Measuring water resources available within an RBD

Transcription

The New European Water Management Geography:
An Indicator-based Analysis of the River Basins Districts
Dorothy Furberg
Master’s Thesis in Environmental Engineering and Sustainable Infrastructure
KTH, Stockholm, Sweden
June 2006
Advisor: Susanna Nilsson, Ph. Lic.
Examinator: Jan-Erik Gustafsson
TRITA-LWR M A S T E R T H E S I S
I SSN 1651-064X
LWR-EX-06-16
Dorothy Furberg
TRITA LWR MASTER
A CKNOWLEDGEMENTS
The author would like to thank first and foremost Sindre Langaas for suggesting the subject of this thesis and for his
encouragement and enthusiasm for the topic. She is also very grateful to Susanna Nilsson for her guidance, patience
and ready willingness to help. Special thanks go to Duncan McConnachie, who patiently answered numerous
questions about GIS and decision support systems, to Jan-Erik Gustafsson, who generously gave of his time and
resources at a moment’s notice and to Jerzy Buczak, whose tireless efforts made this thesis technically possible.
Finally, the author is very thankful for her husband, “the Excel expert.”
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The New European Water Management Geography: An Indicator-based Analysis of the River Basins Districts
Table of Contents
Acknowledgements ................................................................................................................................ii
Abstract ................................................................................................................................................... v
Sammanfattning ..................................................................................................................................... v
Introduction ............................................................................................................................................ 1
Aim .......................................................................................................................................................... 2
The Context for European Freshwater .............................................................................................. 3
Freshwater Problems in the EU ......................................................................................................... 3
The EU Water Framework Directive ................................................................................................. 5
Methods................................................................................................................................................... 6
General Approach............................................................................................................................... 6
Characterization................................................................................................................................... 6
Description of Data Sources .......................................................................................................... 7
Measuring Water Resources Available within an RBD.................................................................... 8
RBD Water Quality: Classification or Comparison?........................................................................ 8
RBD Ranking System ......................................................................................................................... 9
Selection of Indicators for Ranking ................................................................................................ 9
Ranking the Districts.................................................................................................................... 10
Results ................................................................................................................................................... 12
Correlations between Indicator Datasets........................................................................................... 16
Discussion............................................................................................................................................. 17
Data Limitations and Uncertainty Analysis ....................................................................................... 17
Conclusion and Recommendations................................................................................................... 20
References and Data Sources ............................................................................................................ 21
References......................................................................................................................................... 21
Consulted Sources............................................................................................................................. 22
Map Layer and Information/Dataset Sources.................................................................................. 23
Appendixes.............................................................................................................................................. 1
Appendix I: Graduated Color Maps Illustrating the Indicator and Aggregated Indexes ....................... 2
Appendix II: Indicator Information per River Basin District.............................................................. 12
Appendix III: RBD Characterization Maps ........................................................................................ 23
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Dorothy Furberg
TRITA LWR MASTER
Figures and Tables
FIGURE 1: THE DPSIR FRAMEWORK FOR REPORTING ON ENVIRONMENTAL ISSUES ..................................... 6
FIGURE 2: PRESSURE ON AND STATE OF WATER RESOURCES IN THE EUROPEAN RBDS ............................. .12
FIGURE 3: PRESSURE MANAGEMENT PER RBD.................................................................................................... 14
FIGURE 4: PRESSURE MANAGEMENT INDEX PLOT - STATUS VS. PRESSURE PER RBD ................................... 15
FIGURE 5: EEA QUANTITY MEASURING STATIONS………………………………………....………....…18
FIGURE 6: COMPILED WATER QUANTITY MEASURING STATIONS .................................................................... 18
TABLE 1: RIVER BASIN DISTRICT CHARACTERISTICS AND INDICATORS ............................................................. 7
TABLE 2: PROCEDURES FOR OBTAINING THE INDICATOR AND AGGREGATED INDEXES............................. 11
TABLE 3: REPEATEDLY HIGHEST AND LOWEST RANKED RIVER BASIN DISTRICTS ....................................... 13
TABLE 4: RBD INDICATOR-RESULT DATASET CORRELATIONS ......................................................................... 16
TABLE 5: INDICATOR DATA PER RIVER BASIN DISTRICT ...............................................................13(APPENDIX)
TABLE 6: MAXIMUM AND MINIMUM ANNUAL NUTRIENT CONCENTRATION AVERAGES ........18(APPENDIX)
iv
A BSTRACT
This study gives a first indicator-based assessment of the differences and similarities between the river basin districts
(RBDs) established under the EU Water Framework Directive. The RBDs are intended to be the management units
for EU water, which faces the particular problems of eutrophication and regional water scarcity. Yet no harmonized
or easily comparable data currently exists for this new administrative level. Therefore, there is a need for making an
initial assessment of the RBDs, using a set of identical parameters for the whole continent. The analysis was
performed with the help of geographic information systems (GIS) and information sources such as public domain
GIS databases, environmental monitoring databases and other statistics. A major goal of this project was to rank the
river basin districts according to the pressure on and status of their water resources. The results show a clear northsouth dichotomy and that the most serious water situations occur in Western Europe, although a few regional
variations appear for some indicators. The current assessment was limited in terms of the information available and
more comprehensive assessments of the RBDs for comparison and policy-making purposes are needed.
Keywords: Water Framework Directive; River basin districts; Freshwater resources; Pressure
indicators; Status indicators; Indicator index
S AMMANFATTNING
Denna studie ger en första, indikatorbased bild av skillnader och likheter mellan de nya vattendistrikten i Europa,
etablerade med anledning av EU: s ramdirektiv för vatten. Distrikten har en ny administrativ och geografisk struktur
baserad på avrinningsområden för att bättra hantera specifika vattenproblem, som eutrofiering och brister. I
dagsläget finns ingen jämförbar information för alla distrikt, vilket behövs för att kunna bestämma politiken
angående vattenfrågorna i Europa. Denna studie utfördes därför med ett antal identiska parametrar för hela
kontinenten. Med utgångspunkt i ett antal publikt tillgängliga GIS databaser, miljöövervakningsdatabaser och annan
statistik, har en indikatorbaserad analys gjorts. Studien har lagt tonvikten på påverkans- och vattenstatusinformation.
Resultaten visar en tydlig nord-syd gradient, med flest problemområden i Västeuropa, dock finns det för några
indikatorer mer lokala variationer. Studien begränsades av den tillgängliga informationen och flera djupare studier
inom samma ämne behövs.
v
threats, and when combined with socioeconomic indicators, assess the trade-offs at
stake.
I NTRODUCTION
The Water Framework Directive (WFD) is intended to
be a comprehensive encapsulation of European Union
water policy, providing guidelines and requirements
for how Member States are to manage their water
resources. The overall objective of the WFD is to
achieve “good ecological status” for all waters within a
predetermined timescale, and the Directive stipulates a
number of steps that Member States are to take in
order to reach this goal. Probably the most novel of
these are those pertaining to the establishment and
management of River Basin Districts (RBDs), outlined
in Articles 3, 5, 11 and 13 of the WFD.
Indicators are useful because they simplify complex
issues. They can be used in management decisions and
in establishing long-term monitoring programs.
Regional level indicators can aid in natural resource
management and pollution control (Revenga, 2005).
The European Environment Agency (EEA)
continually uses indicators to assess the state of the
environment (e.g. EEA, 2005a, 2005b and 2005c);
however, the indicators are normally presented per
country and not per river basin or RBD.
As the RBDs are supposed to be the management
units for water in the EU and as no harmonized and
easily comparable data and information currently exist
for this new administrative level, there is an urgent
need for making an initial assessment of the RBDs
established under the WFD, using a set of identical
parameters for the whole region.
In March 2005, Member States had to submit their
first analysis of characteristics of each RBD (Article 5
reporting) to the European Commission. These
reports, along with other information and data
collected during WFD implementation will be
gathered and presented in the Water Information
System for Europe (WISE), currently developed by
the
European
Commission
(http://wise2.jrc.it/wfdview/php/index.php). In the
short term, WISE is intended to serve as a publicly
accessible portal for WFD information. In the long
term, the aim is to develop an information system that
contains most of the relevant information for water
resources and management on a European scale.
Although WISE is a very ambitious initiative for
presenting data and information on the status of
Europe’s water resources, it may be questioned if the
information, when available, will be comparable
between countries. The information in WISE is, to a
large extent, based on WFD reporting by EU Member
States. Taking the so-called Article 5 reports as an
example, the reports have been completed according
to a country’s particular standards (and often are only
available in the country’s own language), and the
standards vary widely, which makes comparison
between RBDs very difficult if not impossible.
Additionally, limited effort appears to have been
placed upon harmonizing the characterization for
international districts. Thus, one can imagine that, for
decision-makers especially, it is very difficult to obtain
a comprehensive idea of how the RBDs throughout
Europe compare to one another.
European policy makers and those who influence
policy need clear, brief, comparable information on
the ecological status of water within the RBDs in
order to make decisions (Revenga, 2003):
Leaders in developing and developed countries
– government, private sector, and civil society –
need timely and targeted environmental
indicators to understand the value and use of
ecosystem goods and services, to analyze
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Dorothy Furberg
TRITA LWR MASTER
A IM
The aim of this master’s thesis is to provide a
preliminary and general description and analysis – a
broad assessment – of the river basin districts
established under the EU Water Framework Directive
using a limited set of spatial indicators (namely
population density, percentage of cultivated land, and
water quality and quantity) to produce statistics that
identify and quantify both basic features and pressures.
The major goal is to produce a ranking of the districts,
illustrating which are currently experiencing more or
less pressure on their water resources and which are in
a “better” or “worse” condition in terms of water
status.
Although the information provided by the indicators
used herein is general, concise and perhaps at times
oversimplified, it can be used to make general
assessments and policy decisions. One could view this
thesis work as a useful first “summary” of the
conditions related to water resources within the EU
river basin districts.
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achieve good status in lakes and rivers…
Agriculture is the largest contributor of
nitrogen pollution… to many surface water
bodies (EEA, 2005b).
T HE C ONTEXT FOR
E UROPEAN
F RESHWATER
These contributions of nitrogen and phosphorus
eventually lead to eutrophication of Europe’s waters.
The Helsinki Commission (HELCOM) points out that
reduction targets for diffuse sources of nutrients such
as agriculture have not yet been fulfilled for the Baltic
Sea catchment area (HELCOM, 2004). While some
nutrient trends in rivers generally improved in Europe
during the 1990s, these improvements have not been
enough to halt eutrophication (EEA, 2005b).
Freshwater Problems in the EU
The WFD’s “overall aim is to maintain and improve
the aquatic environment through attention to quality
issues, b[y] incorporating the control of quantity as an
essential ingredient, recognizing the impact that
inadequate quantity could have on the maintenance of
good ecological quality” (Chave, 2001). This objective
directly influenced the choice of indicators for this
work. In essence, it provided the focus; what factors
most influence water quantity and quality in Europe?
Because one of the greatest proportions of water used
in Europe goes to irrigation and because the return
flow is often salinized and polluted from the use of
fertilizers (Lannerstad, 2002), agriculture constitutes
the biggest diffuse source of water pollution leading to
eutrophication of watercourses (EEA, 2005b).
Domestic water use does not consume or even use
much water relatively speaking, but the quality of the
return flow is of concern, especially when it comes to
its nutrient content and especially if minimal or no
waste water treatment takes place. Industry is also
often a significant point source for water pollution,
but can also simply change the temperature of the
water used, which can negatively impact ecosystems
after it is returned to the surroundings (HELCOM,
2004). Eutrophication will continue to be a major
preoccupation as long as these conditions remain
unchanged. Average nitrogen and phosphorous
concentrations were selected as indicators of available
quality of European waters in this project because they
constitute major pollutant contributions from
population and agriculture (HELCOM, 2004).
Perhaps Europe’s greatest water quality-related
problem is eutrophication, a state which produces
ecological changes that may result specifically in the
loss of plant and animal life and even species, and
more generally in a reduction in the water’s ecological
status. Eutrophication also implies negative impacts
on the use of water for human consumption (EEA,
2005a). The European Environment Agency (EEA)
calls eutrophication “one of the major environmental
problems across Europe” (EEA, 2005c). Europe’s
long-standing preoccupation with this problem can be
seen in the fact that preventing eutrophication and
monitoring nutrient concentrations is the objective of
several directives, such as the Water Framework
Directive (2000/60/EC), the Drinking Water
Directive (98/83/EC), the Nitrate Directive
(91/676/EEC), the Urban Waste Water Treatment
Directive (91/271/EEC) and the Surface Water
Directive (75/440/EEC). The earliest of these
directives was adopted in 1975, thus the EU has been
attempting to solve or at least contain this problem for
more than 30 years.
Water scarcity is a growing problem in river basins
where demand is high relative to the available run-off.
Revenga (2005) reminds us that many experts today
predict “that water availability will be one of the major
challenges facing human society in the twenty-first
century and that the lack of water will be one of the
key factors limiting development.” The EEA (2005b)
predicts that “long-term availability of abundant,
reliable and clean water supplies will become more
important in the context of future land-use planning,
especially around the Mediterranean.” In Europe in
particular, regional availability often does not match
regional demand.
Eutrophication is a condition in which an aquatic
system has high concentration levels of nutrients. The
nutrients, particularly nitrogen and phosphorus, are
supplied to the water from either point sources, such
as direct inputs from municipalities or industries, or
diffuse sources, such as agriculture, managed forestry
and urban areas (HELCOM, 2004). The EEA has
stated in its State and Outlook Report on the
European Environment that:
Agricultural/cultivated land also has a significant
impact when it comes to water quantity issues. One
should keep in mind that 65 percent of all water
withdrawals on earth go towards irrigated agriculture,
and that it consumes on average 70 percent of the
water supplied (Shiklomanov, 2000, as presented in
Discharges of urban wastewater have been a
major source of pollution by phosphorus…
Agricultural sources of phosphorus… are both
important and need increased attention to
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Dorothy Furberg
TRITA LWR MASTER
Lannerstad, 2002). Irrigated agriculture takes up about
30 percent of water use in Europe, and 80 percent of
that water is absorbed by crops and evaporates from
fields (EEA, 2005b).
Another less tangible problem that may negatively
impact the quantity of European water resources now
and in the future is that of climate change. Significant
changes in precipitation patterns possibly linked to
climate change can already be seen, causing flooding is
some areas and droughts in others. There has been a
marked decrease in rainfall in southern and central
Europe, especially in summer, while there has been a
noticeable increase in precipitation during the winter
in some northern countries in recent decades (EEA,
2005b). Water stress, in southern Europe especially,
can be expected to increase if these trends continue, as
reduced rainfall and increased evaporation will
increase demand for water for irrigation in particular.
All people require a certain amount of water to
maintain good living conditions and there is also, so to
speak, a certain amount of water that goes to
agriculture and industry per person. The question is if
the current resources available are enough to meet
people’s needs. Water availability (per person per year)
within each RBD was therefore chosen as an indicator
for examination in this thesis work.
While the EU has attempted in the past through
individual-subject directives to improve the quality and
safeguard the quantity of its waters, it has now
developed a more holistic approach to these problems,
and all its water-related problems, through the
adoption of the Water Framework Directive.
4
The EU Water Framework
Directive
Nilsson and Langaas (submitted) have shown that
there are about 105 RBDs in total. Of these, 33% are
international and area-wise, the international RBDs
constitute 70% of the total area of the districts.
The EU Water Framework Directive (WFD), adopted
in 2000, takes an integrated approach to water
management, as opposed to earlier, more fragmented
and at times conflicting, EU water policy. The WFD is
in effect a compilation and harmonization of five
Environmental Action Programs and several
individual, subject-specific directives that were
developed over the period 1973-2000. Recognizing the
need for an integrated and holistic approach to water
management, the European Commission put forward
a proposal for a framework directive for water in 1997,
which was adopted in 2000 (Chave, 2001).
European countries have a number of tasks to
complete with regard to these units according to the
WFD. The Directive (Article 3.2 and 3.3) required
Member States to set up or identify competent
authorities to implement and enforce the directive
provisions in each RBD that lies within the territory of
the Member State by December 2003. Article 5
specifies that competent authorities must provide a
characterization of the RBDs for which they are
responsible, including details of their natural
characteristics, the impact of human activity on water
status and an economic analysis of water use. The
Article 5 report was to be submitted in 2005. The
RBD characterization is ultimately intended to inform
the development of a “program of measures”,
described in Article 11 and to be enacted by 2009.
This program should include all appropriate measures
for the prevention and control of water pollution as
well as for the use and reuse of water. Finally, the
RBD characterization and program of measures
should form integral parts of the River Basin
Management Plan, which must be developed for each
RBD within nine years of adoption of the Directive
according to Article 13. The plan should contain a
description of the RBD, objectives adopted for and
existing pressures on each water body, as well as the
measures to be taken to achieve the objectives. A
description of the public consultation process
regarding the plan must also be included (Chave,
2001).
As stated earlier, the overall objective of the WFD is
to achieve “good ecological status” in terms of both
chemistry and ecology for all waters within a
predetermined timescale set by the Member States for
their waters. The preamble indicates that common
definitions of water quality and quantity should be
developed but that local variations should also be
taken into account. In addition, the Directive’s main
principles illustrate its novel holistic and integrated
approach to water management. The WFD aims to
(Chave, 2001):

Manage water as a whole on a river basin
basis reflecting the situation in the natural
environment;
Use a combined approach for the control of
pollution, setting emission limit values and
water quality objectives;
Ensure that the user bears the costs of
providing and using water reflecting its true
costs; and
Involve the public in making decisions on
water management.
One of the first key steps Member States needed to
take in order to comply with the directive was to
designate their own water management units known as
River Basin Districts (RBDs). Article 3.1 of the
Directive stipulates that Member States must identify
the river basins that exist within their borders and
assign these to river basin districts. They must also
appoint competent authorities to manage the districts.
A river basin district can be defined as an (Chave,
2001):
Area of land and sea made up of one or more
neighbouring river basins together with their
associated groundwaters and coastal waters which
is identified under Article 3(1) as the main unit for
management of river basins.
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Dorothy Furberg
TRITA LWR MASTER
M ETHODS
The DPSIR framework shows the relationship
between “driving forces and the resulting
environmental pressures on the state of the environment
and impacts resulting from changes in environmental
quality and on the societal response to these changes in
the environment” (Smeets & Weterings, 1999, italics
added). By applying this framework to water problems
in the EU, it becomes clear that population can be a
driving force/pressure indicator for eutrophication
and water availability, agricultural land a driving
force/pressure indicator for eutrophication, and
nitrogen and phosphorus concentrations state
indicators for water quality (eutrophication). The
framework in essence helps to distinguish the roles of
the components contributing to an environmental
problem and thus to identify indicators as opposed to
characteristics.
General Approach
In preparation for this project, relevant spatial and
statistical data was collected from public domain GIS
data websites, information centers and statistical
bureaus. The data sources used were selected based on
two main criteria: their spatial coverage (i.e. the whole
of continental Europe) and public availability. These
sources are described in more detail below.
The layer data collected was harmonized, re-projected,
displayed and statistics extracted using ArcGIS 9
software (ArcMap and ArcCatalog). Statistics in the
various tables presented (water quantity and quality,
land cover, population, etc.) were obtained using
Excel.
A characteristic is specific and consists of “a
distinguishing trait, quality, or property” (MerriamWebster, 2006), “a feature that helps to identify, tell
apart, or describe [something] recognizably”
(Dictionary, 2006). An indicator, on the other hand, is
often described as “an instrument used to monitor the
operation or condition of an engine…, reservoir, or
other physical system.” For the project at hand, the
most apt definition is perhaps “a device for showing
the operating condition of some system” (Dictionary,
2006). In this study, that “device” is a characteristic
that is often accompanied by other conditions,
problems or characteristics and thus indicates that a
specific set of circumstances are likely present within
the RBD in question. The indicators used here give an
idea of the pressures on and status of water resources
within the RBDs.
Characterization
With regard to policy making, use of the simple
DPSIR framework can help in identifying causes and
consequences of problems in the water cycle. It can
also be helpful in understanding and reporting on the
relationship between human influences on the
environment and vice versa (see Figure 1):
Drivers Æ Pressures Æ State Æ Impact
↑
↑
↑
↑
Using the RBDs as spatial units, statistics and
summaries per district were extracted. The
characteristics and indicators used are presented in
Table 1:
Responses
Figure 1: The DPSIR Framework for Reporting on
Environmental Issues
6
Table 1: River Basin District Characteristics and Indicators
Characteristic or
Indicator
Characteristic
Characteristic
Indicator
Characteristic
Type
of
Indicator
Pressure
-
Water Availability
Indicator
Status
Nutrient (N/P)
Concentrations
Indicator/
Characteristics
Characteristics
Indicator
Status
m³/person·
year
mg/l
Pressure
Percent
Percent
Area
Population
Population Density
Average Annual
Discharge
Land Cover
Cultivated Land
Selection and use of the indicators is described in the
section on the RBD Ranking System. A more detailed
explanation of the characterization maps and how the
characteristics were obtained is provided in the
Explanatory Notes in Appendix III. A discussion of
the measurement and comparison methods used for
water quantity and quality follows below.
Units
Data Source
km²
People
People/km²
m³
Nilsson, KTH
LandScan 2003
Calculated
EEA Waterbase Datasets
and others (see Section
VIII)
Calculated
EEA Waterbase Datasets
GLC 2000
GLC 2000
slope, land cover, night-time lights and other input
data (Sullivan, 2004). The LandScan files are available
via the internet in ESRI grid or raster binary format.
In terms of population measurement comparison,
official census counts typically characterize residential
populations rather than the ambient populations
estimated in LandScan (Bright, 2002). The spatial
precision of census verification is thus limited to
province or even country level for most of the world.
LandScan therefore proved appropriate for calculating
population of the RBDs, whose limits by nature do
not follow county or country borders.
Description of Data Sources
The definition of RBDs used for the assessment and
ranking was a dataset created by Nilsson (2005). This
dataset is not an official dataset of the RBDs; it is,
however, based on official information available as of
June 2005 from EU Member States and Candidate
Countries except for Greece and Italy. This dataset is,
to our knowledge, the only harmonized dataset of
RBDs currently in existence. With regard to the river
basin districts in Norway, it should be noted that these
are based on one of two proposals currently under
consideration by the Norwegian government and that
some Norwegian RBD borders are not wholly
accurate, i.e. they do not always follow the watershed
boundary. Since Denmark reconsolidated its river
basin districts in 2005 (Nielsen, 2005), the dataset was
updated according to this new information in February
2006. For a more thorough description of the
development of the dataset, see Nilsson et al. (2004)
and Nilsson and Langaas (2006).
GLC2000, a global land cover database with a one
kilometer spatial resolution, was created by an
international partnership of 30 research groups led by
the European Commission’s Joint Research Centre.
The land cover maps are all based on daily data from
the VEGETATION sensor on-board the SPOT 4
satellite, though mapping of some regions involved
use of data from other Earth observing sensors to
resolve specific issues. Detailed legend definition
(FAO Land Cover Classification System), image
classification and map quality assurance were carried
out region by region, and the global product was made
through aggregation of these (Bartholomé & Belward,
2005). The data are available online free of charge for
all non-commercial applications.
LandScan 2003 Global Population Database was
created by Oak Ridge National Laboratory (ORNL) as
part of its Global Population Project for estimating
ambient populations at risk. It is a worldwide
population database compiled on a 30" X 30"
latitude/longitude grid with a spatial resolution of one
kilometer. Census counts (at sub-national level) were
apportioned to each grid cell based on likelihood
coefficients, which are based on proximity to roads,
The GLC2000 dataset was conducive to this project in
that the land cover classes were aggregated further
once the dataset was obtained. Accuracy studies have
shown that GLC2000 fairs well when classes are
aggregated, while reliability falls as classifications
become more numerous and detailed (Giri et al., 2005).
Since cultivated land was the classification of primary
interest in this study, an aggregation that simplified
extraction of statistics from the dataset was feasible.
7
Dorothy Furberg
TRITA LWR MASTER
1 flow unit = 1 million cubic meters per year
The EEA “Waterbase” Datasets are freely available
from the EEA website. They contain reliable and
policy-relevant data collected from EEA member
countries through the Eionet-Water process, which
selects validated monitoring data from national
databases and adds information on the physical
characteristics of the water bodies being monitored
and on the pressures potentially affecting water
quality. The data collected through the Eionet-Water
process are from statistically stratified monitoring
stations and are comparable at the European level.
Waterbase data are primarily used in the production of
the EEA's indicator-based fact sheets (EEA Data
Service, 2006).
These standards have been adapted to the quantity
indicator used in this study (water availability) as
shown below:
No water stress = 10,000 m3/person·yr or more
Risk of water stress = between 9999 and 1667
m3/person·yr
Water stress = less than 1667 m3/person·yr.
RBD Water Quality: Classification or Comparison?
While an outside standard could be used to measure
and compare water availability, finding an appropriate
measure for comparison of water quality proved more
difficult.
Measuring Water Resources Available within an RBD
Average yearly runoff is the measurement that
provides the best means of gauging how much water is
available for use within a given river basin district.
This is because “[t]he renewal rate, not the global
volume of water at a given time determines how much
water is available for use” (Falkenmark and Widstrand,
1992). In order to know how much water one can
sustainably extract from a lake, one must know the
replenishment rate of that lake. Groundwater also has
a connection to run-off in that “[g]roundwater
eventually seeps to the surface in springs, or flows into
rivers” (Falkenmark and Widstrand, 1992).
In 1991, the Swedish Environmental Protection
Agency published a set of guidelines for monitoring
the quality of water in Sweden. The guidelines
particularly addressed quality issues stemming from
nutrients, oxygen and oxygen demanding substances,
light and metals (Swedish EPA, 1991). In particular,
they set class designations based on quantity of total
nitrogen (range: 0.3 – 1.5 mg/l N) and phosphorus
(range: 0.0075 – 0.05 mg/l P). These standards were
designed for and have been used for classifying
Swedish waters. When an attempt is made to widen
the classification to European waters, it becomes
apparent that these standards are strict, as most
European waters fair poorly when these standards are
applied.
This thesis work uses the greatest annual river runoff
measured at medium to low altitude stations as an
estimation of available water resources within an RBD.
If the RBD contains more than one river basin, the
greatest runoff from each basin is selected and these
are summarized for the RBD as a whole. Reasons for
this choice include the fact that this information was
available and that several hydrologists agree that this is
a valid unit for estimation (Lannerstad, 2002):
Several EU directives specify acceptable concentration
levels of nitrate (NO3), which are quite a bit higher
than the Swedish standards (EEA, 2005a):
Drinking Water Directive: maximum allowable
nitrate concentration = 50 mg/l;
Surface Water Directive: guideline nitrate
concentration = 25 mg/l;
Nitrates Directive: must identify waters with
annual average nitrate concentrations of 50
mg/l or higher.
Since river systems… contribute to more
than 90 percent of the global water supply
used by mankind, the mean annual river
runoff is the logical basis for determining
availability and deficits of water resources in
any large region of the world.
In order to determine how much water is enough
water, population within a district was compared to
the amount of water available and the following
standards were applied (Falkenmark and Widstrand,
1992):
These levels are, it seems, representative of a “worst
case scenario”; that is, they indicate that the state of
the water is so poor that action must be taken and that
the water cannot, for the time being, be used as it
normally would. These limits are too high for the
project at hand, where a range of quality levels should
be apparent. The Swedish standard is, on the other
hand, too stringent to be used for Europe-wide
assessment of the quality of water with regard to
nutrient concentrations. Water quality will therefore
Low = or < 100 people per flow unit
Medium (quality or dry season problems) = 100-600
people per flow unit
High (water stress) = 600-1000 people per flow unit
8
only be compared between RBDs and no outside
standard will be applied.
are devoted to agriculture. The percentage of
cultivated land within an RBD can give an indication
of the pressure on water resources in that district,
since it provides an indication of the relative amount
of nutrient pollution from agriculture one might
expect to find in the area.
RBD Ranking System
Selection of Indicators for Ranking
Population is the next important factor when looking
at pressures on water quantity and quality. Population
density was therefore also selected as a key indicator
for pressure placed on water resources. Population
density is the average number of people per square
kilometer and was obtained by dividing the population
of the district by the district area. Population per
district was extracted from LandScan 2003, available at
a resolution of one kilometer.
Spatial indicators are ideal communication tools
to raise awareness of the condition of the
Earth’s
ecosystems
among
different
audiences… Regional spatial indicators not
only inform us about the current condition of,
and pressures on, ecosystems, but also about
the likely capacity of the ecosystem to continue
to provide goods and services to future
generations. (Revenga, 2005)
State indicators
To obtain an idea of the actual state of water resources
in Europe, quality and quantity indicators were
chosen. Water availability was selected as an indicator
of the available quantity of European waters. Run-off
figures, when compared with population, provide
information on roughly how much water is available
per person within a district. Water availability is the
amount of water available per person per year. It was
calculated by dividing the average annual discharge by
the district population. The average annual discharge
per district was obtained from EEA water quantity
datasets and others (see section References and Data
Sources).
The list of indicators of interest for use in this kind of
study is extensive. Below are listed just a few of the
many
considered
for
the
indicator-based
characterization of the RBDs:
•
Health (occurrence of specific diseases, child
mortality rate, etc.);
•
Income (GDP per capita);
•
Acid emissions;
•
Groundwater abstraction;
•
Percentage of population connected to
wastewater treatment plants;
•
Municipal waste (landfill);
•
Water use/consumption;
•
Livestock population density;
•
Moisture index;
•
Protected areas;
Measurements of nutrients, specifically phosphorous
and nitrogen concentrations, were selected as
indicators of quality of European waters. Ammonium,
as representative of nitrogen concentrations, and
orthophosphate, as representative of phosphorous
concentrations, were used to fill information gaps
because measurements of these elements were
available from nearly all the RBDs. The EEA has also
used orthophosphate and ammonium measurements
as a gauge of total phosphorous and nitrogen in rivers.
These indicators “can be used to illustrate
geographical
variations
in
current
nutrient
concentrations.” (EEA, 2005a and 2005b) The mean
total nitrogen concentration, NCavg, or mean total
phosphorus concentration, PCavg, are the mean total
nitrogen, mg/l, or mean total phosphorus, mg/l,
concentrations from all the quality monitoring stations
in the district at which total nitrogen or phosphorus
measurements were taken. Measurements from the
most recent available six or seven years per station
were used. Where total nitrogen and/or phosphorus
data were missing, total ammonium and/or
orthophosphate were used. All information on
nutrient concentrations was provided by EEA water
quality datasets.
• Flooding frequency.
Ultimately, the choice of indicators was most
influenced by what information was available and by
what currently seem to be the biggest freshwaterrelated problems in Europe: water quality as affected
by agriculture and the addition of nutrients and water
quantity as affected by population and amount of
runoff available. Please see the section entitled
“Freshwater Problems in the EU” for more
information about the freshwater situation in Europe.
Pressure indicators
It is important to get some idea of the pressures being
placed on Europe’s water resources. Percentage of
agricultural/cultivated land constitutes a primary
indicator for measuring pressure on water resources,
and was thus chosen as one of the indicators for
examination in this project. The geographic land cover
layer selected, GLC 2000, provides information on
how the land in Europe is used, including which areas
9
Dorothy Furberg
TRITA LWR MASTER
same applied when combining the population density
index and cultivated land index in order to obtain an
aggregated “pressure” index.
Ranking the Districts
A number of possible means of ranking the RBDs
were explored, but few proved to be appropriate for
the task at hand. Among these were Decision Tree
Analysis, the Analytical Hierarchy Process and
Multicriteria Evaluation. The problem with all three of
these methods was that some sort of weighting system
(giving relative importance to each of the indicators)
would have to be used. To obtain a valid weighting
system, experts on European water resources would
need to be consulted and this task simply went beyond
the scope of the thesis.
Dividing a RBD’s score from the aggregated pressure
index by its score in the aggregated status index gives
an idea of how much greater or smaller the pressures
within the RBD are compared to the status of its
waters, and thus also gives an indication of how well
an RBD is managing the pressures that are being
placed on its water resources. This calculation
provided the Pressure Management Index. In addition,
a number of correlations were performed among the
resulting datasets.
Ultimately, a decision was made to carry out two basic
relative rankings in Excel: one in terms of pressure
placed on the water resources and one based on the
current status of the waters within the RBDs. The
pressure ranking is composed of two factors:
percentage of agricultural/cultivated land and
population density. The status ranking is also
composed of two factors: water quality measured by
concentrations of nutrients and water quantity
measured in m3/person·year.
Excel was used to perform simple and composite
rankings of the RBDs based on their indicator scores.
The procedures for obtaining the rankings are
illustrated in Table 2. For each category (indicator), the
average of all the RBD scores was calculated and each
score was then divided by the average. This provided a
number that revealed how many times worse or better
the RBD’s score was in comparison to the category
average, and it is this number that gives the RBD its
rank. This series of operations was performed to
obtain the indicator indexes, namely a population
density index, a water availability index and a
cultivated land index (index referring to the fact that
the ranking is based on the scores’ relationship to the
average). Since a higher score means worse ranking in
each of the categories, scores for water availability
were converted from m3 per person per year to
persons per billion m3 per year.
With a few extra calculations, a nutrient index was also
obtained. Given that there are four nutrient categories
(Ammonium,
Nitrogen,
Phosphorus
and
Orthophosphate) and that a relative score could be
calculated in each category for almost all RBDs, it was
possible to take the average of these per district to
obtain a relative overall “nutrients” score for each
RBD.
The same method was used to combine the water
availability and nutrient indexes and thereby obtain an
aggregated “status” index. The scores from the
indicator indexes for a given RBD were averaged to
provide its score in the aggregated status index. The
10
Table 2: Procedures for Obtaining the Indicator and Aggregated Indexes
Population Density Index for RBDn =
Population Density (pers/km2) for RBDn
AVG (Population Density for all RBDs)
Cultivated Land Index for RBDn =
Cultivated Land (%) for RBDn
AVG (Cultivated Land for all RBDs)
Water Availability Index for RBDn =
Water Availability (pers/m3·yr) for RBDn
AVG (Water Availability for all RBDs)
Water Quality Index for RBDn =
(
Nitrogen (mg/l) for RBDn
AVG (Nit. for all RBDs)
+
Phosphorus (mg/l) for RBDn
AVG (Phos. for all RBDs)
Orthophosphate (mg/l) for RBDn
AVG (Orthophos. for all RBDs)
+
+
Ammonium (mg/l) for RBDn
AVG (Ammon. for all RBDs)
4*
*This number depends on what nutrient information is available for each RBD. For example, for several districts in Ireland, no nitrogen or phosphorus information was
available, so the average of only their orthophosphate and ammonium indexes is taken.
Aggregated Status Index for RBDn = (Water Availability Index for RBDn + Water Quality Index for RBDn) ÷ 2
Aggregated Pressure Index for RBDn = (Population Density Index for RBDn + Cultivated Land Index for RBDn) ÷ 2
Pressure Management Index for RBDn = Aggregated Pressure Index for RBDn ÷ Aggregated Status Index for RBDn
11
)
Dorothy Furberg
TRITA LWR MASTER
graduated color maps that were created based on the
rankings. A map was created for each of the rankings
performed in excel: for the indicator indexes (nutrient
concentrations, water availability, population density
and percentage of cultivated land), for the aggregated
indexes (combined status and combined pressure) and
for the Pressure Management Index (PMI). These are
all located in Appendix I. The maps showing
combined pressure (agricultural and population) on
and combined status (quality and quantity) of water
resources are shown in Figure 2:
R ESULTS
Using the RBDs as spatial units, statistics and
summaries
per
district
are
presented
in
characterization maps in Appendix III.
Perhaps the clearest way to view the rankings and to
get a feel for the results of this study is through the
Figure 2: Pressure on and State of Water Resources in the European RBDs
12
From the individual indicator and combined
pressure/status maps, it becomes clear that there are
some problem districts, if not regions, when it comes
to water quantity and quality and pressures on water
resources. These maps make visibly tangible what
comes across again and again in the rankings; that a
core group of districts repeatedly performs well with
regard to the indicators when compared to the other
districts, and another core group repeatedly fairs
poorly. These groups are listed in Table 3 below.
As can be seen from the table, the RBDs in
Scandinavia (excluding Denmark) fair well when
compared to the rest, while various problem areas
appear in Western Europe. As might be expected
given the information contained in the preceding
maps, Western Europe dominates the list of RBDs
with the greatest percentage of cultivated land and
highest population density, which often translates into
regional hotspots for high nutrient concentrations in
rivers and periodic water stress.
Table 3: Repeatedly Highest and Lowest Ranked River Basin Districts
Repeatedly highest
ranked RBDs:
River Basin District
Countries sharing RBD
Bothnian Bay (1)
Bothnian Sea (2)
Telemark (8)
Sogn og Fjordane (12)
Nordland (16)
Troms (17)
Finnmark (18)
Oulankajoki (22)
Kemijoki (23)
Finland, Sweden
Norway, Sweden
Norway
Norway
Norway
Norway
Finland, Norway, Russia
Finland, Russia
Finland
Numbers in brackets are used for internal reference.
Repeatedly lowest
ranked RBDs:
River Basin District
Countries sharing RBD
Humber (35)
Anglian (36)
Thames (37)
Southeast (39)
Denmark East (63)
Denmark West (66)
Scheldt (75)
Catalonia (90)
Segura (95)
Great Britain
Great Britain
Great Britain
Great Britain
Denmark
Denmark
Belgium, France, Netherlands
Spain
Spain
13
Dorothy Furberg
TRITA LWR MASTER
A more nuanced picture emerges if one looks at the
PMI map (see Figure 3). It indicates that some RBDs
with high pressure on water resources are actually
handling this pressure rather well. This is the case for
the Rhine RBD and for some RBDs in Denmark, for
example. On the other hand, the PMI map also reveals
where pressure management is either minimal because
it is not needed or very weak where it is badly needed.
Several RBDs in southern Spain are not handling their
pressures at all well, since these are not as extreme as
elsewhere in Europe, but still lead to some of the
poorest water quality and quantity conditions. In
addition, it becomes clear that pressure management is
minimal because pressures are low to begin with in
some northern European RBDs. Thus the state of
water resources there is comparatively good without
the authorities having to intervene to protect them.
This is the case for several districts in Sweden and
Finland.
Figure 3: Pressure Management per RBD
14
One can also get a feel for where the RBDs lie in
terms of pressure on and status of water resources
through the graph in Figure 4. The central line
represents the average of the PMI index. Those
districts that have a high pressure index and low status
index, such as Denmark West, are doing very well in
terms of pressure management. The reverse is true of
those who have a low pressure index and high status
index.
It could be considered reassuring that the majority of
RBDs are plotted below the average line, signifying
that for this majority, pressure on water resources is a
more significant problem than the actual state of their
water resources. The caution here is to note that the
state of water resources in certain RBDs is so poor
that their scores pull the average line up a bit, and thus
cause more RBDs to be plotted below the line. In
essence, they make the other RBDs “look better” than
may actually be the case.
5
RBDs
The two lowest ranked RBDs (Segura and
Denmark East) are not shown here because they
are located beyond the extent of the current
scale.
Denmark West
More og Romsdal
4
Eider
Schlei/Trave
Sor- og Trondelag
Guadalquivir
y = 1.04x - 0.03
Rogaland
3
Status Index
Eastern
Eider
Guadalquivir
2
North Western
Thames
Kemijoki
1
Schlei/Trave
Denmark
West
Northumbria
Eastern
Thames
0
Northumbria
0
1
2
3
4
5
Pressure Index
Figure 4: Pressure Management Index Plot - Status vs. Pressure per RBD
15
Dorothy Furberg
TRITA LWR MASTER
The highest correlations occur between the various
nutrients and percentages of cultivated land, with the
strongest correlation of 69% occurring between total
nitrogen and cultivated land percentage. There is also a
strong connection between total nitrogen and
population density and a negative correlation between
the various nutrients and water availability, as can be
seen from Table 4 below:
Correlations between Indicator
Datasets
The correlations performed between the district
indicator-result datasets are interesting in that they
tend to justify the choice of indicators for this study.
Table 4: RBD Indicator-result Dataset Correlations
Nutrient vs. Land Cover Correlations
Forest
Total Ammonium
(TA)
Total Nitrogen
(TN)
Total Phosphorus
(TP)
Orthophosphate
(OP)
Shrub Cultivated Mosaic
Bare
Areas
Water Snow Artificial
Bodies and Ice Surfaces
-0,05
-0,2067
0,35
-0,11
-0,16
-0,28
-0,11
0,02
-0,54
-0,13
0,69
0,2102
-0,38
-0,33
-0,24
0,37
-0,25
-0,06
0,37
-0,15
-0,16
-0,28
-0,10
0,10
-0,38
0,01
0,42
-0,16
-0,20
-0,31
-0,14
0,42
*Critical Value of r
(CVr) =
-0,1829 +/-0,2172 0,1829 +/-0,2172 -0,1829 -0,1829 -0,1829 0,1829
Statistically
Significant?
Yes: all
No
Yes: all
Yes: TN
and OP Yes: all
Yes:
TN
Yes: TN
and OP
Nutrient vs. Water Availability
Correlations
Nutrient vs. Population Correlations
Population
Total Ammonium (TA)
0,10
Total Nitrogen (TN)
0,25
Total Phosphorus (TP)
0,07
Orthophosphate (OP)
0,10
No
Population
Density
0,12
0,51
0,14
0,40
Total Ammonium
Total Nitrogen
Total Phosphorus
Orthophosphate
m3/pers/yr
-0,20
-0,44
-0,1858
-0,24
CVr =
0,1829
0,1829
CVr =
-0,1829
Statistically Significant?
Yes: TN
Yes: TN and
OP
Statistically Significant?
Yes: all
*The correlation must be greater than (if positive) or lesser than (if negative) the critical value of r if the result is to be classified as
statistically significant. A positive correlation exceeding the CVr means that there is only a 5% chance that the correlation is a
random occurrence and thus we can be reasonably confident of the results. (Trochim, 2002)
16
from the agricultural and tourist industries in
particular, is at its peak.
D ISCUSSION
Yet it is important to note that the water availability
indicator (m3/pers·yr) does not account for the
coping capacity of different countries to handle water
shortages (Revenga, 2005). Almost no country uses all
of its available water resources, either because it does
not need to or because it is too expensive to
“mobilize” the rest (Falkenmark, 1992). Spain, as an
alternative to bulk transfer of water, intends to
increase its desalinization capacity in order to meet
water demand in particularly stressed basins (EEA,
2005b). Thus there are alternatives for meeting
demand in Europe’s water stressed regions; it is simply
a question of mobilizing resources and technology in
those areas.
While the north-south gradient evident in the ranking
of the RBDs comes as no real surprise, it is interesting
to note that the repeatedly lowest ranked RBDs are
located exclusively in Western Europe. One might
have expected the major problem areas to appear in
Eastern Europe in the new Member States due to the
previous lack of environmental protection for water
resources, particularly during the Soviet era. Certain
factors may help to explain these results. Agricultural
and industrial activities declined sharply in the former
east bloc in the early 1990s and have only recently
started to increase again. Domestic water use is only
around 40 cubic meters per person per year, compared
to the EU average of 125 cubic meters (EEA, 2005b).
Use is expected to increase, however, as standard of
living rises in these countries. It could be that
pressures on water resources were reduced following
the dissolution of the Soviet Union, which might
explain why the new Member States have faired
relatively well in the rankings, both in terms of
pressure and status. Yet this is only one hypothesis
that requires further investigation. Another caution in
this regard is that almost no quality information for
Russia and Belarus was publicly available, and thus
districts that share parts of these countries may not be
accurately ranked. This will be addressed in more
detail in the next section on data limitations and
uncertainty.
With regard to nutrient concentrations in particular
basins, it is important to recall that this study does not
provide information about trends over time. Since
1992, nitrate concentrations have fallen most markedly
in Denmark, Germany and Latvia (EEA, 2005b). This
is welcome news in light of the fact that RBDs within
these countries are often ranked lowest in terms of
nutrient concentrations. Not all trends are positive,
however. Phosphate levels in the Duero in Spain, for
example, have deteriorated over the past 25 years
(EEA, 2005b).
What may at first glance look like a “water stressed”
region could in reality be an area whose inhabitants
would claim that there are no signs of water stress.
This study did not take groundwater resources into
account and thus some areas that may be ranked as
“water stressed” are in actuality drawing heavily upon
their groundwater stores to compensate for a lack of
surface water. This is the case for Germany and
Denmark (EEA, 2005b). By contrast, Norway uses
almost exclusively surface water resources since they
are so plentiful (EEA, 2005b). But regions that draw
heavily upon groundwater run the risk of lowering the
groundwater table, since groundwater stores take years
if not decades to recharge (Voigt et al., 2004) and of
saltwater intrusion into coastal aquifers, which occurs
particularly in the Mediterranean region (EEA, 2005b).
In this case, the “water stress” ranking is more a signal
that theirs is not a sustainable situation in terms of
water use, even if no immediate warning signs have yet
appeared.
Data Limitations and Uncertainty
Analysis
A particular challenge in this study was the fact that
different types of information had to be obtained from
different sources and then combined to give the
results. The most reliable datasets available were at
times incomplete and had to be complemented with
information from other sources. This was the case for
the EEA water quantity dataset. As can be noted in
Figure 4, measuring stations and runoff information
were missing for Romania and concerned areas of
Serbia and Montenegro, Bosnia and Herzegovina,
Croatia, Belarus and Russia that border the EU (which
is not so surprising given that the EEA would only
seek to provide information concerning EU Member
States). What is surprising, however, is that data for
the Czech Republic, Ireland, Belgium, Poland,
Portugal and Sweden (all EU Member States) are
missing, which constitute large gaps of information in
the dataset. It was therefore necessary to seek quantity
(runoff) information for these regions from other
sources (See Figures 5 and 6). Combining different
datasets for the same indicator is not ideal, and the
results may thus not be as accurate as they otherwise
could have been due to these conditions. But the
80% of water used in European agriculture is
absorbed by crops and evaporates from fields (EEA,
2005b). Evaporative demand is highest in southern
Europe: Spain’s, for example, is twice that of Sweden’s
(Falkenmark, 1992). This condition compounds the
problem of water shortages; when temperatures are
highest, water quantity is at its lowest, while demand,
17
Dorothy Furberg
TRITA LWR MASTER
results are still a useful indication of the state of
water resources in each of the districts, which
can be investigated and complemented with
more in-depth and rigorous studies in the
future.
Figure 5: EEA Quantity Measuring Stations
Figure 6: Compiled Water Quantity
Measuring Stations
18
Turning to our attention to quality issues, the EEA
water quality dataset lacks information for Portugal
and areas of Belarus and Russia that fall within the
borders of some RBDs. Thus the nutrient averages for
the following districts may not be accurate:
•
Finnmark/Tenojoki (18)
•
Vuoksi (19)
•
Oulankajoki-Perameri (22)
•
Daugava (48)
•
Nemunas (49)
•
Douro (83)
•
Tejo-Sado (85)
•
Guadiana-Mira (86)
•
East Estonia (97)
The nutrient averages would in all likelihood be
higher, given the added inputs and level of water
resources protection in these areas, which tends to be
lower than the European average (Library of
Congress, 1996 and Liga, 2003).
19
Dorothy Furberg
TRITA LWR MASTER
EEA (2005b) points out a “lack of common European
definitions and procedures for calculating water
abstraction and freshwater resources.” It is therefore
of vital importance to establish at least common
techniques for water resource accounting and for
estimating water quality as soon as possible. The
intercalibration process outlined in the WFD may
eventually provide these common techniques for
measuring water quality, but the process will (even if
things go smoothly) take years to complete. Interim
common techniques would be very useful for research
on water resources being conducted now and in the
near future.
C ONCLUSION AND
R ECOMMENDATIONS
When looking at the results of this kind of study, one
must keep in mind that “[n]o two rivers are alike; and
no single indicator captures all the factors” (EEA,
2005b). The indicators used for the characterization of
the RBDs provide a general idea of the water resource
conditions in that district – they do not, of course,
account for all the problems or positive conditions
that may also exist. Neither do they show trends over
time, i.e. improvements or degradation of water
resources over the past decade.
It is also widely recognized that there is a lack of
available and/or organized information or datasets for
the kind of study described in this thesis. Revenga
(2005), for example, addresses the issue:
The aim of this thesis is to provide a very general
assessment of the river basins districts created under
the EU Water Framework Directive in order to be
able to compare the districts and assist policy makers
in identifying regions that are most in need of support
in terms of reaching the goal of “good ecological
status” for Europe’s water resources. For the most
part, the aim of providing a general assessment of the
river basin districts was achieved. The results reveal
clear patterns in that, on the whole, northern waters
fair well in a Europe-wide comparison while southern
waters fair poorly in terms of pressure on and status of
water resources. The comparison and rankings also
highlight some regional hot spots. The identification
of these hot spots could be helpful in promoting
cooperation between river basin districts, while
geographically distant, may have common water
problems and information to exchange concerning the
solutions. Time and resources did not allow for all
ambitions for the project to be realized, however. In
the end, it was necessary to set aside the suggestion of
adding other indicators, such as percentage of district
population connected to wastewater treatment plants
and obtaining the GDP per RBD. Perhaps these could
be taken up in a more extensive characterization in the
future.
Much of the limitations of the current
technologies is also accompanied by a lack of
basic data and information, regardless of its
georeferences characteristics… Some notable
examples of these needed datasets include…
data on surface water quantity and quality by
river basin… [and] water use statistics by river
basin instead of by national jurisdiction.
These are precisely the datasets that would be most
useful for the type of study just conducted. Yet these
are also the datasets that are often missing or
incomplete. The EEA (2005c) itself acknowledges the
lack of data:
The northwestern part of Europe is generally
well covered by source apportionment studies,
but there is a shortage of information from the
Mediterranean countries and some eastern
European countries.
It is therefore of utmost importance that reliable and
complete datasets of the type Revenga suggested be
compiled as soon as possible. This would greatly
improve the reliability of studies of this kind and help
to provide a more accurate picture of the state of
water resources in Europe today. In turn, this more
accurate information and reporting would, it is hoped,
help to produce more effective and appropriate water
policy and intervention measures.
What made this study interesting but also particularly
challenging was its lack of precedent. Common
European, or even regional, definitions for what
constitutes good or poor status of water resources are
conspicuously absent, being left to individual Member
States to decide for themselves. Even technical
methods in measuring water resources are missing; the
20
R EFERENCES AND DATA S OURCES
References
Bartholomé, E. and A.S. Belward. 2005. “GLC2000: a new approach to global land cover mapping from Earth
observation data.” International Journal of Remote Sensing Vol. 26, No. 9, 1959-1977. Retrieved June 2006
from
http://www.ingentaconnect.com/content/tandf/tres/2005/00000026/00000009/art00012;jsessionid=dgaf
edq62452.alice.
Bright, E.A. 2002. LandScan Global Population 1998 Database. Oak Ridge National Laboratory. Retrieved June
2006 from http://www.ornl.gov/sci/landscan/landscanCommon/landscan_doc.html.
Chave, P. 2001. The EU Water Framework Directive: An Introduction. London: IWA Publishing.
Dictionary.com. 2006. “Indicator.” “Characteristic.” Retrieved June 2006 from http://dictionary.reference.com/.
EEA, 2005(a). CSI 020 Specification – Nutrients in Freshwater. Copenhagen: EEA. Retrieved February 2006 from
http://themes.eea.eu.int/IMS/ISpecs/ISpecification20041007131957/guide_summary_plus_pub
EEA, 2005(b). The European Environment: State and Outlook 2005. Luxembourg: Office for Official Publications
of the European Communities.
EEA, 2005(c). Source Apportionment of Nitrogen and Phosphorous Inputs into the Aquatic Environment.
Copenhagen: EEA. Retrieved February 2006 from
http://reports.eea.eu.int/eea_report_2005_7/en/EEA_report_7_2005.pdf
EEA Data Service. 2006. “Waterbase – Water Quantity” and “Waterbase – Rivers.” Retrieved June 2006 from
http://dataservice.eea.europa.eu/dataservice/metadetails.asp?id=833.
Falkenmark, M. and C. Widstrand. 1992. Population and Water Resources: A Delicate Balance. Population Bulletin, Vol. 17,
No. 3. Washington, D.C.: Population Reference Bureau, Inc.
Giri, C., Z. Zhu and B. Reed. 2005. “A Comparative Analysis of Global Land Cover 2000 and MODIS Land Cover
Data Sets.” Remote Sensing of Environment 94 (2005) 123-132. Retrieved June 2006 from
http://www.gpa.uq.edu.au/courses/GEOM/2000/giri_modis_lcvalidation_RSE_2005.pdf.
HELCOM and the Baltic Marine Environment Protection Commission, 2004. “Baltic Nutrient Pollution to the
Baltic Sea in 2000.” Baltic Sea Environment Proceedings No. 100. Retrieved February 2006 from
http://helcom.navigo.fi/stc/files/Publications/Proceedings/bsep100.pdf
Lannerstad, M. 2002. Consumptive water use feeds the world and makes rivers run dry. Stockholm: KTH. TRITALWR Master Thesis: 02-13.
Library of Congress. 1996. Russia: Water Quality. Country Studies. Retrieved May 10 from
http://reference.allrefer.com/country-guide-study/russia/russia62.html.
Liga para a Protecção da Natureza. 2003. “Results Overview for Portugal.” WWF Water and Wetland Index – Critical
issues in water policy across Europe. Retrieved May 10 from
http://assets.panda.org/downloads/wwiportugal.pdf.
21
Dorothy Furberg
TRITA LWR MASTER
Merriam-Webster Online. 2006. Retrieved June 2006 from http://www.m-w.com/.
Nilsson, S., S. Langaas and F. Hannerz. 2004. “International River Basin Districts under the EU Water Framework
Directive: Identification and Planned Cooperation.” European Water Management Online, 2004/02,
http://www.ewaonline.de
Nilsson, S and S. Langaas. 2006. International river basin management under the EU Water Framework Directive: An assessment
of cooperation and water quality in the Baltic Sea Drainage Basin. (Submitted to Ambio).
Revenga, C. 2005. Developing indicators of ecosystem condition using geographic information systems and remote sensing. Regional
Environmental Change (2005) 5: 205-214. Springer-Verlag.
Smeets, E. and R. Weterings. 1999. Environmental Indicators: Typology and Overview. Technical Report no. 25.
Copenhagen: EEA.
Sullivan, A. 2004. ORNL Releases LandScan 2003 Global Ambient Population Database. Oak Ridge National
Laboratory. Retrieved June 2006 from
http://www.directionsmag.com/press.releases/index.php?duty=Show&id=10323&trv=1.
Swedish Environmental Protection Agency. 1991. Quality Criteria for Lakes and Watercourses: A system for
classification of water chemistry and sediment and organism metal concentrations. Solna, Sweden: Ingvar
Bingman.
Trochim, W.M. 2002. Research Methods Knowledge Base: Correlation. Retrieved May 3 from
http://www.socialresearchmethods.net/kb/statcorr.htm.
Voigt, H.-J., T. Heinkele, C. Jahnke and R. Wolter. 2004. Characterization of Groundwater Vulnerability to Fulfill
Requirements of the Water Framework Directive of the European Union. Geofísica Internacional (2004), Vol. 43,
Num. 4, pp. 567-574. Retrieved May 10 from http://www.tucottbus.de/umgeo/forschung/projects/geoficica_international_2004.pdf.
Consulted Sources
De Badts, E. 2002. GLC2000: Evaluation of the SPOT VEGETATION Sensor for Land Use Mapping. Green
World Research. Retrieved June 2006 from http://wwwgvm.jrc.it/glc2000/Products/nw_europe/nweurope2000_report.pdf.
EU Commission. 2000. “Directive 2000/60/EC of the European Parliament and of the Council establishing a
framework for the Community action in the field of water policy.” Official Journal of the European
Communities (L 327). Retrieved June 2006 from http://www.wfdinfo.org/Water%20framework%20directive%20UK.pdf.
Gustafsson, J.-E. 1989. Vattenförvaltning i Frankrike. Rapport R21:1989. Byggforskningsrådet.
Hannerz, F. 2002. Harmonisation and applications of transboundary geographic databases of the Baltic Sea Region:
drainage basin characterisation. Master of Science Thesis. Retrieved October 2005 from
http://www.lwr.kth.se/personal/personer/hannerz_fredrik/docs/Fredrik%20Hannerz%20Thesis%20med
ium%20res.pdf.
Heffner Media Group. 2003-2004. Statistics Primer: Critical Values of r. Retrieved May 2006 from
http://allpsych.com/stats/unit4/37.html.
22
Holda, A. 2005. An Analysis of the Environmental Management Elements of the Water Framework Directive and its
Implementation Components. Hamburg University of Technology. Retrieved February 2006 from
http://bin.tec-hh.net/watersketch/pub/WP1%20Directives%20and%20Conventions/Thesis_Holda.pdf
McConnachie, D. 2002. GLC2000Baltic Study: Classifying land cover using GIS, satellite remote sensing and
decision tree analysis. TRITA-LWR Report 3002. ISSN 1650-8610 ISRN KTH/LWR/REPORT 3002-SE
ISBN 91 7283-418-8.
Revenga, C., S. Murray, J. Abramovitz and A. Hammond. 1998. Watersheds of the World: Ecological Value and
Vulnerability. World Resources Institute. ISBN 1-56973-254-x.
Sullivan, C. 2002. Calculating a Water Poverty Index. World Development. Vol. 30, No. 7, pp. 1195-1210. Elsevier
Science Ltd. Retrieved February 2006 from http://www.sciencedirect.com/science/article/B6VC645MDRWD-3/2/013a361da21bd0b59689f77ada5fe6bf.
Yoffe, Shira. 2001. Basins At Risk: Conflict and Cooperation Over International Freshwater Resources. Oregon State
University. Ph.D. Dissertation. Retrieved October 2005 from
http://www.transboundarywaters.orst.edu/projects/bar/index.html.
Map Layer and Information/Dataset Sources
o
o
o
o
o
o
European country layer: ESRI, retrieved November 2005, http://www.esri.com;
RBD layer: Susanna Nilsson, KTH,
http://www.lwr.kth.se/personal/personer/nilsson_susanna/index.htm;
River basin layer: Joint Research Centre Catchment Database, retrieved November 2005,
http://agrienv.jrc.it/activities/catchments/ccm.html;
Land cover layer: Global Land Cover 2000, ECDG Joint Research Centre, retrieved November
2005 from http://www-gvm.jrc.it/glc2000/Products/fullproduct.asp;
Population layer: LandScan 2003, Oak Ridge National Library, retrieved November 2005 from
www.ornl.gov/sci/gist/landscan;
River water quality and water quantity datasets:
“Waterbase – Rivers” and “Waterbase – Water Quantity”, European Environment Agency,
retrieved November 2005 from
http://dataservice.eea.eu.int/dataservice/available2.asp?type=findtheme&theme=water;
CISL'S Research Data Archive (Quantity dataset from across Europe, of particular
importance for this project was the information for Portugal, Poland and Russia), retrieved
February 2006 from http://dss.ucar.edu/datasets/ds552.0/;
The BALTEX Hydrological Data Centre and SMHI (Sweden and some Russia water
quantity dataset), received March 2006 from
http://www.smhi.se/en/menyer/ind_hydro.htm;
The National Water Archive (Scotland, England and Northern Ireland water quantity
dataset), retrieved March 2006 from http://www.nercwallingford.ac.uk/ih/nwa/index.htm;
Wasser Blick (quantity datasets for the north of Germany), retrieved April 2006 from
http://www.wasserblick.net/servlet/is/28997/?lang=en;
Ireland’s Environmental Protection Agency (quantity data for Ireland), retrieved March
2006 from
http://www.epa.ie/PublicAuthorityServices/HydrometricProgrammeandSurfaceWaters/;
Banque HYDRO (quantity data for France), retrieved March 2006 from
http://hydro.rnde.tm.fr/accueil.html.
23
APPENDIXES
1
Dorothy Furberg
TRITA LWR MASTER
Appendix I: Graduated Color Maps Illustrating the
Indicator and Aggregated Indexes
Please note that all color scales and color assignments to RBDs are based on the index values. The figures given next
to the color scale (other than index values) are provided in order to show what indicator value can be associated with
that particular color assignment.
2
3
Dorothy Furberg
TRITA LWR MASTER
4
5
Dorothy Furberg
TRITA LWR MASTER
6
7
Dorothy Furberg
TRITA LWR MASTER
8
9
Dorothy Furberg
TRITA LWR MASTER
10
11
Dorothy Furberg
TRITA LWR MASTER
Appendix II: Indicator Information per River Basin District
12
Table 5: Indicator Data per River Basin District
ID RBD
Bothnian
1 Bay/Tornionjoki
2 Bothnian Sea
3 North Baltic Sea
4 South Baltic Sea
West Sea/Ostfold.
Akerhus. Hedmark.
5 Oppland
6 Indre Oslofjord
7 Buskerud. Vestfold
8 Telemark
Aust- og Vest9 Agder
10 Rogaland
11 Hordaland
12 Sogn og Fjordane
13 More og Romsdal
14 Sor- och Trondelag
15 Nord-Trondelag
16 Nordland
17 Troms
Finnmark/Tenojoki18 Paatsjoki
Vuoksi (Ladoga19 Neva basin)
Country
Mean
Population Total
Mean Total Mean Ortho- Mean Total
Area Cultivated
Density
Nitrogen Phosphorus phosphate Ammonium
2
(mg/l)
(mg/l)
(mg/l)
(km2) Land (%) Population (people/km ) (mg/l)
FI/SE
SE/NO
SE
SE
128190
181841
25231
61727
1
1
12
18
374106
1143140
2555860
2510230
3
6
101
41
0.38
0.41
1.89
1.89
0.02
0.014
0.07
0.04
0.004
0.003
0.03
0.02
0.02
0.02
0.13
0.08
54.16
64.01
6.47
7.20
144779
55991
2531
2869
SE/NO
NO
NO
NO
120559
1743
24699
12501
9
9
6
3
3150480
771150
506659
144845
26
442
21
12
1.38
0.81
1.05
0.30
0.03
0.04
0.04
0.011
0.012
0.012
0.009
0.002
0.06
0.05
0.06
0.02
44.54
14137
10.41
10.68
20540
73727
NO
NO
NO
NO
NO
NO
NO
NO
NO
19593
9174
14027
18251
19010
16463
19039
39435
25469
3
9
4
4
6
8
4
2
2
262529
360165
408847
87870
234075
254572
117887
228943
116015
13
39
29
5
12
15
6
6
5
0.31
0.53
0.39
0.37
0.21
0.24
0.29
0.13
0.09
0.009
0.018
0.011
0.013
0.006
0.014
0.011
0.010
0.005
0.006
0.004
0.003
0.004
0.001
0.004
0.003
0.004
0.002
0.11
0.016
0.012
0.014
0.008
0.018
0.011
0.013
0.006
14.99
2.42
5.57
4.04
3.71
3.15
0.91
7.31
4.68
57108
6724
13631
46004
15867
12361
7720
31908
40355
FI/NO/RU
98777
0
568090
6
0.18
0.006
0.002
0.014
17.74
31227
290682
2
6246170
21
0.80
0.03
0.006
0.06
75.83
12141
FI/RU
13
Average
Water
Annual
Discharge Availability
3
3
(bill. m ) (m /pers·yr)
Dorothy Furberg
ID RBD
Kymijoki20 Suomenlathi
KokemaenjokiSaaristomeri21 Selkämeri
Oulankajoki-Iijoki22 Perameri
23 Kemijoki
24 Aaland
25 North Western
26 Neagh Bann
27 Shannon
28 Eastern
29 Western
30 South Eastern
31 South Western
32 North Eastern
33 North West
34 Northumbria
35 Humber
36 Anglian
37 Thames
38 South West
39 South East
40 Severn
41 Dee
42 Western Wales
TRITA LWR MASTER
Country
Mean
Population Total
Mean Total Mean Ortho- Mean Total
Density
Nitrogen Phosphorus phosphate Ammonium
Area Cultivated
(km2) Land (%) Population (people/km2) (mg/l)
(mg/l)
(mg/l)
(mg/l)
Average
Annual
Water
Discharge Availability
3
3
FI
50792
3
2041580
40
1.40
0.05
0.02
0.10
9.67
4738
FI
68180
8
1738340
25
1.54
0.08
0.04
0.16
18.61
10704
102947
55545
1473
11430
8115
20279
5940
11818
12850
10550
3307
11164
8532
25334
24456
16274
17559
8292
21476
1929
11868
2
0
2
0
4
2
11
2
9
4
3
22
35
64
93
72
27
66
37
16
13
536729
138287
22740
471129
682982
696286
1473640
333468
579321
505260
857021
6634920
2539610
10955400
5013900
14107300
2821230
3369690
5468850
362661
1280700
5
2
15
41
84
34
248
28
45
48
259
594
298
432
205
867
161
406
255
188
108
0.55
0.31
0.03
0.015
0.01
0.004
0.03
0.011
26.93
19.27
50180
139371
5.66
1.77
0.17
0.15
0.12
3.22
0.14
0.27
0.19
1.18
0.20
0.64
0.17
0.04
0.08
0.06
0.10
0.03
0.05
0.04
0.24
0.51
0.13
0.96
0.48
0.69
0.18
0.51
0.19
0.19
0.02
0.09
0.10
0.09
0.04
0.03
0.06
0.07
0.11
0.54
0.14
0.25
0.14
0.15
0.08
0.25
0.11
0.10
0.05
8.33
2.82
9.93
3.88
9.27
6.46
8.99
0.52
6.06
3.06
4.68
1.92
5.82
4.41
1.06
7.48
1.04
5.48
4566
4132
540
93
841
481
595
608
913
1204
428
383
413
1565
314
1367
2869
4282
FI/RU
FI
FI
EI
EI
EI
EI
EI
EI
EI
GB
GB
GB
GB
GB
GB
GB
GB
GB
GB
GB
3.23
0.53
4.20
14
0.56
0.13
ID RBD
43 Solway-Tweed
44 Scotland
45 West Estonia
46 Venta
47 Lielupe
48 Daugava
49 Nemunas
50 Vistula
51 Oder
52 Elbe
53 Eider
54 Schlei/Trave
55 Wamow/Peene
56 Weser
57 Ems
Mean
Average
Population Total
Water
Mean Total Mean Ortho- Mean Total Annual
Density
Nitrogen Phosphorus phosphate Ammonium Discharge Availability
Area Cultivated
3
3
(mg/l)
(mg/l)
(mg/l)
12186
13
457613
38
1.70
0.06
0.03
0.06
7.32
15993
67067
10
4892860
73
0.93
0.07
0.81
0.09
26.26
5366
23160
28
741013
32
2.23
0.06
0.06
0.10
2.61
3520
26517
46
950842
36
2.03
0.07
0.03
0.16
6.09
6401
17876
64
668052
37
5.53
0.37
0.30
1.15
2.08
3116
Country
GB
GB
EE
LT/LV
LT/LV
LV/RU/LT/B
Y
86052
LT/PL/RU
92318
LT/PL/RU 226201
CZ/DE/PL 127422
AT/CZ/DE/
PL
146849
DE
5999
DE
5597
DE
17187
DE
47222
DE/NL
17989
AT/DE/NL/
CH/FR/LU/I
58 Rhine
T/LI/BE
186797
DE/NL/FR/
59 Meuse
BE
35407
AT/CZ/DE/
PL/UA/CH/I
T/SK/SI/YU/
BG/MD/RO/
BA/HR/HU/
AL/MK
60 Danube
806238
61 North Adriatic Sea IT/SI/HR
8951
20
49
56
57
2783050
4890560
26896800
16770600
32
53
119
132
1.87
2.23
3.30
4.60
0.08
0.14
0.30
0.38
0.05
0.08
0.14
0.18
0.15
0.29
0.81
1.07
20.85
15.93
34.82
19.14
7492
3257
1295
1141
57
38
76
70
63
64
24724100
534122
1013550
1483790
9468020
3672370
168
89
181
86
201
204
4.65
3.53
3.69
3.22
4.90
5.13
0.18
0.13
0.17
0.12
0.19
0.15
0.09
0.04
0.09
0.06
0.08
1.65
0.44
0.24
0.23
0.32
0.27
0.23
22.66
0.38
0.56
1.00
10.93
2.78
916
717
554
672
1155
758
42
58329200
312
4.30
0.21
0.11
0.28
82.76
1419
45
9021470
255
6.44
0.23
0.61
0.57
10.24
1135
57
37
82012400
1045860
102
117
3.40
0.92
0.18
0.02
0.11
0.05
0.32
0.15
207.35
3.08
2528
2941
15
Dorothy Furberg
TRITA LWR MASTER
Mean
Average
Water
Population Total
Density
Area Cultivated
3
3
(mg/l)
(mg/l)
(mg/l)
ID RBD
Country
63 Denmark East
DK
3973
75
2318143
583
6.42
0.23
0.14
0.29
0.44
190
65 Sonderjyllands Amt DK
1208
84
64092
53
2.65
0.11
0.02
0.11
0.23
3590
66 Denmark West
DK
3414
78
2999120
878
5.54
0.12
0.05
0.13
5.13
1710
73 Bornholm
DK
587
66
43700
74
10.64
0.15
0.06
0.07
0.014
330
75 Scheldt
NL/FR/BE
35926
82
12536900
349
7.96
0.60
0.41
1.91
5.47
436
Rhone et cotiers
76 mediterraneens
CH/FR
128366
32
15124700
118
1.92
0.21
0.12
0.48
60.00
3593
77 Corse
FR
8731
2
249465
29
0.83
0.09
0.04
0.03
Adour. Garonne.
Dordogne et fleuves
cotiers charentais et
78 aquitains
FR
Loires. cotiers
vendeens et cotiers
79 bretons
FR
Seine et cotiers
80 normands
FR
81 Minho-Lima/Norte ES/PT
82 Cáaado-Ave-Leca PT
83 Douro
ES/PT
Vouga-Mondego84 Lis
PT
Tejo-Sado-Ribeiras
85 do Oeste
ES/PT
Guadiana-Mira86 Ribeiras do Algarve ES/PT
87 Balearics
ES
Cuencas Internas
89 Del País Vasco
ES
117313
39
6886070
59
3.49
0.19
0.09
0.27
33.00
2985
156172
35
12067800
77
4.68
0.23
0.11
0.22
29.61
2453
95010
41299
3617
97766
64
11
15
53
17753400
3995750
1520430
4209210
187
97
420
43
5.58
0.18
0.27
0.11
0.06
0.13
0.20
15.00
11.69
727
2925
1.66
0.15
0.16
0.39
19.05
4526
11558
11
1365530
118
91255
48
10314000
113
0.97
0.73
0.52
2.68
8.73
846
77304
4999
61
43
2280210
809926
29
162
2.23
0.30
1.03
3.95
1733
1730
2
300788
174
16
Mean
Average
Population Total
Water
Density
Area Cultivated
3
3
ID RBD
Country
(mg/l)
(mg/l)
(mg/l)
90 Catalonia
ES
16697
24
5734740
343
5.64
0.52
0.36
4.54
0.45
78
91 Ebro
ES
85616
55
2913220
34
2.49
0.27
0.09
0.48
10.22
3508
92 Galicia Coast
ES
12991
12
1801610
139
0.01
0.04
0.01
0.33
181
93 Guadalquivir
ES
63547
63
4826260
76
5.50
4.03
0.41
2.55
10.77
2232
94 Jucar
ES
40499
56
3406390
84
1.50
0.30
0.25
0.76
0.42
124
95 Segura
ES
21439
71
2360460
110
2.03
0.20
3.02
0.0048
2
96 South
ES
18168
41
1931940
106
1.14
0.17
0.10
0.29
0.045
23
97 East Estonia
EE/RU/LV
60013
25
1104420
18
1.98
0.07
0.04
0.12
14.17
12834
98 Koiva/Gauja
EE/LV
14082
32
232806
17
1.53
0.11
0.21
0.27
2.61
11198
99 Arda-Maritza
BG/TR/GR 52777
67
3477600
66
0.63
0.17
0.85
3.71
1068
100 Kamchia-Veleka
BG/TR
16789
53
964919
57
0.32
0.19
1.03
2.57
2661
Mesta/NestosBG/GR/MK/
101 Struma/Strymonas YU
23277
43
1100100
47
0.17
0.22
0.57
3.03
2756
103 Malta
Malta
333
47
391461
1176
17
Dorothy Furberg
TRITA LWR MASTER
Table 6: Maximum and Minimum Annual Nutrient Concentration Averages
ID
RBD
Country
Bothnian
1 Bay/Tornionjoki FI/SE
2 Bothnian Sea
SE/NO
3 North Baltic Sea SE
4 South Baltic Sea SE
West
Sea/Ostfold.
Akerhus.
Hedmark.
5 Oppland
SE/NO
6 Indre Oslofjord NO
Buskerud.
7 Vestfold
NO
8 Telemark
NO
Aust- og Vest9 Agder
NO
10 Rogaland
NO
11 Hordaland
NO
12 Sogn og FjordaneNO
13 More og Romsdal NO
Soroch
14 Trondelag
NO
15 Nord-Trondelag NO
16 Nordland
NO
17 Troms
NO
Total Nitrogen Total Nitrogen Total
Total
Maximum
Minimum
Phosphorus
Phosphorus
Yearly Average Yearly Average Maximum Yearly Minimum Yearly
(mg/l)
(mg/l)
Average (mg/l) Average (mg/l)
Ammonium
Minimum
Ortho.
Ammonium
Ortho.
Yearly
Minimum
Maximum
Maximum
Yearly Average Yearly
Average
Yearly
(mg/l)
Average (mg/l) Average (mg/l) (mg/l)
0.87
1.20
4.16
9.04
0.11
0.19
0.34
0.24
0.05
0.11
0.20
0.16
0.00
0.00
0.01
0.00
0.02
0.02
0.10
0.12
0.001
0.001
0.0016
0.001
0.12
0.11
1.96
1.33
0.0025
0.006
0.0113
0.0061
5.82
2.94
0.10
0.05
0.12
0.17
0.00
0.01
0.06
0.10
0.001
0.001
0.86
0.49
0.0059
0.001
5.80
0.32
0.36
0.27
0.23
0.04
0.00
0.01
0.055
0.004
0.001
0.001
1.01
0.0197
0.005
0.013
0.58
1.83
4.88
3.94
0.82
0.06
0.09
0.03
0.04
0.03
0.08
0.30
0.16
0.09
0.06
0.00
0.00
0.00
0.00
0.00
0.50
0.036
0.034
0.057
0.006
0.000
0.001
0.001
0.001
0.001
10.00
0.09
0.20
0.17
0.06
0.0005
0.0005
0.0005
0.0005
0.0005
0.61
1.43
0.67
0.23
0.12
0.04
0.02
0.03
0.10
0.12
0.06
0.11
0.00
0.00
0.00
0.00
0.044
0.014
0.088
0.005
0.001
0.001
0.001
0.001
0.25
0.05
0.18
0.019
0.0005
0.002
0.0005
0.001
18
ID
RBD
Country
Finnmark/Tenojo
18 ki-Paatsjoki
FI/NO/RU
Vuoksi (Ladoga19 Neva basin)
FI/RU
Kymijoki20 Suomenlathi
FI
KokemaenjokiSaaristomeri21 Selkämeri
FI
Oulankajoki-Iijoki22 Perameri
FI/RU
23 Kemijoki
FI
24 Aaland
FI
25 North Western
EI
26 Neagh Bann
EI
27 Shannon
EI
28 Eastern
EI
29 Western
EI
30 South Eastern
EI
31 South Western EI
32 North Eastern
GB
33 North West
GB
34 Northumbria
GB
35 Humber
GB
36 Anglian
GB
37 Thames
GB
38 South West
GB
Total
Maximum
Minimum
Phosphorus
Phosphorus
(mg/l)
(mg/l)
Ammonium
Minimum
Ortho.
Ammonium
Ortho.
Yearly
Minimum
Maximum
Maximum
Average
Yearly
(mg/l)
2.60
0.03
0.04
0.00
0.030
0.001
0.32
0.001
7.55
0.19
0.17
0.00
0.094
0.001
2.05
0.0018
7.11
0.36
0.29
0.00
0.128
0.001
2.80
0.0023
7.69
0.31
0.60
0.01
0.165
0.002
2.83
0.0009
1.88
0.58
0.20
0.16
0.12
0.03
0.01
0.01
0.059
0.010
0.001
0.001
0.56
0.046
0.0028
0.0025
257.67
2.34
0.01
1.25
3.83
0.12
0.12
0.00
0.08
0.04
4.01
2.44
3.23
3.23
0.19
0.16
1.08
1.31
0.30
2.35
2.33
0.04
0.06
0.12
0.00
0.27
0.23
0.02
0.320
0.225
0.298
0.279
0.124
0.125
0.178
0.985
3.650
0.590
4.990
1.390
5.390
3.750
-0.003
0.011
-0.040
0.008
-0.005
0.003
0.000
0.020
0.002
0.012
0.010
0.023
0.009
0.000
0.49
0.22
0.75
0.12
0.13
0.46
0.33
0.18
7.83
4.44
4.07
1.52
1.50
0.54
0.0018
0.0275
0.0018
0.0065
0.003
0.0050
0.0084
0.0694
0.0037
0.0001
0.0014
0.020
0.0025
0.000
19
Dorothy Furberg
ID RBD
39 South East
40 Severn
41 Dee
42 Western Wales
43 Solway-Tweed
44 Scotland
45 West Estonia
46 Venta
47 Lielupe
48 Daugava
49 Nemunas
50 Vistula
51 Oder
52 Elbe
53 Eider
54 Schlei/Trave
55 Wamow/Peene
56 Weser
57 Ems
58 Rhine
59 Meuse
TRITA LWR MASTER
Country
GB
GB
GB
GB
GB
GB
EE
LT/LV
LT/LV
LV/RU/LT/BY
LT/PL/RU
LT/PL/RU
CZ/DE/PL
AT/CZ/DE/PL
DE
DE
DE
DE
DE/NL
AT/DE/NL/CH
/FR/LU/IT/LI/
BE
DE/NL/FR/BE
Ammonium
Minimum
Ortho.
Ammonium
Ortho.
Yearly
Minimum
Maximum
Maximum
Average
Yearly
(mg/l)
3.539
0.043
2.16
0.050
0.11
1.506
0.004
1.63
0.0066
0.08
0.693
0.012
0.69
0.010
0.094
0.002
0.30
0.010
0.09
0.119
0.030
0.32
0.015
0.00
51.784
0.002
1.51
0.0044
0.01
0.768
0.002
0.93
0.0055
0.01
0.152
0.001
1.34
0.014
0.03
3.121
0.009
32.00
0.033
0.02
0.308
0.010
1.22
0.023
0.02
4.577
0.001
4.38
0.010
0.02
1.821
0.006
23.36
0.035
0.02
1.651
0.010
18.20
0.010
0.02
0.657
0.005
5.73
0.025
0.09
0.066
0.015
0.53
0.113
0.11
0.255
0.062
0.67
0.081
0.08
0.089
0.029
1.09
0.095
0.06
0.194
0.010
2.60
0.025
0.10
46.660
0.023
0.43
0.050
Total
Maximum
Minimum
Phosphorus
Phosphorus
(mg/l)
(mg/l)
0.67
0.14
0.75
4.20
1.98
4.09
6.37
5.33
46.36
3.46
9.99
30.75
18.03
12.95
5.18
6.55
4.66
11.02
8.96
0.32
4.20
1.42
0.13
0.89
0.75
1.40
0.72
0.48
0.22
0.90
0.82
2.72
1.73
1.98
3.09
2.43
0.16
0.97
0.18
0.22
3.59
0.52
5.37
10.90
13.59
1.04
0.16
0.34
0.18
0.42
0.21
14.62
19.64
0.50
2.05
3.13
1.21
20
0.01
0.02
1.717
49.160
0.000
0.004
6.01
12.67
0.0023
0.013
ID
RBD
Country
AT/CZ/DE/PL
/UA/CH/IT/SK
/SI/YU/BG/M
D/RO/BA/HR/
HU/AL/MK
60Danube
North Adriatic
61 Sea
IT/SI/HR
63Denmark East
DK
Sonderjyllands
65 Amt
DK
66Denmark West DK
73Bornholm
DK
75Scheldt
NL/FR/BE
Rhône et cotiers
76 méditerranéens CH/FR
77Corse
FR
Adour. Garonne.
Dordogne et
fleuves côtiers
charentais et
78 aquitains
FR
Loires. côtiers
vendéens et
79 côtiers bretons FR
Seine et côtiers
80 normands
FR
81Minho-Lima/NorteES/PT
Cáaado-Ave82 Leca
PT
Total
Maximum
Minimum
Phosphorus
Phosphorus
(mg/l)
(mg/l)
Ammonium
Minimum
Ortho.
Ammonium
Ortho.
Yearly
Minimum
Maximum
Maximum
Average
Yearly
(mg/l)
45.48
0.01
3.83
0.002
2.885
0.000
21.15
0.000
2.27
17.98
0.30
2.07
0.10
0.80
0.002
0.08
0.338
0.620
0.002
0.020
2.17
10.92
0.000
0.040
3.08
14.56
13.82
17.55
2.31
1.29
8.94
3.08
0.15
0.25
0.23
7.55
0.09
0.03
0.09
0.03
0.023
0.130
0.100
1.964
0.020
0.007
0.048
0.000
0.15
0.69
0.12
17.00
0.088
0.000
0.050
0.039
9.13
1.13
0.28
0.73
2.95
0.33
0.01
0.01
2.829
0.199
0.003
0.007
15.84
0.15
0.0077
0.0039
19.65
0.55
2.82
0.01
2.364
0.003
13.11
0.0019
12.15
0.95
2.47
0.02
1.658
0.007
3.77
0.0084
12.43
3.01
0.73
2.15
0.02
0.01
0.521
0.367
0.005
0.000
1.69
3.02
0.0084
0.000
21
Dorothy Furberg
TRITA LWR MASTER
ID RBD
Country
83 Douro
ES/PT
Vouga-Mondego84 Lis
PT
Tejo-Sado85 Ribeiras do Oeste ES/PT
Guadiana-MiraRibeiras do
86 Algarve
ES/PT
87 Balearics
ES
Cuencas Internas
89 Del País Vasco ES
90 Catalonia
ES
91 Ebro
92 Galicia Coast
ES
ES
93 Guadalquivir
94 Jucar
95 Segura
96 South
97East Estonia
98Koiva/Gauja
99Arda-Maritza
100Kamchia-Veleka
Mesta/NestosStruma/Strymona
101 s
ES
ES
ES
ES
EE/RU/LV
EE/LV
BG/TR/GR
BG/TR
103Malta
Malta
BG/GR/MK/
YU
Ammonium
Minimum
Total
Ortho.
Ortho.
Ammonium
Maximum
Yearly
Minimum
Phosphorus
Phosphorus
Minimum
Maximum
Maximum
Yearly Average Yearly Average Maximum Yearly Minimum Yearly Yearly Average Yearly
Average
Yearly
(mg/l)
(mg/l)
5.35
0.16
0.71
0.01
0.970
0.000
5.15
0.0066
3.64
0.29
3.80
12.20
0.33
30.53
0.05
4.43
3.86
1.39
25.19
4.64
0.10
0.36
1.85
6.10
2.87
0.47
0.40
0.37
3.719
0.000
30.58
0.000
6.789
0.000
28.35
0.0043
0.00
7.689
0.002
62.16
0.020
0.53
0.02
0.04
0.01
1.109
0.072
0.000
0.000
10.77
0.02
0.000
0.000
4.03
4.60
7.40
0.81
0.66
3.79
3.23
1.36
4.03
0.02
0.01
0.03
0.01
0.01
0.01
0.01
3.914
6.683
1.968
0.882
0.517
4.130
1.470
1.666
0.005
0.007
0.000
0.000
0.003
0.007
0.000
0.009
39.51
25.27
22.80
3.47
0.97
5.63
14.50
6.98
0.010
0.000
0.000
0.010
0.0035
0.020
0.000
0.026
1.43
0.02
4.890
0.000
5.24
0.000
22
0.02
Appendix III: RBD Characterization Maps
23
Dorothy Furberg
TRITA LWR MASTER
Explanatory Notes for the RBD Characterization Maps
The purpose of Appendix III is to provide an illustration and description of the characteristics and
indicators for each River Basin District that has so far been proposed or designated (reported to the
European Commission, Article 3). While the map of each district is somewhat self-explanatory with the
help of the legend, the characteristics and indicators provided require some explanation.
Area is the approximate size of the district in square kilometers, calculated in ArcMap using the RBD map
layer.
Population is the approximate number of people living in the district, calculated in ArcMap by extracting
information from the LandScan layer on a district basis.
Population density is the average number of people per square kilometer. It was obtained by dividing the
population of the district by the district area.
The average annual discharge was calculated by taking the average of the three to seven (depending on
the information available) most recent yearly average measurements of the largest discharge rate (m3/s)
measured within as many river basins as possible located within the district. The averages for the basins
were added together to give a district total, which was then converted to m3/yr.
Water availability is the amount of water available per person per year. It was calculated by dividing the
average annual discharge by the district population.
The water stress ranking was obtained by applying the following standards (adapted from Falkenmark
and Widstrand. 1992):
Low ≥ 10 000 m3/pers/yr = No
10 000 m3/pers/yr > Medium (quality or dry season problems) > 1 666 m3/pers/yr = Risk
High (water stress) ≤ 1 666 m3/pers/yr = Yes
For more information about the selection process of these standards, please see the Methods section of
this report.
The mean total Nitrogen concentration, NCavg, or mean total Phosphorus concentration, PCavg, are
the mean total Nitrogen (mg/l) or mean total Phosphorus (mg/l) concentrations from all the quality
monitoring stations in the district at which total nitrogen or phosphorus measurements were taken.
Measurements from the most recent available six or seven years per station were used. Where total
nitrogen and/or phosphorus data were missing, total ammonium and/or orthophosphate were used.
The standard deviation, σ = √[(∑(x-xavg)2)/N], is from amongst the yearly mean nutrient concentrations
at individual stations (used to calculate the mean total nutrient concentration). While the mean
concentration gives a rough idea of how much N or P is present in the district’s waters, the standard
deviation gives an idea of how much the N or P concentration will vary within the district and what level
of disparity one could expect to find. It is a complimentary measure of variation within the district to
minimum and maximum concentration values because the minimum or maximum could result from
extreme or exceptional circumstances at that station or during that particular year (i.e. they could be
outliers: values that are far from most others in the dataset).
24
Minimum and maximum concentrations, NCmin/max and PCmin/max, are the highest or lowest recorded
yearly mean nutrient concentrations found within the dataset used to calculate the mean concentrations,
i.e. they are either the highest or lowest concentration at a particular monitoring station for a particular
year, taken from the dataset used to calculate the mean N and P concentrations.
The monitoring stations at which minimum and maximum concentrations were measured are highlighted
in red (Nmax), white (Nmin), pink (Pmin) and black (Pmax).
Land Cover categories that were used specifically in this project, and that commonly occur in Europe,
include forest, shrub/herbaceous, cultivated/managed areas, mosaic (a combination of cropland, forest,
shrub and other natural vegetation), water bodies, bare areas, snow and ice, and artificial surfaces such as
asphalt. The land cover information was calculated in ArcMap by extracting information from the
GLC2003 layer on a district basis.
25
Dorothy Furberg
TRITA LWR MASTER
26
27
Dorothy Furberg
TRITA LWR MASTER
28
29
Dorothy Furberg
TRITA LWR MASTER
30
31
Dorothy Furberg
TRITA LWR MASTER
32
33
Dorothy Furberg
TRITA LWR MASTER
34
35
Dorothy Furberg
TRITA LWR MASTER
The remaining characterization maps will be added over the summer (2006) and will be available upon request. Please write to dfurberg@yahoo.se to obtain an electronic copy of
the full set of maps.
36