Nutrient Loads on the North Sea

Transcription

MSc Thesis
Feeding the North Sea
Hendrik Meuwese
May 2007
Delft University of Technology
Faculty of Civil Engineering and Geosciences
Department Water Resources
Section Hydrology
Marine and Coastal Management
MSc Thesis
Feeding the North Sea
Student
Student number
Date
Hendrik Meuwese
1050443
May 2007
Graduation committee
Prof.dr.ir. H.H.G. Savenije
Dr.ir. M.J. Baptist
Ir. G.J. de Boer
Ir. A.N. Blauw
Delft University of Technology
Faculty of Civil Engineering and Geosciences
Department Water Resources
Section Hydrology
TU Delft
TU Delft / IMARES
TU Delft
WL | Delft Hydraulics
Marine and Coastal Management
Cover illustration
Satellite map (Google 2006)
Nutrient sources in the illustration do not represent the nutrient loads in the model.
Preface
This is my master thesis, the end of my master in Water Resources at Delft University
of Technology. The master program Water Resources is taught at the faculty of Civil
Engineering and Geosciences. I am specialized in the field of hydrology.
The master thesis study is done at the institute WL | Delft Hydraulics. Parts of my
master thesis are used in a project for a client of this institute, namely RijkswaterstaatRIKZ. This project is about the nutrient distribution and transboundary transports in
the southern part of the North Sea and finished in December 2006.
The start of my thesis was not always easy, as I did a lot of projects on constructing
buildings and railway stations in the past, while the number of ‘water’ projects was
limited to two: my Bachelor project, the design of the drainage of a polder area, and a
foreign project in Vietnam, about sedimentation in the Tra Khuc River. However, I
managed to deal with a water quality project soon.
In the beginning of my thesis there was a lot of work to do for the RIKZ-project,
which causes that I was familiar with the project very soon. However there was not
enough time to do a literature study. I did this study when the RIKZ-project was
finished, but looking back I would have preferred to do this the other way around.
It was a pleasure to do my master thesis at WL |Delft Hydraulics. The staff of WL |
Delft Hydraulics had a severe contribution to this pleasure as they were always willing
to answer my questions or give me advise. Next to my mentor Anouk Blauw, I would
like to thank Karen van de Wolfshaar, Hans Los and Nicky Villars and of course the
other staff and students for their support. Besides my guidance at WL | Delft
Hydraulics, I would like to thank the committee members of Delft University of
Technology: Prof. Savenije, Martin Baptist and Gerben de Boer, thanks!
This thesis uses data from several authorities. It would be impossible to do this research
without the willingness of those institutes to gather and share their observation data. I
thank the Institute of Oceanography in Hamburg (Lenhart and Pätsch), Institute for
Biogeochemistry and Marine Chemistry (Brockmann), Arbeitsgemeinschaft für die
Reinhaltung der Elbe, Flussgebietsgemeinschaft Weser, Niedersächsischer
Landesbetrieb für Wasserwirtschaft (Engels), the BODC, the BMDC, the OSPAR
Commission, the Royal Dutch Meteorological Institute, the RIKZ/RIZA, Agence de
l'Eau Artois-Picardie and other data sources on the internet.
Delft, 14 May 2007
Hendrik Meuwese
(This is a blank page)
Summary
Eutrophication is a big problem in the North Sea, the nutrient loads on the sea have
increased considerably during the last century and the primary production in the North
Sea has increased by a factor two. An important factor in the eutrophication is the
riverine nutrient loads; a minor contribution is from direct loads and atmospheric
deposition. In order to determine the results of mitigating measures on the riverine
nutrient load, a model that includes all loads in a consistent way is necessary.
The objectives of the thesis are:
To quantify the terrestrial nutrient loads on the southern North Sea in a
consistent way.
To specify the boundary conditions of the southern North Sea regarding
nutrient concentrations in a consistent way.
To determine the relative contribution of these loads to the nutrient
concentrations in the southern North Sea.
The most recent version of the GEM southern North Sea model is used as starting
point in this study, namely the model used during 2nd Maasvlakte studies (De Goede et
al. 2005; Prooijen et al. 2006). The model spans from 1996 to 2003 and simulates the
hydrodynamics and water quality of the southern North Sea. In this thesis the model is
changed regarding the nutrient loads by rivers, the boundary conditions and the
atmospheric deposition. The study includes a two- and three-dimensional model set-up.
The annual terrestrial load has increased compared to the previously used model.
Because a lot of rivers are added in the new model set-up especially in France and the
United Kingdom; the time series of other rivers are updated. The changes apply only to
the loads in the water quality model; the hydrodynamics of the study area are not
changed in this study.
The southern boundary condition regarding nutrient concentrations is not changed, as
the boundary condition in the previous model shows a good agreement with literature
data, except for the nitrate concentration. The nitrate concentration might be
overestimated. The northern boundary condition has been changed as the previous
boundary concentrations were based upon winter concentrations and did not include
seasonal variability. As a consequence the net nutrient transports over the northern
boundary increase, except the total nitrogen load which seems to be overestimated in
the previous model.
The two-dimensional model shows a good agreement with observations, except for
silicate. The disagreement is caused by the estimation of silicate concentrations in
British rivers, this is probably an underestimation.
A three-dimensional model has been applied as the deeper waters of the North Sea are
affected by stratification in the summer period. The northern boundary is divided in a
bottom and surface region in order to account for concentration differences. The
model results of the three-dimensional model are reasonable; however the threedimensional model faces some problems regarding temperature forcing and vertical
mixing. This causes that the spring bloom starts too early and that there is a too high
nutrient concentration in the bottom in the end of the summer and in the whole water
column during the winter period.
Abbreviations
2D
Two-dimensional
3D
Three-dimensional
Be
Belgium
C
Carbon
Chl-a
Chlorophyll-a
Deg
Degrees
Det
Detritus
Dk
Denmark
Fr
France
Ge
Germany
GEM
Generic Ecological Model
kT
Kiloton
KJ-N
Kjeldahl Nitrogen
N
Nitrogen
NH4
Ammonium
Nl
The Netherlands
NO2
Nitrite
NO3
Nitrate
Nw
Noordwijk
P
Phosphorus
PO4
Phosphate
oC
Degrees Celcius
Si
Silicate
T
Temperature
TotalN
Total nitrogen
TotalP
Total phosphorus
Ts
Terschelling
Uk
United Kingdom
Nutrient concentrations are given in gram nutrient per
cubic meter, e.g. gram nitrogen for nitrate and gram
phosphorus for phosphate.
Table of Contents
1.
Introduction _____________________________________________________________ 1
1.1.
1.2.
1.3.
1.4.
1.5.
2.
Model __________________________________________________________________ 5
2.1.
2.2.
2.3.
2.4.
2.5.
3.
Model behaviour ___________________________________________________________ 78
Terrestrial nutrient loads ____________________________________________________ 80
Boundary conditions________________________________________________________ 81
Aggregated overview________________________________________________________ 82
Recommendations _______________________________________________________ 85
7.1.
7.2.
7.3.
8.
Behaviour three-dimensional model ___________________________________________ 65
Nutrient distribution over model area__________________________________________ 70
Transboundary nutrient transport _____________________________________________ 72
Mass balance______________________________________________________________ 74
Conclusion________________________________________________________________ 76
Conclusions ____________________________________________________________ 77
6.1.
6.2.
6.3.
6.4.
7.
Northern boundary _________________________________________________________ 43
Southern boundary _________________________________________________________ 53
Conclusion________________________________________________________________ 59
Model results ___________________________________________________________ 65
5.1.
5.2.
5.3.
5.4.
5.5.
6.
Data availability ____________________________________________________________ 9
Missing data ______________________________________________________________ 14
Model variables ____________________________________________________________ 27
Estuarine retention _________________________________________________________ 29
Hydrodynamics____________________________________________________________ 36
Conclusion________________________________________________________________ 37
Model boundaries________________________________________________________ 43
4.1.
4.2.
4.3.
5.
Modelling software __________________________________________________________ 5
Model grid _________________________________________________________________ 5
Hydrodynamic model________________________________________________________ 5
Water quality model _________________________________________________________ 6
Conclusion_________________________________________________________________ 8
Terrestrial nutrient loads ___________________________________________________ 9
3.1.
3.2.
3.3.
3.4.
3.5.
3.6.
4.
Background ________________________________________________________________ 1
Objective __________________________________________________________________ 3
Scope _____________________________________________________________________ 4
Framework_________________________________________________________________ 4
Reading guide ______________________________________________________________ 4
Model set-up ______________________________________________________________ 85
Terrestrial nutrient loads ____________________________________________________ 85
Boundary conditions________________________________________________________ 86
References______________________________________________________________ 87
Index of Appendices__________________________________________________________ 93
1.
Introduction
This master thesis concerns the nutrient loads on the southern North Sea, the southern
boundary of the study area is in the Channel and the northern boundary is between
Aberdeen and the north of Denmark. Major rivers that drain in this area are the Rhine,
Meuse, Seine, Humber and Elbe. Previous nutrient model studies in this field (De
Goede et al. 2005; Prooijen et al. 2006) are the basis of the research. The nutrients
concerned in this study are nitrogen, phosphorus and silicate.
In section 1.1 some background information about the nutrient loads, water quality,
water movement and history of model simulations in the model area is discussed.
Secondly the objectives of this thesis are discussed in 1.2. The third subsection deals
with the scope of the project. Section 1.4 deals with the framework of the master thesis.
Finally, a reading guide for the report is presented.
1.1.
Background
An overview of the nutrient loads on the North Sea is given by De Jonge et al. (2002);
OSPAR Commission (2003b) and Radach et al. (1990). De Jonge et al. (2002) state that
the riverine nutrient load to the North Sea has increased significantly in the period
1930-1980. They state that the total nitrogen load of the Ems and Rhine has increased
twelve and ten times resp.; the total phosphorus load has increased six and twelve times
resp. These numbers are supported by other reports (e.g. Radach et al. 1990). It is
thought that the nutrient loads have increased because of the introduction of artificial
fertilizers and detergents. In the 1980s mitigating measures were taken to reduce the
nutrient load because the ecosystem in the North Sea was seriously affected by
eutrophication1. The mitigating measures have caused a decrease of nutrient loads in the
period 1985-2000, e.g. the Danish and German phosphate loads were halved and the
nitrate loads from these two countries were reduced by around forty percent (OSPAR
Commission 2003b).
More detailed characteristics of North Sea water are set out in Cadée et al. (2002) based
on long term high water observations in the Marsdiep2. The phosphate concentration in
the Marsdiep has increased four times from 1950 to the mid 1970s. Since the 1980s the
concentrations have decreased. This means that summer observations at the end of the
last century are equal to the concentrations in 1950, but the winter concentrations are
still higher. The winter nitrate concentrations in 1999/2000 are about twice as much as
the 1970s concentrations, the summer concentrations are equal. The silicate
concentrations are more or less constant over the period 1970 – 1990. The chlorophylla concentration, the primary production3 and the Phaeocystis4 blooms have increased
since the 1970s to the 1990s, whereas the algal activity has slightly decreased since the
1990s. These are the first results of de-eutrophication, so the observations in the
Marsdiep show that the water quality in the Dutch coastal zone of the North Sea is
improving.
Eutrophication is the excess load of nutrients on a water body. In general this causes an excessive plant
growth and decay, so the water quality decreases.
2 Most western inlet of the Dutch Wadden Sea between Den Helder and Texel.
3 Primary production is the production of organic compounds from atmospheric or aquatic carbon dioxide,
principally through the process of photosynthesis (Wikipedia 2007).
4 Phaeocystis is a type of algae that produces foam that can accumulate on beaches.
1
1
The nutrient concentration in a specific area, like the Marsdiep, does by no means
originate from the neighbouring coasts only. Residual currents transport nutrients
through the North Sea. The water movement in the North Sea is discussed by Laane et
al. (1996); Lacroix et al. (2004); Salomon et al. (1993) and Simpson (1993). Simpson
(1993) describes that those currents exist because of the tidal forcing, the wind and the
atmospheric pressure. These influences result in a residual current that describes a
counter clockwise rotation. Simpson noted that the North Sea is affected by thermal
stratification by seasonal temperature differences and density differences due to
freshwater discharges. According to Simpson the shallow parts of the North Sea can be
classified as mixed during summer periods because of the strong residual currents. The
deeper parts are stratified during summer. The order of magnitude of the residual
current is described by Laane et al. (1996); Laane states that the travel time of water in
the surface layer from Southampton to the Dutch central coast is around one to one
and a half year. Simpson (1993) did not describe the salinity differences in detail, but
this is elaborated in Lacroix et al. (2004) and Salomon et al. (1993). Salomon describes
the existence of a coastal river of riverine water along the continental coast of the North
Sea. Lacroix focuses on the Belgian coast and describes the classification of three
different water origins in the southern bight of the North Sea as proposed by several
earlier studies (refs. in Lacroix et al. 2004): a zone that is influenced by the English
coast: a central zone of water that originates from the Channel and a third zone of
continental coastal water. However research by Lacroix shows that the influence of the
English coastal zone is smaller than previously assumed. The three different
characteristics in the southern Bight are visible on a satellite image as well, see Figure
1-1. According to Lacroix the counter clockwise residual current pattern does not yield
for the Belgian coast because Lacroix observed that the Rhine and Meuse rivers
influence the water quality in the Belgian coastal waters.
The European Water Framework Directive requires that mitigating measures are taken
for each river. The influence of these measures on the North Sea is not easily
foreseeable as the nutrients are transported through the North Sea by the residual
currents, as stated above. Therefore more insight is required in transboundary5 nutrient
transports. This insight can be obtained by computational models. However the results
of several models on nutrient reduction showed large differences during a recent
OSPAR6 workshop (Hamburg, October 2005). The models differed on several subjects:
the estimation of model variables from observations in rivers and at the model
boundaries; the amount of observations in France and Belgium and the location of the
model boundaries. Another point of debate concerned scenarios: should nutrient
reduction be applied to rivers only or to the boundaries as well?
The Dutch government is represented in the OSPAR by RWS RIKZ7,8. RWS RIKZ has
commissioned WL | Delft Hydraulics to update the existing southern North Sea water
quality model and to simulate the transboundary transports. This project is finished in
December 2006 and described in Blauw et al. (2006). The water quality model makes
use of the GEM9 modelling framework. The history of the GEM model is described in
Transboundary transports are transports of nutrients from one country to the continental waters of a
neighbouring country.
6 OSPAR is the short name of the Convention for the Protection of the Marine Environment of the NorthEast Atlantic. It is the current legislative instrument regulating international cooperation on environmental
protection in the North-East Atlantic.
7 RWS: Dutch Directorate for Public Works and Water Management
8 RIKZ: National Institute for Coastal and Marine Management
9 GEM: Generic Ecological Model
5
2
Smits et al. (1997). They describe how the GEM model is created in the mid 1990s
because the Dutch government would like to get rid of the individual models that were
used within each separate Dutch institute. Because of the large number of models, often
no consensus could be reached about the model results. After several workshops
consensus has been achieved about most parts of the model. The technical
implementation is done using the DELWAQ modelling software of WL | Delft
Hydraulics. One can call it an ecological or water quality model, within this thesis is
chosen to call it a water quality model.
Figure 1-1: The North Sea on March 26, 2007 (NASA 2007).
1.2.
Objective
The riverine loads in the most recent model set-up (De Goede et al. 2005; Prooijen et
al. 2006) needed a major update. An update was needed because only a couple of British
and German rivers were included and the French and Danish rivers were not included
at all. The nutrient loads of British and German rivers were included by time series of
only two years that were cloned to the other years. In order to compare the nutrient
loads of the several countries and rivers in a fair way, their nutrient loads must be
included in the model set-up in a consistent way. This means that the rivers of all
countries bordering the model area have to be included by time series of observations
and with the same number of parameters.
3
The recent model set-up (De Goede et al. 2005; Prooijen et al. 2006) has boundary
conditions that include a seasonal variability on the southern boundary (in the Channel).
The northern boundary conditions (Aberdeen to the north of Denmark) are uniform
over the year. Therefore the second objective is that the model boundaries have to be
composed in a consistent way as well. This implies that the boundary conditions have to
be compared to literature and observations and seasonal variability has to be included
when the number of observation data is sufficient.
A third objective is to investigate the contribution of each country to the nutrient
concentrations in the North Sea. In this way the area of influence of each country is
visualised, so the transboundary loads are indicated. Those simulations are created by
the addition of a tracer to the nutrient of each country. This simulation is done for the
boundary loads as well.
Thus the objectives of this thesis are:
To quantify the terrestrial nutrient loads on the southern North Sea in a consistent way.
To specify the boundary conditions of the southern North Sea regarding nutrient concentrations in a
consistent way.
To determine the contribution of these loads to the nutrient concentrations in the southern North Sea.
1.3.
Scope
The area of interest in this master thesis research is the southern North Sea and
specifically the Dutch Continental Shelf. The southern boundary of the model area is in
the Channel (Southampton – Cherbourg); the northern boundary is between Aberdeen
and the north of Denmark. The research covers the riverine nutrient load and the
boundary conditions regarding nutrient concentrations; the water movement is adapted
from previous studies. However the validity of this hydrodynamic set-up will be
checked. The research covers the period from 1996 to 2003, herein the first year is used
as spin-up year and the subsequent years are considered as model results.
1.4.
Framework
The master thesis research is carried out in collaboration with the Marine and Coastal
Management department of WL | Delft Hydraulics. The research about the riverine
nutrient loads and boundary conditions of the two-dimensional model are used within a
research project for RWS RIKZ, namely Blauw et al. (2006). Other investigations are
independent of this project. The results of transboundary nutrient transports are
adopted from Blauw et al. (2006).
1.5.
Reading guide
The report starts with a description of the model set-up in chapter 2. Secondly the
terrestrial nutrient loads on the model are described in chapter 3. Chapter 4 concerns
the two open model boundaries. Chapter 5 describes the model behaviour of the threedimensional model and the model results. Finally, the conclusions are included in
Chapter 6 and recommendations are given in Chapter 7.
4
2.
Model
This chapter gives an overview of the computational model used. First the modelling
software package itself is discussed in section 2.1. The used model grid is discussed in
section 2.2. The details about the hydrodynamic model software are discussed in section
2.3. Section 2.4 deals with the used water quality model. The chapter ends with a
conclusion in section 2.5.
2.1.
Modelling software
The Delft3D software by WL | Delft Hydraulics is used as modelling software. The
ecological behaviour in the North Sea is simulated by two coupled models. First the
hydrodynamics are simulated in the hydrodynamic Delft3D-FLOW model. The results
of this model are coupled to the water quality model, called Delft3D-WAQ.
2.2.
Model grid
The model uses the Zunogrof grid that contains 4350 computation cells in the twodimensional set-up. A map of the grid layout is given in Appendix A. The size of the
grid cells varies over the model area: the smallest cells are near the Dutch coast (2x5
km) and the largest near the model boundaries coast (20x20 km).
The model has two open boundaries: a southern one in the Channel (Cherbourg –
Southampton) and a northern one between Aberdeen and the north of Denmark.
In the three-dimensional model a sigma layer set-up is used. The percentage depth of
each layer is, from top to bottom, 4.0%, 5.9%, 8.7%, 12.7%, 18.7%, 18.7%, 12.7%,
8.7%, 5.9% and 4.0%.
2.3.
Hydrodynamic model
An existing hydrodynamic model is used, as it is outside the scope of this thesis to set
up a hydrodynamic model. The model was used during the impact studies of the
Maasvlakte 2 land reclamation projects (De Goede et al. 2005; Prooijen et al. 2006) and
is based upon models used in previous projects as the proposed land reclamation for a
new airport island. The mud transport simulations are described in Prooijen et al.
(2006).
The hydrodynamics in the model are calculated by open boundary forcing by the tide,
variable wind forcing and variable river discharges as described in Prooijen et al. (2006).
The boundary conditions regarding water flow are prescribed by forced water levels.
These water levels are extracted from a large hydrodynamic model which covers the
entire continental shelf, the CSM model (Robaczewska et al. 1997). This model does
not take into account the impact of the wind and air pressure. Therefore the flow
through the boundary itself is not necessarily a good simulation of the hydrodynamic
situation.
In addition to the water inflow by the model boundaries, there is water inflow by the
rivers as well. These fresh water inputs cause density differences that affect the
hydrodynamic situation along the coast. Updates of river discharges in the ecological
model have not been applied in the hydrodynamic model in this study, as discussed in
section 2.1.
5
2.4.
Water quality model
Two subjects about the water quality model are discussed in two separate subsections,
namely the used model set-ups and the conceptual model description.
2.4.1.
Model set-ups
The water quality model is not started from scratch, like the hydrodynamic model. The
model set-up by De Goede et al. (2005) and Prooijen et al. (2006) is used also. This setup includes time series of discharges and nutrient concentrations for all major Dutch
rivers. The major rivers in the United Kingdom and Germany are schematised by long
term averages; other rivers as the Seine and smaller foreign rivers are not included. An
overview of all rivers that are in this model set-up is given in section 3.1 on page 9.
The model set-up by Prooijen did not take into account the atmospheric deposition of
nutrients. After recent research by Karen van de Wolfschaar (Blauw et al. 2006)
atmospheric deposition of nitrogen is added to the model by EMEP data of the year
2000 (EMEP 2006). The model set-up of Prooijen including the atmospheric deposition
of nitrogen is called “previous model set-up” from now on.
Within this thesis several water quality model set-ups are discussed. The characteristics
of each model set-up are tabulated in Table 2-1.
Table 2-1: Overview of model set-ups, abbreviations explained in Table 2-2.
Prooijen et al. (2006)
“Previous model”
Blauw et al. (2006)
“New 2D”
“New 3D”
Rivers
North
boundary
Coarse
Uniform
Detailed
Detailed
updated
South
boundary
Atmospheric
deposition
No
Seasons
Yes
Set-up
2D
Seasons
Seasons +
stratification
3D
Table 2-2: Abbreviations used in Table 2-1.
Coarse
Detailed
Detailed updated
Uniform
Seasons
Season
Dutch rivers by time series, others by long term average.
All rivers by time series.
All rivers by time series, Firth of Forth data updated and changed
location for Channel Gent-Terneuzen.
Uniform over seasons
Includes seasonal variability
Includes seasonal variability and stratification
The time step of the water quality models is 30 minutes.
6
2.4.2.
Conceptual model description
This section gives a short overview of the computations in the water quality model
Delft3D-WAQ. A more detailed overview is given in WL|Delft Hydraulics (2005).
The model calculates the mass transport for each grid cell and parameter for each time
step by use of the advection diffusion reaction equation. The simplified advection
diffusion reaction equation is:
M it
t
Using:
M it
M
M
t
t
t
i
t
M
t
t
Tr
t
P
M
t
S
Mass at the end of a time step.
Mass at the beginning of a time step.
M it
t
Time step.
M
t
M
t
M
t
Changes by transport.
Tr
Changes by physical, (bio)chemical or biological processes.
P
Changes by sources, like rivers.
S
The processes that act on the model variables are given in Figure 4-1. A complete
overview of the transport, processes and changes by sources on the nutrient variables is
given in Figure 2-1.
Figure 2-1: Processes on model variables. Like figure 4.1 in Boon et al. (2001). The
processes are explained in Appendix B.
7
This equation is solved by numerical schemes. Several numerical schemes can be used
in the Delft3D. Within this thesis the Flux Correct Transport method (FCT) is used in
the two-dimensional models. This is the most accurate scheme available. The method
combines the upwind scheme and Lax Wendroff method. An estimation of the
concentrations is calculated by the upwind scheme. Afterwards the difference between
the Lax Wendroff method and the upwind method is calculated, the difference is called
the anti-diffusion term. The anti-diffusion term is only applied when no new minimum
and maximum occur, as described by (Boris et al. 1973). The method is named flux
corrected transport because the anti-diffusion term is a flux term and causes mass
conservation.
In the three-dimensional model the FCT scheme is used in the horizontal direction, in
the vertical direction the Crank-Nicolson method is used. The scheme is selected as it
uses the same horizontal scheme as the two-dimensional model. Therefore a fair
comparison is possible with the two-dimensional results. In the vertical direction a
central discretisation is used which prevents artificial mixing as occurs in upwind
schemes.
The computation time for the simulation of one year is about one and a half hour for a
two-dimensional model on a 2.4 GHz computer. The three-dimensional model has a
computation time of over ten hours.
2.5.
Conclusion
In the thesis the Delft3D modelling suite is used. The software package Delft3DFLOW is used to simulate the hydrodynamics and mud transport in the model area; the
package Delft3D-WAQ simulates the water quality processes.
The model set-up is adapted from previous studies in the same area, namely De Goede
et al. (2005) and Prooijen et al. (2006). Atmospheric deposition is added to the model
set-up of the water quality model in these studies; this is regarded as the reference
situation in this thesis and called ‘previous mode’ from now on.
8
3.
Terrestrial nutrient loads
This chapter deals with the terrestrial nutrient loads in the model. The first section
discusses the data sources from which time series of discharge and concentration data
are extracted. Techniques to estimate missing data are discussed in section 3.2. Section
3.3 discusses the way observations are assigned to model variables. In section 3.4 the
ability and necessity of the model software to deal with estuarine retention is
investigated. The difference between the number of rivers in the hydrodynamic model
and the water quality model is explained in section 3.5. The chapter ends with a
conclusion that gives the results of the new input data as well.
3.1.
Data availability
Two types of data sources can be determined: the major rivers of which discharge and
concentration data are available and secondly the smaller rivers and direct loads of
which only annual nutrient loads are available. The data sources of both types are
discussed in separate subsections.
3.1.1.
Major rivers
A tremendous amount of work on time series of riverine observations has been done by
the German scientists Lenhart and Pätsch. They have created time series of the major
Dutch and German rivers (Lenhart et al. 2005) and the British ones (Pätsch 2005).
These time series are very useful within this project.
The time series by Lenhart and Pätsch do not fully fulfil the required model input, as
some parameters are missing, like chlorophyll-a and oxygen. For the Dutch and
German rivers the year 2003 is missing at all. In these cases the concerned authorities
are contacted and missing data is gathered (Arbeitsgemeinschaft für die Reinhaltung der
Elbe 2006; Engels 2005; Flussgebietsgemeinschaft Weser 2006; RIKZ/RIZA 2006).
Data of Belgian, French and Danish rivers is not collected by Pätsch et al. Time series
of Belgian and Danish rivers and small loads are collected via annual OSPAR reports
(OSPAR Commission 2000a; 2000b; 2001a; 2001b; 2002; 2003a; 2004; 2005). The
Western Scheldt is in this thesis regarded as a Belgian river; however the data is
available via Pätsch and Waterbase (RIKZ/RIZA 2006). Data about French rivers is
gathered via the French authorities by internet (Agence de l'Eau Artois-Picardie 2006).
3.1.2.
Other loads
Next to nutrient loads by the major rivers, there are other nutrient sources that
discharge on the North Sea. These sources are e.g. sewerage treatment plants, industry
and loads downstream of the observation locations in major rivers. These loads are
tabulated in annual OSPAR reports (OSPAR Commission 2000a; 2000b; 2001a; 2001b;
2002; 2003a; 2004; 2005) per country and region as annual load in kilotons per year. In
the model these sources are accumulated per geographical region in order to limit the
amount of sources in the model.
9
3.1.3.
Overview
All rivers and smaller sources that are in the new model are given in Figure 3-1. Rivers
that were already in the previous model set-up are indicated in bold; the new rivers are
marked with an asterisk. The data availability is given per load and parameter in Table
3-2. A detailed description about the data availability per country is included in
Appendix C.
Name of source
Belgium
Western Scheldt
Belgian Yser
Belgian Coast
Seine
AA
Authie
Bresle
Canche
Dun
Durdent
French Yser
Saane
Scie
Somme
Wimille
North
Middle
South
Elbe
Ems
Weser
Elbe Region
Ems Region
Weser Region
35
33*
34*
21
32*
29*
27*
30*
23*
22*
26*
24*
25*
28*
31*
54*
53*
52*
49+50
45
47
51*
46*
48*
France
Denmark
Germany
10
Country
Name of source
The Netherlands Haringvliet
Lake IJssel
Nieuwe Waterweg
Noordzee Kanaal
Ch Gent-Terneuzen
EemsDollard
Katwijk
Lauwersmeer
Eastern Scheldt Region
United Kingdom Humber
Solent
Tees
Thames
Wash
Firth of Forth
Tay
Region E1
Region E10
Region E11
Region E13
Region E14
Region E16
Region E2
Region E3
Region E4
Region E6
Region E7
Number in map
Country
Number in map
Table 3-1: River names of Figure 3-1
38
42
39
41
36*
44*
40*
43*
37*
11
19
8
16
13
2
1*
3*
14*
15*
17*
18*
20*
5*
6*
7*
9*
10*
Figure 3-1: Map of model boundaries and nutrient loads, numbers in Table 3-1.
Rivers that were included in the previous model set-up are printed in bold, new loads are
marked with an asterisk.
11
Belgium
Western Scheldt
Belgian Yser
Belgian Coast
France
Seine
AA
Authie
Bresle
Canche
Dun
Durdent
French Yser
Saane
Scie
Somme
Wimille
Denmark
North
Middle
South
Germany
Elbe
Ems
Weser
Elbe Region
Ems Region
Weser Region
The Netherlands Haringvliet
Lake IJssel
Nieuwe Waterweg
Noordzee Kanaal
Ch Gent-Terneuzen
EemsDollard
Katwijk
Lauwersmeer
Eastern Scheldt Region
United Kingdom Humber
Solent
Tees
Thames
Wash
Firth of Forth
Tay
Region E1
Region E10
Region E11
Region E13
Region E14
Region E16
Region E2
Region E3
Region E4
Region E6
Region E7
35
33*
34*
21
32*
29*
27*
30*
23*
22*
26*
24*
25*
28*
1996
31*
54*
53*
52*
49+50
45
47
1996
51*
1996
1996
46*
1996
1996
48*
1996
1996
38
42
39
41
36*
44*
40*
43*
37*
11
19
8
16
13
2
1*
3*
14*
15*
17*
18*
20*
5*
6*
7*
9*
10*
Legend
10 >= values per year
1 < values per year < 10
No values available at all
Data sources and numbers refer to notes in Appendix A
12
O2
CHLA
SI
TOTP
Data source
PO4
TOTN
KJELDAHLN
NH4
NO2
Parameter
NO3
Name of source
Q
Country
Number in map
Table 3-2: Data availability per river.
2003
2003
8
1
2
3
8
8
8
2003
2003
1996 1996 1996
1996 1996
4
1996 1996 1996
2003
2003
2003
8
2003
2003
2003
2003
8
8
8
8
8
8
8
8
8
8
8
5
5
7
7 1996
5
5
5
5
5
6
6
6
6
6
6
6
6
Pätsch
Ospar
Ospar
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Internet
Ospar
Ospar
Ospar
Pätsch
Pätsch
Pätsch
Ospar
Ospar
Ospar
Pätsch
Pätsch
Pätsch
Pätsch
Ospar
Ospar
Ospar
Ospar
Ospar
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Internet
Internet
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
Pätsch
1
Footnotes
No data available, except 5 measurements in 1997
2
No data available, except 7 measurements in 2003
3
No data available in period 2000-2003
4
No data available in 1996 and 2001
5
Silicate is calculated by discharge characteristics, as described in section 3.2.5.
6
Only annual average concentrations are available (Department for Environment, Food
and Rural Affairs 2006)
7
Chlorophyll-a concentration not available, Elbe used
8
Total phosphorus concentration calculated by phosphate (or inverse), see paragraph
3.2.4
1996, etc.
Time series have no missing data, except the year involved
Pätsch
Data is available by Pätsch et al. (2004) or Lenhart et al. (2005). Missing data is added
by observation data from authorities (Arbeitsgemeinschaft für die Reinhaltung der
Elbe 2006; Engels 2005; Flussgebietsgemeinschaft Weser 2006; RIKZ/RIZA 2006)
Internet
Data downloaded from website of Agence de l'Eau Artois-Picardie (2006) or Scottish
Environment Protection Agency (2006)
OSPAR
Annual loads are given in annual OSPAR-reports (OSPAR Commission 2000a; 2000b;
2001a; 2001b; 2002; 2003a; 2004; 2005); the distribution over the year approximated
proportional to precipitation surplus or river discharge see Table 3-4 on page 26.
In Table 3-2 is visible that a couple of parameters are missing in several rivers. Some
techniques are investigated to estimate the missing parameters. These techniques are
discussed in the next section.
13
3.2.
Missing data
There are two types of missing data:
Gaps in the data availability for a short period
Parameters of which no measurements are available at all
For consistency we need to fill those data gaps. The first problem can easily be solved
by interpolation when it concerns a short period. The used interpolation technique is
discussed in the first subsection (3.2.1). When it concerns a longer period, it is preferred
to use average data or data of the preceding or succeeding year. When there is a trend in
the data, it is preferred to use the latter technique as it deals better with trends, but it
does not include climate influences.
The second problem is more difficult to solve, although it is preferred for the model
consistency that all parameters are included in all sources. Therefore the subsection
3.2.2 and following deal with methods to estimate nutrient parameters which are in the
model input, but of which no observations are available in some rivers.
3.2.1.
Used interpolation technique
Lenhart and Pätsch composed time series of discharge and nutrient loads in the major
rivers in the period 1970-2002 (British rivers 1977-2004). Daily time series of discharge
are created by linear interpolation, the time series of nutrient loads are calculated by use
of double linear interpolation. This technique is used in the previous model input as
well. Double linear interpolation means the computation of daily loads using daily
interpolated values for discharge and concentration, in formula:
k
CiLI QiLI
Li
i 1
using:
Li
Load for day i
CiLI
Mean concentration at day i when a concentration value is
available for day i, otherwise linearly temporal interpolated
concentration value.
Mean discharge at day i when a discharge value is available for day
i, otherwise linearly temporal interpolated concentration value.
QiLI
Lenhart and Pätsch list only the result of the double linear interpolation, so it is
unknown whether the nutrient load is based upon measured or interpolated values.
Therefore the measurement interval is unknown.
3.2.2.
Nitrite
Observations of the nitrite concentration are available for the small German and Dutch
sources and all French and Belgium rivers.
In literature a strong and positive correlation to total dissolved nitrogen and nitrate has
been described by Jarvie et al. (1998). This relation has been checked by an analysis of
data of several small French rivers. A plot is given in Figure 3-2.
14
Nitrite+Nitrate vs Nitrate, small French rivers, 1970-2006
14
12
Nitrate [g/m3]
10
8
6
4
2
2
y intercept set to zero, y=0.9912*x, r =0.9992
0
0
2
4
6
8
Nitrite+Nitrate [g/m3]
10
12
14
Figure 3-2: Sum of nitrite and nitrate versus nitrate in small French rivers.
In the plot the strong and positive correlation (r 2 = 0.99) between the sum of nitrite and
nitrate and the nitrate concentration is clearly visible. The nitrate concentration is equal
to 0.9912 times the sum of nitrite and nitrate, which can be rounded to one.
This relation can be used to calculate the nitrite concentration too: the nitrite
concentration is equal to less than one percent of the nitrate concentration. As one
percent is such a small amount, the nitrite concentration is neglected when not included
in the observations.
3.2.3.
Phosphate
The phosphate concentration is given in nearly all rivers, except the Danish ones. The
phosphate concentration in the Danish rivers is calculated by use of the total
phosphorus concentration as described in the next section.
3.2.4.
Total phosphorus
The concentration of total phosphorus has a relation with the phosphate concentration,
as described by Turner et al. (2003). Turner states that phosphate is equal to “46% of
total phosphorus in the Mississippi River watershed sub basins, but 70% in the small
US watersheds”. Turner suggests that the difference in the two numbers was probably
caused by a higher turbulence in larger rivers that causes a higher suspended sediment
concentration and more refractory phosphorus. The relation is subject of research as
the numbers are only based upon American rivers and Turner did not include
information about a linear regression analysis.
Total phosphorus and phosphate observations are extracted from the database when
both observations are available on the same day. The selection is limited to the period
1996 to 2002: 1996 is the first year of the model, 2002 is chosen as last year as there is
no total phosphorus measurements later than 2002 in the major rivers. Interpolated
values are not selected from the database10. Per river the ratio between phosphate and
Data in the datasets of Lenhart and Pätsch is given as daily discharge and loads, by use of double linear
interpolation; this means that no distinction on interpolated or measured values can be made in the database.
More information about the database itself is given in Appendix G.
10
15
total phosphorus is determined for each day that both observations are available;
afterwards the average ratio for each river is calculated and plotted in Figure 3-3. In this
graph a linear regression curve is plotted. The goodness of fit is quite good (r2=0.66),
the relation between phosphate and total phosphorus is: TotalP=1.42×PO4+0.07 .
This relation is implemented in the input of all rivers where total phosphorus is lacking.
Its inverse is implemented in the Danish loads, as phosphate is lacking and total
phosphorus not.
Phosphate versus Total P per river, period 1996-2002
0.6
Seine
0.55
0.5
Dun
Western Scheldt
Total P [g/m3]
0.45
0.4
Canche
0.35
0.3
Noordzeekanaal
Elbe
0.25
0.2
Ems
0.15
0.1
0
0.05
Nieuwe Waterweg
Wimille
Haringvliet
y=1.42*x+0.07, r2=0.66
0.1
0.15
0.2
PO4 [g/m3]
0.25
0.3
0.35
Figure 3-3: The ratio phosphate/total phosphorus in rivers where both parameters are
observed.
3.2.5.
Silicate
The data about silicate is lacking in several stations: Ems, Weser, all British, all French
and the small German and Dutch stations.
The concentration of silica is related to the temperature. Silica dissolves more easily at
higher temperatures, which causes higher silica concentrations in rivers with lower
latitude (Turner et al. 2003). But the variation in latitude around the North Sea is small,
so the long term temperature difference between the different rivers is negligible within
this thesis. Next to the temperature differences by latitude, there is an annual seasonal
pattern in the silicate concentration. This annual trend has about the same period as the
annual diatom bloom. Diatoms consume silica, so one might expect a decrease in silica
concentrations during the summer. This is indicated in Figure 3-4: during increasing
water temperatures the silicate concentration is decreasing.
16
Silicate concentration and temperature in Maassluis
6
30
Si
25
T
4
20
3
15
2
10
1
5
0
1996
1997
1998
1999
2000
2001
Date
2002
2003
2004
Temperature [oC]
Silicate [g/m3]
5
0
2005
Figure 3-4: Silicate concentrations and temperature in Maassluis.
The relation between Silica and temperature is plotted for some Dutch rivers in Figure
3-5. This relation can be considered as significant (R-squared around 0.57). The
goodness of fit increases slightly when a time lag is added to the silicate time series.
Maassluis
IJmuiden
30
r =0.585
20
10
0
0
5
2
r =0.568
20
10
0
0
Silicate [g/m3]
5
10
Silicate [g/m3]
Temperature [oC]
2
Temperature [oC]
Temperature [oC]
30
Schaar van Ouden Doel
30 2
r =0.575
20
10
0
0
5
10
Silicate [g/m3]
Figure 3-5: Silicate concentration vs. temperature for some Dutch rivers.
A different method has to be used to estimate the silica concentration, as there is no
daily temperature data available for all rivers. A relation between the runoff and silica
yield (mass per unit area per year) is studied by Turner et al. (2003). This method is
quite obvious, as the source of silica is the weathering of sedimentary and crystalline
rocks. The weathering depends of course on the soil properties. A map of European
soil properties shows no big differences in European lithology, see Figure 3-6.
17
Figure 3-6: World wide lithology, map by Miotte-Suchet et al. (2003).
As the dimension ‘area’ is in both axes of Turner’s plot, it is possible to remove this
dimension. Besides that, the time period is changed from year to second. The result is a
plot of discharge versus load, which means that the gradient of the linear regression line
is the average concentration: Figure 3-7.
Discharge vs Si load, 1996-2003
4000
Nieuwe Waterweg
3500
y=2.89*x,r2=0.9
Silicate load [g/s]
3000
Elbe
Haringvliet
2500
Seine
2000
1500
Western Scheldt
1000
500
0
Lake IJssel
Noordzeekanaal
0
200
400
600
800
Discharge [m3/s]
1000
1200
1400
Figure 3-7: Discharge versus silicate load for rivers with in the project.
There is an annual trend in the silicate concentration, as mentioned earlier in this
paragraph. Therefore the relation between discharge and silicate load, as described
above and plotted in Figure 3-7, is determined for each month. This results in an
average silicate concentration for each month. The plots are included in Appendix D;
the monthly concentrations are plotted in Figure 3-8.
18
Figure 3-8: Average silicate concentration in rivers per month.
This method has been implemented in the model for all rivers where the silicate
concentration is lacking.
After the implementation of the discharge relationship in the model, several coastline
observations of silicate were collected from the Environment Agency (2006). These
observations were used, together with maritime observations and Scottish riverine
observations, to create dilution plots. In a dilution plot the silicate concentration is
plotted versus salinity. By use of linear interpolation the freshwater silicate
concentration can be estimated, this estimated value can be used in the model input.
The creation of these plots is described in Appendix E. Nevertheless the number of
silicate observations in brackish British waters was too limited to determine an accurate
riverine silicate concentration. Thus the dilution plots cannot be used to estimate the
riverine silicate concentrations.
3.2.6.
Chlorophyll-a
Chlorophyll-a is only measured in the Seine, Elbe and the large Dutch rivers, so the data
is lacking for many rivers. Literature has been studied to find a method to estimate the
chlorophyll-a concentration.
Mei et al. (2005)
Mei et al. (2005) describes a linear relation between chlorophyll-a and nitrate during
April and May in the North Water Polynya (Canada). This relation was checked on the
data available within this project, but the relation was not valid in European rivers.
Neal et al. (2006)
Neal et al. (2006) published a correlation matrix for several measurable quantities during
the spring-summer low-flow periods in British rivers. This matrix indicates some strong
correlations between chlorophyll-a and other quantities:
To river basin and flow
To suspended sediments, boron and soluble reactive phosphorus (SRP), but a
high degree of scatter occurs. The relationship between chlorophyll-a and
boron and SRP look very similar, because of the high relationship between SRP
and boron.
To particulate nitrogen and particulate organic carbon, but there are two
outliers: the Aire and Calder.
19
The correlation of chlorophyll-a versus discharge, suspended solids and phosphate has
been investigated in the Nieuwe Waterweg, but the correlation to all parameters was
very weak during spring-summer (r2 even lower than 0.05). The chlorophyll-a versus
discharge has also been examined in the Elbe, but the correlation has a comparable
goodness of fit as the Nieuwe Waterweg. The relations with boron, particulate nitrogen
and particulate organic carbon cannot be investigated, as there are no time series of
those parameters.
Neal et al. (2006) mentioned an empirical method by Vollenweider (OECD 1982) to
calculate the mean annual chlorophyll-a concentration by the total phosphorus
concentration:
TotP 0.96
Chla= 0.28
Using:
ChlaaMean annual chlorophyll-a concentration
Mean annual total phosphorus concentration
TotP
Neal mentioned some remarks on the Vollenweider method: it calculates annual
averages (so no peak values during summer); uses total phosphorus while SRP is the
most important fraction in the rivers he studies (Humber and Thames) and the equation
was constructed for lakes which have a lower maximum phosphorus concentration than
rivers. The Vollenweider method is applied on the data of the Nieuwe Waterweg and
Elbe and Haringvliet, in spite of the remarks by Neal. The time series, annual averages
and calculated values by Vollenweiders formula are plotted in Figure 3-9.
Nieuwe Waterweg
0.2
Elbe
Haringvliet
0.2
0.12
0.1
0.05
0.15
Chlorophyll-a [g/m3]
0.1
0.15
0.1
0.05
0.08
Observation
Calculated
0.06
0.04
0.02
0
2000
Date
0
2000
Date
0
2000
Date
Figure 3-9: Chlorophyll-a, measured and calculated by Vollenweider.
The chlorophyll-a concentration by Vollenweiders formula does not correspond at all
to the measured chlorophyll-a concentration in all rivers. Thus the Vollenweider
method is not applicable on rivers, as discussed by Neal. The mismatch is probably
caused by parameters as residence time and light limitation.
Decrease in nutrient concentration
This method is investigated after a suggestion by Anouk Blauw. The method uses the
relation between the decrease of nutrient concentrations during spring/summer and the
increase of chlorophyll-a concentrations in the same period.
20
Monthly average observations, 1996-2003
Nitrate [g/m3]
100
50
0
1 2 3 4 5 6 7 8 9101112
Months
1
0.5
0
10
Silicate [g/m3]
Phosphate [g/m3]
Chlorophyll-a [mg/m3]
The analysis is done on all rivers that have chlorophyll-a data during the period of the
model run: Seine, Elbe, Western Scheldt, Haringvliet, Nieuwe Waterweg,
Noordzeekanaal en Lake IJssel. Monthly averages are extracted from the database. A
short look on these average monthly concentrations shows that some rivers have a quite
obvious annual trend; while others have a more flat one, see Figure 3-10.
1 2 3 4 5 6 7 8 9101112
Months
5
0
1 2 3 4 5 6 7 8 9101112
Months
6
4
2
0
1 2 3 4 5 6 7 8 9101112
Months
Elbe
Haringvliet
Nieuwe Waterweg
Noordzeekanaal
Western Scheldt
Lake IJssel
Seine
Figure 3-10: Monthly average observations of chlorophyll-a and nutrients in rivers.
The differences between the maximum, i.e. winter, concentration and the averaged
monthly concentration are calculated per month, per river and per nutrient. In formula
form:
ci
max(c1 , c2 ,.., c12 ) ci
using
ci
ci
Deviation from maximum annual value for month i.
Nutrient concentration in month i.
The result of these calculations are plotted in scatter plots to the average monthly
chlorophyll-a concentration, see Figure 3-11. Linear trend lines are added to each
scatter plot, together with a goodness of fit ratio.
Figure 3-11: Decrease of monthly nutrient concentrations to winter concentrations
plotted to monthly average chlorophyll-a concentration.
21
The goodness of fit is bad for phosphate and silicate, but better for nitrate. The bad
goodness of fit can be explained by the winter months. In the winter months the
observed concentration is around or equal to the maximum concentration, which
causes that in these months the difference between the maximum concentration and the
observed concentration is nearly equal to zero. The relation between the chlorophyll-a
concentration and a number that is around or equal to zero is quite oscillating. This is
showed in monthly plots that are included in Appendix F. The goodness of fit of these
plots is tabulated in Table 3-3.
Table 3-3: Goodness of fit on monthly basis, values bigger than 0.3 are highlighted.
Plots are included in Appendix F.
January
February
March
April
May
June
July
August
September
October
November
December
Nitrate
0.03
0.07
0.28
0.83
0.69
0.93
0.93
0.74
0.80
0.79
0.70
0.78
Phosphate
0.21
0.01
0.00
0.02
0.00
0.01
0.04
0.23
0.80
0.73
0.42
0.00
Silicate
0.00
0.08
0.61
0.89
0.34
0.00
0.01
0.04
0.06
0.61
0.88
0.81
The goodness of fit when the linear regression is calculated per month is better than the
goodness of fit on annual basis, although there is a bad fit during winter months. There
is a distinction with nitrate and the other nutrients, as the goodness of fit of
chlorophyll-a to nitrate is considerably better.
The relation of chlorophyll-a to nitrate for the months April to December is used in the
next model run. In the first three months of the year the relation has a bad goodness of
fit, in this period a linear interpolation of the relation in December and April can be
used.
The relation is not yet implemented in the model, because other subjects in the thesis
have a higher priority. In the current model the chlorophyll-a concentration of the
Weser and Ems, two major rivers, and of a couple of smaller rivers was lacking. In the
Weser and Ems the chlorophyll-a concentration of the Elbe is used, as the Elbe is a
neighbouring river, in smaller rivers no chlorophyll-a concentration is added.
22
3.2.7.
Discharge
The annual OSPAR reports list only annual loads, so there is no distribution over the
year given. These loads can be distributed evenly over a year, but the real discharge is
simulated in a better way when there is an uneven distribution, since nutrient loads have
an unequal distribution over the year: high in winter, low in summer. An easy parameter
to determine this distribution is the discharge, as discharge is generally available for
neighbouring rivers or polder pumping stations. Two cases studies are done in order to
check whether it is allowed to use the distribution of a river discharge or pumping
station over a year as factor for the nutrient load over the year. First the relation
between the nutrient load and discharge in the river Elbe is discussed; secondly the
relation between nutrient load and discharge of the Katwijk pumping station is
discussed.
Elbe case study
The relation between the nutrient load and the discharge in the Elbe River is
investigated by observation data over the period 1996-2003.
The Elbe is selected as all parameters are in its data set and it is likely that the discharge
characteristics of the Elbe are used for the annual distribution of the small sources in its
vicinity. Discharge and nutrient load data of the river Elbe is extracted from the model
input. The average monthly contribution of discharge and nutrient load to the annual
discharge and nutrient load is plotted in Figure 3-12. The graph shows a distribution
that is expected: the nutrient loads have –more or less- the same distribution as the
discharge, while the chlorophyll-a concentration has a different distribution. Thus a
good estimation of the monthly nutrient loads can be made by use of the discharge
characteristics. As the chlorophyll-a concentration does not have the same trend as the
discharge this method is not valid for the chlorophyll-a load. The last result is quite
obvious, as the decrease of nutrients is mainly caused by the increase of chlorophyll-a
during spring and summer. However the chlorophyll-a load is not in the annual OSPAR
reports, so there is no need to fit this parameter on the discharge.
Figure 3-12: Average monthly loads versus discharge in Elbe, 1996-2003.
Chl-a = Chlorophyll-a.
23
Katwijk pumping station case study
The relation between the nutrient load of the Katwijk pumping station and the
precipitation surplus near Katwijk is investigated by observation data over the period
1996-2003.
Actual observations of the Katwijk pumping station are collected from the authority in
charge of the pumping station (Hoogheemraadschap Rijnland 2006). This data set is not
used in the model itself. Nutrient concentrations are observed at two locations
upstream of the pumping station and at one location inside the pumping station. The
observations inside the pumping station are done on a weekly basis; the frequency at
the other locations is less. The observation data inside the pumping station are used in
this analysis as the sampling frequency is high, besides that the observations are in the
same range as the open air observations (see Figure 3-13).
Figure 3-13: Observations of concentrations and discharge around Katwijk pumping
station. RO457: Julianabrug, 900m upstream of the pumping station; RO037: Just
upstream of the pumping station; RO037V: Inside the pumping station.
Daily loads are calculated by use of double interpolation, so discharge and
concentration are first interpolated to daily values and multiplied afterwards. However
the discharge is observed daily, so there was no interpolation necessary in this time
series.
The precipitation surplus is calculated for Den Helder (Airfield De Kooy). The
evaporation is calculated by two methods: first by actual evaporation via the Penman
formula (Anonymous 2005; Van den Akker et al. 2000) and secondly by long term
average Makkink evaporation (period 1971-2000). These two time series are plotted in
Figure 3-14. Both methods give comparable results, the results by the Penman formula
are selected as these are based on actual observations instead of long term average
values.
24
Figure 3-14: Precipitation surplus De Kooy. Data KNMI (2000; 2007).
As both data series (i.e. daily loads and precipitation surplus) are available now the
series can be compared to each other. The average monthly contribution to the annual
load of each parameter is plotted in Figure 3-15. Zeros replace the negative
precipitation surplus during summer.
Figure 3-15: Monthly loads and precipitation surplus as percentage of the annual load
and precipitation surplus.
The assumption that the annual load can be distributed over the year by the
precipitation surplus is correct when the percentages for each quantity are equal for
each month. However it is visible in Figure 3-15 that the percentages are not equal to
each other. The major distinction is that the precipitation surplus is negative during
summer, so zero in this calculation, but the nutrient load is not equal to zero. In the
winter period the percentage of the precipitation surplus is higher than the loads. Thus
the assumption is not valid for the Katwijk pumping station.
A different method to estimate the monthly load by the annual load is to use an even
distribution over the months. This is indicated by the dotted line in Figure 3-15. It is
clear that this method causes an overestimation in summer and underestimation in
winter.
When the method via the precipitation surplus is compared to the even distribution, the
first method creates better estimates as it includes the winter peak also. Therefore it is
the best method available, as it gives an annual trend in the data, although there is some
under- and overestimating.
25
Conclusion
The magnitude of the monthly riverine nutrient load as percentage of the annual load is
of the same order as the monthly water flow as percentage of the annual water flow for
the river Elbe. The assumption is made that this relation yields in neighbouring rivers
too. Therefore the monthly water flow as percentage of the annual water flow of the
river Elbe is used to estimate monthly loads of small rivers and sewerage loads in
Germany and Denmark as percentage of the total load. The method is applied on the
Belgian smaller sources too, by use of the Western Scheldt discharge characteristics.
The assumption that the nutrient load of a pumping station can be estimated by use of
the positive precipitation surplus is not totally correct. However the method is used on
small Dutch loads, as no better method is available. An overview of all the small
stations and their conversion method is given in Table 3-4.
Table 3-4: Overview of calculation methods of monthly loads.
Methods Data source of method
P
R
Belgium
Belgian Coast
Western Scheldt
(RIKZ/RIZA 2006)
Belgian Yser
Western Scheldt
(RIKZ/RIZA 2006)
The
Channel Gent-Terneuzen
Vlissingen (KNMI 2000)
Netherlands Eastern Scheldt Region
Vlissingen (KNMI 2000)
Ems Dollard 11
Den Helder (KNMI 2000)
Katwijk 12
Lauwersmeer
Germany
Ems Region
Elbe (Lenhart et al. 2005)
Elbe Region
Weser Region
Denmark
South Denmark
Middle Denmark
North Denmark
Methods:
P:
Precipitation surplus
R:
River discharge
Country
11
12
Source
Includes Duurswold, Eemskanaal and Nieuwe Statenzijl
Includes pumping stations of Vlotwatering, Scheveningen and Katwijk.
26
3.3.
Model variables
The conversion of nutrient observations in loads and boundaries to model parameters
is done by methods according to Los et al. (1994). Each model variable is discussed
below.
The model variable nitrate includes the nitrite concentration. The nitrite
concentration is very low compared to the nitrate concentration, less than 1%
of the nitrate concentration (see 3.2.2 page 14), so it is not of much interest to
trace nitrite. Therefore the model variable is not included in the modelling
software.
Ammonium is just ammonium.
The model variable phosphate is not represented by the observed orthophosphate concentration, but by the orthophosphate concentration and the
labile phosphorus. Labile phosphorus is assumed to be reversibly adsorbed to
the suspended matter and available to the algae soon. It is estimated by a
quarter of the total phosphorus concentration minus orthophosphate and
detritus phosphorus.
Silicate is just silicon dioxide.
The detritus load is calculated by multiplication of the observed chlorophyll-a
concentration by an averaged chlorophyll-a/nutrient ratio. These ratios are
derived by Los (1991) for fresh water and saline water. An assumption is that
all the freshwater algae will die as soon as they enter the saline water of the
North Sea, so the amount of detritus is equal to two times the living algae
population.
The multiplication by two is not applied on the open boundaries, as there is no
extra mortality of algae here.
Table 3-5: Estimation of nutrient loads from measured riverine data
in present GEM (Blauw et al. 2006).
Model variable
Nitrate (NO3)
Ammonium (NH4)
Phosphate (PO4)
Silicate (Si)
Detritus16 carbon (DetC)
Detritus nitrogen (DetN)
Detritus phosphorus (DetP)
Detritus silicate (DetSi)
Estimation from measured data
NO3 + Nitrite (NO2 )
NH4
PO4 + 0.25 ( TotalP – PO4 – DetP)
SiO2
chlorophyll-a * 0.029 * 2
N = Nitrogen
P = Phosphorus
15 Si = Silicate
16 Detritus is non-living particulate organic material
17 C = Carbon
13
14
27
Unit
(mg N/l) 13
(mg N/l)
(mg P/l) 14
(mg Si/l) 15
(mg C/l) 17
(mg N/l)
(mg P/l)
(mg Si/l)
Alternative methods to compute the detritus fractions are used in other models; these
are tabulated in Table 3-6. The detritus concentrations computed via the methods in
Table 3-6 are higher than the method currently used in the model, which takes into
account the refractory detritus and phosphorus bounded in complexes. The methods
are applied on observation data of Maassluis in Figure 3-16. Because the fate of
refractory detritus18 and phosphorus bounded in complexes19 are not included in the
used modelling software it is not valid to use the alternative methods within this water
quality model.
Table 3-6: Methods to estimate model variables from measured
riverine data (Blauw et al. 2006).
Model variable
PO4
Detritus N
Detritus N
Detritus P
Estimation from measured data
PO4
Kjeldahl-N 20 – NH4
TotalN 21 – NO3 – NO2 – NH4
TotalP 22 – PO4
Unit
(mg P/l)
(mg N/l)
(mg N/l)
(mg P/l)
Figure 3-16: Different methods for the estimation of detritus fractions applied on
Maassluis data (Waterbase). Red dotted line is used in the model, the blue line indicates
different methods. The data are smoothed by a moving average using one year of linear
interpolated observations.
The detritus concentration in the model input is lower compared to the other methods
given in Table 3-6. One can say that the estuarine retention is implemented in this way,
as most of the detritus fraction is assumed to settle in the estuary. The estuarine
retention is discussed in more detail in the next subsection.
Refractory detritus is slowly breaking down detritus.
A complex is the connection between a metal (like Fe2+) and a neutral molecule or a molecule that has free
electrons (like PO43-).
20 Sum of organic nitrogen and ammonium (NH4).
21 TotalN = Total nitrogen concentration.
22 TotalP = Total phosphorus concentration.
18
19
28
3.4.
Estuarine retention
3.4.1.
Introduction
The observation locations in rivers differ sometimes from the location where the rivier
is implemented in the model schematisation. An example is the German river Elbe. The
observations are done in the freshwater part of the river, while time series of these
freshwater observations are just appointed to a marine grid cell, as illustrated in Figure
3-17. An error might be introduced by this schematisation as the chemical properties of
water change when it flows through an estuary (e.g. Billen et al. 1991; Wollast 1983), so
the validity of this method is checked by literature study and a case study of the
Western Scheldt estuary.
Figure 3-17: Map of Elbe region and salinity observations along the estuary.
Data from Beusekom et al. (1996).
3.4.2.
Estuarine processes
Large scale flocculation and settlement of the organic and inorganic particles occurs in
every estuary, because the particles are negatively loaded in riverine conditions and they
absorb cations that are available in the saline water (Wollast 1983). The extent of these
ecological processes is characterized by the morphological and hydrodynamic properties
of an estuary as these properties influence the water circulation, the residence time and
the sedimentation processes. The terrestrial particulate organic matter is at least partly
and often completely removed in the estuarine zone (75% in the Scheldt), dissolved
organic matter is partly removed by coagulation and settling (Wollast 1983).
The efficiency of nutrient removal differs per nutrient and per estuary. The removal
percentage is also dependent on the calculation method as discussed by Blauw et al.
(2001): an estuary not only has flow towards the sea, there is an inflow from the sea as
well. This inflow must also be taken into account when you calculate the total nutrient
removal. However in daily life one is mostly interested in the removal of riverine
nutrients and talks about that removal percentage.
29
Some numbers about nutrient removal in world wide estuaries are given in Figure 3-18.
The data by Soetaert et al. (1995) and the model data include the maritime nutrient load,
the other sources only take the riverine nutrients into account. The model results must
be interpreted with care as not the whole estuary is in the present model set-up, but the
used literature sources take into account the whole estuary. There is quite some scatter
in the numbers. However the nitrogen removal is dominant over the phosphorus
removal, because of the denitrification process. Billen et al. (1991) state that the silicate
removal is depending upon the primary production within an estuary and the burial of
the algae. The primary production is –among other things– dependent on the nutrient
load, so the the x-axis of the silicate removal plot is the nitrogen load on the estuary.
Figure 3-18: Estimates of nutrient removal in different estuaries.
(1) Ochlockonee, USA; (2) Amazon, Brazil; (3) Baltic Sea; (4) Delaware Bay, USA; (5)
Narragansett Bay, USA; (6) Scheldt, NL; (7) Chesapeake Bay, USA; (W) World wide
estimation.
3.4.3.
Western Scheldt case
A closer look is taken at the Western Scheldt area as a lot of information about this area
is available in literature. The ability of the model to simulate the water quality processes
in this estuary is subject of research.
After a description of the model set-up, the simulated concentrations in the model are
compared to observations. Secondly the total load from the estuary towards the sea is
discussed. The section ends with a conclusion.
Model set-up
The model set-up includes two nutrient loads in the Western Scheldt estuary: one
represents the Scheldt river and is implemented near the Dutch – Belgian border
(Schaar van Ouden Doel) and a second one represents the Channel Gent-Terneuzen,
see the map in Figure 3-19. The hydrodynamic model is forced by the inflow of fresh
water in Schaar van Ouden Doel. The location of the upper boundary of the estuary is
different to the models in literature: in literature the upper boundary is usually between
the riverine and estuarine Scheldt, near Rupelmonde.
30
Figure 3-19: Overview of Western Scheldt region in model setup.
Blue boxes are model grid cells.
Model results
Model results over the period 1997-2003 of the new two-dimensional model are
averaged over the axis of the estuary and time, the model results are compared to
averaged observations in the periods 1980-1985, 1990-1995 and 2000-2002. The plots,
included in Appendix H.1, show that the model results are in the same range as the
observations in the periods 1990-1995 and 2000-2002. A more detailed validation is
done for the mouth, because the load from the estuary towards the sea is of interest for
the nutrient balance of the North Sea. In Appendix H.2 time series of model results are
plotted versus observations of the Vlissingen SSVH buoy. The time series show that the
variations in model results and observations are similar, although the peaks do not
always match. An exception are the salinity and phosphate concentration. The salinity is
too low and the phosphate too high.
The salinity at the mouth is always lower than observations, as indicated in Appendix
H.2. The simulated salinity is not only low at the mouth of the estuary, but also in its
direct vicinity, see Appendix H.4. The low salinity values near the coast are a known
distinction between the model results and observations (Prooijen et al. 2006, page 40).
Besides the salinity problems at the mouth, there is another salinity issue in the model
as well. The fresh water discharge of the river Scheldt is implemented in the model setup in Schaar van Ouden Doel, see Figure 3-20. Observations in Figure 3-20 show that
the salinity in Schaar van Oudel Doel is between 0 and 15 ppt, however the salinity of
the fresh water in the hydrodynamic set-up is fixed at 0 ppt. Nevertheless the simulated
salinity in Schaar van Ouden Doel is even higher than the observed values, as depicted
in Figure 3-20. Because of the marine influence in the area.
31
Figure 3-20: Salinity and discharge in Schaar van Ouden Doel.
Comparison to literature
Observation data in the estuary is available in Waterbase (RIKZ/RIZA 2006) for
Schaar van Ouden Doel and a buoy near Vlissingen. Waterbase holds observation data
in the North Sea as well. Observations along the estuary axis are given in smoothed
graphs by Soetaert et al. (2006).
The simulated nutrient load from the Western Scheldt estuary towards the North Sea is
compared to literature. A plot of the nutrient load from the estuary to the North Sea is
given in Figure 3-21, detailed plots are included in Appendix H.5. It is important to
keep in mind that the time period of literature data and the model run differs and that
the nutrient concentrations in the estuary differ over the last decades also. Therefore
long term observation data of Schaar van Ouden Doel and Vlissingen SSVH is plotted
in Appendix H.3 and summarized in Table 3-7.
Table 3-7: Average nutrient observations for buoy Vlissingen SSVH per time period.
Graph in Appendix H.3.
23
Period
Same period as in:
1973 – 1983
1980 – 1986
1991
1997 – 2003
Billen et al. (1985)
Soetaert et al. (1995)
Nixon et al. (1996)
Model period
Average is based upon 1977-1983 as no more is data available.
32
Observation data
NH4
NO3
Si
0.9023 4.56
2.23
0.96
4.43
2.60
0.41
0.70
3.30
PO4
0.08
0.05
Figure 3-21: Nutrient loads from Western Scheldt estuary to sea.
Per parameter the differences between literature and the model result are compared.
The ammonium load is larger in literature than in the model simulation: the output
from the model by Wollast (1981) is two times larger, while the output according to
Soetaert et al. (1995) is more than four times higher. The difference between the model
and Soetaert is explained by the higher ammonium concentrations in the past: the
decrease in load is in agreement with the decrease in observed ammonium
concentrations (see Table 3-7). The difference to Wollast cannot be explained as the
time period is unknown.
The differences in nitrate and total phosphorus load between literature and model
results are comparable to the difference in the observed concentrations too.
There is no literature about a detailed silicate budget for the Western Scheldt.
Nevertheless Billen et al. (1991) states that the silicate removal capacity of the Scheldt
estuary is around 50%. The removal capacity of the estuary in the current model is only
around 20% see (Appendix H.5, right column), but the model does not cover the whole
estuary. In literature, the Scheldt estuary extends towards the freshwater part, near
Ruppelmonde (see Figure 3-19). The silicate concentrations in Ruplemonde are higher
(see Appendix H.1), so a higher removal capacity is achieved. Thus the disagreement
between Billen’s percentage and the removal percentage in the model does not
necessarily indicate an error in the model.
33
Soetaert et al. (2006) discusses the estuarine retention by simple mixing diagrams. From
these plots one can calculate the deviation of concentrations compared to conservative
behaviour by the formula:
D
D
MA
MC
M A MC
100
MA
Deviation between actual mass and conservative mass, in percentage
Total actual mass in the estuary, in kilogram.
The estuary is divided into zones per 50 mmol/l chlorinity (equal to three
salinity units). Per zone the observations are averaged per month and
multipied with the volume of the zone. Finally the average of the monthly
mass per zone is calculated and summed over the whole estuary.
Total conservative mass in the estuary, in kilogram.
A calculation like the actual mass is done, but the concentration is calculated
by linear interpolation of the two end-member concentrations.
It is interesting to compare the model results to the results of Soetaert as Soetaert uses
observations along the estuary that have nearly the same period as the model, namely
untill the year 2002.
First of all the graphs of observation data are digitalised24. Afterwards the conservative
behaviour of the parameters in the estuary is determined via the method described by
Soetaert. However there are some difficulties to reproduce Soetaerts results. The
method requires to calculate total mass per salinity bin, but the observation data is
plotted as concentration versus the chlorinity in the article. Therefore Soetaert uses two
sigmoid best-fit equations to caclulate the volume per bin. The first equation converts
chlorinity to a distance from the estuary mouth, the second equation converts the
distance to a cross section. These formulas are given in the article, but the accuracy of
the calculation decreases as the input to the formulas is based upon grabbed data
instead of observation data.
The percentage deviation to conservative behaviour for each parameter as given in
Soetaert is printed in the title of each subplot in Appendix H.6. The recalculated
percentage deviation is given as well. the difference between those two numbers is often
very high, while you would expect that the values are more or less equal. This
distinction is unexpected because the observed and simulated concentrations had a
reasonable match to each other, as discussed on page 31. Therefore the error must be
caused by the conversion from concentrations to actual mass. Despite of those
differences the method is applied on model data anyway, because Soetaert is the only
article that discusses the estuarine retention in recent years.
The described method is applied on the model results also. However it is not possible
to compare the model results directly to the results published by Soetaert et al. (2006),
as Soetarts analysis includes observations from the mouth (Vlissingen) to 90 kilometer
upstream and the model simulation extents only 60 kilometer upstream.
The digitalisation of the graphs has been done by the Matlab script Grabit. This script is available via the
Matlab File Exchange. Via the script an image is loaded and via the mouse cursor the data points of the graphs
are extracted.
24
34
The actual mass in the estuary is plotted for the observation data in Soetaerts article in
Appendix H.6 in black (1990-1995) and green (2000-2002); in blue the actual mass of
the model results is plotted. The red dotted line indicates the conservative behaviour in
the observations over the same period as the model results. It is clear that the actual
mass per salinity class for Soetaerts data and the model results does not agree, but the
angle of the conservative behaviour line is equal in all plots (excluding the ammonium
plot). The disagreement between the acutal masses over the model estuary is remarkable
as the simulated concentrations in the model match with the observed concentrations
(as discussed earlier in this section and plotted in Appendix H.1). The distinction must
be caused by the multiplication of concentrations and volumes, in order to calculate the
actual mass. The observation data are converted twice via formulas that are derived by
Soetaert and the model results are multiplied with the volume of the model grid.
However in the analysis by Soetaert one is interested in the percentual deviation, so the
results of this analysis can be used to check the model behaviour. The error made in the
calculation of volumes is assumed to be equal over the estuary. The percentage
deviation from conservative behaviour is tabulated in Table 3-8.
Table 3-8: Percent deviation from conservative behaviour over model area.
Model area is lower 60 km of estuary. Data is extracted from plots in Appendix H.6.
Model results
Observation data,
period 2000-2002.
Ammonium Nitrate Oxygen
Phosphate
Silicate
-43 %
-4 %
-33 %
-14 %
-16 %
3%
1%
-28 %
-4 %
6%
The precent deviation from conservative behaviour for nitrate and oxygen is similar in
model results and in Soetaerts data. The match between phosphate and silicate in the
model results and observations by Soetaert is worser, the ammonium behaviour is very
divergent.
The distinction in the phosphate and ammonium behaviour is caused by the
concentrations at the upper boundary of the estuary: the simulated concentrations in
the model are considerably higher than the observations, see Appendix H.1. Because
the concentrationsat the mouth are equal to the observations, the deviation is different
than observation data.
The silicate concentration in the model is quite linear between Schaar van Ouden Doel
and the mouth, but the observed concentrations show a deviation from the linear
behaviour. The deviation causes the negative percentual deviation.
3.4.4.
Conclusion
The simulated concentrations in the mouth of the Scheldt estuary have the same
magnitude as observations in the Western Scheldt. However the salinity in the model is
too low compared to observations. Secondly the nutrient load towards the sea agrees
with literature, when you keep the change in nutrient concentrations from the 1980s to
present in mind. Thus the simulated nutrient load from the Western Scheldt towards
the North Sea can be regarded as good.
35
3.5.
Hydrodynamics
The model performs best when the hydrodynamic model includes the same rivers as the
water quality model; however the new water quality model set-up includes more rivers
than there are in the hydrodynamic model one, as the hydrodynamic model is
independent of the water quality model (see chapter 2).
The total discharge in the hydrodynamic model is compared to the discharge of the
rivers that are in the new water quality model set-up. The results are tabulated in Table
3-9.
Table 3-9: Long term average river discharge in hydrodynamics model and in new input.
Country
Hydrodynamic model setup [m3/s]
(De Goede et al. 2005)
The Netherlands
Belgium
France
Germany
United Kingdom
2873
137
461
1177
516
The input to the hydrodynamic model is not
equal to the discharge of the rivers in the
previous model. Therefore these numbers are
not the same numbers as in Appendix K
Deviation of discharge
in new model set-up.
[%]
0%
0%
44%
-3%
122%
__
These numbers are
based upon the data
given in Appendix K.
It is obvious that the river discharges in the Netherlands, Belgium and Germany in the
new set-up are comparable to the hydrodynamic model. The time series for the Dutch
rivers did not change because in both models they are based upon the Waterbase data
(RIKZ/RIZA 2006). There are no German and Belgian rivers added to the model, so
their discharge is constant too. In France and the United Kingdom several rivers are
added as a consequence the total French flow volume has increased with about 50 %
and the British volume has more than doubled.
In general the presence of density differences near the coast causes that the mixture of
riverine pollutants with sea water declines. Therefore the absence of fresh water near
the French and British coasts causes a larger spread of nutrients than in reality. The
exact impact of the small freshwater volume in France and the United Kingdom is not
investigated. However the large fictitious increase of the annual flow volume is a good
reason to recommend such an investigation in the future.
36
3.6.
Conclusion
First of all the riverine input to the model is discussed in subsection 3.6.1. This
subsection deals with the used and missing data; the assignment of observations to
model variables; the way estuarine retention is implemented and the difference between
the rivers in the water quality model and the ecological mode. Section 3.6.2 gives the
results of the used input data and estimation techniques used. The section discusses the
changes in load per country; the annual trend that is visible in the riverine loads and
gives a graphical overview of the major rivers.
3.6.1.
Input data
All loads from rivers and small sources are included in the model input; this is an
update of the previous model because that model only includes for Dutch rivers
detailed data and estimates of foreign ones. The estimates of nutrient loads from
foreign rivers are replaced by time series and new rivers are added. Time series of small
loads in Belgium, the Netherlands and Germany are available as annual load.
The number of parameters that is available for each load is fluctuating, but the basic
parameters are available for all loads. The number of parameters for the Danish loads is
limited to only three, so it is recommended to update these in the future.
There are two methods investigated to estimate missing nutrient observations:
Total phosphorus is estimated by the phosphate concentration
The silicate concentration by discharge
These methods are currently implemented in the model input. A method to estimate the
chlorophyll-a concentration by nitrate concentration in rivers is investigated too. This
method is not implemented in the model, because the implementation of the
chlorophyll-a estimation has a lower priority than the estimation of the nutrient loads.
Time series of rivers are created by linear interpolation of discharge and linear
interpolation of concentrations, the double linear interpolation technique. The annual
loads are converted to monthly values by use of the positive precipitation surplus for
Dutch sources and discharge characteristics of rivers for the Belgian and German
sources.
The nutrient load from rivers is assigned to model variables in the same way as in
previous model studies that use the GEM model. This means that the nitrate load to the
model is equal to sum of the nitrate and nitrite concentration; phosphate is estimated by
the sum of the observed phosphate concentration and an estimation of labile
phosphorus; the detritus fractions do only take into account the fresh organic matter
and are estimated by the observed chlorophyll-a concentration and averaged
chlorophyll-a/nutrient ratios; the ammonium and silicate load is equal to the observed
concentrations.
The characteristics of the riverine loads are changed during their transition in estuaries.
Literature study shows that especially the estuarine nitrogen removal is significant, the
phosphorus and silicate removal is smaller. However it is not possible to use a fixed
nutrient removal percentrage as the extent of estuarine nutrient removal is influenced
by several parameters A case study of the Western Scheldt estuary shows that the model
is able to deal with estuarine processes, but not all estuaries are included in the model
grid. Therefore nutrient loads from those rivers might be overestimated especially
37
regarding the nitrogen load, but one can say that the estuarine retention is implemented
by the low estimation of detritus fractions. The detritus fractions do only take into
account the fresh organic matter, as most of the detritus fraction is assumed to settle in
the estuary.
More rivers are included in the water quality model than in the hydrodynamic model.
The total water flow of all French rivers in the water quality model is fifty percent
higher than the water flow in the hydrodynamic model. The British annual flow volume
has even more than doubled. The low discharges in the hydrodynamic model for
French and British rivers will cause that nutrients are spreading more widely than in
reality, because the mixture of fresh and saline water prevents the spreading of
nutrients. It is recommended to investigate the exact influence and the necessity to rerun the hydrodynamic model in further research.
3.6.2.
Results
Because of the new input data regarding the terrestrial nutrient loads the terrestrial
nutrient load has changed in comparison with the previous model set-up. These
changes are indicated in this subsection. First the changed load per country is given;
followed by the annual trend in nutrient loads; finally the major rivers are highlighted.
Change per country
The deviation in the riverine nutrient loads between the previous and new model set-up
differs per country. Data about the previous and new model loads is tabulated in
Appendix K. The percent deviations are tabulated in Table 3-10 and plotted in Figure
3-22.
Table 3-10: Percentage deviation of annual riverine load or discharge between previous
model and new model. Positive value means increase.
Nitrate
Phosphate Silicate
Discharge
Belgium
-3%
-9%
6%
-11%
Denmark
N/A
N/A
N/A
N/A
Germany
32%
15%
107%
-1%
The Netherlands
4%
-5%
8%
-9%
United Kingdom
-22%
6%
-56%
79%
France
N/A
N/A
N/A
11%
Load of all countries
22%
21%
8%
4%
N/A: Load not available in previous model set-up.
Figure 3-22: Average riverine load or discharge per country over model period.
Left bars: Previous model set-up; Right bars: New model set-up.
38
In Belgium only two small loads are added compared to the Western Scheldt, the
only load in the previous model.
The Danish loads are not implemented in the previous model; they are implemented
in the new model. The Danish load is equal to about five percent of the Dutch load.
The German loads have increased quite significantly. First of all some smaller loads
are added and the time series of the major rivers are updated: in the previous model
observations of 2002 were copied to the other years, in the new set-up observations are
used for all years. Secondly in the previous model set-up there was no data used in the
Ems and Weser rivers, only in the Elbe River. In the new set-up the silicate
concentration in those two rivers is estimated by use of several major European rivers
as discussed in section 3.2.5 page 16.
In the Netherlands several small loads are added, but on the other hand the time
series of other loads are changed. Major changes are the changed load of the IJsselmeer
because of missing discharge data in the previous model, the large decrease of the
phosphate load of the Eastern Scheldt and the decrease of the load from the
Haringvliet because of a change of the used observation location (Bovensluis was used
in the previous model, Haringvlietsluis is used in the new set-up).
The British loads of nitrate and silicate have decreased significantly, the phosphate
concentration is stable. The load of all rivers that were included in the previous model
set-up has decreased in the new set-up. Some changes are small, but the most show a
deviation of minus 40 percent. The concentrations in the previous model are done in
2002 and cloned in the other years, the discharge data originates from 2001-2002 and is
cloned also. The years 2001 and 2002 can be classified is relatively wet, see Appendix
M.2, so they can cause an overestimation. The decrease of the silicate load is caused
because no silicate observations are available, so the silicate concentrations are
estimated as described in section 3.2.5 page 16.
French water quality data was not implemented in the previous model set-up, the
set-up includes only water discharge. The total French nitrate and phosphate load is
about half of the Dutch load, the French silicate load is smaller.
Annual trend
In this subsection the annual trend in riverine nutrient loads is discussed. The total
annual riverine nutrient load per model set-up is given in Figure 3-23.
Figure 3-23: Annual riverine loads in the three different model set-ups.
The deviation between the annual load in the previous and new model set-up is more or
less constant for each year. A discrepancy is the silicate load in 2001 and the nutrient
loads in 1997 and 2003.
39
An outlier in the annual silicate load is the year 2001. The high load in 2001 is not an
error in the model input; because the high load is observed in nearly all major European
rivers as visible in the plot in Appendix M.1.
The riverine nutrient loads in 1997 and 2003 show a decrease compared to the
previous model. This is explained by the average annual discharge; the average river
discharge in 1997 and 2003 is considerably lower than in the other years, as indicated in
Appendix M.2. In the previous model the discharge of foreign rivers was estimated by
long term averages instead of observations. In the new model all rivers use observation
data, so relative dry years are taken into account in the new set-up.
The total riverine nutrient load per country for the new model set-up is plotted in
Figure 3-24. The load is plotted in kilotons per year and as a percentage. It is striking
that the Dutch rivers (yellow) have a large annual flow volume, but a considerably
smaller contribution to the annual loads. In the British rivers (orange) the situation is
the other way around; they have a small flow volume but a large contribution to the
loads. Thus the British rivers have a higher nutrient concentration than the Dutch ones.
Total N
Total P
20
4000
2000
0
9697989900010203Avg
Total P
100
50
50
50
9697989900010203Avg
0
%
100
0
9697989900010203Avg
9697989900010203Avg
Q
100
%
%
Total N
Belgium
France
Germany
The Netherlands
United Kingdom
Denmark
6000
40
0
9697989900010203Avg
8000
m3/s
500
0
Q
60
kT/year
kT/year
1000
0
9697989900010203Avg
Figure 3-24: Annual and average loads per country.
Upper: in kT/y 25 or m3/s, Lower: as percentage.
Major rivers
The contribution of individual sources to the annual load is not uniform at all. The
accumulated plot in Figure 3-25 shows that about ten of the over fifty sources cause
eighty percent of the total load in the new model set-up.
The nutrient load of the river Seine was not included in the previous model set-up. In
the new model set-up the river is included and is the second largest nitrogen load and
third largest phosphorus load. Therefore the inclusion of the Seine is quite important
for the accuracy of the model.
25
kT = kilotons, 1*106 kilogram.
40
Figure 3-25: Cumulative annual loads and water flow per river.
The top 95% of the rivers are listed, ordered by load.
A three-dimensional overview of the average annual nitrate and phosphate load of each
river is given in Figure 3-26 and Figure 3-27. The difference of magnitude between the
major rivers and the smaller loads is very clear.
41
Figure 3-26: Annual nitrate load per source in new model, period 1997-2003.
Figure 3-27: Annual phosphate load per source in new model, period 1997-2003.
42
4.
Model boundaries
The model has two open boundaries as discussed in chapter 2. The concentrations of
the model variables at the two boundaries are discussed in this chapter. Per boundary
the conditions for a two and three-dimensional model set-up are discussed. Afterwards
those two conditions are compared to each other. The chapter ends with a conclusion
in subsection 4.3 in this conclusion the changed loads over the boundaries because of
the new boundary conditios are discussed as well.
The anthropogenic influence on the boundaries is also discussed. This influence is of
importance during future scenario design, as a border that has an anthropogenic part
must be changed during scenarios that deal with anthropogenic actions, while a pristine
border does not need to be changed.
4.1.
Northern boundary
The northern model boundary is situated between Aberdeen (UK) and the upper north
of Denmark, along the 57th degree latitude. A map of the model grid and its boundaries
is included as Appendix A. The total length of the boundary is over 600 kilometres; the
depth ranges from twenty metres in the coastal waters up to 110 metres. The water in
the east has an anthropogenic influence, because it has flowed along the western
European coasts. The residual current in the eastern part is northwards so a boundary
condition will only have a small influence on the model area. The water quality in the
west can be characterized as pristine, as it has its origin in the Atlantic Ocean. Turrell et
al. (1992) shows that part of the water in the model boundary has a coastal origin, see
Figure 4-1. Observations show only an increase in nutrient concentration and decrease
in salinity in the Danish coastal part (Figure 4-2).
Figure 4-1: Residual currents around northern United Kingdom and Shetland islands by
Turrell et al. (1992), placed on a topographical map. CAW: Coastal Atlantic Water, water
formed in the North Sea during the previous winter.
43
Figure 4-2: Vertical distribution of nitrate in february (left) and salinity in july (right)
near the northern model boundary by linear interpolation. Details about the plots in
section 4.1.2 on page 46. Data from Brockmann et al. (2002) and Radach et al. (1996) over
period 1958-2000.
Besides the horizontal differences in concentrations, there is a vertical distribution: in
summer periods the area is characterized by a stratified system.
The boundary conditions have to deal with these situations. In a two-dimensional
model two different zones can be defined: one influenced by the Atlantic Ocean and a
second one influenced by the European loads. In a three-dimensional model the
stratification can be implemented too. The design of the two boundary conditions is
discussed in two separate sections.
4.1.1.
Two-dimensional model set-up
In the previous model set-up the northern boundary conditions of the inorganic
nutrients are constant in concentration and uniform over the cross section. The
concentrations reflect the open sea conditions in winter, so there is no anthropogenic
influence. This means that the boundary conditions in the eastern part of the boundary
do no match with observations in that region, as there is a strong anthropogenic
influence in this region. The ‘wrong’ boundary conditions do not have serious
consequences for the model, because the residual current near the Danish coast is in
northern direction. The cells of the model nearest to the boundary are a little bit
affected by these ‘wrong’ conditions, but this is not the area that has the main interest.
Boundary conditions of the previous model set-up are compared to observation data
(Brockmann et al. 2002) and literature (Bot et al. 1996; Natural Environment Research
Council 1991; Radach et al. 1997). The boundary conditions in the previous model setup, observations and the new boundary conditions are plotted in Figure 4-3. The
observation locations of the different literature sources are indicated in Figure 4-4.
44
Figure 4-3: Observations in the region near the northern model boundary and the
previous and new boundary conditions over the year.
Figure 4-4: Model boundaries and observation locations (Google 2006).
The observed winter concentrations of nitrate, ammonium and silicate correspond with
the constant concentrations in the previous model. The observed winter concentrations
of phosphate are about double of the old concentrations. The model boundaries are
updated using the observed concentrations in literature, as seasonal variation increases
the model accuracy. The nitrate and phosphate concentration are adopted from the
Eurocat study (Bot et al. 1996). The ammonium concentration is extracted from the
North Sea Nutrient Atlas (Brockmann et al. 2002): monthly averages are calculated
45
from the observations in the region of the model boundary (56.5 degrees (deg) <
latitude <57.5 deg), linear interpolation is used to fill missing months. The silicate
concentration is extracted from Brockmann et al. (2002) too, but the area is restricted to
the same area as used by Bot et al. (1996). The difference in area is caused by the
number of observations: the number of observations of ammonium is scarcer than the
number of silicate observations, so the ammonium observations are averaged over a
larger area in order to create reasonable time series.
The nitrate and phosphate concentrations are based on observations in the upper ten
metre of the water column, so these do not take into account the submerged flow.
Therefore an underestimation of the concentrations is made, as stratification causes that
the concentrations in the deeper parts are higher than the surface ones during summer.
The ammonium and silicate concentrations include the entire water column, as there are
too few observations in the top layer to take a reasonable average, see histograms in
Figure 4-5. It is clear that the whole water column is represented in a more or less even
distribution, so there is no underestimation of ammonium and silicate.
Figure 4-5: Histogram of depth of ammonium (left) and silicate (right) observations.
The decrease of inorganic dissolved nutrient concentrations during spring and summer
is mainly caused by an increase of phytoplankton and detritus: the particulate organic
nutrients. In the old model boundary this conversion was not implemented, which
means that equilibrium between the inorganic nutrients and the organic ones was
established in the first cells of the model. As the decrease of the inorganic nutrient
concentration is implemented in the boundary condition now, the increase of
particulate nutrients must be included too. The concentration of phytoplankton is
estimated by chlorophyll-a concentrations. Average monthly observations of
chlorophyll-a are extracted from the Eurocat study (Bot et al. 1996).
4.1.2.
Three-dimensional model set-up
A three-dimensional model needs boundary conditions for each layer. This section
discusses the design of the boundary conditions by determining the level of
stratification and the boundary conditions for each layer.
Maritime observations are extracted from two databases: the North Sea Nutrient Atlas
(Brockmann et al. 2002) and the NOWESP dataset (Radach et al. 1996). The first one
contains more recent data while the latter one has an enormous amount of data over a
larger time period. The observations are used to quantify the stratification and to create
boundary conditions for each zone, i.e. surface, bottom and Danish coast (see Figure
4-6 on page 47). The British coastal zone is not included as a separate zone as there is
46
no distinction visible between nutrient and salinity observations in that region and the
central Atlantic area.
Observations of nutrients, chlorophyll-a, salinity and temperature are selected in a
region of one degree latitude around the northern boundary. The circumstances in this
area are comparable as there are no big river discharges along the coasts and the water
depth is uniform in north-south direction. An exception is the eastern part, because the
depth has a steep increase in this region just north of the model boundary, see Figure
4-6. The observations done in this area below the depth of the model border are left
out in the analysis, as the circumstances in these deeper waters are different from the
circumstances in the model boundary.
The time period is not limited, in order to have enough data to create monthly cross
section plots. The data ranges from 1958 till 2000. An increase of concentrations over
time is visible in the time series of nutrient observations (e.g. the nitrate concentration
versus time in Appendix I.11). The influence of this trend is not investigated, but a
quick scan shows that the monthly average concentrations increase when only recent
data is used. On the other hand the number of observations decreases in such a way
that no reasonable monthly average concentrations can be calculated for some
parameters. The exact influence of the time period on the nutrient concentrations is
subject of further research.
A couple of observations are left out because they are clear outliners.
Figure 4-6; Bathymetry of North Sea.
Per parameter monthly distribution plots over the depth are created. The distribution is
calculated by linear interpolation in Matlab. There is no plot created when there are less
than twenty values in one month, as this faces problems during the interpolation. The
plots are included in Appendix I.
47
By use of the plots, the location of the three different zones (i.e. Danish coast, surface
and bottom) are determined by good judgement. The border between the bottom and
surface areas is chosen at 40 meter below the water level. The influence of the Danish
coast reaches until the 6th degree longitude. The implementation of these boundary
conditions in the model is given in Figure 4-7. The model uses sigma layers that cause
that layers have the same shape as the bottom, so the border between the different
zones is fluctuating over the cross section.
Figure 4-7: Cross section over northern model boundary. The yellow lines indicate
different zones; the model implementation is given by coloured areas.
Per region the average concentration is calculated per month, this number is used as
boundary condition for that region. The average is calculated in two ways: first a surface
weighed average of the interpolated values is taken and secondly the average of the
observations is calculated per zone. The advantages and disadvantages of both methods
are given in Table 4-1. Plots of both methods are given in Appendix I.9.
Table 4-1: Comparison of different averaging methods.
Surface weighed average
Average of observations
Advantages
Takes into account
the distribution of
concentrations.
Uses observed data.
Disadvantages
Uses interpolated values, dangerous
when number of observations is low
or unevenly spaced.
Uneven distribution of observations
may influence result.
The surface weighed method is considered as the best method because it takes into
account the distribution of concentrations over the cross section. But it is important to
keep the number and distribution of observations in mind, as a low number or uneven
distribution will influence the result significantly. Therefore the results of the surface
weighed method are replaced by the average of observations method when
interpolation problems occur. The result of a particular month is left out when the
number of observations in one month is too low to consider the result as accurate. In
that case interpolation of the preceding and previous month is used. These exceptions
are discussed in Appendix I.10.
48
4.1.3.
Conclusion
In the two previous subsections the boundary conditions for the two-dimensional and
three-dimensional model are composed. In this section the differences between the two
sets boundary conditions are discussed by the plots in Figure 4-8.
Figure 4-8: Boundary conditions of three-dimensional model (Surface, Danish coast and
bottom) are compared to the boundary conditions of the two-dimensional model.
In general the nutrient concentrations in the bottom zone are considerably higher
during summer than in the two-dimensional boundary conditions, because of the
occurrence of stratification.
The two-dimensional boundary conditions are not exactly equal to the average of the
three-dimensional boundary conditions, because the two-dimensional conditions are
based upon different data sources. The two-dimensional nitrate and phosphate
concentration are from the Eurocat study (Bot et al. 1996) that is based upon surface
water observations in the upper 10 meter of the water column in the period 1980-1984,
the three-dimensional boundaries are based upon data in the period 1958-2000. Both
two-dimensional conditions are higher than the three-dimensional conditions. The
ammonium and silicate conditions for the two dimensional model are from the North
Sea Nutrient Atlas (Brockmann et al. 2002), while the three-dimensional conditions are
based upon that atlas and the NOWESP data set (Radach et al. 1996). This means that
the two-dimensional conditions are based upon more recent observation data than the
three-dimensional ones. The chlorophyll-a concentration in the two-dimensional model
boundary is adopted from the Eurocat study (Bot et al. 1996) as well. The peak in the
chlorophyll-a concentration has disappeared in the three-dimensional boundary
condition because the latter boundary condition is based upon more observations and
the spring peak is than smoothed.
49
The two-dimensional and three-dimensional boundary conditions show some
deviations as desribed in the previous paragraph. One can wonder, which conditions are
valid and why? The two boundary conditions differ regarding the used observation data
and the spatial averiging.
The three-dimensional boundary condtions are created upon observations over the
period 1958-2000, the two-dimensional boundary condition is in general based upon
more recent data. An analysis of the influece of the large observation period in the
three-dimensional boundary conditions is not done.
The spatial averaging for the three-dimensional boundary conditions is reasonable as
in the horizontal plane a region near the model boundary is selected, while in the
vertical direction three zones are created (bottom, surface and Danish coastal) which
have equal conditions regarding nutrient concentrations. In the two-dimensional model
the selected area in horizontal and vertical direction should be chosen at those locations
where the residual current is inwards in the model area. However the NOWESP
database was not available during the creation of the two-dimensional model boundary,
so the boundary conditions are based upon the data availability instead of the
hydrodynamic situation.
Altough the three-dimensional model boundary conditions are based upon time
series over a -possibly- too long period, the observation location can be considered as
better than the two-dimensional boundary conditions. Thus the three-dimensional
boundary conditions can be regarded as more accurate.
Antropogenic influence
Long term eutrophication is studied by Pätsch et al. (1997). He concludes that the
North Sea can be divided by a line from the 54 th degree latitude in the United Kingdom
towards the north of Denmark. The area south of this line is affected by eutrophication,
while there are no trends observed in nutrients and/or phytoplankton that are related to
eutrophication in the northern part. The area is depicted in Figure 4-10. The primary
production in the southern area has increased due to the eutrophication, but the
increase in primary production is not proportional to the increase of the input from
riverine nutrients. Thus an antropogenic influence is not directly determined.
The conclusions by Pätsch are compared to observations within the model area. Annual
average concentrations relative to 1987 of the Rhine, the northern boundary and the
Dutch coast are plotted in Figure 4-9. The concentrations are scaled to 1987 as the
nitrate concentration in the Rhine has it maximum in that year. The Rhine and Dutch
coastal observations show a decrease since 1987. This is in line with Pätsch. The
concentrations in the Atlantic part of the northern boundary show no trend at all. The
Danish observations have a lot of scatter, because the annual average concentration is
based upon only a small number of observations so the average is highly dependent on
the season of the observations. Therefore the Danish observations cannot be regarded
as reliable. However the observations in the Atlantic part of the North boundary are
constant, so they agree with the conclusion of Pätsch
50
Figure 4-9: Long term nitrate observations relative to 1987 (dashed line). Annual average
concentration calculated from daily interpolated values. Rhine and Noordwijk data
Waterbase (RIKZ); North boundary Brockmann et al. (2002) and Radach et al. (1996).
According to Pätsch et al. (1997) the situation in the northern part of the North Sea has
not changed significantly in the last century, although there are some slight deviations
caused by variations in the Atlantic water. Pätsch refers to Lindeboom et al. (1996) who
states that the changes in the North Sea ecosystem are caused by changes in the
inflowing Atlantic water. However these changes were not observed by Pätsch, but he
states that there is some uncertainty as the amount of nutrient observations and water
flow data in the northern part of the North Sea is limited.
Thus an increase in eutrophication is visible in the southern North Sea, but a direct link
to the riverine loads is not observed. A look over the boundary itself shows very
constant concentrations in the eastern part and more oscillating observations in the
Danish coastal zone. A link between the oscillating observations in the Danish zone
and an anthropogenic influence is likely, but not proved in this analysis because of the
low number of observations in the Danish part of the boundary.
51
Figure 4-10: Anthropogenic influence in the North Sea according to Pätsch et al. (1997).
52
4.2.
Southern boundary
The southern boundary is situated between Cherbourg (France) and Southampton
(UK), near the 1.5 degree western longitude. A map of the model grid and its
boundaries is included as Appendix A. The length of the boundary is over 100
kilometres; the deepest point is around 65 meters below average water level.
The previous southern boundary conditions regarding nutrient concentrations have a
distribution over the year, in contrast to the old northern boundary conditions that are
constant and uniform.
The validity of the boundary conditions in the previous model and the necessity of
three-dimensional boundary conditions are discussed in two separate paragraphs.
4.2.1.
Two-dimensional model set-up
There is only one article that includes nutrient observations done in the same area as the
model boundary, namely by Bentley et al. (1999). However the observations are done
during monthly cruises in the period December 1994 until December 1995, so there is
only one year of data. As the model will simulate seven years it is not wise to base the
boundary conditions upon one observation year. Therefore those data are not used for
the boundary conditions itself, but can be used for the study of a spatial gradient in the
boundary. Other articles publish nutrient observations in wider area around the model
boundary; their locations are given in Figure 4-11 (copy of Figure 4-4 on page 45).
Figure 4-11: Southern model area (copy of Figure 4-4 on page 45).
The observations in the different literature sources are done in the whole Channel area,
while there are several nutrient sources which discharge into this area such as the Seine
River. Therefore the observations near Calais-Dover are not one to one applicable to
our model boundary. But their data can be used to check the magnitude and seasonal
variability of the boundary conditions. The current boundary conditions are plotted in
Figure 4-12 together with observations from literature.
There is some scatter in the observations, caused by the different observation locations
in the Channel area. Special attention is paid to the observations by Bentley et al. (1999),
because the observations are done in the same region as the model boundary.
53
Figure 4-12: Nutrient concentrations at the old and the upgraded southern model
boundary, in comparison with observations.
The model boundary conditions for phosphate are in the middle of the observations,
the Bentley observations are around the previous boundary conditions. Therefore the
phosphate boundary condition is unchanged. The silicate concentrations show less
scatter, the Bentley observations are slighly above the current boundary. There is no
reason to change the current silicate boundary condition.
Some more research is done for the nitrate observations, as the scatter is very large. The
Benley observations are even below the observations in the western Channel area by
Brion et al. (2004) and Bot et al. (1996), possibly because the Bentley data ranges only
one year. Extra observation data is extracted from the Nowesp data set (Radach et al.
1996) to check whether the order of magnitude of Bentleys data is correct. These
observations are plotted in Figure 4-13. The plot supports the observations done by
Bentely as the nitrate observations have the same order of magnitude, however the
number of observations in this data set is very small. Both Bentley and Radach indicate
a lower nitrate concentration in that region than assumed by the previous boundary
condition, so it is likely that the previous model boundary does not reflect the nitrate
concentrations at the model boundary. Therefore it is recommanded to change the
nitrate boundary condition, however this is not done in this thesis because the
conclusion that the previous model boundary does not reflect to the observations was
drawn in a late stadium of the thesis.
54
Figure 4-13: Monthly nitrate and phosphate concentrations around southern boundary.
Area: -2.5o < Longitude < -1 o; 49.5 o < Latitude < 51 o; data Nowesp (Radach et al. 1996).
There are no ammonium observations to compare with the current ammonium
boundary conditions, so the ammonium concentrations remain unchanged.
Chlorophyll-a was not implemented in the previous model boundary, it is not
implemented in the new boundary condition as only Bot et al. (1996) publishes
observation data.
4.2.2.
Three-dimensional model set-up
In the previous section the boundary is regarded as uniform over its length and depth.
This is not totally correct as discussed by Laane et al. (1993). He observes a horizontal
distribution in the salinity and nutrient observations and a minor vertical distribution.
Laane’s conclusion regarding a gradient over the Channel is checked by use of
observation data. The number of nutrient observations is scarcer than near the northern
boundary, but there are many salinity observations. Salinity observations are used to
determine the mixture of saline water with riverine water. It is assumed that riverine
water is polluted by human activity, so brackish sea water is polluted also. Details about
the observation data are tabulated in Table 4-2 the observation locations are plotted in
Figure 4-14.
Table 4-2: Overview of salinity observation data in Channel region.
Dataset
Region
Period
Radach et al. (1996)
Channel
1959 till 1994
BODC (2001)
BODC (2007b)
Chiffoleau et al. (1999)
Environment Agency (2006)
Middle
Middle
Seine
British
1992 till 1995
1996 till 1997
1994 till 1995
1993 till 1994
55
Depth of
observations
0-10 m 49 %
10-20 m 18 %
20-30 m 17%
30-40 m 11%
> 40 m
3%
Surface
Surface
Surface
Surface
Figure 4-14: Geographical location of observation points per source.
The distribution of the salinity observations over the area is uneven: the resolution in
the coastal waters is significantly lower than the resolution in the middle of the Channel.
The vertical distribution is uneven also: most observations are done in the surface
water. It is important to keep this in mind during the interpretation of the interpolated
plots. Especially the northern coastal part of the model boundary is hardly covered by
observations, except two observations in the Environment Agency set.
Horizontal and vertical salinity distributions are created by use of linear interpolation,
see Figure 4-15. There are two remarks on those plots: the interpolation does not take
into account the presence of the continent and the Island of Wight and secondly the
only riverine salinity observations are in the river Seine (lower right of figure).
Nevertheless it is clear that the middle part of the Channel is more saline than the
coastal parts. Especially the southern part, east of the model boundary is quite fresh.
Separate plots of salinity observations between certain ranges are included as Figure
4-16. These plots support the conclusion from the interpolated plot.
56
Figure 4-15: Horizontal (left) and vertical (right) salinity distribution in Channel area by
linear interpolation.
Figure 4-16: Salinity observations in Channel area within certain ranges.
Thus the circumstances regarding salinity in the coastal parts are identified as different
to the central part. Bentley et al. (1999) is used to check whether nutrient
concentrations behave in the same way. Observed nutrient concentrations over the
transect Cherbourg-Southampton and the current boundary conditions are plotted in
Appendix J. These plots show that generally the coastal nutrient concentrations are
higher than the central concentrations, like the results from Laane et al. (1993).
Therefore the boundary can be divided in three zones: two coastal ones and a central
zone. Nevertheless there is no sufficient nutrient data available to create these boundary
conditions, as the observations by Bentley range only one year and the other sources do
not include coastal observations.
57
Next to the horizontal salinity distribution, the vertical salinity distribution is
investigated. A vertical distribution plot is created by linear interpolation of
observations in a region around the model boundary, see Figure 4-15. The water depth
over the middle of the area (longitude = -1.18 degrees) is included also. The plot
indicates a quite well mixed situation, except in the British coastal waters. This
distinction must be interpreted with care as the interpolation is solely based upon two
surface observations in the British coastal waters and a small number of observations at
depth -10 and -20 meters in the more central part. Thus the vertical salinity distribution
in the British coastal waters is uncertain. The distribution in the other zones can be
classified as well mixed, which means that there is no need to apply three-dimensional
boundary conditions.
4.2.3.
Conclusion
The boundary conditions of the previous model set-up agree with observations in the
Channel area. An exception is the nitrate concentration, it seems that the previous
boundary condition is too high. However the available observations in the same area as
the model boundary are very limited. Therefore the boundary conditions are not
changed with respect to the previous model set-up. It is recommanded to update the
model boundary in the future.
The occurrence of stratification in the model boundary is investigated by salinity
observations. No stratification has been determined. This is is in line with conclusions
by Laane et al. (1993).
The presence of an antropogenic influence is studied by salinity obeservations too. The
presence of riverine water near the coasts is indicated by decreasing salinity
observations and the increase of nutrient concentrations by Bentley et al. (1999) and
Laane et al. (1993). Laane concludes that the higher coastal nutrient concentrations are
related to the riverine influence in the coastal waters. Therefore an anthropogenic
influence is depicted in the coastal waters.
58
4.3.
Conclusion
The model has two open boundaries as plotted in Figure 4-17. The boundary
conditions regarding concentrations for the southern boundary are the same in all
model set-ups, the northern boundary conditions are changed twice; first for the twodimensional model set-up and secondly for the three-dimensional model set-up. The
loads through the boundaries in each model set-up are discussed in this section;
subsections are included for the gross and net loads.
Figure 4-17: Locations of model boundaries.
4.3.1.
Gross loads
The gross loads through the southern boundary remain unchanged, see Figure 4-18.
This is explained by the unchanged model boundaries. There is only a small distinction
in the year 2003: the gross loads differ slightly from the previous model set-up, because
of some small deviations in the winter of 2003. These deviations are plotted in
Appendix N.1. Possibly there is some error in the communication file of the 2002
model simulation to the 2003 simulation. However an error was not found in the input
files and a re-run of the model gave the same results. Therefore the reason for the
deviations is unkown.
Figure 4-18: Gross annual nutrient loads on the southern boundary.
It is possible to check the similarity of the two and three-dimensional hydrodynamics
on the southern boundary, because the boundary conditions regarding nutrient
concentrations are identically in the different models. As stated before there is no
difference in the gross nutrient loads, so the hydrodynamics agree with each other.
59
Because of the changed boundary conditions regarding nutrient concentrations there
are quite some deviations in the gross nutrient transport through the the northern
boundary. The deviations are depicted in Figure 4-19.
Figure 4-19: Gross annual nutrient loads on the northern boundary.
The gross nitrogen load has decreased with about one quarter in the two-dimensional
model set-up compared to the previous model. The strong decrease is caused by the
changed boundary conditions as plotted in Figure 4-20. The boundary conditons in the
previous model set-up are equal to the winter concentrations and uniform over the year,
this causes an overestimation during summer (as discussed in section 4.1.1 on page 44).
The overestimation is corrected in the two-dimensional model set-up by the application
of boundary conditions that include seasonal variability.
The nitrogen concentrations on the surface layer have decreased and the concentrations
on the bottom layer have increased in the three-dimensional model set-up. Apparently
the changed concentrations are more or less proportional to the residual currents in the
layers, because the gross nitrogen load in the three-dimensional model set-up has only a
small deviation to the two-dimensional load.
The total phosphorus load has increased in the new two-dimensional model set-up,
because the winter concentrations have nearly doubled compared to the previous model
set-up. The gross phosphate load decreases in the three-dimensional model set-up,
because the concentrations differ from the two-dimensional boundary conditions.
The gross silicate load decreases considerably in the new model set-up, because the
concentrations decrease compared to the boundary conditions in the previous model.
In the three-dimensional model set-up the gross load increases because the
concentrations in the summer period are higher in the bottom zone.
60
Figure 4-20: Boundary conditions on northern boundary for different model set-ups.
The gross loads, as described above, are interesting to analyze the boundary conditions,
but for the nutrient budget of the North Sea the net load is more important. The net
loads over the boundary are discussed in the next subsection.
4.3.2.
Net loads
The net loads over the boundaries are calculated by the sum of the transport over the
boundary in both directions. When the residual current is outwards the model the
volume of the out flowing water is bigger than the inflowing and so the boundary will
probably act as a sink to the model.
The loads over the boundaries are tabulated in Table 4-3, Table 4-4 and Table 4-5. The
behaviour of the northern boundary is discussed per nutrient. The net load over the
southern boundary is not discussed because the loads are constant, as discussed in the
previous subsection. The net load of the three-dimensional model set-up must be
regarded as a first estimate because the model some problems regarding the
temperature and mixing, as discussed in subsection 5.1 on page 65.
Table 4-3: Average annual total nitrogen loads over boundaries.
[kT N/y]
Southern
boundary
Previous
New 2D
(I) [%]
New 3D
(II) [%]
In
13279
13334
0.4%
13342
0.1%
Out
-12741
-12829
0.7%
-12878
-0.4%
Net
538
505
-6.1%
464
-8.1%
Northern
In
13672
10603
-22.4%
10126
-4.5%
boundary
Out
-13535
-10671
21.2%
-10674
0.0%
Net
138
-67
-148.6%
-548
718%
(I)
Percent deviation to the previous model set-up.
(II)
Percent deviation to the new two-dimensional model set-up.
61
In the northern boundary the inflow in the new two-dimensional model decreases
compared to the previous model with more than twenty percent because of the changed
boundary conditions. The outflow decreases with a slightly smaller percentage. As a
consequence the northern boundary does not act as a total nitrogen source, but as a
sink. In the three-dimensional model the inflow decreases slightly, but the outflow is
constant so the net load increases by a factor eight.
Table 4-4: Average annual total phosphorus loads over boundaries.
[kT P/y]
Southern
boundary
Previous
New 2D
(I) [%]
New 3D
(II) [%]
In
2196
2182
-0.6%
2182
0.0%
Out
-2114
-2102
0.6%
-2105
0.1%
Net
83
80
-3.6%
76
-5.0%
Northern
In
1617
1869
15.6%
1768
-5.4%
boundary
Out
-1642
-1903
15.9%
-1837
-3.5%
Net
-26
-33
26.9%
-69
109%
(I)
(II)
The gross load over the northern boundary has increased with 15 percent in the twodimensional model set-up, the outflow increases also. Therefore the net load over the
boundary has increased slightly. In the three-dimensional model the net outflow
doubles.
Table 4-5: Average annual silicate loads over boundaries.
[kT Si/y]
Southern
boundary
Previous
New 2D
(I) [%]
New 3D
(II) [%]
In
7811
7888
1.0%
7888
0.0%
Out
-7317
-7400
-1.1%
-7352
0.6%
Net
495
488
-1.4%
537
10.0%
Northern
In
11889
9018
-24.1%
10141
12.5%
boundary
Out
-11117
-8370
24.7%
-9280
10.9%
Net
772
648
-16.1%
861
32.9%
(I)
(II)
The average gross load over the northern boundary has decreased with one quarter in
the three-dimensional model set-up. The net outflow has increased with a slightly
higher number. Consequently the net load has decreased with 16 percent. In the threedimensional model the load
62
The net loads per year are summarized in Figure 4-21.
Figure 4-21: Net nutrient loads on the model boundaries; a positive number means that
the boundary acts as a source to the model and a negative number indicates a net sink.
63
64
5.
Model results
In the preceding chapters the loads and the boundary conditions of the previous model
are discussed and changed. The influences of these changes on the riverine loads and
the loads over the boundaries are discussed in paragraph 3.6 and 4.3, page 37 and 59.
In this chapter the simulated nutrient concentrations in the North Sea are discussed.
First the behaviour of the three-dimensional model is discussed, as the model is not
validated before. The validation of the two-dimensional model has been described in
Blauw et al. (2006) and can be considered as good. The simulated nutrient
concentrations in the new model set-up are compared to observations and the previous
model set-up in subsection 5.2. The influence of the nutrient load from the several
countries and the model boundaries on the nutrient concentration in the model area is
discussed in subsection 5.3. The mass balance of the model is discussed in section 5.4.
The chapter ends with a conclusion in section 5.5.
5.1.
Behaviour three-dimensional model
The behaviour of the three-dimensional model has been studied by use of observation
data in the Terschelling and Noordwijk rays, as depicted in Figure 5-1. Those two rays
are selected as long term data over the water column is available.
Figure 5-1: Plot of water depth (m) and locations of observation rays
Terschelling (Ts) and Noordwijk (Nw).
The match of model results to observations is discussed in detail in Appendix L. The
conlusions drawn in the appendix are given here:
Temperature
The water temperature in the three-dimensional model is calculated in the
hydrodynamic model. There is a difference between the modelled values and
observations, the distinction is in the order of three to four degrees celsius. In
the two-dimensional model the water temperature is given by observations
instead of calculations, so the temperature is in good agreement with
observations.
65
Oxygen
The simulated oxygen concentrations are often below observations in summer.
In the surface layer the distinction is around 1 mg/l, but in the bottom layer the
minimum of the simulated concentrations is half of the observed
concentrations.
Nitrate and phosphate
The simulated nitrate and phosphate concentrations in the surface layer are
sometimes better than the simulations in the two-dimensional model. But the
winter nitrate and phosphate concentrations are often too high, especially in
the maritime stations. The high winter concentrations are caused by the high
nutrient concentrations in the bottom layer at the end of the summer, because
of mixing problems. In authumn the water column mixes and the nutrient
concentrations are averaged over the water column. This is illustrated by the
plot in Figure 5-2.
Silicate
The silicate concentration in winter is lower then observations, the simulated
concentrations are often half of the observed winter concentrations. The
distinction is probably caused by the high primary production in the summer
period.
The observed characteristics are illustrated by model results and observations of the
Terschelling175 station in the year 1996. The station Terschelling 175 is selected as the
water depth is around fifty meters and stratification is expected in this station, besides
that there are observations available over the water depth. The year 1996 is used as
spin-up year of the model simulation, so the concentrations in the two-dimensional and
three-dimensional model are the same at January 1 st.
The first graph in Figure 5-2 shows the nitrate concentration over the year. The
nitrate concentration decreases in the the three-dimensional model earlier than in the
two-dimensional model. The nitrate concentration is depleted half a month earlier than
in the two-dimensional set-up. The decrease in the two-dimensional model is in line
with observations. The early decrease in the three-dimensional model is caused by the
different temperature forcing in the three-dimensional model.
The water temperature in the two model set-ups is plotted in the middle plot of
Figure 5-2 together with observations. The difference between the observed
temperature and model temperature in March 1996 is more than four degrees celsius,
the two-dimensional model matches to observations in March. In summer the deviation
between observations and model temperature is more than four degrees Celsius in the
bottom and surface layer, in the middle of the water column the deviation is around two
degrees.
The nitrate concentration in the bottom layer does not match with observations in
the begin of summer at all. This is probably caused by too low vertical mixture. Because
of the absence of mixture the dead algae in the surface layer do not settle down fast
enough to the bottom layer. In the bottom layer the nitrate concentration is lower than
observed in the beginning of summer, but during the summer the dead algae settle
down and reach the bottom layer, as a result the nitrate concentration increases.
66
The absence of mixture is illustrated by the too low oxygen concentrations in the
bottom layer also (not plotted in Figure 5-2). The vertical mixture in the threedimensional model is indicated by the fraction time, i.e. the time fraction that water of a
specific layer spends a specific layer. When the water column is completely mixed the
fraction of all layers is equal to the layer depths, see the winter values. The lower plot in
Figure 5-2 shows the fraction time of the surface layer. The plot indicates that after May
1996 there is no surface water observed in the middle of the water column and near the
bottom. This indicates a too low vertical mixing.
Figure 5-2: Simulated nitrate concentrations, water temperature and fraction of surface
water over water depth in start-up year of the model (1996) for station Terschelling175.
The problems with the model results are discussed with several experienced water
quality and hydrodynamic modellers within WL | Delft Hydraulics (among others Hans
Los, Rob Uittenbogaard and Erik de Geode). Finally the cause of these problems was
found in the climate forcing of the hydrodynamic model. The used model was validated
on residual flows through Dover Strait and the Marsdiep and on salinity in the Dutch
coastal zone. The only purpose of the model was to compute hydrodynamic flows to be
used for simulations with fish larvae (Erftemeijer 2005). Then, salinity concentrations
are essential, while temperature concentrations are not very relevant. Because of lack of
temperature data, the same annual variation in air temperature, relative humidity and
cloudiness was applied in all these eight years, namely data from 1988/1989. The
applied Secchi depth is not from observations as well. This was justified for the fish
larvae simulations. For other applications the model is less relevant. This is sumarized in
Table 5-1. Unfortunately, in the current master thesis this information became available
at almost the end of the project.
Table 5-1: Temperature forcing of different model set-ups.
Model
Hydrodynamic model
Two-dimensional water quality model
Three-dimensional water quality model
67
Temperature forcing
1988-1989, via observations
1996-2003, via observations
1988-1989, coupled to hydrodynamic model
Thus the deviation between the water temperature in 1996 for the two-dimensional
water quality model and the three-dimensional water quality model is caused by the
different temperature forcings. The two-dimensional model is forced by observations in
1996; the three-dimensional model is forced via the calculated temperature in the
hydrodynamic model, but this model uses unfortunately 1988/1989 temperature data.
The deviation of four degrees Celsius between the two models is explained by the
different climate characteristics. The winter of 1988/1989 can be classified as relative
warm and the winter of 1995/1996 is quite cold, this is depicted in Figure 5-3. In this
picture it is visible that 1996 is the coldest year in the model period 1996-2003, so there
is a big difference between the simulated water temperature and the observed
temperature. The differences in other years are smaller, see Appendix L.8.
Figure 5-3: Deviation to average mean air temperature over period 1985-2006. Blue:
month is colder than average; Red: month is warmer than average. Yellow pentagons
indicate the Dutch ‘Elfstedentocht’ skating tour. Data by KNMI (2007).
A new model run is performed for 1996 using a water temperature that is four degrees
Celsius lower26 than the original input file. The results are plotted in Figure 5-4. The
winter temperature is in line with observations, but the summer temperature in the
surface seems to be underestimated. The underestimation in summer is caused by the
smaller temperature difference between the two models in this period. The simulation
shows an significant improvement for the nitrate depletion in spring, because of the
lower water temperature. The spring bloom agrees well to the two-dimensional model
and observations. However the summer nitrate concentrations in the bottom are not in
agreement with observations and the winter concentration is still higher than
observations. These distinctions are probably caused by the stratification problems.
Four degrees Celsius are subtracted from all temperature values in the input file by assistance of Jan van
Beek.
26
68
Figure 5-4: Nitrate and temperature
simulations in 2D and 3D model for
station Terschelling175.
The black lines indicate the model
simulations by a lower temperature
input.
The new 1996 simulation shows that the temperature behaviour in winter has
improved, but the summer temperatures are underestimated. When one wants to
simulate the temperature in the whole model period correctly, one has to calculate the
deviation between the temperature in each month in the 1997-2003 period and the
1988/1989 period subsequently. Although it is technically possible to perform those
calculations, the behaviour of the model is still not reasonable because the model
suffers still the stratification problems and for a correct climate simulation the relative
humidity and cloudiness should be changed to 1996-2003 values as well.
Thus the three-dimensional model results regarding temperature, spring bloom periods
and vertical mixing are not totally correct. However the discussed results of 1996 must
be regarded as an extreme situation, because the temperature deviation between the
winter of 1988/1989 and 1996 is the largest in the whole model period. On the other
hand the model behaviour on other fields is sometimes even better than the twodimensional model, especially the near-shore simulations.
69
5.2.
Nutrient distribution over model area
The average winter, i.e. January and February, nutrient concentrations over the model
area are plotted for each model set-up in Appendix O. The simulated model results are
compared to observations. Detailed information about the used observation data is
given in Appendix O.
In Appendix O the differences between the results of the three model set-ups are
discussed per geographical zone. The conclusions from this comparison are
summarized per parameter below.
Nitrate
Especially in the British coastal waters the simulated nitrate concentrations match better
to the observations than in the previous model set-up. The changes in the other coastal
waters can be explained by the addition of new sources and changed time series of
other rivers, unfortunately there is no observation data available.
In the Channel area the plume of the French rivers is visible in the new model set-up
because these rivers are added to the input. The plume can be explained by good
knowledge, but there is no validation data available to compare with. The model results
in the new set-up, and especially in the three-dimensional one, are bad in the area west
of Denmark. The simulated nitrate concentrations are definitely higher than the
observed ones.
Thus the simulated nitrate concentrations are better near the British coast and probably
better near the French coast. The match near the Dutch and German coast is equal. A
major distinction is the match near the Danish coast, in this area the agreement with
observations is worse especially in the three-dimensional set-up.
Phosphate
A big difference between the three model simulations is the concentration near the
Dutch coast. In the previous and new two-dimensional model the concentrations are
slightly higher than observations. In the new three-dimensional model the simulated
concentration is in line with observations. Near the German coast the situation is the
other way around: the majority of observations indicate the same concentration as the
previous and new two-dimensional model simulate and the three-dimensional model
simulates a lower concentration.
In the north part of the North Sea the simulated concentration in the new twodimensional model is the same as observations, the previous and new three-dimensional
model simulate slightly lower observations.
Thus the new two-dimensional model overestimates the concentrations near the Dutch
coast, but has a good estimation in the German Bight and northern North Sea. The
agreement with observations for the new three-dimensional model is the other way
around.
70
Silicate
The silicate distribution in the North Sea has changed significantly regarding to the
previous model set-up. The concentrations on the central North Sea are decreased,
because the gross load over the northern boundary and the load from British rivers
decreased. The concentrations in the French waters have increased but there is no
validation data available in this area. The distribution in the Dutch waters is stable. The
simulated silicate concentrations in the German coastal waters seem to be improved.
The silicate distribution in the Danish coastal waters is underestimated with respect to
observations in the new two-dimensional model and has slightly improved in the threedimensional set-up.
71
5.3.
Transboundary nutrient transport
Parts of this thesis are used in Blauw et al. (2006), namely the boundary conditions for a
two-dimensional model and the riverine data including the techniques to estimate
missing data. Blauw describes the relative contribution of the riverine loads to nutrient
concentrations in the North Sea by use of a tracer method. A couple of plots created in
this study are given in Figure 5-5 and Figure 5-6.
United Kingdom
Germany
France
Belgium
Figure 5-5: Averaged spatial
distribution (1997 – 2003) of the
fraction of total nitrogen per country.
Plots from Blauw et al. (2006).
The Netherlands
72
The plots show that the contribution of the French riverine nitrogen loads in the Dutch
coastal waters is between 10 and 20 percent. Therefore the absence of the French loads
in the previous model set-up has a significant influence on e.g. the nitrogen
concentration in the Dutch coastal waters. The study by Blauw shows also that the
contribution of Dutch nutrient loads to the nutrient concentration in the Dutch coastal
zone is high 65% of nitrogen in Dutch coastal waters is from Dutch rivers, the
contribution of phosphorus is 35 %. Both nutrients have an equal concentration in the
model boundaries, but the nitrogen load in rivers is higher than the phosphorus load.
Besides the computed influence of the British rivers to the nutrient concentrations, the
influence is also visible by satellite. NASA’s MODIS Terra satellite created a nice image
in March 2007, the image is included as Figure 1-1 on page 3. The image gives a good
overview of the British water that can be recognised by its tan color. The British water
plume has a quite constant distance to the continent, because of the residual flow
through the Channel. The shape of the plume is comparable to the zone of influence in
Figure 5-5 and the existance of the plume is described in Lacroix et al. (2004) as well.
The influence of the boundaries on the nutrient distribution over the North Sea can be
investigated by tracer studies as well.
Southern boundary
Northern boundary
Figure 5-6: Averaged spatial distribution (1997 – 2003) of the fraction of total nitrogen.
Plots by Blauw et al. (2006)
The influence of the southern boundary is still significant (10 till 20 percent) near the
Danish coast. However in section 4.2.1 on page 53 the conclusion is drawn that the
nitrate concentration on the southern boundary might be overestimated, so the
influence might be less than showed in the picture. The northern boundary influences
the whole central North Sea except a small zone along the British coast. The northern
boundary does not influence the Dutch near-shore waters.
73
5.4.
Mass balance
The inflow or outflow trough the model boundaries, the atmospheric deposition,
processes on nutrients and the storage in the model are summarized in mass balances.
In this section the average mass balances of total nitrogen, total phosphorus and silicate
for each model set-up, i.e. the previous model set-up, the new two-dimensional model
set-up and the three-dimensional model set-up are compared to each other. The mass
balances per year are included in Appendix N.2. The mass balances of the threedimensional model must be regarded as a first estimate, because the model has some
problems regarding the vertical mixture as discussed in section 5.1.
Table 5-2: Average annual nitrogen balance for each model set-up.
Previous
[kT/year]
1408
538
138
732
323
New 2D
[kT/year]
1313
505
-67
875
323
[%] (I)
-6.7 %
-6.1 %
-149 %
20 %
0.0 %
New 3D
[kT/year] [%] (II)
1117
-15 %
464
-8.1 %
-548
718 %
878
0.3 %
323
0.0 %
Total load
South boundary
North boundary
Rivers
Atmospheric
Deposition
Denitrification
-579
-506
-13 %
-587
Burial
-1168
-1155
-1.1 %
-558
Storage
17
25
47 %
45
Balance
1
0
0
(I)
(II)
16 %
-52 %
80 %
Compared to the previous model set-up, the biggest change in the new two-dimensional
nitrogen balance is the decrease of the net load through the northern boundary and the
increase of the riverine load. Because of the decrease of the gross load, the northern
boundary changes from a source to a sink in the model. The total nitrogen load on the
model has decreased with seven percent in the new two-dimensional model set-up; the
decrease is comparable to the decrease in the denitrification. The burial has decreased
also, but to a lesser extent. The storage has nearly doubled, but the number is very small
compared to the denitrification and nitrogen load.
In the three-dimensional model the denitrification decreases by fifty percent, probably
because of the mixture problems. The decrease of the burial is of the same order as the
increase of the export over the northern boundary.
Table 5-3: Average annual phosphorus balance for each model set-up.
Previous
[kT/year]
New 2D
New 3D
[kT/year] [%] (I)
[kT/year]
Total load
97
89
-8 %
50
South boundary 83
80
-3.6 %
76
North boundary -26
-33
27 %
-69
Rivers 40
42
5.0 %
43
Processes
-100
-92
-8.0 %
-53
Storage
3
3
0.0 %
4
Balance
0
0
0
(I)
(II)
74
[%] (II)
-43 %
-5.0 %
109 %
2.4 %
-42 %
33 %
The total phosphorus load on the model in the new two-dimensional set-up has
decreased with eight kilotons. This is exactly the decrease in the processes term. In the
three-dimensional set-up the processes term decreases with more than forty percent, the
decrease has the same order of magnitude as the increase in the export over the
northern boundary. This is the same system as the total nitrogen load.
Table 5-4: Average annual silicate balance for each model set-up.
Previous
New 2D
New 3D
[kT/year]
[kT/year] [%] (I)
[kT/year]
Total load
1770
1643
-7 %
1909
South boundary 495
488
-1.4 %
537
North boundary 772
648
-16 %
861
Rivers 503
507
1%
511
Processes
-1793
-1723
-3.9 %
-1961
Storage
20
78
290 %
51
Balance
0
0
0
(I)
(II)
[%] (II)
16 %
10 %
33 %
1%
14 %
-35 %
The silicate load on the model has decreased with 127 kilotons compared to the twodimensional model set-up. The decrease is mainly caused by the changed silicate load
over the northern boundary. The decrease of the silicate load causes a decrease in the
processes in the model and an increase in the storage. In the three-dimensional model
set-up the process term has a small increase than in the two other mass balances, the
model is probably not limited on silicate.
75
5.5.
Conclusion
The conclusions of this chapter are given per subsection.
Model behaviour
The temperature in the three-dimensional model is forced by calculated
temperatures in the hydrodynamic model. These values are two to four degrees Celsius
above the observations. The high water temperature causes that nitrate depletion occurs
half a month earlier than observed and simulated in the two-dimensional model,
because of the high temperature the growth of algae starts earlier. The temperature in
the two-dimensional model is forced by observations, so is in line with the
observations.
Besides the temperature problems, the vertical mixing in the model seems to be too
low. Therefore nitrate concentrations in the bottom layer are too low at the beginning
of summer and increase linearly to a too high concentration at the end of summer.
Probably the dead algae settle down too slowly. Because of the low vertical mixing the
simulated oxygen concentration at the end of summer is equal to half of the observed
concentration.
The high nutrient concentrations in the bottom layer mix during autumn with the
surface water, afterwards the winter concentrations in the whole water column are
nearly double of the observed concentration.
The origin of these problems is in the climate forcing of the hydrodynamic model.
This model was designed for fish larvae transport and not in particular for ecological
processes. One has used climate data of 1988/1989 for all years, in stead of actual
observations. A test case of 1996 is performed using a temperature that is set four
degrees Celsius warmer. T he model results regarding the spring bloom in 1996 are
correct in that test case, but the summer temperature are too warm because the
deviation in summer was less than four degrees. The test case shows that the model will
be able to simulate the spring bloom correct when the model is forced by actual climate
data.
Nutrient distribution
The average surface nutrient distribution in January and February over the southern
North Sea is compared to observations in the same period. Dutch, Belgian, French and
British observations are used. Most parts of the North Sea are covered by these data;
however the number of observation data in the Channel area and the British coastal
waters is limited.
The simulated nitrate concentrations in the new model set-up match better to
observations near the British coast than the previous model. The match near the Dutch
and German coast has not improved, so the match is still good. A major distinction is
the match near the Danish coast, in this area the agreement with observations is worse,
especially in the three-dimensional set-up.
The phosphate distribution in the new two-dimensional model overestimates the
concentrations near the Dutch coast, but has a good estimation in the German Bight
and northern North Sea. The agreement with observations of the new threedimensional model is the other way around.
The silicate concentration is estimated in most British rivers, because observations
were lacking. However the concentrations on the central North Sea are decreased
compared to the previous model set-up and the match to observations is worse. The
riverine silicate concentration is underestimated in the new model set-up.
76
6.
Conclusions
The most recent version of the southern North Sea model set-up is used in this study,
namely the model used during 2nd Maasvlakte studies (De Goede et al. 2005; Prooijen et
al. 2006). The model area extends from the Channel (Cherbourg-Southampton) to the
line between Aberdeen and the north of Denmark. The model results cover the period
from 1997 to 2003 and simulate the hydrodynamics and water quality of the model area.
The water quality is simulated by the Delft3D-DELWAQ software.
In this thesis the input to the water quality model is changed regarding the nutrient
loads by rivers, the boundary conditions and the atmospheric deposition. A threedimensional model set-up is created, because of the stratified situation in the northern
part of the model. The three-dimensional model is not calibrated in this study, so the
two-dimensional calibration constants are adapted.
In the study three different model set-ups can be regarded:
“Previous model”, the model set-up by Prooijen et al. (2006) with the addition
of atmospheric deposition as discussed in Blauw et al. (2006).
“New 2D model”, an update of the previous model by the addition and update
of riverine time series and the addition of a seasonal influence on the northern
boundary condition.
“New 3D model”, the three-dimensional version of the new two-dimensional
model. The northern boundary condition includes stratification during summer
periods in this set-up.
The hydrodynamic model results in Delft3D-FLOW are adapted from De Goede et al.
(2005) and are not updated.
The objectives of the thesis are:
To quantify the terrestrial nutrient loads on the southern North Sea in a consistent way.
To specify the boundary conditions of the southern North Sea regarding nutrient concentrations in a
consistent way.
To determine the contribution of these loads to the nutrient concentrations in the southern North Sea.
In this chapter the behaviour of the model is discussed and the mass balances of the
three model set-ups are given. Afterwards the conclusions on terrestrial nutrient loads,
and the boundary conditions are given. This results in an aggregated model overview.
77
6.1.
Model behaviour
The behaviour of the two-dimensional water quality model is discussed in Blauw et al.
(2006) and can be regarded as good. The three-dimensional model is set up in this
thesis. The model is not calibrated in this study; the behaviour of the model is
compared to observations. Model results of both the average winter nutrient
concentration over the model area and time series of stations in the Terschelling and
Noordwijk ray in the Dutch continental zone are compared to observations. The
analysis shows that the behaviour of the three-dimensional model is not totally in line
with observations for two reasons. First, the climate forcing of the thee-dimensional
model is not by actual observations (like in the two-dimensional model) but by data of
1988/1989. This causes in general a too high water temperature because the year
1988/1989 can be classified as relative hot. Secondly the Secchi depth is not based upon
observations; this causes a too low vertical mixture. In general the climate problems
cause an overestimation of nutrient concentrations in the end of summer in the bottom
zone and because of mixing in the whole water column during winter.
Despite the temperature problems, the three-dimensional model results are often in
agreement to observation data. For near-shore stations the model results are in better
agreement to observations than the two-dimensional model results. Thus the model
results of the three-dimensional model can be regarded as reasonable, when you keep in
mind the climate problems.
A case study of the Western Scheldt estuary shows that the model is able to simulate
estuarine processes. However, for some rivers the observation location is in the
freshwater part of the river, whereas in the model the load is applied much closer to the
river mouth. In those cases concentrations of inorganic nutrients are not corrected for
estuarine retention. This may introduce a small error in the estimated nutrient load.
Literature review has shown that estuarine retention affects in particular the particulate
organic matter concentrations of the river water. This effect is taken into account in the
load estimation method used in this study.
The model behaviour is summarized by graphical representations of the average annual
mass balance over the period 1997-2003 in Figure 6-1. The plots show that in general
the riverine loads have increased in the two new model set-ups compared to the
previous model. The boundary loads have changed as well, because of changed
boundary conditions and processes in the model. The decrease in the processes term in
the three-dimensional model compared to the two-dimensional model is equal to the
increase in the boundary load over the northern boundary. The differences between the
different model set-ups are discussed more detailed in the next subsections.
78
Figure 6-1: Graphical annual average mass balance of total nitrogen, phosphorus and
silicate over the period 1997-2003.
Positive numbers represent a net load towards the model; negative numbers represent a
net removal of nutrients from the model area. The surface area of an arrow represents
the load; the scale is equal for each nutrient.
Blue: riverine loads per country
Red: boundary loads over northern and southern boundary
Magenta: Atmospheric deposition
Cyan: Denitrification, Burial, Processes and Storage.
79
6.2.
The updated model input includes all rivers and smaller sources that drain to the
southern North Sea; this is an improvement to the previous model set-up (De Goede et
al. 2005; Prooijen et al. 2006) that includes only detailed loads of the Dutch and Belgian
rivers and estimations of the German and British major rivers. In the majority of the
rivers there are time series available of concentrations and discharge. The observations
are converted to daily values by use of linear interpolation. Large data gaps are filled
first by preceding observations or average values. Small nutrient loads like polder
pumping stations are implemented in the model by their annual load. This annual load
is distributed over the year proportional to the positive precipitation surplus for Dutch
sources; the distribution of Belgian and German sources over the year is proportional to
riverine discharge characteristics. The average annual nutrient loads per country are
depicted in blue in Figure 6-1.
For most nutrient loads observation data is available for all relevant parameters, except
for the Danish loads. When data is lacking the values are estimated. Estimation
techniques are developed for total phosphorus by the phosphate concentration and for
silicate by the discharge. However during the model validation the silicate estimation
technique seems to underestimate the silicate load in British rivers.
The modelling approach uses a hydrodynamic model that is separate of the water
quality model. Therefore the addition of new rivers and the update of existing rivers in
the water quality model cause a discrepancy with the hydrodynamic model. The total
water flow of the French rivers that are included in the water quality model set-up is
fifty percent higher than the water flow in the hydrodynamic set-up. In the United
Kingdom the water flow in the water quality model has even doubled.
Because of the addition of rivers and the update of existing rivers in the previous model
the total riverine nutrient load to the new two-dimensional model has increased: the
total nitrogen load by 20%, the total phosphorus load by 5% and the silicate load with
only 1%, see Table 6-1. The changes between the new two- and three-dimensional
models are negligible.
Table 6-1: Annual average riverine nutrient load in different model set-ups (1997-2003).
Previous 2D
New 2D
New 3D
[kT/year]
[kT/year]
[%] (I) [kT/year]
Total Nitrogen
732
875
20 %
878
Total Phosphorus
40
42
5.0 %
43
Silicate
503
507
1.0 %
511
(I)
(II)
[%] (II)
0.3 %
2.4 %
1.0 %
The increase of the nutrient loads is not equal to the loads from the newly added rivers.
In general the estimated river load in the previous model was higher than the load given
by time series in the new model set-up. The increase of the silicate load is relatively low
because of the underestimation of the silicate concentration in many British rivers.
Due to the use of time series for 1997-2003 for all rivers the model is able to represent
dry (1997 and 2003) and wet (2001) years.
80
In Blauw et al. (2006) the relative contribution of the riverine and boundary loads to
nutrient concentrations in the North Sea are calculated by use of a tracer method. The
report by Blauw uses the nutrient loads and boundary conditions as determined in this
master thesis study. Blauw et al. (2006) shows that the addition of the French riverine
loads to the model input has a significant influence on the nutrient balance near the
Dutch coast. The fraction French nitrogen about 60 kilometres off shore of the Dutch
coast is between 10 and 20%. This is comparable to the influence of the British rivers.
6.3.
Boundary conditions
The model has two open boundary conditions: a southern one in the Channel near
Cherbourg and Southampton and a northern one from Aberdeen to the north of
Denmark. On these boundaries the residual flow is prescribed by an imposed water
level, the concentrations of the several parameters are derived from literature and
observations.
The southern boundary conditions in the previous model show good agreement with
the literature values, however the nitrate concentration seems to be overestimated. The
southern boundary can be regarded as vertically well mixed, so there is no necessity to
create different boundary conditions for the three-dimensional model set-up.
The northern boundary conditions in the previous model do not include any seasonal
variability; they were based upon winter nutrient observations. This means that in
summer the nutrient load over the northern boundary was overestimated. New
boundary conditions are created by use of literature and observations and do include
seasonal variability. The new boundary condition cause that the northern boundary acts
as a sink for total nitrogen in the new two-dimensional model set-up; the total
phosphorus load over the boundary increases by one quarter; the silicate load decreases
by 16%. This is tabulated in Table 6-2 and depicted in Figure 6-1 as well.
Table 6-2: Average annual net nutrient loads over model boundaries (1997-2003).
Parameter
Boundary
Previous
New 2D
[kT/year]
[kT/year]
New 3D
[%] (I)
[kT/year]
[%] (II)
Total
Nitrogen
Total
Phosphorus
Silicate
(I)
(II)
Southern
538
505
-6.1 % 464
-8.1 %
Northern 138
-67
-149 % -548
718 %
Southern
83
80
-3.6 % 76
-5.0 %
Northern -26
-33
27 %
-69
109 %
Southern
495
488
-1.4 % 537
10 %
Northern 772
648
-16 %
861
33 %
The deeper parts of the North Sea, including the northern boundary, are affected by
stratification in summer periods, so the nutrient behaviour in this area is better
simulated in a three-dimensional model. A three-dimensional model of ten layers has
been created on the same grid as the two-dimensional model; the model has a sigma
layer set-up. Separate boundary conditions are created for this model for the bottom
layer. Different conditions are created for the Danish coastal waters also, because this
area is affected by nutrient rich water from the European rivers. The gross nutrient load
in the three-dimensional model set-up is higher than in the two-dimensional model setup (not listed in this chapter) because the bottom concentrations in the summer are
higher than in the two-dimensional boundary conditions. The net loads over the model
81
boundaries in the three-dimensional model are given in Table 6-2 and Figure 6-1, but
the numbers must be considered with some caution as the model behaviour is not
totally in line with observations, see section 6.1. In the three-dimensional model the
burial of nitrogen halves, which causes that the net load over the northern boundary
increases eight times. The phosphorus load doubles and the silicate load increases by
one third because of the changed boundary conditions and processes in the threedimensional model.
The anthropogenic influence on the boundaries is investigated also. Observations and
literature show an anthropogenic influence on the southern boundary. The salinity
observations near the French and British coast are decreasing, which indicates the
dilution of sea water with polluted riverine water. The order of magnitude of the
anthropogenic influence cannot be investigated as nutrient data is lacking.
At the northern boundary observed nutrient concentrations in Danish coastal waters
are higher than in the central North Sea. This might be caused by an anthropogenic
influence. However a long term trend in this area cannot be investigated as the number
of days when observation data is available is limited. In the part of the northern
boundary that is influenced by the Atlantic Ocean no trend is visible. This is in line with
literature. In the southern part of the North Sea a long term trend in nutrient
concentrations is observed, that is in line with riverine observations.
The influence of the boundaries on the nutrient concentrations in the North Sea is
investigated by Blauw et al. (2006) as well, like the riverine loads. The influence of the
southern boundary to the total nitrogen concentration is still significant (10 till 20
percent) near the Danish coast. However the contribution to nitrogen might be
overestimated as the nitrate concentration on the southern boundary might be
overestimated. The northern boundary influences the total nitrogen concentration in
the whole central North Sea. The northern boundary does not influence the Dutch
near-shore waters. In the Dutch near-shore waters the influence of the Dutch rivers is
very high. The Dutch rivers include nutrient loads from countries upstream of the
Netherlands as well.
6.4.
Aggregated overview
In the preceding subsections the nutrient load on the southern North Sea is explained
in detail. However, one can wonder what the influence of the nutrient loads is on the
nutrient mass in the North Sea. Are the nutrients refreshed every year or ten years? The
residence time in the North Sea is estimated by the annual average net nutrient load 27
and the annual average total mass in the North Sea. The computed residence time is an
average for the whole model area, so the residence is probably shorter in zone with a
high residual current as the Channel area. The data in Table 6-3 show that the residence
time for nitrate in the new model setup is longer than in the previous model because the
annual load has decreased. The net phosphate load was negative in the previous model,
so no residence time can be computed. In the new two-dimensional setup the
phosphate residence time is five years, the residence time is halved in the threedimensional model setup because the net load doubles as the export of phosphate over
the northern boundary decreases. The silicate residence time is equal in the three model
set-ups.
27
Including riverine load, net transport over the boundaries and atmospheric deposition
82
Table 6-3: Annual average net nutrient load and total mass in system (1997-2003).
Nitrate
Phosphate
Silicate
Total net load [kT/y]
Total mass [kT]
Residence time [y]
Total mass [kT]
Residence time [y]
Total mass [kT]
Residence time [y]
Previous 2D
New 2D
New 3D
1712
926
1007
1373
1264
1375
1.4
1.4
0.8
-22
52
100
218
270
266
5.2
2.6
1770
1643
1909
1282
1038
1154
0.7
0.6
0.6
The average total mass of nitrate, phosphate, silicate and its detritus fractions that is
available in the model over a year is plotted in Figure 6-2. The plots show that in the
three-dimensional model the detritus fraction is considerably higher than in the other
models. The increase of the detritus fraction might be explained by the artificial high
water temperatures that causes a higher primary production.
Figure 6-2: Average annual total mass in model area over 1997-2003
83
84
7.
Recommendations
7.1.
Model set-up
There are three recommendations for the hydrodynamic model.
First, the climate data in the hydrodynamic model are observations of the years
1988/1989. The simulated water temperature per layer in the hydrodynamic model is
coupled to the three-dimensional water quality model. The use of historic data instead
of actual observations causes a disagreement to observations for especially the spring
bloom.
Secondly, the Secchi depth data in the hydrodynamic model do not originate from
observations. This causes a too low vertical mixture.
Third, in the water quality model more rivers are included than in the hydrodynamic
model. The annual flow volume of French rivers that are in the new water quality
model is fifty percent higher than the rivers that are included in the hydrodynamic
model. The difference in the British annual flow volume is more than a factor two. The
impact of this discrepancy is not exactly investigated. It is recommended to investigate
the impact on the nutrient distribution and probably re-run the hydrodynamic model.
Thus it is recommended to re-run the hydrodynamic model using actual climate
data, Secchi depth and observed riverine discharges in order to simulate the nutrient
processes in a reasonable way.
The two- and thee-dimensional water quality model is not re-calibrated after the
addition of the new riverine loads and updated northern boundary conditions. It is
recommended to re-calibrate the model in the future.
7.2.
The Danish loads to the model include only nitrate, ammonium and total phosphorus.
These loads are given as annual loads by the OSPAR reports. It is recommended to
update the number of parameters and to change the annual loads to observations or
monthly loads, in order to increase the accuracy of the Danish loads to the North Sea.
The simulated silicate concentrations on the North Sea show that the silicate
concentration near the British coast is underestimated. The silicate concentration in
British rivers is estimated by use of an average concentration of major European rivers,
so this estimation seems to be too low. It is recommended to investigate better silicate
estimation technique for the British rivers.
The influence of the atmospheric deposition is not labelled to each specific country, so
e.g. the plot of French contribution to the nitrogen concentration does only include the
French riverine load and not the atmospheric load. A better estimation of the influence
of each country will be achieved when the atmospheric deposition is labelled too.
85
7.3.
Boundary conditions
The nitrate concentrations in the southern boundary seem to be overestimated
compared to observations. Further research on the nitrate concentrations near the
southern boundary is recommended.
The northern boundary condition for the three-dimensional model is based upon
observation data in the period 1958-2000. The influence of the long term period is not
investigated. It is recommended to study this in the future.
The boundary conditions of oxygen in both boundaries are just forced to 8 mg/l. It is
recommended to update these numbers by observation data.
Chlorophyll-a is currently not implemented in the southern boundary, as observations
were lacking. It is recommended to include chlorophyll-a in the future.
86
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