Water Resources Management 14: 177–189, 2000.
© 2000 Kluwer Academic Publishers. Printed in the Netherlands.
177
Modelling the Effects of Climate Change on Water
Resources in Central Sweden
CHONG-YU XU
Department of Earth Sciences, Hydrology, Uppsala University, Villavägen 16, S-75236 Uppsala,
Sweden, e-mail: Chong-yu.Xu@hyd.uu.se
(Received: 27 January 1999; in final form: 20 May 2000)
Abstract. This article describes investigations into the effects of climate change on flow regimes of
twenty-five catchments (from 6 to 1293 km2 ) in central Sweden. Hydrological responses of fifteen
hypothetical climate change scenarios (e.g. combinations of 1T = +1, +2 and +4 ◦ C and 1P = 0, ±
10%, ± 20%) were simulated by a conceptual monthly water balance model. The results suggest
that all the hypothetical climate change scenarios would cause major decreases in winter snow
accumulation. Significant increase of winter flow and decrease of spring and summer runoff were
resulted from most scenarios. Attendant changes in actual evapotranspiration were also examined
for all climate change scenarios. Despite the changes in seasonal distribution of evapotranspiration,
the change in annual total evapotranspiration was relatively small with the maximum change of 23%
compared with the 76% for mean annual snow water equivalent changes and 52% for mean annual
runoff changes. Such hydrologic results would have significant implications on future water resources
design and management.
Key words: climate change, Sweden, water balance models, water resources
1. Introduction
It is widely recognised that the anthropogenic production of greenhouse gases will
effect many changes in the natural environment. The most obvious of these are
on the climate, for example increased mean global temperatures, and modified
precipitation distributions (Houghton et al., 1995). One of the most significant
potential consequences of changes in climate may be alterations in regional hydrological cycles and subsequent changes in river quantity and quality regimes.
Such hydrologic changes will affect nearly every aspect of human well-being, from
agricultural productivity and energy use to flood control, municipal and industrial
water supply, and fish and wildlife management. The tremendous importance of
water in both society and nature underscores the necessity of understanding how a
change in global climate could affect regional water supplies.
Because the current generation of global climate models are not well suited
to the evaluation of detailed water resources problems, a variety of other impact
assessment techniques and tools must be developed and tested (Xu, 1999a). Hydrologic models provide a framework in which to conceptualise and investigate the
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CHONG-YU XU
relationships between climate and water resources. Various methodologies for simulating hydrological responses to global climate change by using hydrologic models have been reported, which may be described using three categories: (1) Coupling high-resolution regional climate models (RCM) with hydrologic models (e.g.
Hostetler and Giorgi, 1993; Nash and Gleick, 1993); (2) Coupling GCMs with hydrologic models through statistical downscaling techniques (e.g. Wilby and Wigley,
1997); (3) Using hypothetical scenarios as input to hydrologic models (e.g. Arnell,
1992).
Ideally, the climate simulations from the GCMs could be used directly to drive
hydrologic models, which in turn could be used to evaluate the hydrologic and
water resources effects of climate change. However, the performance of GCMs in
the control simulation and the magnitude of the predicated climate change signal
is not certain. Moreover, different GCMs are still giving different values of climate
variable changes and so do not provide a single reliable estimate that could be advanced as a deterministic forecast for hydrological planning. Accordingly, methods
of simple alteration of the present conditions, i.e. hypothetical scenarios methods,
are often used. Many published works were done in this way (e.g. Nemec and
Schaake, 1982; Gleick, 1986, 1987; McCabe and Ayers, 1989; Schaake and Liu,
1989; Lettenmaier and Gan, 1990; Vehviläinen and Lohvansuu, 1991; Panagoulia,
1991; Arnell, 1992; Ng and Marsalek, 1992). Various scenarios have been used
and climate predictions for ‘double CO2 ’ conditions have become a standard (e.g.
Loaiciga et al., 1996).
This article reports some of the results of a larger investigation into the implications of climatic variability and change for river flow regimes in Sweden. The
boreal forest zone has several characteristics that differ from other regions of the
world and which make studies of it imperative in the global context (Thomas and
Rowntree, 1992; Halldin et al., 1998). The earlier stage of the study was presented
in a number of publications. Xu et al. (1996) developed and applied a monthly
water balance model for water balance calculations for Nordic regions. More recently, Xu (1999b) tested the applicability of the model to simulate the hydrological
responses of climate changes. Xu (1999a) reviewed the current state of methodologies for simulating hydrological responses to global climate change. The current
study used the model with a number of hypothetical scenarios of climate change to
estimate impacts in river flow regimes and snow cover in central Sweden.
2. The Model, Study Region and Data
More detailed information about the model’s structure and performance can be
found in Xu et al. (1996). A brief description is given as follows with the help
of Figure 1. The monthly water balance model requires as inputs monthly values
of areal precipitation, potential evapotranspiration and air temperature. The model
outputs are monthly river flow and other water balance components, such as actual
evapotranspiration, slow and fast components of river flow, soil-moisture storage
MODELLING THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
179
Figure 1. Schematic computational flow chart of the monthly water balance model.
and accumulation of snowpack, etc. The model works as follows: precipitation pt
is first split into rainfall rt and snowfall st by using a temperature-index function,
snowfall is added to the snowpack spt (the first storage) at the end of the month,
of which a fraction mt melts and contributes to the soil-moisture storage smt .
Snowmelt is calculated by using a temperature-index method. Before the rainfall
contributes to the soil storage as ‘active’ rainfall, a part is subtracted and added to
interception evaporation loss. The soil storage contributes to evapotranspiration et ,
to a fast component of flow ft and to base flow bt .
The boundary of the study area is defined as the central part of Sweden (about
40 000 km2 , Figure 2). Within this area necessary meteorological data and land-use
data are available for 25 gauged catchments ranging in size from 6 to 1293 km2 .
The landscape of the region is dominated by large lakes and plains separated
from each other by high undulating ridges and rich in faults. The geology is characterised by oldest granites in the northeastern part while sedimentary gneisses
characterise the south. Leptites and hälleflintas are found in the northwestern side
together with some small granite-dominated areas. Forest and agriculture are the
dominant landuse. Forest is a dominant factor in the northwest and agriculture is
concentrated in the south, with meadow and grain cultivation predominant agriculture use.
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CHONG-YU XU
Figure 2. The map of Sweden with the location of the study region.
Within the study region, the following data of at least 10-year duration are available: discharge from 25 stations, precipitation from 41 stations, temperature data
from 12 stations. The daily data from 1981 to 1991 were taken and subsequently
integrated to the monthly values for use in the study. Areal precipitation was calculated by Thiessen method. Land-use data are also available for the corresponding
catchments. A general information about the catchment characteristics is presented
in Table I.
3. Hypothetical Climate Change Scenarios
The preferred source of data for using in the assessment of impacts of climate
change is the general circulation model (GCM). Given the limitations of GCMs
grid-point predictions for regional climate change impacts studies, one of the most
widely used methods of scenario generation has been to estimate average annual
changes in precipitation and temperature for a region, and then apply these estimates to adjust historic time series of precipitation and temperature. In the simplest
procedure, the generation of climate scenarios consists of two steps:
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MODELLING THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
Table I. General information of the study catchments (1981.1–1991.12)
Station
Abbr.
Code
Area
Mean
prec.
(km2 )
Meana
evap.
Mean
runoff
Lake
(mm)
Forest
Open
field
(%)
Åkesta Kv.
Åkers Krut.
Bergsh.
Berg
Bernsh.
Ak
Ar
Be
Bg
Bs
2216
2249
2300
2218
1573
727
214
21.6
36.5
595
60.1
60.3
55.6
63.9
78.0
40.0
43.3
40.2
45.3
43.4
21.6
17.6
16.3
22.2
34.9
4.0
5.2
0.2
0.0
8.6
69.0
66.3
69.5
71.4
77.3
27.0
28.5
30.3
28.6
14.1
Dalkarlsh.
Fellingsbr.
Finntorp.
Gränvad
Härnevi
Dl
Fb
Ft
Gr
Ha
2206
2205
2242
2217
2248
1182
298
6.96
167
312
76.4
62.6
65.9
59.4
60.2
42.4
39.9
43.9
41.3
38.8
35.2
24.6
22.1
19.9
22.8
7.5
6.0
4.7
0.0
1.0
74.6
63.8
95.3
41.1
55.0
17.9
30.2
0.0
58.9
44.0
Hammarby
Kåfalla
Kringlan
Karlslund
Lurbo
Hb
Kf
Kl
Ks
Lu
2153
1532
2229
2139
2245
891
413
294
1293
122
73.3
81.0
78.3
69.7
60.8
43.1
43.6
44.3
43.4
37.0
30.9
36.9
34.2
27.0
25.6
9.5
6.2
7.6
6.6
0.3
80.9
80.8
87.2
62.7
68.2
9.7
13.0
5.2
30.7
31.5
Odensvibr.
Ransta
Rällsälv
Sävja
Skräddart.
Ob
Ra
Rs
Sa
Sd
2221
2247
2207
2243
2222
110
197
298
722
17.7
63.6
59.8
79.3
59.7
66.7
41.7
38.2
43.1
40.4
41.6
23.3
22.4
38.4
19.7
25.3
6.3
0.9
7.4
2.0
2.5
71.0
66.1
78.8
64.0
96.1
22.7
33.0
13.8
34.0
1.4
Skällnora
Sörsätra
Tärnsjö
Ulva Kv.
Vattholma
Sn
So
Ta
Ul
Va
1843
2220
2299
2246
2244
58.5
612
13.7
976
293
55.0
59.7
59.7
61.2
60.6
39.9
33.2
39.1
44.2
41.2
16.2
28.3
22.2
16.7
20.3
10.4
1.1
1.5
3.0
4.8
44.5
61.0
84.5
61.0
71.0
45.1
37.9
14.0
36.0
24.2
65.2
41.3
24.9
4.3
70.4
25.3
Mean
a Actual evapotranspiration calculated by the model.
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CHONG-YU XU
Table II. Hypothetical climate change scenarios
Scenario no.
1T (◦ C)
1P (%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
–20
1
–10
1
0
1
10
1
20
2
–20
2
–10
2
0
2
10
2
20
4
–20
4
–10
4
0
4
10
4
20
1) Estimate average annual changes in precipitation and temperature using either
GCM results or historical measurements of change, or personal estimates (typically, 1T = +1, +2 and +4 ◦ C and 1P = 0, ±10%, ±20%).
2) Adjust the historic temperature series by adding 1T and, for precipitation, by
multiplying the values by (1 + 1P/100).
In practice, these annual changes were distributed during the year by various methods. For example, Nemec and Schaake (1982) and Ng and Marsalek (1992) assumed constant distributions of climatic changes, and multiply historical precipitation records by constant factors and adjusted historical temperatures by constant
increments. Sanderson and Smith (1990) used GISS (Goddard Institute of Space
Studies) scenarios of predicted monthly changes in temperature and precipitation.
The general procedure for estimating the impacts of hypothetical climate change
on hydrological behaviour has the following stages:
1) Determine the parameters of a hydrological model in the study catchment using
current climatic inputs and observed river flows for model validation.
2) Perturb the historical time series of climatic data according to some climate
change scenarios.
3) Simulate the hydrological characteristics of the catchment under the perturbed
climate using the calibrated hydrological model.
4) Compare the model simulations of the current and possible future hydrological
characteristics.
In this study, the long-term hydrological response was simulated for climate change
scenarios associated with a base case (nominally, present climate conditions), as
well as 15 hypothetical climate change scenarios. The 15 scenarios are combinations of plus 1, 2 and 4 ◦ C, and minus and plus 0, 10 and 20% precipitation
(Table II). The changes were applied uniformly to monthly values of the historical
time series. This set of scenarios is in general in agreement with the values provided
for the Nordic region in other studies (e.g. Kaas, 1993a, b).
MODELLING THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
183
4. Hydrological Response Analysis
It is very important for water resources managers to be aware of and prepared
to deal with the effects of climatic change on streamflow and related variables.
Analysing different hydrologic variables will indicate which hydrologic variable
is most affected by changes in the climate. Obviously, streamflow is essential in
order to provide an indication of the extent of impacts of climatic change on water
resources. Streamflow represents an integrated response to hydrologic inputs on
the surrounding drainage basin area and therefore affords good spatial coverage.
Since it is expected that climatic change will result in a diversity of environmental
responses, hydrologic variables other than streamflow, are also included in this
study. The hydrologic variables selected to describe the alternative hydrologies are
(a) annual and monthly average catchment runoff, (b) annual and monthly average
snow water equivalent over the catchments, (c) annual and monthly average catchment evapotranspiration. The results are not discussed for each individual catchment, instead, the average values calculated from the 25 catchments are presented
and discussed. The results represent the regional hydrological response to climate
change scenarios.
4.1.
SNOW WATER EQUIVALENT
The long-term mean monthly snow water equivalent over the region of central
Sweden for all the alternative climates is shown in Figure 3. A marked reduction in
average snow water equivalent for all the alternative scenarios was presented. The
combined scenarios of temperature increase by 4 ◦ C (1T = 4 ◦ C, 1P = ±20, ±10,
0%) produced the maximum reduction in snow water equivalent. The maximum
value in February reduced from 98 mm for the present condition to 34 mm for
the scenario 1T = 4 ◦ C, 1P= –20%, and the snow-cover free period increased
from five months (May to October) to seven months (April to November). These
changes reflect the fact that for temperature increase by 4 ◦ C, the temperature is the
basic factor of snow storage control in relation to precipitation. The other combined
scenarios of temperature increase by 1 and 2 ◦ C, and for all precipitation changes
caused progressive reduction in average snow water equivalent from the wetter to
the drier climate.
For the mean annual values, the minimum reduction of snow water equivalent
(13%) was produced with the scenario 1T = 1 ◦ C and 1P = 20%, and the maximum
reduction (76%) with the scenario 1T = 4 ◦ C and 1P = –20% (Figure 4).
4.2.
EVAPOTRANSPIRATION
The actual evapotranspiration, as calculated by the water balance model, depends
on soil moisture and potential evapotranspiration. During the cold and wetter period
(October–April) the actual evapotranspiration remained completely unaffected by
the precipitation changes (Figure 5). During the warm and drier period (May–
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CHONG-YU XU
Figure 3. Monthly mean snow water equivalent for the study region calculated from 15
climate change scenarios and 25 catchments.
September) the actual evapotranspiration went up for precipitation increase and
dropped for precipitation reduction.
Despite the changes in seasonal distribution of evapotranspiration, the change
in annual total evapotranspiration was relatively small with the maximum change
of 23% compared with the 76% for mean annual snow water equivalent changes
and 52% for mean annual runoff changes (Figure 4).
4.3.
RUNOFF
The significant changes in seasonal distribution of regional runoff for all 15 hypothetical scenarios are shown in Figure 6(a) (1T = 1 ◦ C, 1P = ±20, ±10, 0%),
Figure 6(b) (1T = 2 ◦ C, 1P = ±20, ±10, 0%), and Figure 6(c) (1T = 4 ◦ C, 1P =
±20, ±10, 0%), respectively.
Summer (June, July, August) runoff in 14 of the 15 cases dropped considerably
in relation to the observed summer runoff. The summer runoff that resulted from
MODELLING THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
185
Figure 4. Percent change of annual mean snow water equivalent, runoff and evapotranspiration for the study region calculated from 15 climate change scenarios and 25 catchments.
Refer to Table II for the corresponding temperature change and precipitation change for each
scenario number.
1T = 1 ◦ C and 1P = 20% went up a little, reflecting the fact that the temperature
increase was too small to cause early snowmelt and significant increase of evapotranspiration. The summer runoff drop is more obvious for the driest simulated
climates. For a temperature increase by 4 ◦ C and a precipitation decrease by 20%,
the summer runoff dropped by about 80%.
Winter (December, January, February, March) runoff increased in 10 of the 15
cases combined with any temperature increase and positive or no precipitation
change. The maximum winter runoff increase reached 80% above the base case
runoff was caused by a temperature increase of 4 ◦ C and a precipitation increase
of 20%. These two high-value variables raised up the winter runoff through earlier
snowmelt and rainfall increase. The maximum winter runoff reduction resulted
from temperature increase by 1 ◦ C and precipitation decrease by 20% reflecting
the fact that the snowmelt mechanism could not operate due to the low value of the
temperature increase, while the precipitation dropped at the maximum percentage.
In the base case and 10 of the 15 combined scenarios of temperature increase
by 1 and 2 ◦ C and for all precipitation changes, the spring flow peaked in April,
while for the other five scenarios (1T = 4 ◦ C), the peak shifted one month earlier
(to March).
On the annual base, the temperature increase by 1, 2 and 4 ◦ C combined with no
precipitation change produced annual runoff down by 7, 12 and 21%, respectively
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CHONG-YU XU
Figure 5. Monthly mean evapotranspiration for the study region calculated from 15 climate
change scenarios and 25 catchments.
(Figure 4). The maximum reduction and increasing of annual runoff were –51 and
35% caused by 1T = 4 ◦ C, 1P = –20% and 1T = 1 ◦ C, 1P = 20%, respectively.
5. Conclusions
The following conclusions can be reached by this study: (1) The three temperature
increases, associated with all precipitation changes, could result in substantial de-
MODELLING THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
187
Figure 6. Monthly mean runoff for the study region calculated from 15 climate change
scenarios and 25 catchments.
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CHONG-YU XU
creases in average snow accumulation in the study region. (2) The climatic input
changes led to a significant redistribution of the streamflow within a year. The
changes caused by scenarios 1T = 4 ◦ C combined with precipitation changes
showed a strong increase in discharge over the whole winter period and especially
at the beginning and the end of the winter. (3) Increased temperature could increase
spring and summer actual evapotranspiration, this could counterbalance the effect
of a precipitation increase during summer and the change in discharge was the
smallest in summer.
It is necessary to make clear at this juncture that the climate change scenarios
used in this study should not necessarily be seen as the future climates in the region:
They are primarily designed to show the sensitivity to change within a reasonable
interval.
Acknowledgements
This research is a part of my work within SWECLIM (Swedish Regional Climate Modelling Programme). I also received research funding from NFR (Swedish
Natural Science Research Council). The author appreciates the helpful comments
made by the reviewer.
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