CENTRAL RESEARCH INSTITUTE
LITHUANIA ENVIRONMENTAL
PROTECTION AGENCY
FOR COMPLEX USE OF WATER
RESOURCES ‐ BELARUS
River Basin Management and Climate Change Adaptation in the
Neman River Basin
Assessment of climate change impacts on
qualitative characteristics of surface water
resources in the Neman River Basin
Colophon
Project title:
River Basin Management and Climate Change Adaptation in the
Neman River Basin
Report title:
Assessment of climate change impacts on qualitative characteristics of
surface water resources in the Neman River Basin
Issued in:
December 2012
Prepared by:
Paul Buijs
Disclaimer
The views expressed in this report are those of the author and do not represent the views of the UNECE, UNDP or other
Project Partners
Table of Contents
1
Introduction ............................................................................................................................ 5
2
Possible impacts of climate change on surface water quality ................................................. 7
2.1
General mechanisms ........................................................................................................... 7
2.2
Physico‐chemical parameters.............................................................................................. 8
2.3
Nutrients ........................................................................................................................... 11
2.4
Hydrobiological parameters .............................................................................................. 13
3
Climate Change in the Neman River Basin ............................................................................ 15
3.1
Observed trends 1961 ‐ 2010. ........................................................................................... 15
3.2
Projections 2021 – 2050 .................................................................................................... 18
4
Projections for surface water quality in the Neman River basin............................................ 24
4.1
Departure points ............................................................................................................... 24
4.2
Water temperature ........................................................................................................... 26
4.3
Dissolved oxygen ............................................................................................................... 28
4.4
Nutrients ........................................................................................................................... 30
4.5
Hydrobiological parameters .............................................................................................. 34
5
Ramifications for surface water quality monitoring programmes ......................................... 37
5.1
Candidate surface water quality parameters .................................................................... 37
5.2
Monitoring sites ................................................................................................................ 38
5.3
Routinely monitored parameters ...................................................................................... 38
5.4
Sampling frequencies ........................................................................................................ 40
5.5
Laboratory analysis ........................................................................................................... 40
5.6
Synthesis ........................................................................................................................... 41
6
Discussion .............................................................................................................................. 44
7
Conclusions and Recommendations ..................................................................................... 47
7.1
Conclusions ....................................................................................................................... 47
7.2
Recommendations ............................................................................................................ 47
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Abbreviations and acronyms
A1B
B1
CCLM
BOD
CEN
COSMO
DO
IPCC
ISO
Ntotal
Ptotal
relatively high‐emission scenario
low‐emission scenario
COSMO‐ClimateLimited‐areaModelling
biochemical oxygen demand
European Committee for Standardisation (Comité Européean de Normalisation)
COnsortium for Small scale Modelling
dissolved oxygen
Intergovernmental Panel on Climate Change
International Standards Organisation
total nitrogen
total phosphorus
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Preface
The underlying study has been conducted as part of the “Pilot project on river basin
management and climate change adaptation in the Neman river basin”. This project is implemented
by the United Nations Economic Commission for Europe (UNECE) Convention on the Protection and
Use of Transboundary Watercourses and International Lakes (Water Convention) and UNDP Belarus,
with funding from Finland and Sweden through the Environment and Security Initiative (ENVSEC).
The overall objective of the project is to improve integrated river basin management and
transboundary cooperation in times of a changing climate in the Neman river basin. The project aims
to strengthen the capacity to adapt to climate change of the countries sharing the Neman River
through supporting dialogue and cooperation on the needed steps to design an adaptation strategy
in the transboundary context. It will aim to reach a common understanding on future water
availability and water use taking into account possible climate change impacts.
The author would like to thank Alena Bagadiazh (project’s national senior expert on water
quality, Belarus), Audrius Šepikas (River Basin Management Division, Lithuanian Environmental
Protection Agency), Iryna Usava (UNDP project manager) and Vladimir Korneev (team leader of the
Belarusian project’s team) for supplying information and data. Without their contributions, this
report would have been much emptier. The author would like furthermore to express his gratitude
to the following people for reviewing the draft report: Nickolai Denisov, Egidijus Rimkus, Audrius
Šepikas and Edvinas Stonevičius.
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1 Introduction
Table 1 summarises the observed effects of climate change and its observed/possible impacts on
water services as reported by the Intergovernmental Panel on Climate Change (IPCC) in Chapter 4 of
the Technical Paper VI – Climate change and water (IPCC 2008).
Due to increase in surface water temperature, reductions in dissolved oxygen content, mixing
patterns, and self purification capacity, as well as increase in algal blooms are among the possible
impacts linked to surface water quality. Also other observed climate change effects can affect
surface water quality.
Table 1
Observed effects of climate change and its observed/possible impacts on water services [IPCC, 2008]
Observed effect
Observed/possible impacts
Increase in atmospheric temperature

Reduction in water availability in basins fed by glaciers that are
shrinking, as observed in some cities along the Andes in South
America
Increase in surface water temperature


Reductions in dissolved oxygen content, mixing patterns, and
self purification capacity
Increase in algal blooms
Sea‐level rise

Salinisation of coastal aquifers
Shifts in precipitation patterns

Changes in water availability due to changes in precipitation
and other related phenomena (e.g., groundwater recharge,
evapotranspiration)
Increase in interannual precipitation
variability

Increases the difficulty of flood control and reservoir utilisation
during the flooding season
Increased evapotranspiration



Water availability reduction
Salinisation of water resources
Lower groundwater levels
More frequent and intense extreme
events

Floods affect water quality and water infrastructure integrity,
and increase fluvial erosion, which introduces different kinds
of pollutants to water resources
Droughts affect water availability and water quality

Assessing — let alone: forecasting — potential impacts on the quality of surface waters adds yet
another dimension to the already complex nature of climate change. Not only is the functioning of
aquatic ecosystems notorious for its complex dynamics and interactions. Also ‘relatively simple’
physico‐chemical quality parameters like dissolved oxygen and nutrients, or, for that matter, the
temperature of surface water bodies, are not that straightforward.
With the resources available for the underlying study, it was merely feasible to prepare a mainly
descriptive overview of possible climate change impacts on qualitative characteristics of surface
water resources in the Neman River Basin. Nevertheless, some of the results give some preliminary
semi‐quantitative indications about possible impacts.
The further outline of this document is as follows. Chapter 2 contains a general introduction to
possible impacts of climate change on surface water quality. Chapter 3 summarises observed
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meteorological and hydrological trends in the Neman River Basin during the period 1961 – 2010, as
well as the projections for the period 2021 – 2050. Examples for projections for possible impacts of
climate change on the surface water quality in the Neman River basin are elaborated in Chapter 4.
Chapter 5 addresses some of the requirements of surface water quality monitoring programmes in
the context of climate change. Chapter 6 is dedicated to discussing findings of this study. Chapter 7
contains the main conclusions and recommendations.
Figure 1
The Neman River Basin (in: Korneev et. al. 2011)
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2 Possible impacts of climate change on surface water quality
2.1
General mechanisms
Climate change is generally associated with the following phenomena:
 The average atmospheric temperature is expected to increase due to climate change.
Furthermore, extreme temperatures might occur more frequently during certain
seasons.
 Meteorological changes can lead to changes in the water cycle. For example, the
amount of precipitation largely determines the surface runoff and therewith the
discharges of rivers and volumes of lakes. Projections differ; in some parts of the world,
more precipitation is forecasted during certain seasons, whereas in other parts less
precipitation may occur; seasonal patterns might shift. Also here, extremes (more/less
precipitation) might occur more frequently during certain seasons.
The potential impacts on surface water quality comprise physical parameters (e.g. water
temperature, dissolved oxygen), chemical parameters (e.g. nutrients), and hydrobiological
parameters (e.g. phytoplankton, fish). A more detailed list with possible impacts is included in Table
2.
Table 2
Physical
Biogeochemical
Biological
1
Most important impacts of climate change –notably changes in temperature and precipitation‐ on the
1
aquatic ecology (adopted from: Kosten, 2011)
Higher water temperature and less ice coverage
Longer stratification period (earlier start and later mixing) in deep systems
More shallow transition layer in deep systems
Temporary stratification in shallow systems
Increased runoff
Increased peak discharges and erosion in running waters
Decreased discharges of running waters, either running waters drying up
Higher temperatures –possibly in combination with lower discharges– lead to larger
day/night fluctuations in oxygen concentrations
Higher probability of periods with very low oxygen concentrations
Higher internal phosphorus‐ and nitrogen loading due to increased releases from sediment
Higher external phosphorus‐ and nitrogen loading due to changes in the precipitation
regime and related runoff and water inlet/outlet operations
Accelerated denitrification due to higher temperatures
Increased sulphur concentrations due to higher groundwater tables in sulphur‐containing
soils during wet winters
Increased CO2 concentrations in water
Possible increase of emission of greenhouse gasses from waters
Increased chloride concentrations due to evaporation
Higher phytoplankton biomasses due to increased loading with nutrients
Enhanced growing season of phytoplankton, with higher phytoplankton biomasses in
spring and autumn
No clear increase in maximum phytoplankton biomass during summer in case nutrient
loading remains the same
Share of cyanobacteria in total phytoplankton biovolume will increase
Size and biomass of zooplankton decreases, resulting in decreased predation of
It might be good to notice that the study of Kosten (2011) was conducted with the Netherlands as primary focus.
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`phytoplankton
Flushing of macrofauna during peak discharges
Species‐composition of water plants changes due to warmer winters (with less ice)
Increased chances on dominance by floating water plants in smaller water bodies
Fish communities change with rising temperatures: less cold stenothermal fish and
possibly more benthic fish species
Extended fish spawning season, resulting in an extended period of small zooplankton‐
predating fish
Climate change is not the most important factor in dispersal and immigration of new
species. However, climate change could facilitate settling and strongly affects abundance
of exotic species, with warmer winters leading to higher abundance.
Climate change could affect surface water quality both directly as well as indirectly. An increase
of the water temperature due to higher air temperatures is an example of a direct impact. An
example of an indirect impact is changes in the dilution capacity of rivers and lakes due to
lower/higher water volumes, which could result in higher/lower concentrations of pollutants.
Obviously, changes in the air temperature can lead to changes in the temperature of surface
water bodies (refer to section 2.2.1 for more information). Water temperature is an important
variable for the functioning of aquatic ecosystems. The rates of biological and chemical processes
depend on temperature. Aquatic organisms from microbes to fish are dependent on certain
temperature ranges for their optimal health. Optimal temperatures for fish depend on the species:
some survive best in colder water, whereas others prefer warmer water. Temperature affects the
oxygen content of the water (section 2.2.2), the rate of photosynthesis by aquatic plants, the
metabolic rates of aquatic organisms, and the sensitivity of organisms to toxic wastes, parasites, and
diseases.(2) Higher water temperatures can increase the release of phosphorus and nitrogen from
bottom sediments (IPCC 2007, Kosten 2011, Solheim et.al. 2010).
Changes in the hydrological cycle can influence surface water quality in several ways. Firstly, the
discharges of rivers and volumes of lakes are largely determined by the surface runoff, which again
heavily depends on precipitation and evapotranspiration. Higher/lower precipitation would lead to
higher/lower discharges and volumes. The sunshine duration is also an important factor influencing
the runoff regime, since it affects the intensity of evaporation. Higher/lower river discharges and
lake volumes imply higher/lower dilution capacities. Under a similar loading with pollutants, their
concentrations will become lower with increasing discharges/volumes and vice versa.
Surface runoff can furthermore affect the flux of substances like nutrients and pesticides. Higher
intensity and frequency of floods and more frequent extreme precipitation events will give increased
surface runoff and erosion, increasing nutrient loads to the surface water (Depla et. al. 2009, IPCC
2007, IPCC 2008, Kosten 2011, Loeve 2006, Solheim et.al. 2010). Heavy rainfall may account for a
significant proportion of annual phosphorus transfer from agricultural soils under arable crops
(Solheim et.al. 2010). Increased rainfall can lead to more frequent incidences of stormwater
overflows, thus introducing pollutants from municipal sewage systems (Solheim et.al. 2010).
2.2
2.2.1
Physico‐chemical parameters
Water temperature
Relations between surface water and air temperatures are not necessarily linear. Shallow waters
are more prone to changes in air temperature than deeper waters, notably deep stratified lakes.
2
http://water.epa.gov/type/rsl/monitoring/vms53.cfm
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Water bodies with larger volumes/discharges will respond less and slower to air temperature
changes. One study on various streams indicates an increase in the water temperature of about 0.6
— 0.8 °C for every 1°C increase in air temperature (Morill et.al. 2005). Projected increases in surface
water temperatures are often 50 to 70 % of the projected increases in air temperature (Solheim
et.al. 2010).
Textbox 1: Cooling water
During the period 1910 – 2010, the annual average water temperature of the River Rhine at
o
the Dutch‐German border increased with about 3 C. The average air temperature increased
o
with about 1 C during the past century, thus cannot explain the increased water
temperature. It is estimated that about 2/3 of the total increase is caused by using the river
for cooling purposes (thermal discharges) upstream and about 1/3 due to climate change
[CIW, 2004].
2.2.2
Dissolved oxygen
Mere physical parameters determine the solubility of dissolved oxygen (DO) in surface water
bodies: water temperature, barometric pressure and salinity. DO contents will decrease with higher
water temperatures, lower barometric pressure and/or higher salinity.
Table 3 below has been prepared in accordance with the principles applied by the United States
Geological Survey (USGS 2011).3 (Refer also to Annex 1 for equations for computing dissolved
oxygen.) Salinity is not a relevant factor for fresh surface waters, associated with levels <0.05 ‰.
Differences in altitude in the Neman Basin (via barometric pressure) could theoretically lead to
slightly different DO solubility (0.2 – 0.3 mg O2/l; compare Table 3).
Table 3
Computed DO solubility at various water temperatures and altitudes (salinity= 0.05 ‰; standard
barometric pressure = 1 atm = 760 mm Hg)
Water temperature
o
( C)
DO
[mg O2/l]
Altitude = 0m
Altitude = 200m
0
14.6
14.3
5
12.8
12.4
10
11.3
11.0
15
10.1
9.8
20
9.1
8.9
25
8.3
8.1
30
7.6
7.4
In the northern hemisphere, air temperatures vary seasonally with generally higher
temperatures during the summer and lower temperatures during the winter.4 Therewith, also
seasonally varying DO concentrations can be expected, as illustrated in Figure 2 (in which a
‘Gaussian distribution’ of the air temperature over the year has been assumed).
3
4
Dissolved oxygen concentrations can also be calculated on‐line at http://water.usgs.gov/software/DOTABLES/
DO concentrations in surface waters can also vary throughout the day (diurnal cycle), with generally higher concentrations during
daytime and lower concentrations in the night.
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Calculated maximum Dissolved oxygen [mg O2/l]
Water temperature [oC]
30
14
25
12
20
10
8
15
6
10
4
5
Water temperature [oC]
Calculated maximum Dissolved oxygen [mg O2/l]
16
2
0
January
0
February
April
May
July
September
October
December
o
Figure 2 Calculated DO solubility [mg O2/l] for water temperatures between 0 and 25 C (with barometric pressure=
1 atm and salinity= 0.05 ‰)
Other factors can determine DO concentrations as well. On the one hand, oxygen is consumed
by the metabolism of organisms like fish and bacteria, while on the other hand oxygen is produced
by for example submerged water plants and phytoplankton. Anthropogenic pollution, notably with
organic material, can negatively affect the oxygen concentrations in surface waters.
A clear example of the latter is shown in Figure 3 below. During the 1960s and early 1970s, the
River Rhine was that much polluted, that dissolved oxygen concentrations were frequently below
4 mg O2/l and even could reach levels below 2 mg O2/l. Such low oxygen contents are critical or even
lethal for aquatic species like fish. Meanwhile, the situation has drastically improved due to pollution
remediation measures.
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16
14
12
10
8
6
4
2
2010
2007
2005
2003
2000
1998
1996
1994
1990
1988
1986
1985
1984
1983
1982
1980
1979
1978
1977
1976
1975
1973
1972
1971
1970
1969
1967
1966
1965
1964
1963
1961
1960
1959
1956
1954
1952
0
Figure 3 DO concentrations [mg O2/l] in the River Rhine at the Dutch‐German border, 1952‐2011
Despite the severe pollution during the 1960s and early 1970s, DO concentrations could though
still reach levels up to 10 mg O2/l during the winter, as illustrated in Figure 4.
12
10
8
6
4
2
Sep‐74
Apr‐74
Nov‐73
Jun‐73
Jan‐73
Aug‐72
Mar‐72
Nov‐71
Jun‐71
Feb‐71
Sep‐70
Apr‐70
Nov‐69
Jun‐69
Jan‐69
Sep‐68
Apr‐68
Nov‐67
Jul‐67
Feb‐67
Oct‐66
May‐66
Dec‐65
Aug‐65
Apr‐65
Nov‐64
Jun‐64
Feb‐64
Sep‐63
May‐63
Dec‐62
Jun‐62
Aug‐61
Mar‐61
Oct‐60
May‐60
Jan‐60
0
Figure 4 DO concentrations, in [mg O2/l], in the River Rhine at the Dutch‐German border, 1960 ‐ 1974
2.3
Nutrients
Nutrients (nitrogen and phosphorus compounds) are mentioned in many literature dealing with
climate change and surface water quality (for example: Depla et. al. 2009, IPCC 2007, IPCC 2008,
Kosten 2011, Loeve 2006, Solheim et.al. 2010, Whitehead et. al. 2008).
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Nutrients are important parameters for the functioning of aquatic ecosystems. However,
over‐enrichment with nutrients can lead to eutrophication, negatively affecting the quality
conditions of surface waters (compare also Figure 5 below).5
Higher air temperatures will increase mineralization and releases of nitrogen, phosphorus and
carbon from soil organic matter (Solheim et.al. 2010). Moreover, an increase in runoff and erosion
due to greater precipitation intensity should result in an increase in pollutants transport, especially
after a drought period (Solheim et.al. 2010). Furthermore, release of phosphorus from bottom
sediments in stratified lakes is expected to increase, due to declining oxygen concentrations in the
bottom waters (Depla et. al. 2009). Higher water temperatures accelerate the degradation of
organic matter in water and sediment, therewith releasing nitrogen and phosphorus compounds
(Kosten 2011). Then again, with higher water temperatures, concentrations of nitrogen compounds
in the water column would be reduced due to enhanced denitrification processes (Kosten 2011).
Figure 5 Some relationships now established that link climate change and eutrophication symptoms (in: Moss et.al.
2011)
2.3.1
Other physico‐chemical parameters
Depla et. al. (2009) mention the following physico‐chemical parameters whose properties might
also be affected by climate change: pH, dissolved organic matter, inorganic micropollutants (metals)
and organic micropollutants (like pesticides and pharmaceuticals) (Depla et. al. 2009). Observations
for micropollutants are though still rather scant and not unambiguous.
5
The EU Directive 91/271/EEC concerning urban wastewater treatment gives the following definition: ‘eutrophication’ means the
enrichment of water by nutrients, especially compounds of nitrogen and/or phosphorus, causing an accelerated growth of algae and
higher forms of plant life to produce an undesirable disturbance to the balance of organisms present in the water and to the quality of
the water concerned.
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2.4
Hydrobiological parameters
2.4.1
Phytoplankton
An increase in algal blooms is one of the possible effects of climate change documented by,
among others, the IPCC (IPCC 2008; see also Chapter 1). Algal blooms are also a clear expression of
eutrophication.
An increase in algal blooms could be caused by two main, complementary, mechanisms:
 Growth of algae might be accelerated with higher water temperatures and sunshine
duration. Kosten (2011) though mentions that these effects are not unambiguous
(Kosten 2011).
 Due to a combination of factors, concentrations of nutrients can increase, which can
result in additional growth of algae (section 2.3).
Actually, Moss (2011) mentions even a third mechanism: “Piscivorous fish generally become
scarcer with eutrophication, and the ultimate effect, through an increase in foraging fish and a
decline in zooplankton grazers, is an increase in algae” (Moss et. al. 2011).
Factors affecting algae will most likely have similar effect on several other phytoplankton species
as well.
Cyanobacteria (also known as blue‐green bacteria or blue‐green algae) are strictly spoken no
algae, but their growth will be determined by similar mechanisms. The cyanotoxins produced by
cyanobacteria can cause for example skin irritation, nausea and other symptoms with bathers. They
furthermore can negatively affect the quality of surface water abstracted for the preparation of
drinking water.
2.4.2
Fish
Fish populations could be affected in various ways. Without being conclusive, following
mechanisms might apply.
Some possible impacts can be tied directly to water temperatures:
 Fish are ectothermic (‘cold‐blooded’) organisms. With overall increasing water
temperatures, populations could shift from coldwater fish, including salmonid fish like
salmon and trout, to warmwater fish, including cyprinid fish like carp (Kosten 2011,
Solheim et.al. 2010).
 For fish, there are two important kinds of limiting temperatures: the maximum
temperature for short exposures and a weekly average temperature that varies
according to the time of year and the life cycle stage of the fish species. Reproductive
stages (spawning and embryo development) are the most sensitive stages.6 Table 4
shows water temperature criteria for some fish species. Changes in the water
temperature might affect (the) spawning (period), notably for fish requiring colder
water, like salmon and trout.
 The solubility of dissolved oxygen decreases with increasing water temperatures (see
also section 2.2.2). Lower oxygen concentrations can be detrimental for fish, with
salmonid fish generally being more sensitive than cyprinid fish.
6
http://water.epa.gov/type/rsl/monitoring/vms53.cfm
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7
Table 4 Maximum average temperatures for growth and short‐term maximum temperatures for selected fish
Species
Atlantic salmon
Bluegill
Brook trout
Common carp
Channel catfish
Largemouth bass
Rainbow trout
Smallmouth bass
Sockeye salmon
a
b
Max. weekly
average temp. for
growth (juveniles)
20 °C
32 °C
19 °C
‐‐‐
32 °C
32 °C
19 °C
29 °C
18 °C
Max. temp. for
survival of short
exposure (juveniles)
23 °C
35 °C
24 °C
‐‐‐
35 °C
34 °C
24 °C
‐‐‐
22 °C
Max. weekly
average temp. for
a
spawning
5 °C
25 °C
9 °C
21 °C
27 °C
21 °C
9 °C
17 °C
10 °C
Max. temp. for
embryo spawning
b
11 °C
34 °C
13 °C
33 °C
29 °C
27 °C
13 °C
23 °C
13 °C
‐ Optimum or mean of the range of spawning temperatures reported for the species
‐ Upper temperature for successful incubation and hatching reported for the species
Eutrophication can affect fish in various ways. Firstly, lower oxygen concentrations are often
part of eutrophication phenomena. Secondly, eutrophication can change the whole balance and
composition of aquatic species, including fish (compare for example Figure 5 in section 2.3).
2.4.3
Macrophytes
Temperature and hydrology are considered to be important factors for the development and
reproduction of water plants. Climate change might lead to less coverage with submerged water
plants. Softer winters (with less ice) further can change the species composition. Notably the
temperature in winter and spring can be important for the area where water plants will grow
(Kosten 2011). More complex interactions, also affecting water plants, are depicted in Figure 5 in
section 2.3.
2.4.4
Phytobenthos
Kosten (2011) mentions that light, nutrients, pH, oxygen‐ and salt contents are relevant factors
for phytobenthos communities (Kosten 2011). Changes in the ice coverage might strongly affect the
species composition. Increased turbidity in standing waters (due to extreme runoff either growth of
algae) could lead to light limitation and therewith possibly disappearance of phytobenthos (Kosten
2011).
7
http://water.epa.gov/type/rsl/monitoring/vms53.cfm
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3 Climate Change in the Neman River Basin
3.1
Observed trends 1961 ‐ 2010
Over the period 1961‐2010, the mean annual air temperature in the Neman River Basin
increased statistically significant by 1.7 °C (Figure 6). The most significant changes occurred by the
end of the 1980‐ies. Since then, only once in 1996 the average annual air temperature was lower
than the long term average of 6.4 °C (Rimkus et. al. 2012).
Figure 6 Dynamic of mean annual air temperature in Neman river basin in the period 1961‐2010 (in: Rimkus et. al.
2012)
The largest changes were recorded during the winter months (Figure 7). A statistically significant
(α = 0.05) rise of air temperature was measured in January, April, July, and August. Meanwhile, in
May‐June and autumn, air temperatures remained almost unchanged. A statistically insignificant
negative trend was recorded in June (Rimkus et. al. 2012).
Figure 7 Monthly (X‐axis: month number) air temperature changes per 50 years in Neman river basin in the period
1961‐2010 (in: Rimkus et. al. 2012;)
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The annual precipitation gradually increased in the Neman River Basin during the period 1961 –
2010 (Figure 8). Contrary to the air temperature, the sign of precipitation amount changes in
individual months of the year differs a lot (Figure 9). Statistically significant positive changes were
recorded in January and February. Although the absolute values of positive changes are the largest
in July but due to very high rainfall amount and variability in summer the change is not statistically
significant (α = 0.05). Large increase was also recorded in October. Meanwhile, in the second half of
spring rainfall amount decreased. The largest negative changes were measured in April. The
precipitation amount declined in September and November as well (Rimkus et. al. 2012).
Figure 8 Dynamic of precipitation amount in Neman river basin in the period 1961‐2010 (in: Rimkus et. al. 2012)
Figure 9 Monthly (X‐axis: month number) precipitation amount changes per 50 years in Neman river basin in the
period 1961‐2010 (in: Rimkus et. al. 2012)
Comparison of the 1961‐1985 and 1986‐2010 periods revealed the most pronounced increase in
winter precipitation amount in the central part of the Neman River basin (up to 40‐50%). Decrease
of precipitation amount was observed in spring in the large part of basin area. Rainfall has increased
throughout the investigated area in summer. The precipitation amount in autumn remained almost
constant (Rimkus et. al. 2012).
Evaluation of the changes of the number of days with the snow cover in different months of the
cold period of the year shows that such a number slightly increased in October and November
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whereas it strongly declined in the period from December to April. The largest negative changes
were recorded in January and March (Rimkus et. al. 2012).
Sunshine duration increased during the 1961‐2010 period in the Neman River Basin. Sunshine
records of six meteorological stations indicate increase of annual sums from 50 till 250 hours (4‐13%)
during the 50‐year period. The largest positive changes were recorded in April and May (Figure 10).
Only in following months, changes are statistically significant. Meanwhile, in July strong growth in
both rainfall and sunshine duration was recorded. Negative trends in sunshine duration recorded in
June, while changes during the cold season are very minor (Rimkus et. al. 2012).
Figure 10 Monthly (X‐axis: month number) sunshine duration changes per 50 years in Neman river basin in the
period 1961‐2010 at the Šilutė and Utena meteorological stations (in: Rimkus et. al. 2012)
Textbox 2: Generic climate conditions in Neman Basin (Korneev et al 2011)
The climate in the Neman river basin is moderately continental. Humid Atlantic air masses
prevail most of the year. Air masses from the continent add continental features to the
climate, particularly in the East and Southeast. The climate in the river basin area is
o
transitional from maritime to continental. Mean annual temperature is +6 C, including ‐
o
o
4.9 C in January and +17 C in July. Westerly and south‐easterly winds prevail. Mean annual
precipitation is 600 – 700 mm, except in the Vilija/Neris basin, where it exceeds 650‐800
mm. 75% of annual precipitation is rain, 65% is vaporised, and around 32% are transformed
into land runoff. Mean annual wind speed is 3.5 – 4 m/s.
The large basin area in Belarus causes higher monthly temperature variations, ranging from
20 °С to 32 °С from West to East. Mean temperature rises from the Northeast to the
Southwest during the year and to the Southeast during the warm season. Mean annual air
temperature is 5 °С in the Northeast, 5.5 °С in the North and 6.5 °С in the South and
Southeast. On average, the temperature increases by 0.5 °С per each 200 km southward. In
the warm period, astronomic and sun radiation factors determine the sublateral pattern of
the air temperature. Mean air temperature in July ranges from 17.5 °С in the North to
18.5 °С in the South of the basin area, and mean January temperature from ‐6,5 °C in the
Northeast to ‐5 °С in the Southwest. In the cold season, the patterns of atmospheric air
circulation determine the submeridional direction of the isotherms. On average, the
temperature drops by 0,5°С per every 100 km eastward. The Neman river basis is located in a
sufficiently wetted zone. High air humidity, extensive cloud cover and a favourable
temperature pattern are conducive to large precipitation. Mean annual precipitation is 560‐
Assessment of climate change impacts on qualitative characteristics of surface water resources in
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17 | P a g e
620 mm, reaching 700 mm and above in the Novogrudok and Slonim plateaus. Precipitation
is unevenly distributed throughout the year, with 70% falling out in the warm season
(including over 1/3 in July – August). Precipitation is the lowest in February – March, the
period of low cyclonic activity. Most of the precipitation is rain, and only 10 – 15% is snow.
Soil surface vaporization ranges from 450 mm in the Northwest to 600 mm in the Southwest.
Water surface vaporization is 600‐630 mm. Relative humidity is generally high, at 84 – 90% in
the spring and 66 – 78% in the summer. Humidity factor ranges from 0.9 – 1.0 in the North to
0.8‐0.9 in the South, with the exception of the Novogrudok plateau, where the ratio is 1.0 –
1.2.
In the Lithuanian section of the basin area, the climate changes from maritime within 12 – 15
km of the coastline to continental in the East. Mean annual air temperature is 6‐7 °С, ranging
from a high of 7.4‐7.6 °С on the Baltic coast to 5.8 °С in the East of the basin area. Mean
temperature in January is ‐1.4 and ‐5.2 °С, respectively. In July, mean temperature is highest
at the Baltic Coast (17.6 °С) and lowest (16.4 °С) in Zemaitijos Plateau. Mean annual
precipitation is 660 mm, ranging from less than 570 mm in the North to over 900 mm in
Zemaitijos Plateau. Two‐thirds of the precipitation occurs in the warm period from April to
October.
3.2
Projections 2021 – 2050
Projections were calculated on the basis of output data of the CCLM model (COSMO‐
ClimateLimited‐areaModelling; COSMO ‐ COnsortium for Small scale Modelling). Two greenhouse
gas emission scenarios were considered:
 A1B: relatively high‐emission scenario;
 B1: low‐emission scenario.
The mean annual air temperature in the basin is expected to rise by 1.4 °C–1.7 °C according to
different climate scenarios, with a 2.0 °C – 2.8 °C increase in winter and 0.7 °C – 1.1°C increase in
summer. An air temperature rise is projected for all months of the year (Figure 11). The largest
changes under both climate scenarios will likely occur during the cold season of year. This coincides
with observed trends over the period 1961‐2010. Model projections shows that the air temperature
will increase by nearly 3 °C under A1B scenario in December, February and March. During the
summer months smaller changes are foreseen (mostly below the 1 °C). For the first half of the year
larger changes are predicted under A1B climate scenario, while the second half of the summer will
be warmer according to B1 climate scenario (Rimkus et. al. 2012).
Figure 11 Projected monthly (X‐axis: month number) air temperature changes during the year in the near term
future (2021‐2050) in the Neman River Basin according to A1B and B1 climate scenarios. Changes are relative to the base
period of 1971‐2000 (in: Rimkus et. al. 2012)
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Precipitation amount at the beginning of the 21st century will increase in the Neman basin area.
The annual precipitation amount will increase by 73 mm according to A1B scenario and by 28 mm
according to B1 scenario. CCLM model output data according to A1B scenario foresee precipitation
amount increase in all months of the year (Figure 12). The largest relative changes predicted for
March‐April (35 and 28% respectively), while for the period from May till September the smallest
changes (up 10%) are foreseen. According to B1 climate scenarios positive changes will be less
significant and in some months negative trend should be recorded. The most rapid rise (16%) is
expected in March, April and October. Meanwhile in January, May and July decrease in precipitation
amount is very likely (Rimkus et. al. 2012).
Figure 12 Projected monthly (X‐axis: month number) precipitation changes during the year in the near term future
(2021‐2050) in the Neman River Basin according to A1B and B1 climate scenarios. Changes are relative to the base
period of 1971‐2000 (in: Rimkus et. al. 2012).
CCLM model outputs data foresee decrease in sunshine duration in the first half of the 21st
century. Under A1B scenario the average sunshine duration will decrease by 112 hours, while
projected reduction according to B1 scenario is 69 hours. Relatively the largest sunshine duration
decrease will be observed during the winter months (Figure 13). In December negative changes will
exceed 20% under both climate scenarios. The lowest changes (5%) are foreseen for the period from
May to September. Only in January sunshine duration will increase (11%) according to B1 climate
scenario. It coincides with air temperature and precipitation projections (in: Rimkus et. al. 2012).
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Figure 13 Projected monthly (X‐axis: month number) sunshine duration changes during the year in the near term
future (2021‐2050) in the Neman River Basin according to A1B and B1 climate scenarios. Changes are relative to the base
period of 1971‐2000 (in: Rimkus et. al. 2012).
If, according to empirical relationships, the air temperature in the near‐term future (2021‐2050)
will increase with 1.0 °C, the average number of days with snow cover would reduce to 55 days (A1B
scenario). According to B1 scenario, the air temperature will rise with 0.4 °C and the number of days
with snow cover will decrease till 67 days (in: Rimkus et. al. 2012).
Run‐off changes are addressed in two reports that have been prepared under this project; the
findings of both approaches are comparable (Stonevičius and Štaras 2012), (Stonevičius et. al. 2012).
According to A1B emission scenario the annual runoff is likely to increase in the north western part
of Neman basin in 2021–2050 compared with 1961–2009 (Figure 14, left). In the south eastern part
of the basin the runoff will decrease in majority of catchments. In some small catchments in
Belarusian part of Neman basin the annual runoff change is positive, but the changes merely reach
5%. According to B1 emission scenario the annual runoff is likely to slightly increase in major part of
Neman basin (Figure 14, right) (Stonevičius and Štaras 2012).
Figure 14 Projected annual runoff changes (%) in 2021–2050 to compare with 1961–2009 according to A1B (left) and
B1 (right) scenarios (in: Stonevičius and Štaras 2012)
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The projected maximum monthly runoff, which can be related to the magnitude of the
spring flood, will decrease in the majority of Neman basin (Figure 15). In most sub‐catchments
the projected maximum monthly runoff in 2021–2050 is likely to be from 10 % to 20 % lower
than in reference period 1961‐2009. The largest decrease of maximum monthly runoff is
expected in the Belarusian part of Neman basin. The decrease of maximum monthly runoff is
likely to be related to warmer winters with more frequent thaws and consequently less water
accumulated in snow cover before flood (Stonevičius and Štaras 2012).
Figure 15 Projected maximum monthly runoff changes (%) in 2021–2050 to compare with 1961–2009 according to
A1B (left) and B1 (right) scenarios (in: Stonevičius and Štaras 2012)
The largest projected runoff change will be in the January‐ February (Figure 16). The increase of
January‐February runoff in 2021‐2050 can be related to the earlier start of spring, increased winter
precipitation and increased frequency of thaws. According to B1 scenario the largest increase is
likely to be in the Lithuanian part of basin and in the eastern catchments of Belarusian part (Figure
16, right). The smallest changes are projected for catchments on Lithuanian‐Belarusian border. The
spatial distribution of January‐February runoff changes based on A1B emission scenario is more
scattered (Figure 16, left). According to A1B scenario the changes of January‐February runoff in
Lithuanian part of Neman basin are likely to be smaller than according to B1 scenario.
The climate change will also affect the warm season (May‐September) runoff. The forecasts of
May‐September runoff changes calculated according to B1 scenario shows slightly drier conditions in
most parts of Neman basin than the ones calculated according A1B (Figure 17). The decrease of
May‐September runoff suggests that in some parts of Neman basin the hydrological droughts may
be more frequent or with higher magnitude then they were in the 1961‐2009. In other catchments
the risk of droughts in 2021‐2050 during May–September period can be lower than it was in 1961‐
2009 (Stonevičius and Štaras 2012).
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Figure 16 Projected January‐February runoff changes (%) in 2021–2050 to compare with 1961–2009 according to
A1B (left) and B1 (right) scenarios (in: Stonevičius and Štaras 2012)
Figure 17 Projected May – September runoff changes (%) in 2021–2050 to compare with 1961–2009 according to
A1B (left) and B1 (right) scenarios (in: Stonevičius and Štaras 2012)
The changes of magnitude of maximum monthly runoff are concomitant with the shift of
maximum monthly runoff towards the beginning of the year in many of analyzed catchments. In
1961‐2009 the maximum monthly runoff in almost all catchments was in April. In 2021‐2050 the
maximum is likely to occur more frequently in March (Figure 18) (Stonevičius and Štaras 2012).
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Figure 18 The example of maximum monthly runoff calculated according A1B and B1emision scenarios shift from
April in 1961‐2009 to March in 2021‐2050 (in: Stonevičius and Štaras 2012)
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4 Projections for surface water quality in the Neman River basin
4.1
Departure points
The use of computer models will be inevitable for well‐founded projections for the possible
impacts of climate change on the surface water quality in the Neman River basin. Neither the tools,
nor the time, nor all necessary data were available for conducting model‐based exercises under this
study. Nevertheless, some basic examples have been elaborated, for two purposes:
 Even basic exercises might already reveal some indicative results.
 The cases could become a source of inspiration for follow‐up studies.
The cases built upon two core features of the projected changes:
a) Projected increase in air temperature.
b) Projected changes in the runoff (regime).
4.1.1
Projected increases in air temperatures
Table 5 summarises the projected air temperature increases under the two scenarios per
meteorological season. The main differences between both scenarios are in the projected air
temperature increases for the meteorological spring and winter.8 The projections for autumn are
equal in both scenarios. The differences in the increases of the air temperatures projected for the
summer are minor.
Table 5 Projected air temperature increases 2021 – 2050 per meteorological season
Scenario
Spring
Summer
Autumn
Winter
AIB
2.1 °C
0.7 °C
1.3 °C
2.6 °C
B1
1.5 °C
0.9 °C
1.3 °C
1.8 °C
The projected air temperature increases according to scenario A1B have been selected for
elaboration in this chapter.
4.1.2
Projected runoff changes
A ‘lean‐but‐mean’ approach is used for a semi‐quantitative assessment of possible impacts of
projected changes in runoff. Runoff changes can result in changes in the discharges of rivers and the
volumes of lakes, therewith affecting their dilution capacity. Assuming a similar loading with
substances, concentrations will be lower with higher discharges/volumes and vice versa. (Which is,
of course, a gross simplification; please also refer to the Chapteres 2 and 6.) The examples have
been elaborated for nutrients.
Considering physico‐chemical parameters, lower discharges/volumes are likely to cause more
(pollution) stress, notably in warmer periods. During the summer, decreases in the runoff up to 20%
(compared to the period 1961 – 2009) might occur in some parts of the basin (compare Figure 17 in
section 3.2). In the corresponding examples below, a 20% reduction in the discharge/volume during
the summer is assumed, implying an increase in the concentration of nutrients with 25%. The
calculations have been performed for all data, hence not distinguishing different parts of the basin.
8
For the Northern hemisphere, the meteorological spring begins on 1 March, summer on 1 June, autumn on 1 September, and winter on 1
December.
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This can be considered as a ‘worst‐case scenario’, since projected runoff changes during the summer
are generally less than ‐20% and could even increase with up to +20% in some parts of the basin;
Figure 17.
It should be noticed that during periods with increased runoff also other pollutants than
nutrients (like organic substances, pesticides, et cetera) might enter surface water bodies, e.g. to
surface runoff or erosion (refer also to Chapter 2).
4.1.3
Available monitoring data
The following sets of surface water quality monitoring data (in addition with data about river
discharges) have been made available:
 Lithuania:
o Raw surface water quality monitoring data for selected rivers the period 2000‐
2010 (refer to Annex 2 and Figure 19). Additional data were provided for the
period 2001‐2010, with only some of the monitoring sites coinciding with the
aforementioned data set (refer to Annex 3). The latter data set has been used
for section 4.2 on water temperatures and preparation of the figures in section
4.4.
 Belarus
o Annual average concentrations/values for selected parameters over the period
1996 – 2010 for monitoring sites situated inside the Belarusian part of the
Neman River Basin.
Figure 19
9
Surface water quality monitoring sites selected by the project for application of the joint
9
classification schemes
Refer also the presentation “Progress of the pilot project since February, 2011”
http://www1.unece.org/ehlm/platform/download/attachments/24707075/Neman.pdf
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Unfortunately, the surface water quality data provided for the Belarusian part of the basin were
not suitable for the envisaged exercises. Many surface water quality parameters vary seasonally, as
also will be shown in the sections further below. Such seasonal variations are relevant, also because
the projected climate changes differ over the seasons (Chapter 3). Seasonal variations are masked
by annual average concentrations/values and cannot be inferred from the latter. Therefore, the
examples elaborated in this chapter are solely based on the monitoring data provided for the
Lithuanian rivers.
4.2
Water temperature
A summary of statistics of water temperatures of selected monitoring sites along Lithuanian
rivers over the period 2001‐2010 are included in Table 6 below.10 It is important to notice that these
are statistics for the aggregated data. For example, the maximum water temperature of 8.3 oC in
January was measured at the site 98 (Levuo, ziotyse) in 2001, whereas the highest temperature of
28.5 oC in July was measure at site 265 (Jura, ties Mociskiais) in 2006. Although minimum and
maximum water temperatures represent actual cases, it seems more prudent to base an overall
characterization on the ranges between the 5‐ and 95‐percentile values.
o
Table 6 Statistics for aggregated monthly water temperatures, in [ C], of selected Lithuanian monitoring sites
(period 2001‐2010)
Statistic
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
minimum
0.0
0.0
0.0
0.6
2.0
10.0
12.1
11.8
9.8
5‐percentile
0.1
0.1
0.2
2.8
10.5
13.4
16.4
16.0
average
1.3
1.3
2.4
7.6
14.7
18.0
20.5
95‐percentile
4.4
4.8
6.0
12.0
19.5
21.9
maximum
8.3
11.7
10.0
20.2
22.1
27.6
Oct
Nov
Dec
1.0
0.1
0.0
11.5
3.7
1.5
0.1
19.7
15.2
9.1
4.7
2.3
24.7
23.3
19.3
13.5
7.9
5.3
28.5
26.8
22.0
16.2
12.8
7.1
The month with coldest water temperatures was January, whereas the warmest water
temperatures were generally recorded in July.
Increases in surface water temperatures could be estimated between 60 ‐ 80% of the increases
in air temperature (compare section 2.2.1). In this study, it is assumed that an increase in air
temperature of 1 oC would result in an increase in the surface water temperature of 0.75 oC (75%
increase). Combined with the projected air temperature increases, possibly related increases in
surface water temperatures are shown Table 7.
Table 7 Example of projected air and water temperature increases 2021 – 2050 per meteorological season (A1B
scenario)
Scenario
Spring
Summer
Autumn
Winter
Projected increase in air temperature (A1B scenario)
2.1 °C
0.7 °C
1.3 °C
2.6 °C
Example projected increase in surface water
temperature
(75% of projected air temperature increase)
1.6 °C
0.5 °C
1.0 °C
2.0 °C
(*)
10
(*)
please refer to Textbox 3 as well
The original set with Lithuanian surface water quality monitoring data did not include water temperatures. Additional data were
provided, but this data set did not match all monitoring sites in the original dataset. The water temperature calculations of Lithuanian
rivers are based on the data for the monitoring sites listed in Annex 3.
Assessment of climate change impacts on qualitative characteristics of surface water resources in
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Textbox 3: Limitations in projected water temperatures
In all examples further below, the calculated projected increases in surface water
temperature (Table 7) were simply added to the statistics of the selected
Lithuanian monitoring sites (Table 6). This is a gross simplification, notably for the
winter season. After all, a projected increase in the air temperature during the
o
winter of 2.6 C does not imply as if there would be no longer ‘sub‐zero’ air
temperatures! As mentioned in Textbox 1 (section 3.1): in Belarus, the mean
January temperature varies from ‐6,5 °C in the Northeast to ‐5 °С in the Southwest,
whereas in Lithuania this varies between ‐1.4 at the Baltic coast and ‐5.2 °С in the
eastern part of the basin. Also under the A1B and B1 climate change scenarios
there will still be freezing air temperatures during the winter, perhaps except for
sections nearer to the Baltic Sea. Surface waters still will get frozen, either will
o
reach ‘near‐to‐zero’ water temperatures, whether the air temperature is ‐6 C or ‐
o
3 C (of course, ‘the colder, the better’...). Taking all of this into account, the
examples further below should be considered more like being ‘worst‐case
scenarios’.
Adding the temperatures mentioned in Table 6 to the corresponding months in Table 7, results
in the example of projected water temperatures in Table 8 and Figure 20. The possibly highest
increases in surface water temperatures would be expected in the winter period (December –
February).
o
Table 8 Projected water temperatures ), in [ C], of selected Lithuanian monitoring sites, period 2021 – 2050 (A1B
scenario)
Jan
Statistic
(*)
minimum
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
(*)
Dec
(*)
2
2
1.6
2.2
3.6
10.5
12.6
12.3
10.8
2
1.1
2
5‐percentile
2.1
2.1
1.8
4.4
12.1
13.9
16.9
16.5
12.5
4.7
2.5
2.1
average
3.3
3.3
4
9.2
16.3
18.5
21
20.2
16.2
10.1
5.7
4.3
95‐percentile
6.4
6.8
7.6
13.6
21.1
22.4
25.2
23.8
20.3
14.5
8.9
7.3
10.3
13.7
11.6
21.8
23.7
28.1
29
27.3
23
17.2
13.8
9.1
maximum
(*)
Feb
see also Textbox 3
30
30
25
25
20
20
15
15
10
10
5
5
0
0
Jan
Feb
minimum
Mar
Apr
May
5‐percentile
Jun
Jul
average
Aug
Sep
Oct
Nov
95‐percentile
Dec
maximum
Jan
Feb
minimum
Mar
Apr
May
5‐percentile
Jun
Jul
average
Aug
Sep
Oct
95‐percentile
Nov
Dec
maximum
o
Figure 20 Monthly water temperatures ), in [ C] of selected Lithuanian rivers. Left: period 2001‐2010. Right:
projected 2021 – 2050 (A1B scenario; also refer to Textbox 3).
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4.3
Dissolved oxygen
Theoretically, the DO solubility decreases with about 1 mg O2/l per 5 oC increase in water
temperature (a bit more with colder and a bit less with warmer water temperatures; compare also
Table 3 in section 2.2.2).
An example of differences in DO per season between measured versus projected water
temperatures is shown in Table 9, extending on the projected increases in water temperatures of
section 4.2.
Table 9 Example of calculated DO decreases due to higher surface water temperatures in Lithuanian monitoring
sites
Average water temperature 2001‐2010 [°C]
Calculated DO [mg O2/l]
Projected surface water temperature 2021‐2050 [°C]
Calculated DO [mg O2/l]
Difference in calculated [mg O2/l]
Winter
(*)
Spring
Summer
Autumn
8.3
19.4
9.7
2.8
11.8
9.2
11.4
13.5
9.9
19.9
10.7
4.8
11.3
9.1
11.1
12.8
0.5
0.1
0.3
0.7
DO calculated with salinity= 0.05 ‰ and standard barometric pressure = 1 atm = 760 mm Hg
(*)
see also Textbox 3
Relatively larger decreases in DO solubility could be expected in winter (however, refer also to
Textbox 3 in section 4.2) and spring, but fortunately also higher DO concentrations can be expected
in these seasons due to the lower water temperatures (section 2.2.2). The summer, however, can be
the more critical period with respect low dissolved oxygen levels, although the calculated projected
decrease in DO solubility due to an increase in air temperature during this season is quite small
(‐0.1 mg O2/l). The summer being a potentitally more critical season is a/o related to enhanced risks
for eutrophication.
Joint surface water quality assessment criteria for the Neman River Basin have been agreed
during the project.11 The agreed limits for dissolved oxygen are mentioned below.
Table 10 Joint assessment criteria for dissolved oxygen in the Neman River Basin
Status
O2, mg/l
High Good
Moderate
>8.5 8.5‐7.5 7.49‐6.0
Poor
Bad
5.99‐3.0 <3.0
Table 11 contains statistics about the DO concentrations in the Lithuanian rivers listed in Annex
2. Again, it should be noticed that these are the statistics off all data aggregated per month. From
the minimum concentrations one can derive that in some of the rivers there have been cases of
critical DO contents —qualifying as ‘poor’ status— even during the winter season.
11
Compare for example the presentation “”Progress of the pilot project since February, 2011”
http://www1.unece.org/ehlm/platform/download/attachments/24707075/Neman.pdf
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Table 11 Monthly DO concentrations, in [mg O2/l], of selected Lithuanian monitoring sites (period 2000‐2010)
Statistic
minimum
5‐percentile
average
95‐percentile
maximum
Jan
5.0
7.2
10.6
13.9
15.1
Feb
3.3
6.7
10.7
13.9
16.1
Mar
3.3
7.3
10.4
13.4
15.4
Apr
4.4
7.5
10.3
13.1
17.0
May
5.4
7.3
10.0
12.7
14.5
Jun
4.4
5.8
8.8
11.5
16.7
Jul
0.5
4.9
8.5
11.8
15.5
Aug
0.4
5.3
8.2
11.4
15.9
Sep
0.4
5.7
8.9
12.2
16.4
Oct
0.0
6.5
9.2
12.1
13.4
Nov
5.3
7.1
10.2
13.2
14.6
Dec
5.2
7.7
10.9
14.2
17.6
The highest decrease in DO solubility would be expected during the winter season under
scenario A1B: 0.7 mg O2/l. Table 12 contains the average winter (December – February) DO
concentrations at the selected monitoring sites along Lithuanian rivers. At the majority of the sites,
they would qualify as ‘high status’, but there are some exceptions. A shift from ‘good’ to ‘moderate
status’ (based on the average winter concentrations) might be expected at sites with DO
concentrations between 7.5 and 8.2 mg O2/l, separately highlighted in Table 12. Based on the 2000‐
2010 data, for example at the site with station ID 40 (Nevezis, auksciau Raudondvario) more cases of
‘moderate status’ could be expected, based on the average winter concentrations.
Table 12 Average winter DO concentrations, in [mg O2/l], of selected Lithuanian monitoring sites (period 2000‐
2010)
Station ID
1
18
20
26
33
40
41
43
50
56
62
65
70
77
133
137
150
151
217
218
219
265
266
268
269
271
327
359
361
401
2000
11.5
11.1
10.5
10.3
10.0
10.7
11.1
9.2
11.3
8.2
9.7
10.1
10.3
9.9
10.5
11.2
2001
12.2
13.6
11.7
10.8
11.4
11.1
12.2
8.8
12.5
7.1
9.1
9.4
11.6
11.9
12.2
10.6
2002
9.7
12.0
9.6
10.2
9.9
7.1
8.9
10.8
10.5
7.1
11.2
11.6
11.8
11.0
9.0
10.2
2003
10.2
12.7
10.6
10.7
11.8
8.2
10.2
9.6
11.3
9.6
10.1
10.6
11.7
9.3
9.8
10.1
2004
11.5
12.6
10.9
5.2
9.4
7.6
8.5
9.6
8.6
9.1
10.4
10.6
11.3
10.8
8.6
5.8
2005
12.3
14.0
12.5
6.6
8.7
10.2
8.8
9.6
8.7
6.7
9.2
9.4
11.9
10.8
8.2
6.4
5.9
10.9
9.3
9.0
8.3
12.9
12.6
11.7
13.1
13.9
8.8
7.9
6.4
5.0
2006
9.3
11.5
8.5
8.9
5.9
6.8
4.4
10.2
7.3
7.6
9.4
9.9
9.9
9.2
6.3
8.3
10.2
4.9
5.2
3.3
12.0
10.3
11.9
10.3
11.8
11.9
8.3
8.3
8.0
2007
12.2
9.6
5.3
8.4
9.7
9.7
8.1
8.6
9.2
7.4
6.6
7.8
11.9
8.9
8.9
8.4
10.0
10.8
9.0
9.9
8.2
9.1
7.6
8.0
9.9
9.2
11.8
6.3
6.9
8.7
2008
10.3
9.6
8.6
8.0
8.8
8.1
8.4
8.2
6.2
8.3
7.6
8.6
8.9
9.1
7.2
7.3
8.6
9.0
7.2
8.9
6.1
11.1
10.2
8.6
2009
10.1
13.0
11.7
9.3
11.0
12.3
12.5
11.6
10.3
7.9
9.5
7.0
7.9
9.1
9.2
7.0
6.2
9.4
9.6
8.6
10.3
7.0
9.6
10.3
8.7
9.5
9.5
7.7
12.3
11.3
10.9
2010
9.1
7.0
7.9
9.4
10.0
10.8
9.2
10.0
9.6
10.2
8.7
7.3
9.5
5.5
7.5
7.1
7.3
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
29 | P a g e
As already mentioned in section 2.2.2: the DO concentrations are not only determined by
physical factors. For example, degradation of organic material can result in lowered DO levels. This
degradation is based on biological processes, which are more intense with warmer water
temperatures. However, how much a projected increase in the water temperature of 0.5 oC would
affect an oxygen level decrease due to degradation of organic material remains speculative. Possibly,
this could be addressed by model studies for selected cases. Nevertheless, by intuition one would
not expect a drastic effect on the rate of degradation with an increase in water temperature of
0.5 oC.
Furthermore, it is important to notice that expected effects of climate change not only comprise
an increase in the overall air temperatures, but also more frequent occurrences of extremes, with air
temperatures being substantially higher than the average associated with the corresponding season.
This would also translate into the DO concentrations. From this point of view, the above examples
might be considered as being a bit conservative.
4.4
Nutrients
Nutrients were selected for elaborating examples for possible impacts of projected runoff
changes.
When addressing nutrients, it is first of all good to notice that (as with water temperature and
dissolved oxygen) also nutrients can show seasonally varying concentrations. This is illustrated in
Figure 21 and Figure 22, indicating generally higher concentrations during the winter and lower
concentrations during the summer periods.
4.0
3.5
3.0
2.5
2.0
1.5
1.0
Jul/2010
Oct/2010
Jan/2010
Apr/2010
Jul/2009
Oct/2009
Jan/2009
Apr/2009
Jul/2008
Oct/2008
Jan/2008
Apr/2008
Jul/2007
Oct/2007
Jan/2007
Apr/2007
Jul/2006
Oct/2006
Jan/2006
Apr/2006
Jul/2004
Oct/2004
Apr/2004
Dec/2003
Jun/2003
Sep/2003
Jul/2002
Oct/2002
Oct/2001
Apr/2002
0.0
Apr/2001
Jul/2001
0.5
Figure 21 Ntotal concentrations (sum NO2, NO3, NH4 and organic nitrogen), in [mg N/l], over the period 2001 – 2010
at monitoring site with Station ID 13: Nemunas, auksciau Rusnes, auksciau Leites
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
30 | P a g e
0.30
0.25
0.20
0.15
0.10
P total
Jul/2010
Oct/2010
Apr/2010
Jul/2009
Oct/2009
Jan/2010
Jan/2009
Apr/2009
Jul/2008
Oct/2008
Apr/2008
Jul/2007
Oct/2007
Jan/2008
Jan/2007
Apr/2007
Jul/2006
Oct/2006
Apr/2006
Jul/2004
Oct/2004
Jan/2006
Apr/2004
Dec/2003
Jun/2003
Sep/2003
Oct/2002
Oct/2001
Apr/2001
Jul/2001
0.00
Apr/2002
Jul/2002
0.05
PO4‐P
Figure 22 Ptotal and PO4 concentrations, in [mg P/l], over the period 2001 – 2010 at monitoring site with Station ID
13: Nemunas, auksciau Rusnes, auksciau Leites
This phenomenon can partly be explained by the uptake of nutrient by e.g. water plants and
phytoplankton, with accelerated assimilation rates and growth during the summer season. Highest
chlorophyll‐a concentrations (which can be related with algae and other phytoplankton) at site 13
were indeed found during the summer season, as shown in Figure 23.
90
80
70
60
50
40
30
20
Nov/2010
Jul/2010
Sep/2010
May/2010
Jan/2010
Mar/2010
Nov/2009
Sep/2009
Jul/2009
Mar/2009
May/2009
Jan/2009
Nov/2008
Jul/2008
Sep/2008
May/2008
Jan/2008
Mar/2008
Nov/2007
Sep/2007
Jul/2007
May/2007
Mar/2007
Jan/2007
Sep/2006
Nov/2006
Jul/2006
May/2006
Jan/2006
0
Mar/2006
10
Figure 23 Chlorophyll‐concentrations, in [µg/l], over the period 2006 – 2010 at monitoring site with Station ID 13:
Nemunas, auksciau Rusnes, auksciau Leites,
In fresh surface waters, risks for eutrophication —including algal blooms— are normally highest
during the summer season. Nutrient levels (in fresh surfacewaters notably phosphorus compounds)
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
31 | P a g e
are important factors enabling eutrophication. It is assumed that the surface water quality
assessment criteria for total nitrogen and total phosphorus indicate enhanced risks for
eutrophication, starting from the ‘moderate status’.
Table 13 Assessment criteria for Ntotal and Ptotal
Status
Ntotal, mg/l
Ptotal, mg/l
High
<2.00
<0.10
Good
2.0‐3.0
0.10‐0.14
Moderate
3.01‐6.0
0.141‐0.23
Poor
6.01‐12.0
0.231‐0.47
Bad
>12.0
>0.47
As quite a basic approximation for possible impacts of runoff changes, the average summer
concentrations over the years 2000‐2010 have been multiplied by a factor 1.25 (section 4.1.2).
The results for Ntotal are summarised in Table 14. Several shifts from ‘good’ to ‘moderate’ status
can be noticed. Notably, this happens at the sites with the Station ID 40 (Nevezis, auksciau
Raudondvario) and 41 (Susve, ziotyse), but also at several other monitoring sites. However, during
the summer the status at the majority of the sites would remain ‘high’ either ‘good’, as far as Ntotal is
concerned.
Table 14 Average summer Ntotal concentrations, in [mg N/l], of selected Lithuanian monitoring sites: period 2000‐
2010 and multiplied by 1.25
Station ID
1
18
20
26
33
40
41
43
50
56
62
65
70
77
133
137
150
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
cur
pro
2000
1.1
1.4
0.8
1.0
1.4
1.8
0.6
0.8
0.7
0.9
1.9
2.4
2.6
3.2
1.5
1.8
1.1
1.4
0.8
1.0
0.7
0.8
0.6
0.8
1.1
1.4
1.5
1.9
1.0
1.2
0.9
1.1
2001
1.2
1.5
0.9
1.1
1.5
1.8
0.8
1.0
1.7
2.2
4.1
5.1
3.6
4.5
1.0
1.2
1.5
1.8
0.8
0.9
0.6
0.7
0.6
0.8
1.0
1.3
1.0
1.3
1.6
2.0
1.7
2.1
2002
1.1
1.4
0.7
0.8
1.2
1.5
0.6
0.7
1.2
1.5
2.4
3.0
3.1
3.9
1.0
1.3
1.8
2.3
0.7
0.9
0.5
0.7
0.7
0.8
1.0
1.2
1.3
1.7
0.9
1.1
1.0
1.2
2003
1.2
1.5
0.7
0.9
1.4
1.7
0.8
1.0
0.6
0.7
1.8
2.2
1.7
2.1
1.4
1.8
1.1
1.4
1.8
2.3
0.5
0.6
0.6
0.7
0.9
1.1
0.9
1.2
0.7
0.9
1.0
1.3
2004
1.5
1.9
1.2
1.4
1.9
2.4
1.8
2.3
0.8
1.0
3.0
3.8
5.0
6.3
1.6
2.0
1.1
1.3
2.1
2.6
1.0
1.2
1.1
1.4
1.1
1.4
1.8
2.2
1.1
1.3
1.2
1.5
2005
1.4
1.7
1.2
1.5
1.9
2.3
0.8
1.0
1.1
1.4
2.8
3.5
3.5
4.4
1.3
1.7
2.1
2.6
1.2
1.5
1.4
1.7
0.9
1.1
1.3
1.6
1.5
1.9
1.8
2.3
2.6
3.3
2.5
3.1
2007
2.3
2.9
0.7
0.9
1.6
2.0
1.0
1.3
2.6
3.2
4.0
5.0
2.9
3.6
0.9
1.1
3.0
3.7
1.1
1.4
0.5
0.7
0.5
0.6
1.6
2.0
1.7
2.1
2.4
3.0
2.6
3.3
2.1
2.6
2008
2.3
2.9
0.9
1.1
1.9
2.3
0.7
0.9
1.2
1.5
3.0
3.7
4.3
5.4
0.9
1.1
1.8
2.2
1.0
1.2
0.6
0.7
1.2
1.5
1.8
2.3
1.2
1.6
1.1
1.4
0.9
1.1
2.9
3.6
2009
2.2
2.8
0.9
1.2
2.6
3.3
0.8
1.0
3.0
3.7
3.9
4.8
0.6
0.7
1.8
2.3
0.6
0.8
0.3
0.4
0.4
0.5
1.4
1.8
1.5
1.9
1.3
1.6
2.6
3.3
3.1
3.8
2010
2.0
2.5
1.9
2.3
0.8
1.0
2.2
2.7
3.8
4.7
3.6
4.5
1.0
1.2
1.5
1.9
0.5
0.6
0.5
0.7
1.9
2.4
2.2
2.8
2.0
2.5
2.9
3.6
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
32 | P a g e
Station ID
151
2000 2001 2002
cur
pro
217 cur
pro
218 cur
pro
219 cur
pro
265 cur
pro
266 cur
pro
268 cur
pro
271 cur
pro
327 cur
pro
401 cur
pro
cur= current (period 2001‐ 2010)
pro= projected (current concentration * 1.25)
2003
2004
2005
1.3
1.6
1.1
1.4
1.4
1.7
2.7
3.4
1.6
2.0
1.2
1.5
2.0
2.5
1.0
1.3
1.1
1.4
2.4
3.0
2007
1.8
2.2
2.5
3.2
2.8
3.5
3.1
3.9
1.2
1.5
0.8
1.1
1.8
2.3
0.7
0.9
0.7
0.9
3.6
4.5
2008
2.2
2.7
1.8
2.3
1.5
1.9
1.7
2.1
0.8
1.0
0.8
1.0
0.9
1.1
0.7
0.8
0.7
0.9
1.3
1.6
2009
2.0
2.5
1.2
1.5
1.1
1.4
1.7
2.1
0.9
1.1
0.8
1.0
1.2
1.5
1.8
2.2
0.7
0.9
1.7
2.1
2010
1.4
1.8
1.0
1.3
2.1
2.6
1.3
1.7
3.3
4.1
The results for Ptotal are shown in Table 15. It can be noticed that during the period 2001 – 2010
the status at more sites and years was worse during the summer based on the Ptotal concentrations
when compared with Ntotal (Table 14). Obviously, this could further deteriorate in case in the future
rivers’ discharges would diminish during the summer.
The sites with the Station ID 62 (Zeimena ties Kaltanenais) and 65 (Zeimena zemiau Pabrades)
are also worth noticing. While generally qualifying as ‘high’ status for most of the years during the
period 2001 – 2010, both sites were ranked as ‘poor’ status in combination with the average
summer 2003 Ptotal concentration. At both sites there were peak concentrations of 0.5 mg P/l in the
August 2003 samples. The river discharges at both sites during the summer of 2003 were though
comparable with the other years, so this seems not to be an explanatory factor.
Table 15 Average summer Ptotal concentrations, in [mg P/l], of selected Lithuanian monitoring sites: : period 2000‐
2010 and multiplied by 1.25
Station ID
1
18
20
26
33
40
41
43
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
2000
0.13
0.17
0.08
0.10
0.24
0.30
0.07
0.08
0.03
0.04
0.31
0.39
0.03
0.03
0.10
0.12
2001
0.11
0.13
0.14
0.18
0.34
0.42
0.08
0.10
0.08
0.10
0.28
0.35
0.05
0.06
0.11
0.14
2002
0.11
0.14
0.11
0.14
0.30
0.38
0.08
0.10
0.06
0.07
0.46
0.57
0.03
0.03
0.14
0.18
2003
0.12
0.15
0.12
0.15
0.39
0.48
0.09
0.12
0.07
0.09
0.39
0.49
0.04
0.05
0.47
0.59
2004
0.13
0.17
0.09
0.11
0.47
0.59
0.09
0.11
0.06
0.08
0.17
0.22
0.02
0.03
0.29
0.36
2005
0.12
0.15
0.06
0.07
0.19
0.24
0.07
0.09
0.06
0.08
0.54
0.68
0.06
0.08
0.10
0.13
2007
0.08
0.10
0.06
0.08
0.21
0.26
0.07
0.09
0.09
0.11
0.18
0.23
0.06
0.08
0.08
0.10
2008
0.09
0.11
0.07
0.08
0.45
0.57
0.06
0.08
0.04
0.05
0.26
0.33
0.07
0.09
0.11
0.14
2009
0.09
0.11
0.08
0.09
0.90
1.13
0.07
0.08
0.17
0.21
0.04
0.05
0.10
0.12
2010
0.11
0.13
0.16
0.20
0.06
0.07
0.08
0.10
0.15
0.18
0.04
0.06
0.10
0.12
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
33 | P a g e
Station ID
50
56
62
65
70
77
133
137
150
151
217
218
219
265
266
268
271
327
401
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
Cur
Pro
2000
0.11
0.14
0.08
0.09
0.04
0.05
0.06
0.07
0.09
0.11
0.17
0.21
0.06
0.07
0.22
0.28
2001
0.18
0.22
0.07
0.09
0.08
0.10
0.07
0.09
0.09
0.11
0.18
0.22
0.10
0.13
0.27
0.34
2002
0.15
0.19
0.09
0.11
0.05
0.06
0.06
0.07
0.09
0.12
0.12
0.15
0.10
0.12
0.22
0.27
2003
0.13
0.17
0.39
0.48
0.24
0.30
0.25
0.31
0.11
0.13
0.25
0.31
0.08
0.10
0.26
0.32
2004
0.06
0.08
0.15
0.18
0.07
0.09
0.08
0.11
0.12
0.15
0.21
0.27
0.06
0.07
0.23
0.29
2005
0.15
0.19
0.07
0.08
0.07
0.09
0.05
0.07
0.09
0.12
0.11
0.14
0.09
0.11
0.19
0.24
0.19
0.24
0.08
0.10
0.13
0.16
0.08
0.10
0.37
0.47
0.08
0.10
0.07
0.09
0.10
0.13
0.05
0.07
0.05
0.06
0.17
0.22
2007
0.11
0.13
0.07
0.09
0.02
0.03
0.06
0.08
0.07
0.08
0.13
0.17
0.08
0.10
0.26
0.33
0.08
0.10
0.07
0.09
0.13
0.16
0.09
0.11
0.17
0.21
0.11
0.14
0.09
0.11
0.17
0.21
0.06
0.08
0.05
0.06
0.18
0.23
2008
0.09
0.11
0.08
0.10
0.04
0.05
0.05
0.06
0.08
0.10
0.16
0.20
0.08
0.10
0.21
0.26
0.08
0.10
0.09
0.11
0.12
0.15
0.06
0.07
0.12
0.15
0.09
0.12
0.07
0.09
0.09
0.12
0.05
0.06
0.04
0.05
0.24
0.30
2009
0.10
0.13
0.07
0.08
0.02
0.03
0.05
0.07
0.06
0.08
0.10
0.13
0.08
0.10
0.23
0.29
0.08
0.10
0.07
0.09
0.13
0.16
0.08
0.09
0.14
0.17
0.09
0.11
0.07
0.09
0.09
0.11
0.25
0.31
0.04
0.05
0.60
0.75
2010
0.13
0.17
0.03
0.04
0.05
0.06
0.08
0.11
0.18
0.23
0.10
0.12
0.17
0.22
0.13
0.16
0.12
0.15
0.14
0.18
0.07
0.09
0.17
0.21
Shifts towards worse categories in examples above could be interpreted as enhanced risks for
eutrophication, including algal blooms, in case future runoff will decrease during the summer (while
furthermore assuming that the nutrient emissions will not change).
It is important to keep in mind that the above examples are merely included for illustrating
possible cases. As mentioned in the section 3.2: possible decreases in the runoff (up to ‐20%) during
the summer are projected for parts of the basin only.
4.5
Hydrobiological parameters
Aquatic ecosystems are highly complex systems, which could not be properly addressed during
this study. This chapter is constrained to mentioning some tentative, descriptive observations.
Increased algal blooms might indeed be possible in some parts of the basin, due to higher
nutrient concentrations in the summer season because of reduced runoff, as indicated in section 4.4.
Assessment of climate change impacts on qualitative characteristics of surface water resources in
the Neman River Basin
34 | P a g e
Possible increases in water temperatures during autumn and winter might somehow affect (the
spawning of) fish like salmon and trout. In Lithuania, fishing is prohibited during the salmon
spawning period, from October 16 – December 31 (HELCOM, 2011). Polutskaya (2005) mentions that
“The first groups of Sea trout going upstream usually reach spawning grounds in Belarus in the
middle of October. The spawning lasts until approximately the end of November when the water
temperature is 2 to 6 degrees centigrade. The Baltic salmon usually arrives in November and the
spawning may last until the end of December” (Polutskaya 2005). The example of projected average
water temperatures in Table 8 indicates average water temperatures in November and December of
respectively 6 and 4 oC (compared to 5 and 2 oC during the period 2001‐2010; Table 6). Thus, a later
start of the spawning period might well be considered to be a possibility.
The above observations should though be considered more as reflections, rather than supported
projections.
While referring to Table 2 in section 2.1, it is good to notice that the report of Kosten (2011) has
been prepared with a certain focus on the Netherlands. There are some parallels in the climate
change projections, like winters becoming milder and increased runoff in winter and spring due to
higher precipitation (refer also to Textbox 4). On the other hand, for example the winter
temperatures in the Netherlands are higher than inside the Neman Basin. Thus, the examples in
Table 2 in section 2.1 should be regarded with some care.
Textbox 4
Climate change scenarios for the Netherlands
The Netherlands have a temperate maritime climate influenced by the North Sea and
Atlantic Ocean, with cool summers and moderate winters. Daytime temperatures vary from
2 °C – 6 °C in the winter and 17 °C – 20 °C in the summer. Since the country is small there is
little variation in climate from region to region, although the marine influences are less
inland. Rainfall is distributed throughout the year with a dryer period from April to
September. Especially in fall and winter strong Atlantic low‐pressure systems can bring gales
and uncomfortable weather. Sometimes easterly winds can cause a more continental type of
weather, warm and dry in the summer, but cold and clear in the winter with temperatures
sometimes far below zero. Holland is a flat country and has often breezy conditions,
although more in the winter than in the summer, and more among the coastal areas than
12
inland.
According to the 2006 scenarios of the KNMI (Koninklijk Nederlands Meteorologisch Instituut
= Royal Dutch Meteorological Institute), winters will become milder and summers warmer.
The amount of precipitation increases in the winter and spring. Whether precipitation in
summer and autumn will increase either decrease strongly depends on the future airflows.
Extreme situations both in temperature and precipitation will occur more frequently (Kosten
2011).
Readers are further recommended to consult the website Climate Change and Freshwater
(“Indicating the status of freshwater ecosystems under changing climatic conditions”), a product of
‘Euro‐Limpacs’, a major European Union ‐funded Integrated Project: http://www.climate‐and‐
freshwater.info/
12
http://www.weatheronline.co.uk/reports/climate/HollandThe‐Netherlands.htm
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The website contains the following types information, while distinguishing rivers (small and
large), lakes and wetlands in cold, temperate and warm ecoregions:
 Presently used assessment systems for aquatic ecosystems in Europe and how they
address Climate Change effects.
 Case studies addressing the effects of Climate Change on aquatic ecosystems.
 Indicators potentially suited to detect the effects of Climate Change on European
aquatic ecosystems.
 Aquatic species which are affected by (or benefiting from) Climate Change.
There are too many details on this website to be summarised in this report. Suffice to say that
the information that can be found on this website definitely extends on what has already been
provided in the previous chapters.
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5 Ramifications for surface water quality monitoring programmes
For the purposes of the joint classification exercise, already more details were obtained for the
Belarusian and Lithuanian parts of the basin (see also section 4.1.3). Information available for the
Kaliningrad Oblast of the Russian Federation was rather more descriptive. Due to time constraints,
the underlying study had to extend on related work already done during the project as such. This
explains why the inventory in this chapter does not include details about the Kaliningrad Oblast of
the Russian Federation.
5.1
Candidate surface water quality parameters
While referring to the Chapters 2 and 4, the following sets of parameters could be considered
being relevant and indicative in the framework of climate change. Candidate physico‐chemical
parameters are listed in Table 16.
Table 16
Candidate physico‐chemical parameters
Category
Parameter
Thermal conditions
water temperature
Changes in air temperatures will also be reflected
in changes in the temperature of surface waters.
Oxygenation
conditions
dissolved oxygen
There is a direct, physical relation between water
temperature and dissolved oxygen contents.
Nutrient conditions
Justification
oxygen saturation
nitrate (NO3)
nitrite (NO2)
ammonium (NH4)
organic/Kjeldahl‐nitrogen
One potential impact of climate change could be
enhanced risks for eutrophication, with nutrient
conditions being important causal factors.
 Parameters are furthermore indicative for
anthropogenic pollution stresses.
total phosphorus (P)
ortho‐phosphates (PO4)
Other
biochemical oxygen demand
(BOD5; BOD7)
Indicative for anthropogenic pollution stresses
(organic substances).
 Furthermore indicative for potentially lowered
dissolved oxygen concentrations.
Salinity
mineralization
 Salinity can affect the dissolved oxygen contents
(albeit in normal conditions not significantly in
fresh surface waters).
 Parameters could be useful as ‘explanatory
variables’ for supporting certain observations or
hypotheses.
total dissolved salts
‐
chloride (Cl )
sulphate (SO4)
salinity [‰]
conductivity
Acidification status
pH
 Climate change could affect the acidification
status, although this seems to be more an issue
for seas and oceans, rather than inland fresh
surface water bodies.
 pH might be useful as an ‘explanatory variable’,
not expensive to be monitored.
The most common groups of hydrobiological quality parameters are listed in Table 17.
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Table 17
Common hydrobiological quality elements
Group
Benthic invertebrate fauna
Phytoplankton
Phytobenthos
Macrophytes (water plants)
Fish fauna
Zooplankton
Phytoplankton appears to be the fastest responding among the hydrobiological quality elements
to decreases in nutrient concentrations, as can be derived from Table 18. When assuming
comparable response rates to changes in water temperature, then phytoplankton could become one
of the more promising candidate hydrobiological monitoring parameters in the context of climate
change. Furthermore, phytoplankton can be indicative for algal blooms, one of the phenomena that
could be observed/expected more frequently under climate change.
Table 18
Typical response rates after a decrease in nutrient concentrations in surface waters (Loeve et. al.
2006)
Water chemistry
Sediment chemistry
Algae communities
Diatoms
Macrophytes
Benthic invertebrate fauna
Phytoplankton
Zooplankton
Fish
5.2
Days
+
Weeks
+
+
+
+
Months
Years
+
+
+
+
+
+
+
+
+
+
+
Monitoring sites
Belarus operates 43 surface water quality monitoring sites in the Neman Basin, encompassing in
total 35 rivers, lakes and reservoirs. The surface water quality monitoring networks of Lithuania
comprises 866 sites along rivers, lakes and reservoirs. Both countries have natural background
monitoring sites in those parts of the basin that are expected minimally to be impacted by
anthropogenic activities. From these respectable numbers of sites one can infer that there should be
no lack of surface water quality monitoring locations that could be used for monitoring possible
impacts of climate change.
5.3
Routinely monitored parameters
Table 19 gives an overview for which of the candidate physico‐chemical parameters are actually
monitored on a routine basis.
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Table 19
Routine monitoring of the candidate physico‐chemical parameters in BY and LT
Parameter
Belarus
Lithuania
water temperature
yes
yes
dissolved oxygen [mg O2/l]
yes
yes
oxygen saturation [%]
no
yes
nitrate (NO3)
yes
yes
nitrite (NO2)
yes
yes
ammonium (NH4)
yes
yes
Kjeldahl‐nitrogen
yes
no
total nitrogen
no
yes
total phosphorus
yes
yes
ortho‐phosphates (PO4)
yes
yes
yes
BOD5
no
yes
BOD7
yes
yes
yes
chloride (Cl )
yes
yes
sulphate (SO4)
yes
yes
salinity [ /oo]
no
no
conductivity
yes
yes
pH
yes
yes
biochemical oxygen demand
mineralization
total dissolved salts
‐
o
Most selected candidate physico‐chemical parameters are routinely monitored in both
countries. The lack of monitoring of oxygen saturation, mineralization, total dissolved salts and/or
salinity in one or both countries should not be considered a problem, since equivalent parameters
are being monitoring (dissolved oxygen, chloride, sulphates and conductivity).
Lithuania measures biochemical oxygen demand (BOD) over a period of seven days (BOD7),
whereas Belarus measures BOD over a period of five days (BOD5). In principle, one would expect
Lithuania reporting systematically higher concentrations because of this two days’ difference.
However, the actual differences are expected to be marginal, but there were no comparative data
available for substantiating a possible difference.
Lithuania analyses total nitrogen, which is not the case in Belarus. However, total nitrogen can
be calculated as the sum of Kjeldahl nitrogen (comprising organic nitrogen and NH4_N) + NO3_N +
NO2_N.
The status concerning routine monitoring of hydrobiological parameters is summarised in Table
20.
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Table 20
Routine monitoring of hydrobiological parameters in BY and LT
Parameter
Belarus
Lithuania
Benthic invertebrate fauna
yes
yes
Phytoplankton
yes
yes
Chlorophyll‐a
no
yes
*
Phytobenthos
yes
yes
Macrophytes (water plants)
no
no
Fish fauna
no
yes
Zooplankton
yes
yes
*
5.4
phytoperiphyton
Sampling frequencies
Common sampling, analysis and/or measurement frequencies13 for of physico‐chemical
parameters are as follows:
 Belarus
o 12 per year (monthly) at most monitoring sites along rivers;
o 7 per year (during the main phases of the hydrological regime) at selected
monitoring sites along rivers;
o 4 times per year at lake monitoring sites.
 Lithuania
o 12 (monthly) per year at monitoring sites along rivers;14
o up to 9 times per year at freshwater lake monitoring sites (no further details
were obtained).
Hydrobiological parameters are commonly measured
 Belarus
o 3 times per year.
 Lithuania
o Frequencies vary per parameter, like for example: benthic invertebrate fauna 2
times per year, phytobenthos 1 per year, and chlorophyll‐a 4‐12 times per
year15.
5.5
Laboratory analysis
In Belarus, six regional laboratories are involved in the analysis of physico‐chemical
parameters.16 In Lithuania, this concerns nine laboratories of the Regional Environmental Protection
Departments. The methods for laboratory analysis are summarised in Table 21; more details can be
found in Annex 5.
Table 21
Summary of methods of analysis in BY and LT
Parameter
BY
*
LT
13
For example, in Belarus at most stations samples are taken twelve times per year (monthly). However, at various sites salinity‐related
parameters are analysed seven times per year (using the same water samples). Measurements in this context applies to using portable
equipment for measuring parameters directly in the field, normally comprising: water temperature, dissolved oxygen, pH, and
conductivity.
14
During the period 2000 – 2010, frequencies were higher at some sites during some of the years.
15
In the monitoring data provided for Lithuanian rivers, chlorophyll‐a was though monitored only at few sites and not covering the whole
period 2001‐2010.
16
In Baranavichy, Hrodna, Lida, Maladzechna, Slutsk, and Smargon.
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*
Parameter
BY
LT
water temperature
national method
national method
dissolved oxygen
national method
EN 25814:1999
oxygen saturation
‐
nitrate (NO3)
national method
nitrite (NO2)
national method
ammonium (NH4)
national method/ISO
Kjeldahl‐nitrogen
national method
total nitrogen
‐
calculated
ISO 7890‐3:2005
EN 26777:1999
**
EN ISO 7150‐1:1998
‐
EN ISO 11905‐1:2000
total phosphorus
ISO 6878‐2005
EN ISO 6878:2004
ortho‐phosphates (PO4)
ISO 6878‐2005
EN ISO 6878:2004
chloride (Cl )
national method
sulphate (SO4)
national method
EN ISO 10304‐1:1999 /
EN ISO 9297:1998
EN ISO 10304‐1:1999
conductivity
ISO 7888‐2006
EN 27888:2002
pH
ISO 10523‐2009
ISO 10523:1994
biochemical oxygen
demand
national method
(BOD5)
ISO 5815‐1:2003
(BOD7)
‐
*
Information for the Alytus Regional Environmental Protection Department; the prevailing methods are listed (refer to
Annex 2 for further details).
**
During the visit to the laboratory of the Ecological Inspectorate in Grodno under the field trip of October 2012, it was
mentioned that NH4 is analysed with an ISO method.
Lithuania uses EN either ISO methods for analyses of all parameters.17 In Belarus, four (five
when including NH4) parameters are analysed in accordance with ISO methods; the other
parameters are analysed with ‘national methods’ (compare Annex 4 for more details).
5.6
Synthesis
First of all it can be concluded that both countries maintain well‐developed surface water quality
monitoring networks and programmes. Furthermore, it can be noticed that possible climate change
impacts on qualitative characteristics of surface water resources in the Neman River Basin do not
necessarily imply drastic changes in the routine surface water quality monitoring programmes.
Potentially indicative and relevant physico‐chemical parameters are already included in the
routine monitoring programmes. The main point of attention here might be to assure the
comparability of monitoring data. According to verbal information received during the visit to the
laboratory of the Ecological Inspectorate in Grodno under the field trip of October 2012, Belarusian and
Lithuanian laboratories situated inside the Neman Basin conduct several joint sampling exercises per
year, enabling certain comparisons of collected monitoring data. In the future, further
harmonisation of laboratory analysis methods may be considered, as well as the participation in
international proficiency testing programmes.
Most hydrobiological parameters are routinely monitored on a routine basis, although
macrophytes are not included in the programmes of both countries. For Lithuania, monitoring of
hydrobiological parameters (except zooplankton) is prescribed by the Water Framework Directive.
Whether to aim at complete coverage of hydrobiological parameters is partially a strategic choice for
17
EN standards are issued by CEN: Comité Européen de Normalisation (European Committee for Standardization); ISO standards are
issued by the International Standards Organization.
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Belarus and the Kalinigrad Oblast of the Russian Federation. However, all countries could at least
consider including chlorophyll‐a in the routine surface water quality monitoring programmes.
Chlorophyll‐a can be used as an indicator for phytoplankton biomass and therewith for observing
possible algal blooms. Sampling and analysis of chlorophyll‐a does not imply substantial additional
efforts.
Sampling frequencies could become major point of attention. Noticing that a) projected climate
change effects vary for the four seasons (section 3.2) and b) many parameters fluctuate seasonally
(compare the examples in Chapter 4), then at least monthly sampling frequencies prevail. In
practise, this might meet problems during the winter when surface water bodies could be frozen,
but still also during the winter sampling should be envisaged.
Even monthly sampling might still not suffice for detecting future trends, like for example
possible increases in overall water temperatures. Refer to Textbox 5 for a theoretical basis for
optimising sampling frequencies.
Textbox 5
Optimising sampling frequencies
The main basic assumption for optimising the sampling frequencies is stable variances.
When a recommended trend is known, the required number of samples can be estimated,
using the variance detected in the historical time‐series. Vice versa, when the monitoring
frequency is given, the minimum detectable trend could be calculated.
These statistical relations are based on the equations for the trend detection of
Lettenmayer:
where:
Tr
crit
NT
2
s
n*
detectable trend
trend test statistic based on a given critical value α and a reliability level (1‐
β)
(historical) variance
total number of independent samples for the total period of interest
crit
In this equation a value N T =2.927 is commonly used, which is calculated on basis of
18
the following assumptions: 0.05 or 5% and (1‐ 0.1 or 10%.
The statistical equation above could be used in a correct way only for independent samples.
Due to seasonality, trend or auto‐correlation, in practice water quality samples are mostly
not independent. In the framework of recommended trends, the variance is calculated after
removing trends and seasonality, instead of calculating the variance of the raw data.
18
The α‐level, or significance level, is the probability of incorrectly rejecting a tested null hypothesis (type I error), e.g. there is no trend.
The ‐level, or lack of power, is the probability of failing to reject the null hypothesis, when in fact it is false (type II error). The α‐level is
a "management tool" to make a decision, whereas the ‐level is dependent on both a dataset analysed and statistical tests used.
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Rather than increasing the sampling frequencies and sets of (hydrobiological) parameters at all
monitoring sites, it would be more prudent to select a limited number of sites for more intensive
monitoring programmes, aimed at detecting possible future trends.
Initially, one might consider selecting surface water quality monitoring locations situated along:
 the Neman/Nemunas River (e.g. in the upper, middle and lower stretch of the river
inside each country);
 the Vilya/Neris River (e.g. in the upper, middle and lower stretch of the river inside each
country);
 a smaller tributary in each country;
 a ‘large’ lake/reservoir (depth >3m) in each country;
 a ‘small’ lake/reservoir (depth <3m) in each country.
Surface water quality monitoring locations in the vicinity of hydrological posts would prevail in
order to be able to add quantitative aspects in the assessments. Locations (having been) exposed to
(nearby) pollution sources are to be avoided.
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6 Discussion
From the available literature one can derive that there is a general consensus that climate
change can also affect the qualitative characteristics of surface water resources, involving physico‐
chemical and hydrobiological parameters.
The key question is to which extent possible effects might have to be anticipated in the Neman
River Basin. There are certain differences between the climate change scenarios A1B and B1, but not
such that they would make a big difference in terms of possible impacts on the surface water
quality. Hence, the key is first of all in estimating the possible impacts of the projected climate
changes more in general, notably increases in air temperature and changes in the runoff.
The use of computer models will finally be inevitable for making well‐founded projections for
the possible impacts of climate change on the surface water quality in the Neman Basin.
 The basin is comprises a diverse set of freshwater surface water bodies, including
smaller and larger rivers, shallow and deep lakes and several reservoirs. Each category
has its own characteristics and therewith potential responses to changes in external
factors.
 Projections will have to include scenarios for emissions of substances from point and
non‐point sources.
 Projected climate changes in the Neman River basin are not uniformly, but differ
between the various parts of the basin.
 Aquatic ecosystems are complex, with numerous factors interacting with each other.
Characteristics of individual parameters will have to be taken into consideration as well.
 Projected climate changes are not a certainty ...
Neither the tools, nor the time, nor the data were available for conducting model‐based
exercises under this study. Nevertheless, some of the indicative findings of Chapter 4 are worth
mentioning.
Please though notice that most examples are limited to the selected monitoring sites along
Lithuanian rivers. The situation in lakes and reservoirs could be different (including assessment
criteria). For example, standing waters like lakes and reservoirs are more prone to eutrophication
than running waters.
While merely tied to projected increases in air temperature, overall increases in water
temperatures during the period 2021‐ 2050 could amount to about to about 0.5 oC in the summer to
maximal 2.0 oC in the winter (refer to section 4.2, while noticing that the estimates for the winter
might be exaggerated). The increasew in water temperature would physically translate into a
decrease of dissolved oxygen levels of about 0.1 mg O2/l in the summer to 0.7 mg O2/l in the winter.
A decrease of 0.1 mg O2/l during the summer is rather insignificant. Dissolved oxygen levels during
the winter are generally sufficiently high not to be seriously affected by a decrease of 0.7 mg O2/l.
Based on selected Lithuanian monitoring sites along rivers, the 5‐ respectively 95‐percentile DO
levels during the winter range between 7.2 and 14.0 mg O2/l, with an average of 10.7 mg O2/l;
section 4.3. Sites with relatively low DO concentrations are probably affected by (local) pollution.
Implying, that projected decreases in DO during the winter due to increased water temperatures
would be merely relevant for those sections of the river where pollution is still not properly
remediated in the period 2021 – 2050.
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The indicative computations for possible impacts of a 20% decrease in runoff during the
summer imply higher risks for eutrophication in some parts of the basin. This would be a/o the result
of higher nutrient concentrations (notably phosphorus compounds) due to lower dilution capacities
of rivers and lakes (section 4.4).
Of course, this includes as underlying assumption that nutrient emissions would not change
during the period 2021 – 2050. The countries situated inside the Neman River basin are still in the
process of preparation and implementation of measures aiming at a reduction of nutrient emissions.
Nevertheless, the indicative results for nutrients of this study might justify pursuing more detailed,
model based follow‐up studies.
Textbox 6
Nutrient emissions
Anthropogenic nutrients emissions involve both point and diffuse sources. Discharges of
municipal wastewater are a clear example of point sources of nutrient pollution. A major
diffuse source is runoff and leaching of nitrogen and phosphorus compounds from
agricultural lands, resulting from the application of fertilisers.
Climate change could affect diffuse nutrient loading in a number of ways (refer also to
section 2.3):
 Increased runoff due to increased precipitation could also lead to increased surface
runoff and erosion of nutrients. The projected maximum monthly runoff, which can
be related to the magnitude of the spring flood, will though decrease in the majority
of Neman basin. The increase of January‐February runoff in 2021‐2050 can be
related to the earlier start of spring, increased winter precipitation and increased
frequency of thaws (section 3.2).
 Higher air temperatures will increase mineralization and releases of nitrogen,
phosphorus and carbon from soil organic matter.
 Release of phosphorus from bottom sediments in stratified lakes might increase,
due to declining oxygen concentrations in the bottom waters.
 Higher water temperatures accelerate the degradation of organic matter in water
and sediment, therewith releasing nitrogen and phosphorus compounds. It is
though good to notice that generally relatively small increases in water
temperatures might be anticipated during the summer season (section 4.2).
Eutrophication affects aquatic ecosystems as a whole, including its organisms. Organisms could
be affected via other ways as well. For example, fish like salmon and trout might respond to warmer
water temperatures during the late autumn and winter by adjusting their spawning season.
However, this study allows merely for speculations about possible impacts of climate change on
hydrobiological conditions of the Neman River Basin.
It is good to notice that climate conditions inside the Neman Basin apparently already changed
during the past decades, in orders of magnitude comparable to projected changes (Chapter 3).
Implying, that some of the impacts that could be anticipated for the future, might have commenced
already right now. It would be worth examining historic data, including for hydrobiological
parameters, for possible changes/trends that could be related to the changes in climatic conditions.
Considering adaptation strategies, the following could be raised from the preliminary findings of
this study. The examples in Chapter 4 indicate that (a combination of) climate change effects could
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lead to enhanced risks for eutrophication. Implying, that further control and reduction of
anthropogenic nutrient emissions becomes even more important. From Chapter 4 one could derive
that notably pollution with phosphorus compounds is critical (at least in the case of Lithuanian
rivers, but there is no reason to assume differently for other parts of the Neman River Basin).
 Considering point sources of pollution —municipal wastewater discharges and other
direct wastewater discharges from specific industrial activities, like food industry—
additional phosphorus and nitrogen removal (tertiary treatment) may have to be
considered. Phosphorus loads in municipal wastewaters can further be achieved by
banning or minimising of phosphate in relevant domestic products, like laundry cleaning
products (Solheim et. al. 2010).
 Regarding diffuse sources of pollution, among others fertiliser, manure and slurry
management measures. A rational and planned application of fertilisers/manure/slurry,
which is well‐timed and which takes account of local parameters such as soil type and
structure, is a wide‐spread tool for reducing nutrient leaching and which can have an
enormous impact on water quality. By keeping nutrient levels as close as possible to
plant requirements over time, excess nutrient in the soil, which can be flushed out e.g.
by precipitation, is reduced to a minimum (Solheim et. al. 2010).
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7 Conclusions and Recommendations
While emphasising that the underlying study merely provides with descriptive overviews of
possible climate change impacts on qualitative characteristics of surface water resources in the
Neman River Basin, the following conclusions and recommendations are worth mentioning.
7.1
Conclusions



7.2
From the available literature one can derive that there is a general consensus that
climate change can also affect the qualitative characteristics of surface water resources,
involving physico‐chemical and hydrobiological parameters.
Preliminary findings indicate that in the Neman River Basin under the A1B climate
change scenario:
o Water temperatures might increase most during the winter season, since also
the highest increases in air temperatures are projected for this season.
o With increased water temperatures, the solubility of dissolved oxygen will
decrease. However, during the coldest (winter) season, dissolved oxygen
contents are theoretically highest. Thus, unless surface water bodies are already
under —severe– pollution stress, the possible decreases in dissolved oxygen
contents during the winter (due to higher water temperatures) are not expected
to be critical.
o The risks for eutrophication during the summer season might increase, notably
in those parts of the basin where runoff is expected to decrease during this
season. Notably pollution with phosphorus compounds could be critical.
The surface water quality monitoring programmes in Belarus and Lithuania are well
developed and already comprise many physico‐chemical and hydrobiological parameters
considered to be relevant either indicative in the context of possible impacts of climate
change on the quality of surface waters. However, sampling frequencies might not
always suffice for detecting (future) trends in the development of certain surface water
quality parameters.
Recommendations


The examples in this study are mainly based on physico‐chemical monitoring data
collected at Lithuanian rivers throughout the period 2000 ‐ 2010. In possible follow‐up
studies it is recommended to extend the sets of monitoring data to: lakes and reservoirs,
as well as hydrobiological monitoring data, and to include the monitoring data available
for Belarus and the Kaliningrad Oblast of the Russian Federation. Because of seasonal
variations, as well as differences between project climate changes throughout the year,
raw monitoring data should be used for assessments and as a basis for projections; in
many cases, annual average values are not suitable. Finally, the use of computer‐models
will be inevitable for preparing projections about developments of surface water quality
in the Neman River Basin, including the possible effects of climate change.
Anticipating the detection of possible (future) trends in the further development of
surface water quality, it is recommended to consider implementing more intensive
surface water quality programmes at a limited number of monitoring sites in the basin.
‘More intensive’ implies higher sampling frequencies and more parameters. Sites could
limited for example to: the upper, middles and lower sections of the Neman/Nemunas
river, each in Belarus, Lithuania and Kaliningrad Oblast; the upper, middles and lower
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

sections of the Vilya/Neris River, each in Belarus and Lithuania; selected smaller
tributaries, as well as a selection of a smaller/larger lake and reservoir inside each
territory.
Climate change effects could be observed already during the period 1960 – 2010. It
would be worth examining historic surface water quality monitoring data, including
physico‐chemical as well as hydrobiological parameters, for possible changes/trends
during the same period and to assess whether changes (if any) could be related to
climatic change.
The —indicative— results of this study imply that a combination of climate change
effects could lead to enhanced risks for eutrophication. This indicates that further
control and reduction of anthropogenic nutrient emissions will become even more
important. In terms of adaptation strategies, among others the following could be
suggested:
o Regarding point sources of pollution —municipal wastewater discharges and
direct wastewater discharges from specific industrial activities like food
industry—additional phosphorus and nitrogen removal (tertiary treatment) may
have to be considered. Phosphorus loads in municipal wastewaters can further
be achieved by banning or minimising of phosphate in relevant domestic
products, like laundry cleaning products.
o Regarding diffuse sources of pollution, among others fertiliser, manure and
slurry management measures. A rational and planned application of
fertilisers/manure/slurry, which is well‐timed and which takes account of local
parameters such as soil type and structure, is a wide‐spread tool for reducing
nutrient leaching and which can have an enormous impact on water quality. By
keeping nutrient levels as close as possible to plant requirements over time,
excess nutrient in the soil, which can be flushed out e.g. by precipitation, is
reduced to a minimum.
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7.3
References
CIW, 2004
CIW beoordelingssystematiek warmtelozingen. Ministerie van Verkeer en
Waterstaat, Commissie Integraal Waterbeheer.
http://www.helpdeskwater.nl/publish/pages/634/ciw42004‐
11beoordelingssystematiek_warmtelozingen.pdf
Delpla, I. ; Jung, A.‐V.; Baures, E.; Clement, M.; Thomas, O. 2009
Impacts of climate change
on surface water quality in relation to drinking water production. Environment International 35
(2009) 1225–1233. http://jlakes.org/web/impacts‐climate‐change‐surface‐water‐quality‐EI2009.pdf
HELCOM, 2011
Salmon and Sea Trout Populations and Rivers in Lithuania – HELCOM
assessment of salmon(Salmo salar) and sea trout (Salmo trutta) populations and habitats in rivers
flowing to the Baltic. Sea. Balt. Sea Environ. Proc. No. 126B.
http://www.helcom.fi/stc/files/Publications/Proceedings/BSEP126B_LT.pdf
IPCC, 2007
Climate Change 2007 – Impacts, Adaptation and Vulnerability. Contribution
of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change, 2007. http://www.ipcc.ch/publications_and_data/ar4/wg2/en/contents.html
IPCC, 2008
Technical Paper VI: Climate change and water
http://www.ipcc.ch/publications_and_data/publications_and_data_technical_papers.shtml#.UDW0
eqO9OK0
Korneev, Vladimir; Rimkus, Egidijus; Sepikas, Audrius; Shalygin, Andrey. 2011
Management of the Neman River basin with account of adaptation to climate change ‐
Baseline Study Report Belarus, Lithuania, Russian Federation. August 2011.
http://www1.unece.org/ehlm/platform/download/attachments/25133069/Report_Neman_Baseline
_Study_English_Version_final.pdf
Kosten, Sarian. 2011
Een frisse blik op water. Over de invloed van klimaatverandering op
de aquatische ecologie en hoe je de negatieve effecten kunt tegengaan. Stichting Toegepast
Onderzoek Waterbeheer (STOWA), Amersfoort; Rijkswaterstaat Waterdienst, Lelystad. STOWA
report number 2011‐20. ISBN 978.90.5773.524.0. http://www.stowa.nl/upload/publicaties/2011‐
20%20(2).pdf
Loeve, R.; Droogers, P.;Veraart, J. 2006
Klimaatverandering en waterkwaliteit. Wetterskip
Fryslân. FutureWater Report 58. http://www.futurewater.nl/downloads/2006_Loeve_FW58.pdf
Morrill, Jean C.; Bales, Roger C.; Conklin, Martha H. 2005
from Air Temperature: Implications for Future Water Quality.
https://eng.ucmerced.edu/people/rbales/CV/PubsM/98
Estimating Stream Temperature
Moss, Brian; Kosten, Sarian; Meerhoff, Mariana; Battarbee, Richard W.; Jeppesen, Erik; Mazzeo,
Néstor; Havens, Karl; Lacerot, Gissell; Liu, Zhengwen; Meester, Luc, De; Paerl, Hans; Scheffer,
Marten. 2011. Allied attack: climate change and eutrophication. Inland Waters (2011) 1, pp. 101‐
105. https://www.fba.org.uk/journals/index.php/IW/article/viewFile/359/263
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49 | P a g e
Polutskaya, Nina. 2005
Atlantic Salmon in Rivers of Belarus. Coalition Clean Baltic.
http://www.ccb.se/documents/AtlanticSalmoninRiversofBelarus.pdf
Rimkus, Egidijus; Korneev, Vladimir; Pakhomau, Aliaksandr; Stonevičius, Edvinas. 2012
Climate Change in the Nemunas River Basin: Observed Trends and Future Predictions.
Vilnius, Minsk 2012. UNDP/UNECE Project River Basin Management and Climate Change Adaptation
in the Neman River Basin.
http://www1.unece.org/ehlm/platform/download/attachments/25690532/REPORT_climate_Nemu
nas.pdf
Solheim, Anne Lyche; Austnes, Kari; Eriksen, Tor Erik; Seifert, Isabel; Holen, Silje. 2010
Climate change impacts on water quality and biodiversity. Background Report for EEA
European Environment State and Outlook Report 2010. European Topic Centre on Water, Technical
Report 1/2010.
http://icm.eionet.europa.eu/docs/Climate_impacts_on_water_quality_and_biodiversity_29_Nov_2
010_final_2.pdf
Stonevičius, Edvinas; Korneev, Vladimir; Rimkus, Egidijus; Pakhomau, Aliaksandr. 2012
Runoff Change in the Neman River Basin. Vilnius, Minsk 2012. UNDP/UNECE Project River
Basin Management and Climate Change Adaptation in the Neman River Basin.
http://www1.unece.org/ehlm/platform/download/attachments/25690532/REPORT_current_runoff
_draft.pdf
Stonevičius, Edvinas; Štaras, Andrius. 2012 Projections of Runoff Changes in Nemunas Basin
Rivers according to WatBal model. Vilnius, Minsk 2012. UNDP/UNECE Project River Basin
Management and Climate Change Adaptation in the Neman River Basin.
http://www1.unece.org/ehlm/platform/download/attachments/25690532/Runoff_projections_Wat
Bal.pdf
USGS. 2011
Office of Water Quality Technical Memorandum 2011.03. Change to
Solubility Equations for Oxygen in Water. U.S. Geological Survey.
http://water.usgs.gov/admin/memo/QW/qw11.03.pdf
Whitehead, P., Butterfield, D. and Dr Wade, A., 2008.
Potential impacts of climate change
on water quality and ecology. Science Report – SC070043/SR1. Environment Agency UK,
http://cdn.environment‐agency.gov.uk/scho0508bocw‐e‐e.pdf
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50 | P a g e
Annex 1:
Computation of the solubility of dissolved oxygen
The United States Geological Survey uses the Benson and Krause equations for computation of
the solubility of dissolved oxygen (DO). More information can be found in the document that can be
downloaded at http://water.usgs.gov/admin/memo/QW/qw11.03.pdf
with:
DO=
DOo=
Fs=
Fp=
dissolved oxygen concentration in (mg/l)
baseline concentration in freshwater
salinity factor
pressure factor
Benson and Krause baseline concentration in freshwater, DOo
with:
T=
water temperature in Kelvin (T = t(°C) + 273.15)
Benson and Krause salinity factor, Fs
with
S=
T=
salinity in parts per thousand (‰)
water temperature in Kelvin (T = t(°C) + 273.15)
Benson and Krause pressure factor, Fp
with:
P=
u=
θo=
the barometric pressure in atmospheres
the vapor pressure of water in atmospheres
related to the second virial coefficient of oxygen
with:
t=
water temperature in degrees Celcius (oC)
with:
T=
water temperature in Kelvin (T = t(°C) + 273.15)
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Notes
For calculating the barometric pressure as a function of the altitude, the following equation can
be applied (http://www.waterontheweb.org/under/waterquality/oxygen.html):
where P= pressure (atm) at altitude h (km) relative to standard partial pressure (Pst) at 760 mm
Hg or 101.325 kpa at sea level.
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Annex 2:
STATION ID
219
151
62
271
218
269
26
150
18
20
41
33
40
50
65
43
1
70
77
137
133
265
266
401
217
268
359
327
56
361
Selected Lithuanian monitoring sites with surface
water quality data for the period 2000‐2010
NAME
Ziezmara ties Paparciais
Ula‐Pelesa ties Kasetomis
Zeimena ties Kaltanenais
Akmena auksciau Pagramacio
Dubysa ties Kaulakiais, ties keliu Nr.225
Salanta ties Nasrenais
Sesupe Lenkijos pasienyje
Jiesia ties Jiestrakiu
Veivirzas ties Veivirzenais
Sysa zemiau Silutes
Susve ziotyse
Dubysa auksciau Seredziaus
Nevezis auksciau Raudondvario
Neris auksciau Kauno
Zeimena zemiau Pabrades
Neris ties Buivydziais
Nemunas auksciau Druskininku
Merkys zemiau Puvociu
Akmena‐Dane ziotyse
Sesupe Kaliningrado pasienyje
Sventoji ziotyse
Jura ties Mociskiais
Minija ties Suvernais
Rausve ties Nadrausve
Sesuvis ties Taubuciais
Vilka ties Gudais
Sventoji ties Bindzeliskiais, ties Pasiles upelio
Sventoji ties Sabaliunais (zemiau Andrioniskio)
Sirvinta auksciau Sirvintu
Josvainis ties Oreliais (netoli ziociu)
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Annex 3:
River
Akmena ‐
Dane
Bartuva
Birveta
Daugyvene
Dubysa
Jura
Laukesa
Levuo
Merkys
Minija
Musa
Nemunas
Lithuanian monitoring sites for calculation of
average water temperatures
Station
ID
Monitoring point
74
76
77
78
79
105
99
33
218
21
22
265
106
95
96
97
98
69
70
161
14
ties Tubausiais
auksciau Klaipedos
ziotyse
auksciau Skuodo
zemiau Skuodo
Baltarusijos pasienyje
ziotyse
auksciau Seredziaus
ties Kaulakiais, ties keliu
Nr.225
auksciau Taurages
zemiau Taurages
ties Mociskiais
zemiau Zarasu
auksciau Kupiskio
zemiau Kupiskio
auksciau Pasvalio
ziotyse
auksciau Varenos
zemiau Puvociu
auksciau Valkininku
auksciau Plunges
15
16
17
266
84
85
86
1
zemiau Plunges
zemiau Gargzdu
zemiau Priekules
ties Suvernais
auksciau Kulpes
zemiau Kulpes
zemiau Salociu
auksciau Druskininku
2
zemiau Druskininku
3
4
5
zemiau Druskininku
auksciau Alytaus
auksciau Alytaus
zemiau Alytaus
zemiau Alytaus
auksciau Prienu
6
7
8
11
13
127
auksciau Kauno
zemiau Smalininku
auksciau Rusnes, auksciau
Leites
auksciau Rusnes, auksciau
Leites
Skirvyte auksciau Rusnes
135
zemiau Kauno ties Zapyskiu
12
River
Neris
Nevezis
Sesupe
Sesuvis
Sidabra
Sirvinta
Skroblu
s
Susve
Sventoji
Station
ID
48
49
50
130
36
37
38
39
auksciau Jonavos
zemiau Jonavos
auksciau Kauno
zemiau Jonavos
auksciau Panevezio
zemiau Panevezio
auksciau Kedainiu
zemiau Kedainiu
40
26
27
28
29
137
23
217
87
88
56
57
142
auksciau Raudondvario
Lenkijos pasienyje
zemiau Kalvarijos
auksciau Marijampoles
zemiau Marijampoles
Kaliningrado srit. pasienyje
ties Skirgailiais
ties Taubuciais
zemiau Joniskio
Latvijos pasienyje
auksciau Sirvintu
zemiau Sirvintu
ziotyse
71
41
52
53
54
55
133
138
zemiau Dubininku
ziotyse
auksciau Anyksciu
zemiau Anyksciu
auksciau Ukmerges
zemiau Ukmerges
ziotyse
ziotyse
ties Sabaliunais (zemiau
Andrioniskio)
ties Bindzeliskiais,ties Pasiles
ziotym
auksciau Silutes
zemiau Silutes
auksciau Birzu
zemiau Birzu
ties Trecionimis
327
Sysa
Tatula
Veivirza
s
Venta
Virvycia
Zeimen
a
Monitoring point
359
19
20
92
93
94
18
80
ties Veivirzenais
auksciau Kursenu
81
zemiau Kursenu
82
83
zemiau Mazeikiu
zemiau Pateklos
62
ties Kaltanenais
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River
Nemunelis
Neris
Station
ID
136
89
357
43
44
45
46
47
Monitoring point
zemiau Kauno ties Kulautuva
zemiau Panemunio
Tabokine
ties Buivydziais
auksciau Vilniaus
zemiau Vilniaus
zemiau Vilniaus
zemiau Vilniaus
River
Station
ID
63
64
65
141
Monitoring point
zemiau Svencioneliu
auksciau Pabrades
zemiau Pabrades
auksciau Vaisniunu
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Annex 4
Laboratory analysis of physico‐chemical parameters
Belarus
Parameter
Water
temperature
Method,
Standard
Principle
Руководство по химическому
анализу поверхностных вод
суши. Под редакцией А.Д.
Семенова. – Л.:
Гидрометеоиздат, 1977 г. – с. 19‐
21
Сборник МВИ.ч.3.‐ Мн.: НТЦ
"АПИ", 1998. ‐ с.154‐163.
Методика выполнения
измерений (МВИ) температуры
при помощи ртутного
термометра; термометр
электронный
Nitrate (NO3)
Сборник МВИ.ч.1.‐ Мн., 1997. ‐
с.209‐214.
Nitrite (NO2)
Сборник МВИ.ч.2.‐ Мн., 1997. ‐
с.144‐149.
Ammonium
(NH4)
Сборник МВИ.ч.1.‐ Мн., 1997. ‐
с.167‐174.
Kjeldahl‐
nitrogen
Унифицированные методы
анализа вод. под ред.
Ю.Ю.Лурье. ‐ М.: Химия, 1973.‐
с.110‐112.
СТБ ИСО 6878‐2005. Качество
воды. Определение фосфора
спектро метрическим методом с
молибдатом аммония.
СТБ ИСО 6878‐2005. Качество
воды. Определение фосфора
спектро метрическим методом с
молибдатом аммония.
Унифицированные методы
анализа вод. под ред.
Ю.Ю.Лурье.‐М.:Химия,1973.‐с.47.
Сборник МВИ.ч.3.‐ Мн.: НТЦ
"АПИ", 1998. ‐ с.181‐185.
МВИ концентрации нитратов
фотомет‐рическим методом с
салициловой кислотой
метод фотометрический;
МВИ концентрации нитритов
фотометрическим методом с
реактивом Грисса
метод фотометрический;
МВИ концентрации азота
аммонийного фотометрическим
методом с реактивом Несслера
метод фотометрический;
МВИ концентрации азота общего
по методу Кьельдаля (без
приборов)
Dissolved
oxygen
Total
phosphorus
Ortho‐
phosphates
(PO4)
Total dissolved
salts (сухой
остаток)
‐
Chloride (Cl )
Sulphate (SO4)
Сборник МВИ.ч.1.‐ Мн.: НТЦ
"АПИ", 1997. ‐ с.142‐148.
МВИ концентрации кислорода
титри‐метрическим методом
Detection
limit/
accuracy
‐
Д ‐ св.0,05
мгО2/дм3;
П ‐ 0,3 %
в
Д ‐ 7‐10
мгО2/дм3
Д ‐0,5‐70
мг/дм3;
Д ‐ 0,005‐0,3
мг/дм3;
П ‐ 50‐33%
Д ‐ 0,1‐10
мг/дм3;
П‐50‐25%
Д ‐ 1‐200
мг/дм3
метод спектрометрический;
Д ‐ св.0,005
мг/дм3
метод спектрометрический;
Д ‐ св.0,005
мг/дм3
МВИ величины сухого остатка
гравиметрическим методом
метод гравиметрический;
МВИ концентрации хлоридов
титриметрическим методом с
нитратом серебра
метод титриметрический; Весы
лабораторные электронные СР
225Д
МВИ концентрации сульфатов
турбидиметрическим методом
метод турбидиметрический (без
‐
Д ‐ св. 10
мг/дм3; П‐
0,6 %
в
Д ‐ 100‐200
мг/дм3
Д ‐ 1,0‐15
мг/дм3;
П ‐ 10%
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56 | P a g e
Conductivity
pH
Biochemical
oxygen
demand, BOD5
СТБ ИСО 7888‐2006.Качество
воды. Определение удельной
электрической проводимости
СТБ ISO 10523‐2009 Качество
воды. Определение рН
Сборник МВИ.ч.1.‐ Мн.: НТЦ
"АПИ", 1997. ‐ с.102‐116.
приборов)
метод кондуктометрический;
‐
метод потенциометрический (pH
метр)
МВИ концентрации БПК
стандартным методом
определения
стандартный метод (без
приборов)
Д‐ 2‐12 ед.
рН.
Д ‐ св. 1,0
мгО2/дм3;
П ‐ 13 ‐ 1,0 %
Lithuania
Parameter
Method’s principle
Standard
Unit
BOD7
BOD7
BOD7
BOD7
N total
Electrochemical
Electrochemical
Electrochemical
Electrochemical
Spectrometric, mineralizing with K2S2O8 (potassium
persulphate)
Flow injection
Flow injection
Spectrometric, using amonium molybdenum
Ion exchange chromatography
Titrimetric
Flow injection
Spectrometric
Spectrometric
Flow injection
Flow injection
Spectrometric
Iodinemetric
Electrochemical
Calculation
Electrometric
Electrometric
Flow injection
Spectrometric, using amonium molybdenum
Electrometric
turbidimetric
Ion exchange chromatography
Instrumental
Spectrometric
ISO 5815‐2:2003
ISO 5815‐1:2003
EN 1899‐2:2000
EN 1899‐1:2000
EN ISO 11905‐1:2000
mg O2/l
mg O2/l
mg O2/l
mg O2/l
mg/l
EN ISO 11905‐1:2000
EN ISO 15681‐1:2005
EN ISO 6878:2004
EN ISO 10304‐1:1999
EN ISO 9297:1998
EN ISO 11732:2005
EN ISO 7150‐1:1998
EN 26777:1999
EN ISO 13395:2000
EN ISO 13395:2000
ISO 7890‐3:2005
EN 25813:1999
EN 25814:1999
mg/l
mg/l
mg/l
mg/l
mg/l
mg N/l
mg N/l
mg N/l
mg N/l
mg N/l
mg N/l
mg/l
mg/l
ISO 10523:1994
ISO 10523:2009
EN ISO 15681‐1:2005
EN ISO 6878:2004
EN 27888:2002
‐
EN ISO 10304‐1:1999
‐
ISO 10260:1992
‐
N total
P total
P total
Cl
Cl
NH4‐N
NH4‐N
NO2‐N
NO2‐N
NO3‐N
NO3‐N
O2
O2
O2 saturation
pH
pH
PO4‐P
PO4‐P
Conductivity
SO4
SO4
Temperature
Chlorophyll a
mg P/l
mg P/l
µS/cm
mg/l
mg/l
°C
µg/l
The main method is highlighted in bold. Other methods are used when there are no possibilities to use the main
method.
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