation, Forest Stewardship Council (FSC) and Program
for the Endorsement of Forest Certification (PEFC)
certification rules, as well as consideration for other
ecosystem services such as cultural heritage, biological
diversity, hunting, etc. The consequences of these decisions and the effects of different ownership structures
have a tangible effect on the forest landscape mosaic. In
Sweden, private individuals own 50 % of the productive
forestland, while big private and government owned
companies dominate the other half. In 2011, e.g. the
average notified areas for planned regeneration felling
were 3.4 and 7.0 ha felling−1 for individual and other
owners, respectively (Swedish Forest Agency 2012).
This areal difference has a landscape-scale effect on
subsequent forestry operations, potentially affecting water quality throughout the following rotation period.
Model results suggest that forestland contributes
40 % of nitrogen (N) and 35 % of phosphorus (P) net
diffuse pollution from Sweden to the sea, of which final
felling areas contribute 7 % (N) and 2 % (P) (Brandt
et al. 2009; HELCOM 2011). Hence, at the national
level, final felling is of minor importance for nutrient
loadings to the sea. Locally, however, final felling may
have substantial impacts on stream water quality. The
density of perennial headwater streams is high, ca
Environ Monit Assess
1 km km−2, in the Swedish boreal landscape (Bishop
et al. 2008; Ring et al. 2008). Due to this, boreal water
bodies may fail to reach good ecological status
according to the EU Water Framework Directive
(Eriksson et al. 2011; Löfgren et al. 2009a), and
local groundwater aquifers may be contaminated
with nitrate (Futter et al. 2010).
From a policy point of view, it is important to understand forestry effects on surface waters from a landscape
perspective. The EU Water Framework Directive
(2000/60/EC), implemented in the Swedish
Environmental Act, demands remedial actions for water
bodies not achieving good ecological status. In Sweden,
there are approximately 23,000 surface water bodies, of
which 44 % had moderate ecological status or worse in
2009 (Swedish Water Authorities 2010). Many of these
water bodies drain catchments with mixed land cover
and a mosaic of managed forest stands. It is therefore
important for the forestry sector and water authorities to
be able to identify where, in the forested landscape,
special precautions are necessary and where it can be
assumed that forestry plays an important role for water
quality.
In this study, we take this landscape approach and use
water chemical data from almost 200 randomly selected
headwater streams in the hemiboreal zone (southern
boreal and boreonemoral) of southwest and central
Sweden for assessing the impact of forest and wetland
status on stream water quality. The subsample of
streams represents hemiboreal headwaters within an
area of 43,000 km2, which equals 10 % of the total land
area of Sweden. Using data from the Swedish National
Forest Inventory (NFI), satellite images and the probabilistic classifier method, the relations between typical
forestry parameters (basal area, biomass, increment, tree
species composition, clear-felled area) and stream water
concentrations of nutrients, organic matter and ions
affecting acid-base chemistry were quantified. The results were assessed in the context of different climate,
sulphur and nitrogen deposition, as well as marine
influences.
Material and methods
Stream selection
In each of two different regions of Sweden, 100 firstorder headwater streams were selected randomly. In
central Sweden, the selected headwater subcatchments
were located within the river Dalälven catchment. In
southwest, the headwater catchments were located within the catchments of four major rivers: Viskan, Ätran,
Nissan and Lagan (Fig. 1). The headwater streams were
identified based on a ‘virtual hydrological network’
generated from a 50 m×50 m digital elevation model
(Nisell et al. 2007). The object-oriented database contains water bodies, watersheds and their topological
relations. The datasets contain raster data on flow accumulation and flow direction as well as vector data on
river reaches, lakes and contributing areas on a national
scale. The database includes 930,000 stream reaches/
arcs, and in this study, headwaters are defined as arcs
with a start node without discharge from another
upstream arc. The threshold value for accumulated
flow, defining the initiation of a headwater stream
in the landscape, varied between 1.1 and 3.5 l s−1
across Sweden.
The water divide for each headwater catchment was
modelled using the same elevation data, while land
cover and roads were mapped from digital versions of
the topographic map (1:50,000) and road map
(1:100,000), respectively. Metria (http://www.metria.
se/Startpage/) provided all geographical data except
the ‘virtual network’. One hundred headwaters
fulfilling the criteria of, being longer than 2,500 m (to
ensure that the stream was not ephemeral), having a
distance of <500 m to a driveable road at the outlet,
without lakes and urban areas and with <5 %
agricultural land in the catchment, were randomly
selected in each region. Later quality control, based on
land cover data classified from satellite images (25 m×
25 m pixel, GSD-Marktäckedata, Metria) and the
county administrations’ surface water liming register,
showed that some catchments were not suitable and
therefore excluded as they either were limed,
contained some urban area or had more than 5 %
agricultural land.
The small shares of agricultural land in these forested
catchments (≤4.9 %) are non-fertilized grasslands used
for extensive grazing and with nutrient losses similar to
those found from forests (Brandt et al. 2009). Based on
experimental data from field trials, production region,
crop rotation, fertilizer levels, soil texture, harvest and
climate, the nitrogen losses from grazing land have been
estimated by model simulations (HBV-N, Brandt and
Ejhed 2008). These simulations indicate an average
nitrogen concentration of 0.5 mg N/L in Dalälven and
Environ Monit Assess
Fig. 1 Main catchments with randomly selected headwater catchments. Green Dalälven catchment, blue southwest region (Viskan, Ätran,
Nissan and Lagan catchments)
0.7 mg N/L in the southwest. Based on the mean nitrogen concentrations 0.41 and 0.85 mg N L−1, respectively, in this investigation, the share of grazing land must
be >55 % in order to increase the nitrogen concentration
with more than 10 % in Dalälven. Thus, the low number
of catchments (40 out of 179) with small shares of
agricultural land (≤4.9 %) does not tangibly affect the
models. It is the same with the low share of water
surface area. Analysis of GSD-Marktäckedata showed
that 21 catchments included surface water pixels
representing the stream itself as well as tarns, ponds,
etc. which lacked visible connectivity to the stream. In
Environ Monit Assess
19 of the catchments, the water surface area was <1 %,
while in 4 catchments the range was 1–5 %. All these
catchments were accepted. Therefore, the final number
of catchments was 84 in the southwest and 95 in the
Dalälven region. Due to the length of a tree generation
(>50 years), average climate data for the period 1961–
1990 were used and obtained from the Swedish
Meteorological and Hydrological Institute (SMHI)
(Raab and Vedin 1995). We excluded grazing land and
water surface area from the models reported here.
Water sampling and chemical analyses
Water was sampled during four different seasons (Table 1).
Except for the summer sampling in Dalälven 2009, which
was conducted by Swedish University of Agricultural
Sciences (SLU) staff, the sampling was performed by the
county administration boards in Dalarna, Västra Götaland,
Jönköping and Halland. The collected water was sent to
the laboratory at the Department of Aquatic Sciences and
Assessment, SLU, for chemical analyses, which were
initiated within 1 day of sampling.
The analytical methods are accredited by the
Swedish Board for Accreditation and Conformity
Assessment (www.swedac.se) and follow the Swedish
standard methods. pH was measured in a through-flow
cuvette using a Radiometer PHM 210 Precision pH
meter at ambient pCO2 pressure (pH). Total organic
carbon (TOC) was measured using a Shimatzu TOC
5050 analyzer with ASI-502 sample injector following
acidification. Analysis of calcium (Ca2+), magnesium
(Mg2+), sodium (Na+) and potassium (K+) was performed by inductively coupled plasma atomic emission
spectroscopy (ICP-AES) (Varian Vista Ax Pro) and of
sulphate (SO42−) and chloride (Cl−) by ion chromatography (LDC ConductoMonitor III). Ammonium (NH4+,
indofenol method), nitrate (NO3−, sulphanilamide
Table 1 Sampling dates in the headwater streams in the catchments
of Dalälven and Viskan, Ätran, Nissan and Lagan (southwest)
Dalälven
Southwest
May 31–June 7, 2010
Spring
August 10–September
2, 2009
May 2–5, 2011
Autumn
September 12–15, 2011 September 12–15, 2011
Summer
Late autumn November 7–11, 2011
April 4–7, 2011
November 28–December
1, 2011
method after Cd reduction), total phosphorus (TP, molybdenum method after persulphate digestion) and phosphate (PO43−, molybdenum method) were photometrically analyzed (Bran Luebbe Autoanalyzer 3). Total
nitrogen (TN) was measured using a TNMI-module
equipped Shimatzu TOC-VCPH analyzer. Iron (Fe)
and manganese (Mn) were analyzed with ICP-AES
(Varian Vista AX Pro). Further information on the analytical methods, including analytical precision and limits
of detection, can be found at the department’s website
(SLU 2014a). Except for NO3−, all observations were
above the limits of detection. The latter is 1 μg L−1 for
NO3−, which was used as NO3− concentration on samples below this limit. Acid-neutralizing capacity (ANC,
μeq L−1) is calculated according to Reuss and Johnson
(1986):
ANC ¼ Ca2þ þ Mg2þ þ Kþ þ Naþ − SO4 2− þ NO3 − þ Cl−
μeq L−1
ð1Þ
Forest and wetland status classification
The status of forests and wetlands was classified using
the non-parametric probabilistic classifier method (Yu
and Ranneby 2007a; b) based on remote sensing data
(LandsatTM and Spot in Dalälven and southwest, respectively), forest and wetland data from the Swedish
NFI (tree species composition, tree age, tree biomass,
average increment, basal area etc.) and final felled areas
(Swedish Forest Agency and Metria). The NFI data was
from 2005 to 2009, representing 5-year average tree
growth and tree biomass excluding other vegetation
(SLU 2014b). The classification of deciduous trees
was improved by using satellite images (AWifs)
representing time periods after leaf fall. At the pixel
level, seven forest and two wetland variables (Table 2)
were quantified and aggregated to catchment area. The
portion of deciduous trees was classified before conifer
classes were determined. The pixel size was 25×25 m in
Dalälven and 10×10 m in the southwest. Accumulated
final felled area during the last 10 years, based on
polygons obtained from the Swedish Forest Agency,
constituted the eighth forest class. In northern Sweden,
the general view is that final felling affects N and P
concentrations for 10–15 years, while the same figure is
around 5 years in southern Sweden (Löfgren 2007 and
references therein). A period of 10 years has been used
for both regions, due to the restricted number of studies
Environ Monit Assess
Table 2 Probabilistic classifier
method derived forest and wetland classes (variables and units)
used for the BMA modelling
Land cover
Name
Variable
Unit
Forest
Growth
Average increment
m3 ha−1 year−1
Biomass
Average total biomass
Kton ha−1
>70 % spruce
Norway spruce ≥70 %
share of catchment %
Wetland
>70 % pine
Scots pine ≥70 %
share of catchment %
>50 % deciduous
Deciduous trees >50 %
share of catchment %
20–50 % deciduous
Deciduous trees [20 %, 50 %)
share of catchment %
>70 % mixed conifers
Mixed conifers ≥70 %
share of catchment %
Clear-cut
Final felled area
share of catchment %
Wetland forested
Basal area ≤3 m ha
share of catchment %
Wetland non-forest
Basal area >3 m2 ha−1
share of catchment %
supporting these generalizations. Both drained and nondrained forests are most probably represented in the
selected catchments, and potential effects on water
chemistry are likely in the studied streams. However, it
is not possible to add drainage as a spatial parameter in
the models since no data are available on where and to
what extent the forests are drained. For the same reason,
fertilization or ash return is not included as a parameter
in the models. The probabilistic classifier method derives unbiased area estimates for small areas and deviating areas based on information about the probability
distribution at pixel level (Yu and Ranneby 2007a, b).
Slowly weathering granite, gneiss, porphyry and
sandstone are the most abundant minerals in the studied
catchments (Swedish Geological Survey, data not
shown). Except for two catchments in the central part
of the Dalälven region (1 and 13 % of the catchment
area, respectively), calcium-rich soils (calcite, dolomite,
etc.) do not occur in any of the catchments. Thus, the
impact of calcium-rich soils is negligible in all catchments except one, and including this as a parameter
would not add any extra information to the models.
Variations in lithology, mineral composition and soil
chemistry between catchments are taken into account
by the random sampling, which has a consequence of
reducing the explanatory power by the models.
2
−1
parameters (Table 2) as explanatory variables. An advantage of the BMA method is that effect sizes are not
calculated from one single model but instead calculated
based on all considered models (in this case, 1,024),
weighted by the posterior model probabilities. Thus, this
method takes model uncertainty into account. The posterior inclusion probability for each explanatory variable
is also estimated. This is a measure of how important
this variable is for explaining observations and the fraction of positive coefficients, conditional on inclusion.
The BMA analyses were performed using R 3.00 and
the package ‘BMS’. In the BMA analysis, all possible
models were run and evaluated, and uniform model
priors were used. The performance of the models was
assessed by 10-fold cross-validation.
The explanatory variables in the models are assumed
to be constant over the short-term (decadal) time scale
and related to spatial variations in forest and wetland
properties (Table 2). Climatic and other temporal parameters were not used in the models. In order to test
whether there were seasonal differences in explanatory
power, models were created based on water chemistry
from four different seasons. Both seasonal and regional
(Dalälven and southwest) models were used to interpret
possible mechanisms behind the variation in water
chemistry.
Stream water chemistry models
Results
The relations between forest and wetland status and
stream water chemistry were studied using Bayesian
model averaging (BMA) (Feldkircher and Zeugner
2009). For each chemical constituent (log10 transformed), models were created with landscape
Catchment characteristics
A summary of catchment characteristics for the studied
first-order streams is presented in Table 3. There are
Environ Monit Assess
Table 3 Catchment properties in the two regions Dalälven and southwest
Dalälven
Southwest
Min 25 (%) Median 75 (%) Max Mean Min 25 (%) Median 75 (%) Max
Mean
Catchment area (ha)
106
163
209
252
620
221
24
79
105
149
279
117
Precipitation (mm year−1)
650
750
750
850
950
780
750
950
950
1,050
1,250 996
MAT (°C)
0.5
1.5
2.5
3.5
4.5
2.6
4.5
5.5
5.5
6.5
6.5
5.9
Vegetation period (days)
140
160
160
170
180
164
190
190
200
200
210
196
5.4
−1
NH4-N deposition (kg ha
−1
year ) 1.1
1.3
1.5
1.6
2.1
1.5
3.5
4.6
5.4
6.2
8.0
NO3-N deposition (kg ha−1 year−1) 1.8
2.3
2.4
2.5
3.0
2.4
4.3
5.1
5.4
5.5
5.8
5.3
SO4-S deposition (kg ha−1 year−1)
1.3
1.8
1.9
2.0
2.8
1.9
3.8
4.6
4.9
5.1
5.7
4.8
Cl depositiona (kg ha−1 year−1)
2
–
–
–
7
–
10
–
–
–
50
–
Forest growth (m3 ha−1 year−1)
0.8
1.8
2.3
2.9
4.1
2.3
0.8
3.5
4.3
5.0
7.0
4.2
Forest biomass (kton ha−1)
30
52
65
83
110
68
22
84
97
109
146
95
Wetland forested (%)
0.3
8.2
11.8
17.3
41.2
12.9
3.7
10.6
14.4
18.9
36.0
15.0
Wetland non-forest (%)
0.2
5.9
9.1
14.8
32.3
10.5
0.6
2.8
4.6
11.3
64.7
9.1
Clear-cut (%)
0.0
2.5
8.3
13.5
45.7
9.8
0.0
6.7
10.9
17.9
55.8
12.9
>70 % spruce (%)
1.1
6.1
9.5
14.6
29.7
10.8
1.3
15.5
22.7
28.5
63.3
22.9
>70 % pine (%)
6.4
16.9
23.5
33.7
65.7
25.7
2.3
7.5
11.1
14.7
27.3
11.4
>50 % deciduous (%)
0.7
4.1
5.5
7.0
13.3
5.7
0.5
2.3
3.6
6.9
23.3
5.1
20–50 % deciduous (%)
4.1
11.4
14.7
18.7
33.5
15.4
3.8
9.7
11.6
14.3
24.3
12.3
>70 % mixed conifers (%)
1.6
5.5
8.4
10.5
18.4
8.4
1.2
6.4
8.9
11.3
27.0
9.2
Agricultural land (%)
0
0
0
0
2.4
0.1
0
0
0.2
2.1
4.9
1.2
a
Throughfall data 1995–2012 from the Swedish Throughfall Monitoring Network (http://www.krondroppsnatet.ivl.se/)
tangible differences in catchment properties between the
two regions. Based on the non-parametric Wilcoxon test
(JMP 9.0.0, p<0.05), the catchments in the Dalälven
region were larger, located at higher altitude with lower
mean annual temperatures and precipitation and a higher
proportion of Scots pine compared with catchments in
the southwest (Table 3). In the latter region, atmospheric
deposition of sulphate-sulphur (S), inorganic nitrogen
(N) and chloride (Cl−); forest growth; forest biomass;
the proportion of Norway spruce and proportion of
clear-cuts were all higher (Table 3). Historically, the
difference in S deposition between the regions has been
even larger (Westling and Lövblad 2000). The forest
growth figures in Table 3 may seem low for both regions, but they represent average production for the
entire catchment including both productive forestland
and impediments such as wetlands, bare rock, etc.
Stream water chemistry—regional differences
The concentrations for most studied elements were, on
average, significantly (Wilcoxon test, p<0.05) higher in
the southwest than in the Dalälven region (Table 4).
Concentrations of TN and TP were generally twice as
high in the southwest compared with Dalälven. In both
regions, organically bound nitrogen (OrgN) dominated,
reflecting the differences in TOC concentrations with
tangibly higher levels in the southwest. However, the
proportion of inorganic nitrogen (TIN), especially
NO3−, was at least 3-fold higher in the southwest. The
most pronounced difference was for Cl−, which was, on
average, more than five times higher than the concentrations in the southwest, followed by Na+, which had a
3-fold difference or more between the two regions.
Also, SO42− concentrations were at least twice as high
in the southwest. The mobile mineral acid anions SO42−,
Cl− and NO3− were in large excess in the southwest
compared with Dalälven. However, for the balancing
base cation Ca2+, the difference in concentrations between regions was small, with higher average concentrations in Dalälven during all times of the year except
summer. This was in contrast to Mg2+ and K+, which
followed the general pattern of higher concentrations in
the southwest. Dalälven streams had much lower
A
TOC (mg L−1)
TN (μg L−1)
OrgN (μg L−1)
NO3-N (μg L−1)
NH4-N (μg L−1)
TIN (μg L−1)
TP (μg L−1)
ResP (μg L−1)
PO4-P (μg L−1)
pH
ANC (μeq L−1)
Ca2+ (μeq L−1)
Mg2+ (μeq L−1)
Na+ (μeq L−1)
K+ (μeq L−1)
SO42− (μeq L−1)
Cl− (μeq L−1)
NO3− (μeq L−1)
Fe (μg L−1)
Mn (μg L−1)
B
TOC (mg L−1)
TN (μg L−1)
OrgN (μg L−1)
NO3 (μg L−1)
NH4 (μg L−1)
TIN (μg L−1)
TP (μg L−1)
ResP (μg L−1)
PO4 (μg L−1)
pH
ANC (μeq L−1)
12.3
304
265
53
4
63
8
4
4
6.6
191
139
50
64
10
45
23
4
683
41
22.9
837
544
229
84
319
18
13
5
5.0
82
8.8
238
206
19
1
20
6
3
3
6.2
124
87
32
52
8
29
15
1
460
21
18.2
645
464
134
39
187
13
10
3
4.8
48
30.9
1,857
927
557
775
949
50
45
9
6.8
297
27.9
1,108
593
951
65
956
79
74
14
6.9
440
371
133
293
16
133
312
68
2,800
870
8.0
259
236
2
2
9
5
3
2
4.4
27
2.6
62
56
1
3
4
2
0
1
4.3
57
26
11
23
0
4
7
1
10
1
20.6
590
490
27
13
48
13
7
5
5.0
97
15.4
313
300
3
6
10
8
5
3
5.1
134
87
28
36
3
9
14
1
650
26
25.6
759
615
55
28
117
17
10
6
5.4
159
22.0
453
422
4
8
15
11
7
4
5.8
179
128
41
52
5
16
21
1
1,100
43
35.2
967
789
106
74
217
24
15
9
6.1
277
27.8
596
562
10
11
21
16
11
5
6.3
241
166
60
69
7
27
31
1
1,850
97
51.7
2,013
1,729
616
569
718
66
44
32
7.5
1,199
58.3
2,050
1,779
719
255
727
44
39
21
7.1
560
466
158
380
16
90
407
51
14,000
4,400
Min 25 (%) Median 75 (%) Max
Min 25 (%) Median 75 (%) Max
Dalälven
2.5 6.6
85
187
79
166
1
6
1
1
2
7
1
4
0
1
1
2
4.9 5.8
29
90
15
61
8
22
24
41
1
6
8
22
6
11
1
1
10
243
1
11
Southwest
6.3 14.7
166 511
149 357
5
83
1
16
7
117
5
10
2
7
1
2
4.2 4.6
−11 26
Summer
Spring
12.3
393
305
4
3
7
8
4
2
4.0
42
3.8
94
92
1
1
2
2
0
1
4.2
67
37
12
23
3
5
11
1
24
1
Min
34.7
787
764
9
10
20
16
12
3
4.4
105
17.3
340
333
3
3
6
7
4
3
4.9
138
89
29
37
7
10
16
1
670
26
43.0
1,010
958
23
17
44
21
17
5
4.6
136
22.4
435
414
4
5
12
11
7
3
5.7
176
121
39
49
9
14
22
1
1,050
48
52.3
1,260
1,174
48
33
86
30
22
7
5.0
243
27.8
555
517
8
9
19
14
11
4
6.2
235
160
57
63
11
29
33
1
1,700
94
77.0
2,833
1,983
135
799
850
63
44
20
6.9
756
46.7
1,076
930
205
41
213
37
27
20
6.9
656
529
154
182
18
167
124
15
3,700
1,300
25 (%) Median 75 (%) Max
Autumn
8.6
377
256
5
4
10
5
2
2
4.0
−17
1.8
64
35
4
1
6
1
0
1
4.6
51
24
12
24
1
7
7
1
11
2
20.9
566
470
37
21
71
11
7
4
4.4
40
9.5
215
177
12
4
20
5
2
3
5.9
137
87
32
43
6
15
16
1
613
20
28.8
763
586
73
44
136
15
10
5
4.7
73
14.8
288
249
25
8
41
8
4
4
6.3
174
121
41
54
8
21
22
2
970
45
35.6
970
783
128
79
199
23
15
7
5.0
123
20.2
413
344
50
19
72
12
7
5
6.6
221
161
58
74
11
32
35
4
1,800
110
67.6
2,401
1,555
483
1,054
1,190
52
31
21
6.9
487
37.5
849
733
249
194
277
82
79
17
7.2
666
519
192
278
18
124
326
18
5,200
1,200
Min 25 (%) Median 75 (%) Max
Late autumn
Table 4 Concentrations in randomly selected headwater streams in A the River Dalälven catchment and B the southwest region during different seasons (see Table 1
Environ Monit Assess
506
118
675
27
140
717
35
11,000
600
31
42
107
5
23
106
1
640
20
There were significant differences (Wilcoxon test,
p<0.05, JMP 9.0.0) between seasons for most water
chemical variables, except for between summer and
autumn in Dalälven and between summer and late autumn in the southwest (Table 4). The variables associated with TOC, i.e. primarily dissolved organic matter,
but also TN and TP, had low concentrations in spring
and late autumn and high concentrations during summer
and autumn. Similarly, the base cations, with the exception of K+, had the lowest concentrations in the spring.
Highest base cation concentrations, again with the exception of K+, were recorded during summer. In contrast, SO42− and TIN, especially NO3−, had the highest
concentrations in spring and the lowest concentrations
in autumn (Table 4). Due to the seasonality in ion
composition, ANC was highest in summer. This was
reflected also in the high pH in southwest streams. In
contrast, pH was highest in spring and late autumn in
Dalälven.
During summer, the concentration differences in
TOC, TN, TP and pH between regions were generally
smaller than during the other seasons (Table 4). The
relatively similar concentrations between regions coincided with low coefficient of determination for seasonal
models of TOC, TN, TP and pH for the summer period
(Table 5).
25 %, 25th percentile; 75 %, 75th percentile
16
16
66
7
18
47
1
10
16
42
35
112
13
53
97
6
795
42
59
45
142
15
65
129
10
1,100
59
84
58
162
18
81
158
16
1,500
85
302
103
1,495
37
165
1,575
40
3,500
380
33
36
120
1
8
92
1
130
12
80
64
176
8
37
146
2
1,675
48
120
82
193
11
54
170
4
2,450
69
200
103
223
16
81
212
8
3,425
103
1,112
259
793
48
160
802
44
12,000
1,100
26
31
81
3
6
69
1
890
19
63
53
138
7
28
118
1
2,325
51
82
68
160
9
36
144
2
3,000
77
138
85
183
13
47
160
3
4,550
120
700
135
534
24
109
465
10
14,000
2,200
61
58
164
12
48
193
3
1,500
51
89
71
192
15
56
222
5
2,000
73
117
88
216
18
70
251
9
2,850
110
concentrations of strong mineral acid anions. With the
exception of spring snowmelt, this was reflected in wellbuffered conditions with tangibly higher ANC and pH
compared to streams in the southwest. The generally
higher strong mineral acid anion concentrations in the
southwest are in accordance with other independent data
from national surface water monitoring programs
(Löfgren et al. 2010b; Wilander et al. 2003).
Stream water chemistry—seasonal differences
Ca (μeq L−1)
Mg (μeq L−1)
Na (μeq L−1)
K (μeq L−1)
SO4 (μeq L−1)
Cl (μeq L−1)
NO3 (μeq L−1)
Fe (μg L−1)
Mn (μg L−1)
Min 25 (%) Median 75 (%) Max
25 (%) Median 75 (%) Max
Min 25 (%) Median 75 (%) Max
Min 25 (%) Median 75 (%) Max
Min
Summer
Spring
Table 4 (continued)
Autumn
Late autumn
Environ Monit Assess
Landscape and stream water chemistry relations
Figure 2 shows the co-variation between forest and
water chemistry parameters in spring and summer,
which are the seasons when the models had highest
and lowest coefficients of determination, respectively
(Table 5). In both cases, the first two components (loading plots, principal component analysis, JMP 10.0) explain 44 % of the variation in the dataset. The first
component represents regional differences, while the
second component represents gradients related to the
0.20
0.15
0.35
ANC
Ca2+
Mg2+
0.41
0.64
0.80
0.42
0.40
K+
SO42−
Cl−
Fe
Mn
Na
0.72
0.03
0.38
ResP
0.54
0.37
TP
pH
0.59
TIN
PO4-P
0.64
NO3-N
NH4-N
0.18
0.42
0.79
0.61
0.39
0.77
0.45
0.17
0.06
0.13
0.24
0.29
0.29
0.48
0.47
0.49
0.57
TN
0.25
0.46
0.55
0.66
TOC
+
Dalälven
Southwest
0.25
0.60
0.82
0.54
0.06
0.78
0.43
0.14
0.10
0.35
0.15
0.33
0.35
0.37
0.40
0.38
0.61
0.52
0.20
0.48
0.82
0.59
0.37
0.78
0.45
0.12
0.13
0.49
0.04
0.28
0.25
0.40
0.46
0.37
0.63
0.51
0.26
0.48
0.81
0.59
0.31
0.76
0.42
0.14
0.12
0.38
0.46
0.32
0.32
0.46
0.49
0.45
0.59
0.46
0.37
0.48
0.49
0.54
0.24
0.48
0.37
0.35
0.22
0.18
0.20
0.15
0.19
0.49
0.30
0.50
0.48
0.49
0.32
0.48
0.38
0.50
0.35
0.50
0.41
0.49
0.40
0.22
0.11
0.41
0.38
0.22
0.22
0.26
0.52
0.47
0.42
0.47
0.51
0.44
0.29
0.54
0.46
0.52
0.43
0.18
0.13
0.17
0.19
0.31
0.11
0.29
0.42
0.31
0.38
0.50
0.57
0.41
0.27
0.51
0.34
0.35
0.27
0.17
0.02
0.18
0.13
0.40
0.41
0.40
0.57
0.46
0.37
0.48
0.49
0.47
0.29
0.51
0.40
0.43
0.33
0.19
0.46
0.23
0.22
0.35
0.26
0.36
0.50
0.43
0.28
0.12
0.60
0.65
0.22
0.45
0.31
0.07
0.29
0.25
0.06
0.05
0.06
0.15
0.30
0.24
0.16
0.21
0.03
0.35
0.42
0.32
0.10
0.32
0.14
0.25
0.25
0.27
0.12
0.09
0.13
0.11
0.37
0.04
0.28
0.27
0.38
0.30
0.48
0.50
0.07
0.48
0.09
0.05
0.06
0.18
0.04
0.06
0.05
0.05
0.18
0.08
0.15
0.24
0.24
0.36
0.50
0.53
0.39
0.58
0.16
0.07
0.06
0.18
0.04
0.05
0.05
0.05
0.09
0.11
0.07
0.20
0.23
0.28
0.50
0.50
0.19
0.46
0.17
0.11
0.17
0.22
0.46
0.06
0.07
0.09
0.23
0.12
0.17
0.23
Spring Summer Autumn Late autumn All seasons Spring Summer Autumn Late autumn All seasons Spring Summer Autumn Late autumn All seasons
Constituent Both regions
Table 5 Coefficient of determination (r2 values) for the best models based on seasonal data from both regions and separated for Dalälven and southwest
Environ Monit Assess
Environ Monit Assess
proportion of wetlands in the catchments. The proportions of deciduous trees, mixed conifers and clearfellings were not related to any of these gradients, while
forest production, biomass, Norway spruce and Scots
pine were related to the regional gradient. The proportions of mixed conifers and deciduous trees were related
to principal components 3 and 4, respectively,
explaining 11 and 7 % of the variation. In spring, the
chemical constituents were also related to this regional
gradient except for Ca2+, which was related to the wetland gradient, and PO43− and Mn, which were unrelated
to either component. In summer, the chemical constituents arranged somewhat differently, and the wetland
gradient became more important for pH, TOC, TN, TP
and Fe (Fig. 2).
The three best predicted chemical variables were
Na+, Cl− and SO42− (0.5≤r2 ≤0.8, Table 5). These variables were all well predicted both in the combined
modelling of both regions and in the separate regional
models (Table 5). Total nitrogen was also relatively well
predicted (0.5≤r2 ≤0.7, Table 5), generally better than
TOC and TIN (0.3≤r2 ≤0.6 and 0.3≤r2 ≤0.4, respectively), but the regional TN models were less precise, especially in the southwest (r2Southwest <0.28, r2Dalälven <
0.57). Typically, for most variables, explanatory power
was not as good for the regional models as for the main
models (Table 5).
In general, forest growth was the most important
explanatory variable in the models. Forest growth occurred in nearly all of the top models as a positive factor
for concentrations of the chemical species (Table 6a).
Clear-cuts had positive mean coefficients for many
chemical variables, most notably TIN and SO42−. Also,
Cl−, Mg2+, Na+ and K+ had positive coefficients for
clear-cuts, whereas Ca2+ did not (Table 6a). Clear-cuts
also appeared as a positive factor in some of the top
models for TOC, TN and TP (Table 6a). The all-season
mean coefficient of 1.3 for TIN (Table 6b) corresponds
to about 20 times higher TIN from a 100 % clear-cut
catchment compared to a catchment without clear-cuts.
For a 10 % clear-cut area, i.e. approximately the mean
clear-cut area in this study (Table 3) (and Sweden), this
corresponds to an approximately 35 % increase in TIN
leaching, compared to growing forests. The corresponding all-season mean coefficients for the regional models
(data not shown) were, on average, lower, suggesting 9
and 17 % increased TIN leaching for 10 % clear-cut
catchments in the southwest and Dalälven, respectively.
For TOC, TN and TP, the mean coefficients for all
seasons were 0.12, 0.32 and 0.2, respectively
(Table 6b), which corresponds to 2 %, 8 % and 5 %
increase, respectively, in a catchment with 10 % clearcut area. For these variables, the effect was negligible
when the two regions were analyzed separately. For
Na+, Mg2+ and K+, the mean coefficients for all seasons
indicated an approximately 5-fold increase in 100 %
clear-cut catchments, and there was a tangible effect of clear-cuts in both regions.
The proportion of wetland was an important predictor for many of the chemical variables. Typically, forest
wetlands with a basal area >3 m2 ha−1 were more
important than non-forested. Based on the best allseason models (Table 6b), the highest positive mean
coefficients for forested wetlands were found for Fe
(3.2), Mn (2.4) and TOC (1.3), while there were large
negative coefficients for pH (−4.1) and Ca2+ (−1.3). The
other base cations had small positive mean coefficients.
TN, TP and TIN had positive mean coefficients.
For many of the chemical variables, there were negative all-season mean coefficients for the forest classes
(Table 6b). This was the case for all tree classes, i.e. all
of the Norway spruce, Scots pine and deciduous dominated stands, as well as the mixed forest classes. In the
regional models, the negative signs often disappeared
for both southwest and Dalälven (data not shown). In
many cases, there were opposing signs for the dominating tree species mean coefficients between the two
regions.
Discussion
Uncertainties
A study like this includes a long chain of various uncertainties, which, to some extent, is possible to control
(water sampling, chemical analyses, forest inventories,
satellite images, model selection, parameter estimation
etc.), but how the uncertainty propagates through the
assessment is difficult to quantify. Uncertainty related to
stream water sampling is generally less than the analytical precision for the chemical analyses (Löfgren et al.
2010a), and compared with the water chemical concentration gradients in this study (Table 4), these uncertainties are of no importance. For uncertainties related
to forest inventory data and remote sensing data, information can be obtained by contacting Swedish NFI
(http://www.slu.se/en/webbtjanster-miljoanalys/forest-
Environ Monit Assess
Fig. 2 Co-variations between
modelled parameters in spring
and summer, which are the
seasons when the models had the
highest and lowest coefficients
of determinations, respectively
(Table 5). Loading plots
(principal component analysis)
a
b
Response variables
TOC
1
2
3
Predictors (catchment properties)
Intercept
1.03
1.06
1.30
Growth
0.13
0.06
0.12
Biomass
Wetland forested
0.89
1.30
1.10
Wetland non-forest
Clear-cut
>70 % spruce
−1.06 −0.54 −0.92
>70 % pine
−0.89
−0.76
>50 % deciduous
−1.35
20–50 % deciduous
−0.98
>70 % mixed conifers
Response variables
TIN
1
2
3
Predictors (catchment properties)
Intercept
0.25
−0.26 1.43
Growth
0.53
0.32
0.29
Biomass
Wetland forested
1.88
Wetland non-forest
2.01
3.02
Clear-cut
2.21
2.20
>70 % spruce
−2.78
−2.55
>70 % pine
−1.07
−1.64
>50 % deciduous
20–50 % deciduous
−1.77
>70 % mixed conifers
Response variables
pH
1
2
3
Predictors (catchment properties)
A
2.81
0.19
0.40
0.14
2
ANC
1
4
−1.61
3
4
0.58
2.80
−2.67
2
−3.65
Cl−
1
−1.91
−1.90
1.97
0.08
0.55
3
−0.75
1.69
0.12
0.48
4
3.07
0.18
−0.29
−2.05
0.60
1.67
1.43
0.23
1.06
2
3
−1.34
−0.69
0.40
0.10
0.54
0.11
ResP
1
−2.68
−1.22
−1.03
1.31
0.12
3
1.85
0.87
−2.02
−0.89
1.39
0.20
0.98
4
1.21
0.13
−2.93
−1.45
0.37
0.58
3
1.30
0.28
−3.61
−2.69
0.43
1.28
2
4
2
4
2.66
3.02
−1.95
0.54
−0.67
NO3-N
1
TP
1
1.75
0.97
1.02
1.65
0.17
4
4
−1.36
−1.01
−1.34
0.81
2.64
0.17
3
−2.09
1.20
0.92
0.60
−0.64
2.06
0.15
2
−1.63
−1.33
−1.02
−1.11
−1.18
1.99
0.82
0.62
TN
1
4
SO42−
1
−0.90
0.61
PO4-P
1
−4.58
−3.67
−5.34
−2.62
−3.23
0.54
1.55
NH4-N
1
2
−1.28
−0.69
−2.32
0.12
0.77
2
−2.25
2.38
2.89
1.05
0.24
−0.19
2
3
−0.90
0.11
0.40
3
3.16
1.88
1.13
0.22
−0.49
3
4
0.04
0.54
4
3.17
1.85
1.52
0.28
−0.46
4
Table 6 (A) Regression coefficients for the best models based on seasonal data from both regions (Dalälven and southwest). (B) Regression coefficients for the best models based on both
regions and all seasons
Environ Monit Assess
0.00
−5.37
2.80
5.67
2.79
4.48
5.57
−4.81
6.59
−0.33
Response variables
Ca2+
1
2
3
Predictors (catchment properties)
Intercept
−0.74 −0.79 −0.87
Growth
0.12
0.09
Biomass
Wetland forested
−1.55 −1.32 −1.28
Wetland non-forest
Clear-cut
>70 % spruce
−0.94 −1.24
>70 % pine
>50 % deciduous
20–50 % deciduous
>70 % mixed conifers −2.06 −1.62
Response variables
Fe
1
2
3
Predictors (catchment properties)
Intercept
1.51
2.08
2.63
Growth
0.19
0.16
0.26
Biomass
Wetland forested
3.19
3.16
2.82
Wetland non-forest
1.26
1.34
Clear-cut
0.75
>70 % spruce
−1.77
>70 % pine
−1.29
>50 % deciduous
−1.69
20–50 % deciduous
>70 % mixed conifers
B
Response variables
TOC TN
NO3-N
Predictors (catchment properties)
Growth
0.11
0.17
0.39
Biomass
0.00
0.00
0.00
Intercept
Growth
Biomass
Wetland forested
Wetland non-forest
Clear-cut
>70 % spruce
>70 % pine
>50 % deciduous
20–50 % deciduous
>70 % mixed conifers
Table 6 (continued)
0.04
0.23
2.80
1.15
1.06
2.28
0.13
3.70
TIN
0.35
0.00
NH4-N
0.31
0.00
2.91
Mn
1
4
−0.54
−1.04
0.68
−1.67
0.08
Mg2+
1
−0.29
−0.42
0.21
−1.36
−1.43
−0.67
4
6.37
3.70
−5.52
6.05
−0.30
0.11
0.00
TP
2.53
1.12
0.09
2
0.98
0.99
−1.89
0.14
2
0.22
0.07
0.00
0.16
0.00
ResP
2.58
2.57
0.86
0.13
3
0.52
−0.88
−0.55
−1.48
0.12
3
−0.45
−0.44
0.21
0.04
0.06
0.00
PO4-P
2.40
2.50
0.87
0.13
4
0.57
−0.63
−0.55
−1.47
0.11
4
−0.27
−0.58
0.28
ANC
0.01
0.00
−0.22
0.00
0.64
0.87
−1.39
−0.93
−1.22
−1.58
0.28
2
−2.60
−2.17
−2.24
−1.36
−1.28
−1.26
0.40
pH
−0.78
0.47
−1.19
−0.82
−1.37
−1.30
0.21
Na+
1
−2.55
−1.95
−2.13
−1.41
−1.58
−1.41
0.40
0.39
0.00
Cl−
0.61
−1.53
−0.95
−1.27
−1.39
0.24
3
−2.26
−1.85
−1.69
−1.21
−1.13
−1.27
0.34
0.23
0.00
SO42−
0.52
0.77
−1.33
−0.89
−1.10
−1.48
0.25
4
−2.86
−2.38
−2.19
−1.74
−1.61
−1.10
0.42
0.06
0.00
Ca2+
0.70
0.77
−0.48
−2.48
0.13
0.11
0.00
Mg2+
−1.87
0.80
1.22
−2.78
0.15
2
1.45
0.98
K+
1
−2.17
0.30
0.00
−1.80
0.20
0.00
0.24
0.00
Na+
−0.75
−1.95
3
1.20
−2.12
0.23
0.00
0.11
0.00
K+
0.76
−0.67
−2.42
0.14
4
1.03
−1.95
0.22
0.00
0.18
0.00
Fe
0.14
0.00
Mn
Environ Monit Assess
2.45
0.10
0.10
−0.12
−0.23
1.18
0.02
−0.08
Environ Monit Assess
0.15
0.31
0.38
−2.33
−1.81
−1.27
−1.30
−0.63
−0.02
0.02
1.17
−0.02
−0.01
−0.02
0.13
−0.01
−1.30
0.00
0.00
−0.57
0.06
0.04
0.06
−1.09
0.01
0.31
0.72
−0.32
−0.25
−0.02
0.12
−0.23
0.08
0.25
0.68
−1.28
−0.86
−1.09
−0.06
−0.17
−0.11
0.26
0.70
−0.21
0.06
−0.53
−0.03
0.00
3.16
0.54
0.13
−0.62
−0.49
−0.34
0.00
0.02
statistics/contact-us/) and Metria (http://www.metria.se/
Startpage/Contact-us/), respectively.
For the probabilistic classifier method, the entropy
gives uncertainty on pixel level. For a given number of
classes, the entropy is maximized when all classes have the
same probability. This maximum entropy increases with
an increasing number of classes. By dividing the calculated entropy with the maximum entropy, comparisons can
be made for different levels of aggregation, yielding an
unbiased estimate (Yu and Ranneby 2007a, b). Standard
statistical practice ignores model uncertainty, leading to
risk for overconfident inferences and decisions. Bayesian
model averaging (BMA) provides a coherent mechanism
for accounting for this model uncertainty (Feldkircher and
Zeugner 2009). Hence, in this study, the coefficients of
determination for the best models (Table 5) are affected by
the aggregated uncertainty for the entire assessment chain.
1 spring, 2 summer, 3 autumn, 4 late autumn
Wetland forested
Wetland non-forest
Clear-cut
>70 % spruce
>70 % pine
>50 % deciduous
20–50 % deciduous
>70 % mixed conifers
Table 6 (continued)
1.28
0.18
0.12
−0.62
−0.46
−0.48
−0.26
−0.02
0.83
0.38
0.32
−1.06
−0.69
−0.43
−0.41
−0.20
−0.06
0.69
1.26
−1.98
−0.95
−0.08
−0.28
−0.75
2.24
1.70
0.93
−1.14
−0.92
−1.62
−0.56
−0.32
0.44
1.30
1.30
−1.58
−0.76
−0.22
−0.43
−0.44
0.79
0.02
0.20
−0.59
−0.41
0.04
−0.90
−0.02
1.63
0.11
0.10
−0.53
−0.67
0.74
−1.60
−0.08
0.14
0.08
0.16
−0.37
−0.11
−0.41
−0.10
−0.11
−5.33
−0.15
−0.04
0.03
3.09
1.04
4.96
−0.70
−0.27
0.01
0.02
−0.15
0.04
0.08
0.13
−0.03
Two distinctly different regions
The differences in concentrations between the two regions were substantial. The importance of region is also
indicated by the first principal component in the PCA
analysis, which explained approximately 30 % of the
variation in the dataset (Fig. 2). For most water quality
variables, the 25th percentile in the southwest was approximately equal to or even higher than the 75th percentile in the Dalälven region (Table 4). Similarly, there
were also large variations between regions for some of
the explanatory variables (Table 3). Forest growth and
biomass were significantly higher in the southwest compared to Dalälven. Forest growth overlapped in the
lower range 0.8–4.1 m3 ha−1 year−1, but with values
up to 7.0 m3 ha−1 year−1 in the southwest, while forest
biomass was typically about 30 kton ha−1 higher in the
southwest (Table 3). In addition, the relative abundance
of dominating tree species differed between the two
regions, and Norway spruce was more abundant in the
southwest and Scots pine more common in Dalälven
(Table 3). The two distinctly different regions with
different levels in stream water concentrations for most
variables and landscape characteristics have resulted in
models that reflect these differences.
The separation of both water chemistry and landscape data into two clusters, each representing one of
the two regions, implies that any explanatory variables
separating the two regions are likely to be included in
the statistical models. Models with region, latitude or
longitude as dependent variables show that forest
Environ Monit Assess
In the models presented here, the large differences in
forest growth between Dalälven and the southwest have
a significant role in separating the regions (Table 3). If
forest growth is removed from the list of potential predictors, forest biomass becomes the most important
explanatory variable. Forest growth and biomass are
strongly related to each other (r2 =0.80). As already
stated, latitude (r2 =0.83) or mean annual temperature
(r2 =0.82) could also be used to separate the two different regions. Due to their geographical location, with the
southwest region neighbouring the Kattegat Sea, deposition of chloride, sulphate-sulphur (r2 =0.82) and TIN
(r2NO3 =0.81 and r2NH4 =0.73) may also separate the two
regions (Table 7).
The relations with latitude and temperature are to be
expected since both latitude and altitude are highly
correlated to forest production and biomass. These geographical factors are used in Sweden for estimating
growth both at tree and stand levels and are used as
substitutes for climatic gradients (Ekö 1985; Söderberg
1986). In this study, forest biomass and production are
estimated independently of latitude with the probabilistic classifier method, creating models from satellite images and ‘ground truth’ measurements by the Swedish
NFI. Additionally, variations within each catchment are
accounted for by the probability for each land cover,
forest growth and biomass class at pixel level (Yu and
Ranneby 2007a). Hence, the differences between the
regions with respect to forest production and biomass
are well documented. However, it remains to identify
whether it is forest production per se or some other
variables which are correlated with forest production
that have the causal relationships with water chemistry.
When each region was analyzed separately, forest
growth was not an important predictor in the southwest
for some of the chemical variables, whereas it was in
Dalälven. This was the case for TOC, TN, TP and K+,
which all are variables related to biological production.
For the Dalälven region, there was a relatively strong
positive correlation between mean annual temperature
and forest growth (r=0.63) whereas this was not the
case in the southwest (r=0.11, Table 8). The long-term
mean annual temperature varied with almost no overlap
in the ranges 0.5–4.5 and 4.5–6.5 °C, respectively
(Table 3). Consequently, the growing season varied
between 160 and 180 days in Dalälven and between
190 and 210 days in the southwest (Table 3). Water is
Table 7 Coefficient of determination (r2 values) for the best
models based on catchment properties (Table 2) from both regions
and separated for Dalälven and southwest
Table 8 Regression coefficients (r values) between selected variables and forest growth (Table 2) for both regions and separated
for Dalälven and southwest
Dependent variable
Southwest
Dependent variable
characteristics (Table 2) are highly related to region and
latitude (r2 ≥0.8, Table 7), while longitude is less important (r2 =0.28). In both regions separately, the northsouth location is well simulated by the models
(r2 ≈0.6). In Dalälven, longitude is also important
(r2 ≈0.5) and corresponds to temperature and precipitation gradients. The Dalälven catchment stretches from
low-productive mountain forests in the west to productive managed forests in the east, resulting in a good
relationship between forest parameters and longitude.
Due to these ecoregional differences, results must be
interpreted with great caution. This has been dealt with
by making separate models for each region and by
evaluating the explanatory variables in relation to expected water chemical effects due to forest production
(assimilation/mineralization of nutrients: TIN and K+
and formation of organic matter: TOC, TN and TP),
atmospheric deposition (SO42−, Cl−, Na+) and the share
of wetlands (TOC, base cations, pH, Fe, Mn).
Forest production
Both areas
Dalälven
Both areas
Dalälven
Southwest
Region
0.80
Not relevant
Not relevant
Temperature
0.74
0.63
0.11
Latitude
0.83
0.59
0.62
Latitude
−0.72
−0.67
0.11
Longitude
0.28
0.54
0.36
Longitude
−0.31
0.56
−0.17
Temperature
0.82
0.52
0.54
Altitude
−0.70
−0.84
−0.05
NHx deposition
0.73
No relation
0.51
NHx deposition
0.67
−0.05
0.08
NOx deposition
0.81
0.51
0.62
NOx deposition
0.70
0.57
−0.13
SO4 deposition
0.82
0.51
0.48
SOx deposition
0.73
0.59
0.10
Environ Monit Assess
another important factor for forest growth and the longterm precipitation was, on average, 200 mm larger in the
southwest compared to the Dalälven region (Table 3). In
summary, the climatic gradients, which affect forest
production, are stronger in the Dalälven region than in
the southwest, possibly explaining the better models for
the former area.
In Sweden, nitrogen is the most limiting nutrient for
forest growth, and there is a strong relation between N
availability and forest production (Binkley and Högberg
1997; Tamm 1991). Atmospheric deposition is an important N input to forest ecosystems (op. cit.). During
this investigation, the atmospheric deposition of TIN
varied without overlap between 8–14 and 3–5
kg N ha−1 year−1 in the southwest and Dalälven, respectively (Table 3). Due to high rates of atmospheric deposition, N fertilization is prohibited in the southwest
(Swedish Forestry Act). In the Dalälven region, N fertilization was performed on <6,000 ha in 2011 (Swedish
Forest Agency 2012), which corresponds to <0.3 % of
the area of productive forestland. Hence, the randomly
selected headwater streams in Dalälven are not likely to
be affected by N fertilization to any large extent.
Interestingly, there is no relation between NHx deposition and forest growth in either region. Only the
Dalälven area exhibited a positive correlation between
forest production and NOx deposition (Table 8). This
indicates that the forest N demand is primarily governed
by N availability coupled to N mineralization in the
soils. In a short-term perspective, the annual N deposition is an extra N source for the forest ecosystem, but it
does not determine the production level. In the time
perspective of multirotation periods, however, atmospheric deposition must have been a major source of N
to the hemiboreal landscape, strongly affecting soil
productivity.
In both regions, relatively low inorganic nitrogen
(TIN) concentrations during summer and autumn compared to spring and late autumn concentrations (Table 4)
indicate less efficient N retention outside of the growing
season. Additionally, it is evident that forests in the
southwest have a more open N cycle, exporting more
N to receiving streams regardless of season compared
with the Dalälven region. This indicates that high forest
production and enhanced losses of nitrogen to surface
waters are related somehow (see below).
A positive relationship between forest growth and
dissolved organic matter has previously been reported
by Lauerwald et al. (2012) who found that net primary
production was positively related to DOC concentrations in streams across the USA. In a Finnish study,
stream water concentrations of TN, TIN and TP were
positively related to stem volume in the catchment
(Mattson et al. 2003). In Sweden, differences in net
primary production have been suggested as a reason
for decreasing soil water DOC concentrations with increasing latitude (Fröberg et al. 2006). It has also
been proposed that the amount of soil organic matter in soils across Sweden is related both to temperature (Callesen et al. 2003) and N deposition
(Kleja et al. 2008).
For TIN, K+, OrgN and TOC, forest growth per se is
likely not a factor that has a significant direct effect on
stream water chemistry. Instead, the elevated concentrations of many elements at sites with high growth can
probably be attributed to factors simultaneously affecting forest production and stream water chemistry.
Climate (temperature and precipitation), extensive soil
element pools and N deposition are the most likely
candidates. Results presented here support a hypothesis
that the potential losses of organic species and inorganic
nutrients is higher in nutrient-rich stands with a climate
favouring leakage compared with stands of lower fertility and a climate less conducive to element losses.
However, the mechanisms may differ between water
chemistry variables depending on whether they are produced (DOC, TON) or assimilated (TIN, K+) by
vegetation.
In the southwest where winter temperatures are often
above 0 °C and precipitation primarily in the form of
rain, the prerequisites are created for high groundwater
levels and leakage of organic matter to streams throughout the year. At base flow, during summer and autumn,
peatlands and riparian soils may be important sources of
organic matter (Löfgren and Cory 2010; Winterdahl
et al. 2011), yielding high concentrations of TOC, TN
and TP (Table 3). In the Dalälven region, winter precipitation (December to March) generally accumulates as
snow with few or no melt events, creating continuously
descending groundwater levels and decreasing TOC
concentrations in run-off. This is further accentuated in
spring, when large volumes of snowmelt water may
dilute mobilized organic matter (Table 3). As in the
southwest, organic matter is derived primarily from
wetlands during base flow.
A high nutrient demand during the growing season
makes TIN (and K+) less available for leakage, while
winter, early spring and late autumn are potential
Environ Monit Assess
periods for tangible losses (Tables 3 and 4). Both net
mineralization and atmospheric deposition may contribute to excess TIN (and K+) concentrations in run-off
during the dormant season. The atmospheric N deposition is much higher in the southwest compared with
Dalälven, and from the forest production figures, it
could be assumed that mineralization is too. The prerequisites for higher TIN concentrations in the southwest (Tables 3 and 4) are therefore fulfilled.
The models reflect two distinctly different regions,
and the influence of dominant tree species should therefore be interpreted with great caution. For many chemical variables, there was a negative influence of all
dominant tree species in the model for both regions
combined (Table 6b). This negative influence usually
disappeared in the separate regional models (Table 6a).
There is a complex multifactor interaction between forest production and the amount of Norway spruce and
Scots pine. Forest growth increases, e.g. with Norway
spruce in the southwest, but not in Dalälven. If region is
used as response variable, the relations are positive for
forest growth and negative for these two tree species.
Based on an ANOVA, forest growth and Scots pine are
significantly related (p<0.05) as well as the three-way
interaction between forest growth, Scots pine and
Norway spruce. This suggests that the negative influence of dominant tree species on the chemical variables
in the models should be interpreted as statistical artefacts
rather than real effects.
Forest management
Clear-cuts are well known to result in increased N
concentrations in soil water (e.g. Akselsson et al.
2004; Futter et al. 2010) and surface waters (e.g.
Löfgren et al. 2009b; Rosén et al. 1996). Where in the
catchment, clear-cutting occurs and the existence of
buffer zones along a stream may also be important
factors for N and P export (Löfgren et al. 2009b). On a
large scale, however, the effects of clear-cuts on the
nutrient loads to the sea surrounding Sweden are low
(Brandt et al. 2009; Futter et al. 2010).
The data presented here suggest a strong increase of
TIN after complete clear-cutting, which generally includes also soil tillage and planting (see Introduction),
of a headwater catchment. Mean coefficients for the
whole dataset (Table 6b) suggest as much as a 20-fold
increase at 100 % clear-cut compared with a nonharvested area (mean coefficient for clear-felling=1.3,
log10TIN=1.3*clear-cut), although this figure is not
well constrained and is considerably lower in the regional models. On a larger scale, some fraction of the
total N and P loads from forests to the sea may be
attributed to clear-cutting. In 2006, 4.3 % of the forestland in the southwest region which drains to the
Kattegat was clear-felled (Brandt et al. 2009). Based
on the all-season best model, this corresponds to a TIN
concentration increase of 10 %. For the Kattegat, Brandt
et al. (op. cit.) estimated a 10 % increase in gross
nitrogen load from forestland due to increased TIN
leakage from clear-felled areas. Changed run-off after
clear-cutting was not taken into account in these load
estimates (op. cit.), implying a 10 % TIN concentration
increase.
In other studies where large fractions of small catchments have been clear-cut, there have been significant
effects on C, N and P in streams (Löfgren et al. 2009b).
Vuorenmaa et al. (2002) reported 40–70 % higher P and
a more than 3-fold increase in N after clear-cutting 80 %
of a catchment in Finland. Hence, on a headwater catchment scale, the impact on stream water concentrations
may be significant if a large proportion of the catchment
is harvested within a short period of time. In the data
presented here, up to about half of the area of a single
catchment was classified as clear-cut. The results for
TOC in relation to clear-cuts are similar in magnitude
to those observed by Laudon et al. (2009), who reported
an up to 50 % increase in stream water DOC concentrations after harvesting large parts of boreal catchments in
Northern Sweden. Mean coefficients (Table 6b) in this
study suggest an increase of about 30 % in TOC concentration after complete harvest. Thus, the contribution
from clear-cuts to total transport of TOC on a large scale
(i.e. clear-cut area ~10 %) was estimated to be only a
few percent, but the effect on a smaller scale may be
significant if large portions of the catchment are subjected to harvest.
Removal of base cations with trees is another effect
of forest harvest, potentially contributing to acidification
of soils and water (Zetterberg et al. 2013). Increased
leaching of base cations reinforces the negative effect of
clear-cutting (Titus et al. 1998). In this study, positive
relationships between clear-cuts and Na+, K+ and Mg2+,
but not Ca2+, were found. Mean coefficients (Table 6b)
suggest a 10–20 % increase in base cation concentrations from forest catchments on a large scale (i.e. with
about 10 % of the area clear-cut) as a result of clearcutting in Sweden. In addition, SO42− had positive mean
Environ Monit Assess
coefficients for clear-cuts. The SO42− leakage and
higher NO3− loss cause increased base cation leaching
in order to maintain ion balance in solution (Zetterberg
et al. 2013). Kreutzweiser et al. (2008) concluded in a
review that forest harvest might result in increased
leaching of base cations. Rosén et al. (1996) found
distinct increases in K+, inorganic and organic N in
experimentally clear-cut catchments in Central
Sweden, accompanied by less distinct increases for
Na+, Ca2+, Mg2+, Cl− and SO42−. Increased leakage of
K+ but no effects on Ca2+ was also found after clearcutting in northern Sweden (Löfgren et al. 2009b).
Following harvest and regeneration in the studied
regions, the most extensive forest operation is thinning
on one or more occasions. N fertilization, ash treatment,
etc. are rare or prohibited. The relations between such
activities and water quality have not been evaluated per
se, but the effects of thinning operations are included in
the production and biomass estimates. As already stated,
production per se does not seem to have a causal relation
with water quality, indicating few or negligible effects of
thinning.
Atmospheric deposition
Based on all data, the stream water variables that gave
best model fits (Table 6b) were Na+, Cl− and SO42−
(0.6<r2 ≤0.8, Table 5), which are all known to be
closely related to atmospheric deposition (Löfgren
et al. 2010b). As already shown, forest growth, temperature, precipitation and atmospheric depositions are
closely correlated across Sweden, and good fits for these
variables are therefore expected. In the two separate
regions, the model fits were lower (r2 ≈0.5, Table 5),
but still the best among the studied water chemical
variables. The good model fits for these constituents,
poorly coupled to forest growth, strongly support a conclusion that it is not forest production per se that causes
increased organic matter and nutrient concentrations in
stream water.
Wetlands
The proportion of forested wetland (basal area
≥3 m2 ha−1) was included as a positive factor in models
for TOC, TN, TP, Fe and Mn and as a negative factor for
Ca2+, pH and ANC (Table 6). The importance of wetlands is also indicated by the second principal component in the PCA analysis, explaining approximately
15 % of the variation in the dataset (Fig. 2). There were
also some tendencies for a positive relationship for nonforested wetlands (basal area <3 m2 ha−1), for the aforementioned variables and for Na+, K+, Mg2+ and TIN
(Table 6b). Higher concentrations of dissolved C, N and
P in streams from catchments with large contributions
from wetlands have repeatedly been shown in other
studies (e.g. Dillon and Molot 1997; Laudon et al.
2004; Johnston et al. 2008; Lauerwald et al. 2012).
The positive effect of wetlands on these variables was
observed in the models for both regions and in the
separate regional modelling. There was also a logical
sequence for the influence of wetlands in the models
(Table 6). Wetlands had the largest influence on TOC
(100 % organic), followed by TN, which is dominated
by organic species and then by TP, which includes both
organic P, but also a significant fraction of inorganic P.
For TIN, the connection to wetlands was weak.
Fe and Mn are two redox sensitive elements, and the
positive relationship with wetlands is in agreement with
catchments in Ontario, Canada (Dillon and Molot
1997), New York, USA (Maranger et al. 2006), and
Finland (Kortelainen et al. 2006). The negative relationship between wetlands and Ca2+ is explained by the
strong connection to mineral weathering for this element
and the restricted hydraulic connectivity with minerals
in peat-dominated areas. The negative relation between
wetlands and ANC, which also affect pH, is a result of
lower base cation concentrations. Besides ANC, pH is
also affected by organic acidity derived from peat.
Conclusions
This assessment indicates that element losses from
forest-dominated mosaic landscapes are related to factors associated with forest production but that does not
necessarily mean that it is the forest production per se
that causes the excess losses. Instead, factors simultaneously affecting forest production and stream water
chemistry, with climate (temperature and precipitation),
extensive soil pools and N deposition, are the most
likely candidates for the water chemical gradients.
However, some relationships between catchment
properties, forestry practices and water chemistry are
likely to be direct effects. This is shown by clear differences in explanatory variables between the chemical
models and the model of region. Neither wetlands nor
clear-cuts were important factors for modelling of
Environ Monit Assess
region (Table 6) but are included in many of the models
for stream water chemistry, thus suggesting an actual
effect of these factors on the water chemistry (e.g. TOC,
TN, TIN, TP, pH and ANC).
Hence, the forestry sector could use this type of
models in order to identify water bodies where special
precautions are necessary and water authorities could
improve the estimates of element leakage from mosaic
landscapes and to make more accurate source apportionments, thereby quantifying the possibilities to reduce
e.g. nutrient loads to the sea. In the latter case, however,
water discharge and between-years variation are necessary to take into account. Currently, the nutrient reduction targets within the Baltic Sea Action Plan
(HELCOM) have been criticized for not properly estimating the different background leakage from different
land cover, causing unachievable reduction targets in
e.g. hemiboreal areas (Swedish Farmers 2013).
In the context of forest management, these results
indicate that focus should be on improving harvesting
techniques at clear-felling and associated regeneration
practices such as soil tillage in order to reduce negative
impacts on the headwater stream chemistry. At least at a
local scale, this may improve water quality. The effects
of thinning operations, performed later in the forest
succession, seem to be low or negligible.
Acknowledgments This work was financed by the Swedish Environmental Protection Agency; the Swedish Energy Agency; the
county administration boards of Dalarna, Västra Götaland, Halland
and Jönköping; the Department of Aquatic Sciences and Assessment
(SLU) and the research program ForWater financed by the Swedish
Environmental Research Council for Environment, Agricultural Sciences and Spatial Planning. We would like to thank the field and
laboratory staff for excellent work with sampling and chemical analyses. We also thank associate professor Martyn Futter, SLU, for
valuable comments and linguistic corrections.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use,
distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
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