jan Leeuwen et al.

Model domains

0 |
|

(X
:Cefas, JRC
Deltares
UHH-HZG |,

7 IFREMER
Oldenburg "-
RBINS
3MHI

—z
- 0 U
Longitude [deg. E]

9°

IGURE 1

Model domains of the different models used to calculate pre-
zutrophic marine conditions. Note that the JRC (Joint Research
Centre, EU) and Cefas model domains are identical, though thei'
acOsvstem models are different

2.2 Available observational data

Observations were obtained from the OSPAR Common
Procedure Eutrophication Assessment Tool (COMPEAT, see
GitHub - ices-tools-prod/COMPEAT, ICES, 2022) for validation
and weighting purposes. This tool is applied in the OSPAR
Comprehensive Procedure (COMP) for the determination of the
gutrophication status of marine areas based on observational data
submitted by each member country. The observations, which are
che same as those used in the official eutrophication assessments,
2ave a higher spatial and temporal coverage in coastal areas than in
offshore waters (see Appendix B; Supplementary Materials). Thus,
spatial averages of the observations over assessment areas that
comprise coastal and more open waters may not be fully
zepresentative of these assessment areas. Simulated area-averaged
results will always be representative of the full area, complicating a
direct comparison with the observational averages (see, e.g., Garcia-
Garcia et al., 2019). Observations were available for DIN, DIP and
Chl. Due to the low confidence in the in-situ Chl observations
‘Appendix B; Supplementary Figure 3) related to data scarcity (both
‚emporally and spatially), satellite data (a reanalysis product, Van
der Zande et al., 2019; Lavigne et al., 2021) were added to the Chl
observational database in COMPEAT. To calculate a combined
seasonal mean concentration per assessment area from in-situ and
Earth Observation (EO) data a weighting factor was applied, based
on the OSPAR confidence rating of in-situ data availability which is
implemented in the COMPEAT tool. Where the combined
‚emporal and spatial confidence of in-situ Chl was high, 50:50
“in-situ/EO) data was used to derive the assessment area mean. If
in-situ confidence was moderate, 30:70 (in-situ/EO) was used, while

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10.3389/fmars.2023.1129951

where in-situ confidence was low, 10:90 (in-situ/EO) was applied
(OSPAR, 2022a). Coastal waters are optically challenging
environments for satellite wavelength observations, due to shallow
depths and high levels of suspended matter. Retrieving accurate Chl
estimates from satellite data is therefore more challenging in coastal
waters than in offshore waters (Lavigne et al., 2021).
Observational time series were extracted for the stations with
the most complete temporal coverage over the simulated period
(available in the ICES COMPEAT tool), for validation purposes.
Unfortunately, using these data results in a strong spatial bias, as
most observations were obtained in near-shore waters of the
southern North Sea. In addition, data from long-term observation
stations were provided by Cefas (station Stonehaven) and PML
(station WCO-L4). See Supplementary Figure 2 in the
zupplementary materials for the locations of the used stations.

2.3 Model validation

Each model used in this exercise has been validated separately
before this application (see Supplementary Materials Appendix D). A
common validation procedure was applied using a subsection of the
COMPEAT data, representing short-term and long-term time series,
augmented with 2 additional long-term stations (Stonehaven, WCO-
L4) to allow for a comparison of model performance (Figure 2).

Following the approach of Eilola et al. (2011) and Edman and
Omstedt (2013), we used a combination of correlation coefficient
and the root mean square difference (RMSD) scaled by the standard
deviation of the observations to assess the skills of the different
ecosystem models compared to the observations. This comparison
was done for each station individually and later the station results
were combined to assess the overall skill for each model (Figure 2).
Overall, most model systems have a good to acceptable model skill.

| UHH-HZG

SMHI
RBINS

Oldenburg

‚RC

+ DIP
= DIN
$ Chlorophyll-:

| IFREMER

eltares

> as
1 - Correlation coefficient
FIGURE 2

Model skill assessment for DIN, DIP and Chl for all participating
models (in respective areas where the number of observations is the
nighest). The horizontal axis shows 1 - the correlation coefficient
oetween model values and observations, the vertical axis shows the
‚oot mean square difference (RMSD) divided by the observational
standard deviation (STD). The closer the values are to the origin, the
zloser the model results are to observations. The inner field (x-axis:
I-1/3, y-axis: 0—1) indicates good agreement and strong
correlation between model and observations. The middle field (x-
axis: 1/3—-2/3, y-axis: 1-2) indicates reasonable agreement and
moderate correlation between model and observations. The outer
Äeld indicates poor agreement between model and observations.
SMHI values for DIN and DIP are off the scale at (0.35, 3.61) and
(0.32,3.42), respectively. Negative correlations did not occur.

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5
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5
50

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N
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ıme P/'

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Longitude [deq. E]

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ug Chl/l
4

»o vw u
Lonaitude [deq. E1

x Chl-a
3

an
Current state

u. ur u.
ongitude Ideg. E]

“IGURE 7
Neighted ensemble results for the current state (2009-2014 average), for surface DIN (A), DIP (B) and Chl (C).

3.4 Differences between current and pre-
eutrophic state

The difference between the ensemble mean values for the pre-
eutrophic state and the current state exhibits up to 50 - 60% less
dissolved inorganic nutrients in the coastal zones in the pre-
eutrophic state (Figure 9). Almost no changes are observed in
oceanic areas, with at most a 1% difference. Note that in general a
decrease in nutrient input can lead to local increases in some
dissolved nutrients, as the input reduction of the limiting nutrient
will decrease primary production, thus reducing nutrient uptake
and causing a possible local increase in non-limiting nutrients. DIN
ıevels were up to 62% lower in the pre-eutrophic state than in the
current state, particularly along the Dutch and German coast. Both
DIP and Chl concentrations were up to 40% lower in the pre-
eutrophic state than in the current state. In contrast there is no effect
of the DIP concentration within most of the Channel area and the
coastal region of France while the difference for Chl lies around 20%
in the Channel area and increases at the French coast. Also for the
Eastern North Sea area, east of the Dogger Bank, the difference
petween the two simulations is higher for Chl than for DIP, but still
‚ower than for DIN.

"rontiers in Marine science

Other eutrophication effects include N:P ratio changes,
increased net primary production and low oxygen levels near the
bed through remineralization of excess organic material by bacteria.
Figure 10 shows the differences for the unweighted (due to lack of
observations) ensemble mean differences for these eutrophication
related phenomena. Both the N:P ratio and net primary production
were much smaller for the pre-industrial state in the coastal zones
(maxima of 35% and 30% respectively) than in the current state.
Note that the net primary production reported here relates
predominantly to pelagic production, as the models do not
include macrophytes. Pre-eutrophic conditions are known for
extensive macrophyte presence (Nienhuis, 1996), and would thus
be characterized by a larger benthic primary production
contribution. Near bed oxygen levels were higher in the Southern
Bight of the North Sea, the English Channel area and the coastal
parts of the Bay of Biscay under pre-eutrophic conditions. The
Meuse plume is the only area where near bed O, levels were slightly
lower (<1%) in the historic state. Oxygen values in the Bay of Biscay
are dominated by 1 of only 2 contributing models: without
weighting due to lack of observations outlier values can have
disproportionate influence. The high value for O, difference in
the Gironde Plume (GDPM, -50.7%) is deemed artificial, and this

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jan Leeuwen et al.

‚ol
5%
2
ä
E
3

Current State, CWMTI: Channel well mixed tidal influenced

u 2007
Current State, CWMTI: Channel! well mixed tidal influencead
4RSN4* 4RATARA 4904458

10.3389/fmars.2023.1129951

Current State, CWMTI: Channel well mixed tidal influenced

1
14:
13

Inn

# observations

Ibservations
JEFAS

+ Deltares
IFREMER

RC
Ildenburg
RBINS
a SMHI
A UHH-HZG

‚Jbservations
2009-2014 mean
Model mean !
Model Weiaghted Mean

-IGURE 5
Annual results per model for area Channel Well Mixed Tidal Influenced (CWMTI, area 18): (A) DIN, (B) DIP and (C) Chl. The grey bars denote the
>bservational values per vear, including their standard deviation. Note the low number of observations for DIN and DIP

Supplementary Figures 9-15). Chl results show both large over- and
underestimations. Note that the observational mean over 2009-
2014 for an area may be biased towards the coast and fair-weather
conditions. Limited numbers of observations per year can also
introduce bias for individual areas, e.g. by missing
concentration peaks.

indicators are found in the Scheldt plume (SCHPMI1, SCHPM2),
Meuse plume (MPM), Rhine plume (RHPM), and Seine plume
(SPM), and to a lesser extent in the Elbe plume (ELPM). The
weighted ensemble approach is thus capable of simulating known
coastal gradients.

3.3 Pre-eutrophic state
3.2 Current state

The horizontal distribution of the weighted ensemble results for
che present day for surface winter DIN, DIP and growing season
Chl are displayed in Figure 7. High concentrations are found for all
hree variables in near-coastal and river plume areas, and in the
Southern Bight of the North Sea. Highest concentrations for these

Current State, SNS: Southern North Sea
8 - 85 57

zurrent State, SNS: Southern North Sea
STOMBS SAND SREFOSE SARFUN

The horizontal distribution of the weighted ensemble results for
the pre-eutrophic state (or HS) for surface winter DIN, DIP and
growing season Chl are presented in Figure 8 and as a table in
Appendix G (Supplementary Materials). Offshore results are similar
to the current state results, but coastal areas (typically influenced by
rivers) show consistent lower concentrations for all parameters.

Current State, SNS: Southern North Sec
| [5

2010

}
Ih

„A
1
IM

+ observations

Jbservations
°EFAS
deltares
FREMER
RC
Ildenburg
ZBINS
ar SMHI
JHH-HZG '

a Observations
2009-2014 mean
Model mean
Model Weighted Mean

Wi
*

:IGURE 6

Annual results per model for area Southern North Sea (SNS, area 11): (A) DIN, (B) DIP and (C) Chl. The grey bars denote the observational values per
year, including their standard deviation. The scale of subfigure B has been adjusted to show the individual results better: the DIP observational
standard deviation for years 2010, 2011, 2012 was 1.38, 8.94 and 3.45 respectively.

"rontiers in Marine Science

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van Leeuwen et al.

Maritime and Fisheries Fund. Deltares and IFREMER were partly
supported by the Jerico-S3 project, funded by the European Union’s
Horizon 2020 research and innovation programme under grant
agreement No 871153. RBINS received financial support from
BELSPO through the project ReCAP, which is part of the Belgian
research program FED-tWIN.

Acknowledgments

We like to thank Thomas Neumann (IOW) and Stiig Markager
‚Aarhus University) for their contribution to derive pre-eutrophic
Joundary condition for the Baltic Sea outflow. A very special thanks
:©o Hjalte Parmer for providing the ICES data that are used within
ıhe COMPEAT tool. We thank the members of ICG-Eut and TG-
COMP for the fruitful discussions, as well as OSPAR for
commissioning this work. Part of the maps in this manuscript
were made using the free package M_Map: Pawlowicz, R., 2020.
“M_Map: A mapping package for MATLAB”, version 1.4m,
[Computer software], available online at www.eoas.ubc.ca/-rich/
map.html. Computing facilities for Deltares were provided by the
DECI resource Cartesius based in The Netherlands at SURFsara
with support from PRACE. The support of Maxime Moge from
SURFsara, The Netherlands is gratefully acknowledged. UK riverine
data was processed from raw data provided by the Environment
Agency, the Scottish Environment Protection Agency, the Rivers
Agency (Northern Ireland) and the National River Flow Archive.
French water quality data was provided by Agence de l’eau Loire-
Bretagne, Agence de l’eau Seine-Normandie and IFREMER, while
low data was provided by Banque Hydro. German and Dutch
riverine data was provided by the University of Hamburg (Johannes
Paetsch, Hermann Lenhart), with some additional German river
data supplied by IOW (Ulf Graewe). Irish flow data was provided by
Hydrodata and the Environment Protection Agency (Hydronet),
while water quality data was obtained from OSPAR RID reports.
Norwegian flow data was supplied by NVE’s Anne Fleig (afl@

10.3389/fmars.2023.1129951

nve.no), water quality data was obtained from NIVA (www.niva.no)
and Tore Hegäsen (tore.hogaasen@niva.no). Danish water quality
data was provided by the National Environmental Research
Institute (NERI). Water quality data for Baltic rivers was
provided by the University of Stockholm and the Baltic Nest
(www.balticnest.org/bed). Spanish data was provided by Dr. Luz
Garcia (while at Cefas, UK). Portuguese data was obtained from Dr.
Amelia Araujo (Cefas, UK). Dr. S. M. van Leeuwen, NIOZ,
Lansdiep 4, ‘t Horntje, Texel, the Netherlands, pers. comm.

Conflict of interest

The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.

The reviewer JB declared a shared affıliation Helmholtz Centre
for Materials and Coastal Research with the author CS to the
handling editor.

7ublisher’s note

All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
teviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.

Supplementary maz:eria!

The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmars.2023.1129951/
‚ull#supplementary-material

Referenc2s

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autrophication assessment. Ambio 39 (1), 59-69. doi: 10.1007/s13280-009-0006-7

Arabi, B., Salama, M. S., Pitarch, J., and Verhoef, W. (2020). Integration of in-situ
and multi-sensor satellite observations for long-term water quality monitoring in
coastal areas. Remote Sens. Environ. 239, 111632. doi: 10.1016/j.rse.2020.111637

Berdalet, E., Fleming, L. E., Gowen, R., Davidson, K., Hess, P., Backer, L. C., et al.
(2016). Marine harmful algal blooms, human health and wellbeing: challenges and
opportunities in the 21st century. Mar. Biol. Assoc. 96 (1), 61-91.

Billen, G., and Garnier, J. (1997). The phison river plume: coastal eutrophication in
response to changes in land use and water management in the watershed. Aquat.
nicrobial Ecol. (Cambridge, UK: Cambridge University Press) 13 (1), 3-17. doi:
10.3354/ame013003

Billen, G., Garnier, J., Deligne, C., and Billen, C. (1999). Estimates of early-industrial
inputs of nutrients to river systems: implication for coastal eutrophication. Sci. Total
Anviron. 243, 43-52.

Billen, G., Silvestre, M., Grizzetti, B., Leip, A., Garnier, J., Voss, M., et al. (2011).
“Nitrogen flows from European watersheds to coastal marine waters,” in The European
nitrogen assessment: sources, effects, and policy perspectives. Ed. M. A. Sutton
(Cambridge, UK: Cambridge University Press), 271-297. Available at: https://

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:entaur.reading.ac.uk/28381/1/Chapter%2013%20EN A%20Billen%20et%20a1%
202011.pdf.

Brockmann, U., and Topcu, D. H. (2002). Nutrient atlas of the central and northern
North Sea.

Burkholder, J. M., Tomasko, D. A., and Touchette, B. W. (2007). Seagrasses and

utrophication. /. Exp. Mar. Biol. Ecol. 350 (1-2), 46-72.

Ciavatta, 5., Brewin, R. J. W., Skakala, J., Polimene, L., de Mora, L., Artioli, Y., et al.
2018). Assimilation of ocean-color plankton functional types to improve marine
2cosystem simulations. j. Geophys. Res. C Oceans 123 (2), 834-854. doi: 10.1002/
20177C013490

Claussen, U., Zevenboom, W., Brockmann, U., Topcu, D., and Bot, P. (2009).
"Assessment of the eutrophication status of transitional, coastal and marine waters
within OSPAR,” in Eutrophication in coastal ecosystems (Dordrecht: Springer), 49-58.

Cloern, J. E. (2001). Our evolving conceptual model of the coastal eutrophication
»roblem. Mar. Ecol. Prog. Ser. 210, 223-253. doi: 10.3354/meps210223

Conley, D. J., Paerl, H. W., Howarth, R. W., Boesch, D. F., Seitzinger, S. P., Havens,
K. E., et al. (2009). Controlling eutrophication: nitrogen and phosphorus. Science 323
‚5917), 1014-1015. Available at: https://citeseerx.ist.psu.edu/document?repid=
'ep1&type=pdf&doi=89c56da0503c6d284fee938932e1c20112e6197e.

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DIN

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A DIP
N

10.3389/fmars.2023.1129951

HS
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Ss

a
> |
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>
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&
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= 1
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50

-10 5 u ;
Longitude [deg. E1
Chl-a

] HS
>
Cs

HS
0
38

> a
Lonaitude [deg. E]

HS:
>
Cs

Difference

HS
>
De
Lonaitude [deg. E1

“IGURE 9

difference between the weighted ensemble results for the current state (CS) and the pre-eutrophic state (historic scenario, HS) for DIN (A), DIP
B) and Chl (C). Green colours indicate areas where the pre-eutrophic levels were lower than those of the current state. Note that the colour bar
>xtends to -5% only, indicatina areas where pre-eutrophic levels were sliaghtly hiaher than current levels.

different formulations of ecological processes and exclude the
influence of the underlying hydrodynamic model we compared
relative changes in chlorophyll mean concentrations with relative
changes in nutrient concentrations (Figure 12). Some models, such
as the Deltares and RBINS models show decreases in Chl
concentrations almost proportional to decreases in DIN
concentrations (close to the black line). Other models such as the
JRC model and Oldenburg model show a much smaller response in
Chl concentrations to decreasing winter nutrient concentrations.
Aere we see how a relative reduction in winter nutrient
concentrations (pre-eutrophic state compared to current state,
[CS-HS]/CS) induces a relative reduction in mean Chl. The
relative reductions in winter DIN were in general stronger (up to
75%) than the reductions in winter DIP (up to -55%) in the
individual model results. Due to the already achieved P reduction
measures between peak discharges in the 1980’s and the current
state period, we observe smaller differences for winter DIP between
pre-eutrophic and current loads in our model study. The
corresponding reductions in mean Chl reach 60% at most. In
many areas, the required reductions in nutrients to reach the pre-
eutrophic mean Chl seem higher for DIN than for DIP. Although
the relationship between winter nutrients and mean Chl is non-

“rontiers in Marıne © zie.17e

linear and perturbed by other factors like the underwater light
climate, grazing and regeneration of nutrients, the results suggest
that certain areas are less sensitive to further DIN reduction than
others. These are mainly coastal areas where DIP is likely the
limiting nutrient already, due to the successful reductions in
riverine P loads (Billen et al., 2011).

Discussion

4.1 Eutrophication on the European shelf
We have presented our ensemble results for the pre-eutrophic
state of marine waters on the European Shelf. This estimate of pre-
eutrophic conditions follows the steps taken by OSPAR to move
towards a harmonized and integrated eutrophication assessment
across the North-East Atlantic, taking into account the Water
Framework Directive (WFD; EC, 2000) and Marine Strategy
Framework Directive (MSFD; EC, 2008), which require the
definition of a common ‘baseline’ to which the eutrophication
status of waters can be compared. The approach, to define
ecologically relevant threshold levels for eutrophication indicators

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100

‘90

10.3389/fmars.2023.1129951

KO
so}
60}

20
E

‘——z 0
U 40 60 80 100
% Red. Winter DIN ([CS-HSVCS, %)

Deltares
IFREMER |
JRC
Stine]
RBINS
SMHI
UHH-HZG!

20 40 60 80 100
% Red. Winter DIP ([CS-HSVCS, %)

“IGURE 12
delative reduction of growing-season mean Chl as a function of the relative reduction of winter DIN (A) and winter DIP (B). Each dot indicates a
narine area for one model and models are differentiated by colours

Although the models deliver fairly good results for nutrient
concentrations, the modelled Chl concentrations are often lower
ıhan expected, and this must be considered carefully in any further
application. In all applied models the Chl concentration is mainly
determined by nutrient availability, light availability and grazing
pressure. Differences can thus stem from the complexity of included
nutrient recycling processes, hydrodynamic differences in nutrient
and suspended particulate matter transport, inclusion of benthic
storage and release of nutrients, inclusion of a separate sediment
resuspension model and the complexity of zooplankton
representation. For example, the Deltares model does not include
zooplankton, while the SMHI model has the lowest number of
pelagic state variables, indicating lower pelagic complexity
‘Appendix E). Whether these model characteristics contribute to
ihe observed high Chl concentrations from these models needs
further careful analysis though. The same applies to those models
:hat have consistently low Chl predictions compared to
observations. Lack of phytoplankton species resolution in the
models (usually 2-6 different functional groups) can also play a
part in underestimating Chl levels (unlikely to capture a single
species sudden bloom event well), as can the applied Chl:C ratio
Ased to calculate the Chl concentrations in models based on the
simulated phytoplankton biomass. Reappraisal of individual model
results and possible model improvement is thus a key part of
ensemble modelling.

Structural diversity of the models, parametric uncertainties,
differences in spatial resolution, in boundary conditions and in
‚orcings will necessarily cause differences between model estimates.
Although some of these issues have been solved in this exercise by
applying identical loads, forcings and boundary conditions, there is
still variability in model responses. This variability is desirable as it
displays a range of possible outcomes, and ensemble modelling
approaches are used to explore and quantify this diversity. Though
parametric variation for each ensemble member would enhance
confidence in the individual results even further, a separate
parametric ensemble for each contribution to the overall
ensemble is generally unfeasible due to computational and

"rontiers in Marıne © 12...

arm

financial restraints. Note that we applied the weighting method
by Almroth and Skogen (2010) in a fundamentally different way
{rom the original article: they used it to enhance the quality of the
modelled current state in order to compare it against thresholds
whereas in this study we applied it to a pre-eutrophic scenario
which can be used to derive thresholds.

An objective way to further reduce uncertainties is to resort to
weight-averaged values, estimated from the comparison between
model outputs and observations, and apply these weights to the
individual model results before taking the ensemble average
(Almroth and Skogen, 2010). The present exercise used this
weighted-ensemble-mean method to provide pre-eutrophic
values, or reference values, for the indicators of eutrophication in
coastal and shelf areas. For this the availability of observational data
in the COMPEAT tool was essential. More observational evidence
would therefore also increase confidence in the weighted
ensemble result.

4.4 Ensemble modelling as a tool for
marine management

In the past several single model approaches have been used to
estimate the pre-eutrophic state of marine systems (Schernewski
and Neumann, 2005; Schernewski et al., 2015; Kerimoglu et al.,
2018), including using multiple single models to cover a larger area
(Desmit et al., 2018). Ensemble modelling addresses the inherent
uncertainties in single model results and is increasingly applied in
marine response studies (Almroth and Skogen, 2010; Lenhart et al.,
2010; Eilola et al., 2011; Meier et al., 2019; Friedland et al., 2021;
Stegert et al., 2021) despite the higher efforts involved. These efforts
include the necessity to combine a variety of modelling groups and
their individual models, as well as agreement to a common protocol
to ensure comparable results, agreement on suitable scenarios and
to a common analysis of the obtained scenario results. Individual
funding issues can undermine this common approach, as can
technical issues as demonstrated here (lack of results from one

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‚an Leeuwen et al.

Stegert, C., Lenhart, H. J., Blauw, A., Friedland, R., Leujak, W., and Kerimoglu, O.
(2021). Evaluating uncertainties in reconstructing the pre-eutrophic state of the north
Sea. Front. Mar. Sci. 8, 637483. doi: 10.3389/fmars.2021.637483

Timmermann, K., Christensen, J. P. A., and Erichsen, A. (2021). Establishing
-hlorophyll-a reference conditions and boundary values applicable for the river basin
management plans 2021-2027 (Aarhus, Denmark: Aarhus University, DCE —- Danish
Centre for Environment and Energy), 32. Available at: http://dce2.au.dk/pub/SR461.
df.

Van der Zande, D., Lavigne, H., Blauw, A., Prins, T., Desmit, X., Eleveld, M., et al.
(2019). Coherence in assessment framework of chlorophyll a and nutrients as part of the
EU project Joint monitoring programme of the eutrophication of the north Sea with

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satellite data’. 106, (Ref: DG ENV/MSFD Second Cycle/2016). Available at: https://
www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=
21ahUKEwjJ2MmS6-r9AhUph_0HHRilC6cQFnoECBQQAQ&url=https%3A %2F%
2Fwww.informatiehuismarien.n1%2Fpublish%2Fpages%2F163016%2F2_chlorophyll_
zatellite_data_rev.pdf&usg=AOvVaw2irzU2-BcCBAYDDThMvYWV.

van Leeuwen, 5., Tett, P., Mills, D., and van der Molen, J. (2015). Stratified and
aonstratified areas in the north Sea: Long-term variability and biological and policy
mplications. J. Geophys Res C Oceans 120, 4670-4686. doi: 10.1002/2014JC010485

Venohr, M., Hirt, U., Hofmann, J., Opitz, D., Gericke, A., Wetzig, A., et al. (2011).
Modelling of nutrient emissions in river systems-MONERIS—methods anc
»ackground. Int. Rev. Hydrobiology 96 (5), 435-483. doi: 10.1002/iroh.201111331

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van Leeuwen et al.

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autrophic conditions compared to current conditions (except in the Meuse
Alume and Seine Plume areas). Chlorophyll concentrations were estimated to
be as much as „40% lower in some areas, as were dissolved inorganic
phosphorus levels. Dissolved inorganic nitrogen levels were found to be up to
650% lower in certain assessment areas. The weighted average approach reduced
model disparities, and delivered pre-eutrophic concentrations in each
assessment area. Our results open the possibility to establish reference values
for indicators of eutrophication across marine regions. The use of the new
assessment areas ensures local ecosystem functioning is better represented
while political boundaries are largely ignored. As such, the reference values are
‚Ess associated to member states boundaries than to ecosystem boundaries.

<EYWORDS
autrophication, North Sea, OSPAR ICG-EMO, DIN, DIP, nutrients, chlorophyll
3cosystem modelling

ı Introduction

Nutrient inputs into the marine environment predominantly
come from riverine inputs, direct discharges and atmospheric
deposition. Elevated nutrient concentrations may lead to
ındesirable increases in primary production, and subsequent
degradation of the sinking organic matter can lead to oxygen
deficits near the seafloor (Diaz and Rosenberg, 2008; Greenwood
et al., 2010; Große et al., 2016). This process, called eutrophication,
is related to an increase in nutrient loads from anthropogenic
sources (Jickells, 1998; Nixon, 2009). Additional symptoms of
marine eutrophication include harmful algae blooms (Schoemann
et al., 2005; Riegman et al., 1992) and loss of seagrasses (Burkholder
et al, 2007), resulting in qualitative changes in the local marine food
web. The smelly foam on beaches left in the wake of Phaeocystis
blooms are well known to the general public and tourist’s industries,
but toxins released by some algae blooms also directly threaten
numan economic interests and human life, usually via
(consumption of) affected marine resources (Berdalet et al., 2016).

Eutrophication effects became increasingly evident in the North
Sea around 1980, and it was broadly recognized that this phenomenon
was related to anthropogenic sources. The regional sea convention for
:he North-East Atlantic OSPAR (www.ospar.org) defined
eutrophication as “the enrichment of water by nutrients causing an
accelerated growth of algae and higher forms of plant life to produce an
undesirable disturbance to the balance of organisms present in the
water and to the quality of the water concerned, and therefore refers to
he undesirable effects resulting from anthropogenic enrichment by
nutrients” (OSPAR, 1998, p. 53), confirming the cause-effect
zelationship with anthropogenic sources. Following the early
evidence of eutrophication, OSPAR applied a source-oriented
approach since 1988, through limiting inputs of nutrients and
organic matter to levels that do not give rise to adverse effects on the
marine environment. The proposed reduction was very successful for
phosphorus (which is caused mainly by point-sources, e.g. sewage) but
less so for nitrogen (caused mainly by diffuse sources, e.g. agriculture)

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öl» 8

‚Claussen et al, 2009; Conley et al., 2009). As a result, though
eutrophication effects have declined since their 1980’s peak, they are
even now persistent in many western European coastal areas. The latest
application of OSPAR’s Common Procedure (COMP3, OSPAR, 2017)
still identified large parts of the southern North Sea along the Belgian,
Dutch, German and Danish coasts as so-called “problem areas” or
“potential problem areas” with respect to eutrophication, with smaller
areas along the French and British coasts also characterized as such.
The Kattegat was also defined as a “problem area”, as were smaller
parts along the Swedish and Norwegian coasts.

Recovery from a eutrophic state can be a lengthy process (>
decades, McCrackin et al., 2017), and does not always lead to the
ecological state observed before eutrophication occurred (Duarte
et al., 2008; Oguz and Velikova, 2010). It is therefore critical to
establish appropriate restoration goals for eutrophied areas
(McCrackin et al., 2017). The objective of the presented work is
to quantify the pre-eutrophic state of the Northwest European Shelf,
based on an ecosystem modelling ensemble approach applied by
OSPAR’s ICG-EMO (Intersessional Correspondence Group on
Ecosystem Modelling). Here the pre-eutrophic state is defined as
the situation around the year 1900, and is by no means a pristine or
anthropogenically undisturbed state. To account for regional
differences in performance between the models, the ensemble
mean applies a weighting method (Almroth & Skogen, 2010)
based on the level of agreement between current model
simulations and current observations. These weights are then
applied to construct the ensemble-simulated pre-eutrophic state,
thus providing a sophisticated estimate of the mean pre-eutrophic
concentrations, which can serve as baseline for eutrophication
assessments. This paper presents the harmonized modelling
approach, the applied ensemble weighting method, the underlying
assumptions, as well as the resulting estimates of pre-eutrophic
nutrient and phytoplankton concentrations. These values can
support the elaboration of policy thresholds for eutrophication
that are coherent across national boundaries. We demonstrate
that an ensemble modelling approach can help to define pre-

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jan Leeuwen et al.

TABLE 3 Pre-eutrophic nutrient concentrations/loads? As percentage of
:urrent values for the Droaden and Darss sills in the Baltic Sea

gl
NO3
NH
DON
204
86

91%

model results. We applied these weights to calculate ensemble
model averages for the OSPAR areas defined for the COMP4
assessment (section 2.4). Almroth and Skogen (2010) used this
weighted ensemble approach to derive a better estimate of the
current state, which was then assessed against the eutrophication
criteria of the time. Here, we apply this method to obtain weights
based on validation of current state results. We then applied these
weights to the historic results and estimate the area’s pre-
gutrophic state.

The applied weighting method is given by Eq. 1-Eq. 4 and is
based on model results for the current state and the available
observations from the COMPEAT tool. It relies on observational
concentrations being available in each area over the chosen COMP3
veriod (2009-2014). As such, the weighting is applied to winter DIN
and DIP and growing season mean Chl results. When observations
were not available for a given area, we used the unweighted
ensemble mean (ie. a classical averaging was applied), but DIN,
DIP and Chl weights were also applied to Total N, Total P and Chl
P90, respectively. For Chl, the observations involved both in situ
and satellite observations. In any given area, the cost function C?
(Eq. 1) was calculated for each model £ and parameter P (e.g. DIN),
with P °S and P model S CS referring to the current state,
observational and simulated values, respectively. Model results
were averaged over the years 2009-2014 before application in the
cost function: as such, a one-to-one comparison of individual
stations is not included in the method. Individual model results
for the cost function are shown in Appendix E, for the parameters
DIN, DIP and Chl. Weights W were then calculated per model and
per assessment area (Eq. 2) with B=0.1 an arbitrary constant to
avoid division by small numbers in case of good model fits
‚Almroth and Skogen, 2010). The weights were then normalized
using all contributing models in the area for each parameter (Egq. 3,
with N the number of contributing models). Normalized weights
from the current state were then applied to the model results
obtained from the historic scenario (Eq. 4). Note that the number
of contributing models varies with area and parameter, with a
maximum of 7 (Southern Bight of the North Sea) and a minimum
of 2 (Gulf of Biscay).

mean Pmodel 5 CS _ ynean pn
std. pobs

Eg. ]

AT
CE

+ P

Eqg. 2

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10.3389/fmars.2023.1129951

N
Wnorm® = = Wf Eq
De Wi

1 N

pP P del <= HS1

WMAHsı = Zr X (Wnormi +meanPf"“ ) Eq. 4
Si Wnorm; 1
As the cost function is based only on the current state results, it
‚nherently neglects differences in the individual model responses to
che historic scenario, which is inevitable in absence of sufficient
observations for the historic scenario. Weighted ensemble results
are generally more robust than those of the individual members, as
model strengths are enhanced and model weaknesses are reduced
by the applied weighting.

3 Resu!'-

3.1 Annual results per model

First we show results for 2 areas in more detail: the Channel
Well Mixed Tidally Influenced area (Figure 5) and the Southern
North Sea area (Figure 6) (see Figure 3 for the locations of these
assessment areas). Annual values per individual model are shown,
as well as the number of available observations per year and their
mean annual value. The overall observational mean, ensemble
model mean and the weighted ensemble model mean over the
original COMP4 assessment period (2006-2014) are also provided.
Additional selected areas are shown in (Appendix H;
Supplementary Figures 9-15). All data presented here are spatially
averaged over the area and temporally averaged over the
individual years.

Through the ensemble-weighted-mean method a more robust
estimate can be made of nutrient and chlorophyll concentrations.
Model estimates for specific variables in specific areas and years
show large variability, with between-model variability generally
larger than interannual variability within an area. The ensemble
nodel mean (light-blue diamonds in Figures 5, 6) tends to be closer
to the observed concentrations (black squares) than the individual
model results. The weighted ensemble mean is even closer to the
observed concentrations (red asterisks), but the ensemble model
mean cannot get closer to the observations than the closest model
result (e.g. Figure 6B, all models underestimate winter DIP
concentrations in this area). The effectiveness of the weighted
ensemble mean approach strongly depends on the availability and
tepresentativeness of observation data per assessment area. This is
illustrated by Figure 5, where for both DIN and DIP only one
observation was available in 6 years, leading to large uncertainty in
the observational data that the weights are based upon. In contrast
there are many more observed data available for chlorophyll in the
same area, thanks to earth observation data. Note that in-situ
observations for Chl tend to be higher than the mean EO Chl data.

In general, all models capture the yearly observational mean for
DIN, DIP and Chl well for most areas. However, differences
between models for each parameter exist. DIN results display
high variability (overestimation as well as underestimation) but
DIP is usually close to the observational range (Figures 5, 6;

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van Leeuwen et al.

observational data gathering, the applied approach, negotiations and
overall methodology. Therefore their submitted current state results are
‚ncluded in Appendix F, but are not included in the presented analysis.

2.5 Current state scenario

The current state scenario (hereafter, CS) applied the ICG-EMO
database of European rivers for the riverine nutrient inputs of the
selected period. This database contains daily values for flow and
autrients for 368 rivers discharging onto the European Shelf,
tollowing optimization to daily values from originally sourced
observational data (Lenhart et al., 2010; ICG-EMO, 2021). For an
overview of the rivers included see Appendix A (Supplementary
Materials). The average atmospheric deposition rates for NO, NH3z
and Total Nitrogen over the North Western Continental Shelf as
estimated by EMEP (EMEP, 2020) were used for atmospheric input
of nutrients, with values provided in Table 2.

For the open sea boundaries, information from CMEMS
(Copernicus Marine Service, EU, https://marine.copernicus.eu/) was
used: NORTHWESTSHELF_REANALYSIS_PHY_004_009 (https://
doi.org/10.48670/moi-00059) for the physical requirements and
NORTHWESTSHELF_REANALYSIS_BIO_004_011 (https://
doi.org/10.48670/moi-00058) (Ciavatta et al., 2018) for the
chemical and biological state variables (both 0.067 x 0.111 degree
spatial resolution with 24 depth levels). The exchange between the
Baltic Sea and North Sea in the Kattegat and Skagerrak area is very
complex (deep stratified waters, different layers flowing in different
directions). To simplify the inflow of nutrient-rich Baltic waters into
:he North Sea the boundaries of the participating models were
selected to be at two shallow sills where flow patterns are less
complicated: Darss sill and Drogden sill. Estimates from simulated
Baltic Sea discharges by DHI for recent years provided monthly mean
climatologies for the water flows at the two sills. For nutrient
concentrations recent observations (2009 —- 2014) near the sills
were used to provide monthly mean climatologies (flow and
autrients: Stiig Markager, pers. comm.). For silicate, an annual
mean estimate of 10.4 uM was used (Mantikci, 2014).

2.6 Pre-eutrophic scenario

The pre-eutrophic or historical scenario (hereafter, HS) should
‚eflect the state of European Shelf marine waters before major
anthropogenic nutrient inputs occurred. Here, we follow the

TABLE 2 Average N deposition rates for the current (2009-2014) and
historic (1890-1900) time periods over the North Western Continental
Shelf and the respective ratios (Schöpp et al., 2003).

TOxN 183.57
NH; 167.49
TotalN 351.07

U... 4

105 7°

0.63

„32.2

0.38

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10.3389/fmars.2023.1129951

definition of the project Joint Monitoring Programme of the
Eutrophication of the North Sea with Satellite data (JMP-
EUNOSAT, Enserink et al., 2019) which uses a period around the
year 1900 during the European industrialization but before
agricultural intensification. In the 19” century there were likely
already first signs of eutrophication in freshwater systems and
coastal waters (e.g. Billen & Garnier, 1997, Billen et al., 1999), but
impacts in coastal waters were probably limited to a more local scale
(Nixon, 2009). Most importantly, the end of the ya
centuryprecedes the establishment of the Haber-Bosch process
that industrialized the production of inorganic nitrogen fertilizers
(first demonstrated in 1909 with first industrial-level production
starting in 1914, Kissel, 2014). Furthermore, anecdotal evidence of
high-water transparency and seagrass coverage (two important
quality indicators for eutrophication effects, Reise and Kohlus
(2008)) indicate good water quality status in the coastal waters of
the German Bight during this period (Brockmann et al., 2002). In
the closely connected Baltic Sea, the same time period is used as a
reference (Schernewski and Neumann, 2005), as in the Kattegat and
the Belt Seas evidence exists of extensive macrophyte fields around
1900 (Krause-Jensen et al., 2021), which severely declined due to
disease and eutrophication. Frederiksen et al. (2004) show further
evidence of eelgrass decline in Danish coastal waters since 1940
following increasing nutrient pressures.

The JMP-EUNOSAT project applied pre-eutrophic load
estimates from a dedicated simulation of the watershed model E-
HYPE (see https://hypeweb.smhi.se/for the HYPE model suite, with
E-HYPE the European application), representing conditions
around the year 1900 (Enserink et al., 2019). These loads, which
did not include hydrological or morphological changes in river
basins (e.g. reservoir construction, dams and barriers, etc.), are
simulated per coastal area and are not necessarily associated with
actual rivers. Nevertheless, this dataset provides a consistent set of
pre-eutrophic nutrient loads going into the marine environment on
the European Shelf. Local, more detailed studies offer additional
information. Kerimoglu et al. (2018) describe historic riverine loads
for the German Bight based on simulations of the detailed
catchment model MONERIS (Venohr et al., 2011; Gadegast &
Venohr, 2015). They found significantly lower historical DIP
levels for major German rivers compared to the E-HYPE
historical scenario. Danish authorities commissioned a similar
study where two independent water quality models (one Bayesian,
one mechanistic) simulated undisturbed conditions for Danish
:ivers (Timmermann et al., 2021). This study found differences in
historical coastal DIP loads (compared to JMP-EUNOSAT) up to
„10%. Stegert et al. (2021) used estimates of historical river inputs
from both MONERIS and E-HYPE and compared their influence
on the nutrient and chlorophyll-a concentrations in the North Sea.
They found higher marine nutrient concentrations, particularly in
coastal zones, if the E-HYPE values were applied, with coastal zone
DIP differences of 40% in the German Bight.

An expert group consisting of, amongst others, members from
LCG-EMO and ICG-Eut (Intersessional Correspondence Group on
Eutrophication) defined the pre-eutrophic scenario as using the E-
HYPE historic N load percentages (E-HYPE estimate of percentage
difference between the historic state and current day loads) for all

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van Leeuwen et al.

participating model). Both pecuniary and technical issues can result
n gaps in geopolitical coverage of the ensemble result that can
ainder international acceptance of derived policy products.
However, the benefits of ensemble modelling are equally clear:
‚.ncreased confidence in the results (due to the inclusion of different
models with their own specific strengths), more insight into model
dynamics and the opportunity for individual model development
(based on the ensemble results and individual performance) and a
nigher level of acceptance on the international (policy) stage
compared to single model results.

Thus, ensemble modelling is a suitable approach to help tackle a
variety of ecological issues and their management in the marine
environment. This could include dispersal of harmful dissolved
substances, marine litter dispersion (by using particle tracking
models), circulation pathways of pathogens (by using epidemiological
bio-physical models) and impacts of these and other stressors on
ecosystem services (coupled ecosystem models). Models are extremely
zuited to test different policy options, quantify single and combined
stressor impacts and predict future marine environmental conditions
and their impact on anthropogenic derived usage. They can do this on
5soth small (harbours, estuaries, bays) and large (basins, oceans) scales,
providing a broad answer to marine ecosystem response that augments
observational evidence and dedicated experimental work. It is therefore
anticipated that ensemble modelling will be increasingly used in marine
management issues.

5 Conclusions

This study presented a weighted ensemble modelling approach
‚oO estimate the pre-eutrophic state of the marine ecosystem on the
European Shelf. Eight modelling centers from countries around
Europe participated with their most suited ecosystem model,
:hough only seven delivered results on time. Inputs and boundary
conditions were aligned as much as possible to focus on the models’
response to pre-industrial riverine and atmospheric nutrient levels.
As expected, results showed lower nutrient concentrations in the
pre-eutrophic state in most coastal areas, whereas offshore areas
showed minimal change compared to the current state. DIN, DIP
and Chl levels were at most 62%, -40% and „40% lower in the pre-
eutrophic state than they are now, respectively, with most changes
occurring in the southern North Sea, the Irish Sea and coastal Bay of
Biscay areas. Net primary production was also lower in the historic
scenario, with reductions up to -35% concentrated in the South-
eastern North Sea and the Irish Sea. N:P ratio showed little change
ın offshore areas, but strong changes in coastal areas, which moved
closer to the Redfield ratio in the historic scenario. Pre-eutrophic
results for near-bed oxygen levels showed improvements in known
problem areas such as the Oyster Grounds. Overall, coastal areas
show more sensitivity to DIP reductions than DIN reductions.

The resulting concentration estimates for key eutrophication
indicators like surface winter DIN, DIP and growing-season
chlorophyll-a can be used as a basis for assessments as well as
nolicy measures to combat marine eutrophication. It also illustrates
he potential of modelling to support marine management.
However, the weighted ensemble method relies on observations,

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lc 4

10.3389/fmars.2023.1129951

and more and more spatio-temporally balanced observations are
needed, particularly in offshore areas, to augment the applied
weighting method and reduce uncertainty even further. As such,
this work highlights the need for (more) extensive monitoring
programmes. Models can help in this respect by optimizing
existing and new observational efforts. While models are able to
focus on local ecosystem functioning, they also consider the
continuity of transboundary transport and processes across large
areas. This model specificity is particularly useful in systems where
data collection remains a challenge, such as the ocean. In that sense,
nodels will continue to be useful for policy initiatives in coastal
nanagement, and uptake by marine managers is encouraged.

The ensemble approach presented here has demonstrated its
use for policy purposes by defining a baseline for nutrient reduction
measures; it may be useful for other environmental questions as
well. For eutrophication modelling the next step should be to
consider climate change impacts on the marine environment, and
how these changes impact on derived thresholds for eutrophication
indicators, both in the immediate and intermediate (policy) future.

Data availlability statement

The datasets presented in this study can be found in online
repositories. The riverine input data used for all scenarios can be
found here: https://doi.org/10.25850/nioz/7b.b.vc. The other open
sources are mentioned in the manuscript.
Author contributions

Model simulations were performed by AB, LV, AvdL, CS, XD,
GL, OK, IB, TS, RF and MP. All authors contributed to the
ensemble methodology and conditions, which was led by H-JL.
TP led the discussions with associated groups within OSPAR, while
LF performed most of the work related to COMPEAT. SL provided
the riverine data, collected the individual results and produced the
final tables and most of figures, with additional analysis figures
provided by XD and RF. All authors contributed to the article and
approved the submitted version.

-unding

We would like to thank the Swedish Agency for Marine and
Water Management for their support and financial contribution to
this work. CS and RF were supported by the Umweltbundesamt
(UBA, grant no. 3718252110 and 3720252020). Supercomputing
power was provided to RF by HLRN (North-German
Supercomputing Alliance) and to CS by Deutsches Klima-
Rechenzentrum (DKRZ). OK was supported by the Deutsche
Forschungsgemeinschaft (DFG, KE1970/2-1). MP acknowledges
the Pöle de Calcul et de Donnees Marines (PCDM) for providing
supercalculator DATARMOR {storage, data access, computational
resources}. SL was supported by Rijkswaterstaat and NIOZ.
Deltares was supported by Rijkswaterstaat and the European

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‚an Leeuwen et al.

&
55
id
3
x
A
50

DIN

mal N/I
B
In DIP
55.

zZ
rn
ww
3
>55
5
3
5
—_
50

10.3389/fmars.2023.1129951

mol ?/l

464

15 -10 5 0 ö
8 Lonaitude Idea, E1

10
ug Chl/I
A

KO
> U 5
Longitude [deg. E]

„5 Chl-a

Sn
Pre-eutrophic state
D
%
+55

- U S
Longitude [deg. E:

“IGURE 8
Neighted ensemble results for the pre-eutrophic state (+ 1900), for surface DIN (A), DIP (B) and Chl (C). The colour bar scale is identical to that of
:igure 7

may also affect the adjacent water bodies (Gulf of Biscay Coastal
Waters or GBCW: -22.1%)

3.5 Ecosystem sensitivity

The presented results clearly show the elevated nutrient
concentrations in the coastal zone in the current state, compared
:o the pre-eutrophic state. The accompanying net primary
»roduction values as a function of winter total N and P
concentrations are displayed in Figure 11 for all areas calculated
by each model.

In the current state (Figure 11A) the relationship between
modelled Total N and Total P tends to reproduce the Redfield
ratio in offshore areas (characterized by low concentrations for both
variables) but not in coastal areas where TN concentrations are high
due to anthropogenic river loads. Areas showing N:P ratios far from
Redfield are found close to the delta rivers outlets (Rhine, Meuse,
Scheldt; areas RHPM, MPM, SCHPMI1, SCHPM22), but also in the
Humber plume (HPM) and the Seine plume (SPM) areas. This is a
direct result of the successful phosphate loads reduction and less
successful nitrogen loads reduction (Conley et al., 2009), showing

"rontiers in Marine Lcience

higher impact in coastal than in offshore areas. N:P ratios for pre-
zutrophic conditions (Figure 11B) are much lower in river plume
areas than in the current state scenario. The N:P ratio varies
between 55-25 molN molP-1 in the current state and between 30-
15 molN molP-1 in the pre-eutrophic state in coastal areas (with
sxception of the Meuse Plume and Seine Plume, which remain on
high N:P ratios). Note that the Redfield ratio is an average value
applicable to global oceanic conditions, and that it is subject to high
variability in the short term especially in coastal waters (Falkowski,
2000). There are exceptions where the dual reduction does not
significantly change the N:P ratio, which remains as high as 42
moIlN molP* or even 50 molN molP* for e.g. the Meuse Plume
area. Despite differences between models (results not shown), net
primary production (NPP) decreases in the pre-eutrophic scenario
compared to present conditions (Figure 10B).

Since all models apply the same river loads, the differences in
IN and TP concentrations between models in coastal areas are due
to other differences in the models’ ecological and hydrodynamic set-
up, such as the model grid resolution and domain and ecological
process formulations. The proximity of boundary conditions may
"nfluence concentrations in an area, for some of the smaller domain
models this can be near river plume areas. To focus on the impact of

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van Leeuwen et al.

eutrophic values for indicators of eutrophication across vast marine
zegions, while keeping a focus on local ecosystem functioning and
on the continuity of transboundary processes.

? Methcas

2.1 Ensemble method overview

Eight modelling centres participated in the ensemble modelling
exercise. To ensure comparable results between the different models
.n the ensemble, some harmonized model inputs were prescribed in
a joint protocol. These included all external sources of nutrients:
:iverine nutrient loads, atmospheric deposition and boundary
conditions. S imulation period and model output variables were
also prescribed. Meteorological forcing was not prescribed, to allow
the participants to use the same forcings as applied in published
validation results. Each partner also used its standard bathymetry,
‚or the same reason. Boundary conditions were taken from a shared
source. All partners were asked to submit results for 2009-2014 (the
COMP3 assessment period) for variables aligned with the
eutrophication assessment protocol by OSPAR: Dissolved
Inorganic Nitrogen (DIN, surface layer), Dissolved Inorganic
Phosphorus (DIP, surface layer), Total Nitrogen (TN, depth-
averaged), Total Phosphorous (TP, depth-averaged) and the
nitrogen to phosphorus (N:P, depth-averaged) ratio for the winter
period (December-February). Chlorophyll (Chl, surface layer),
chlorophyll 90’ percentile (Chl P90, surface layer) and light
attenuation (Ky, surface layer) were averaged over the growing
season (March-September), while near-bed oxygen levels (O,, near-
bed layer) and net primary production (netPP, depth-integrated)
were considered over the whole year. Models with no benthic
compartment applied a three-year spin up period to move from

10.3389/fmars.2023.1129951

initial conditions. Models with a benthic compartment capable of
nutrient storage applied a longer, suitable spin up period for the
historic scenario to arrive at an equilibrium between benthic
nutrients and the applied nutrient inputs.

The participating modelling centres were: the Cefas (Centre for
Fisheries and Aquaculture Science, Lowestoft, UK), Deltares
(Netherlands), IFREMER (L’Institut Francais de Recherche pour
V’Exploitation de la Mer, France), JRC (Joint Research Centre in
[spra, Italy but representing the EU), the University of Oldenburg
(Oldenburg, Germany), RBINS (Royal Belgian Institute of Natural
Sciences, Belgium), SMHI (Swedish Meteorological and
Hydrological Institute, Sweden), and the University of Hamburg
together with the Helmholtz Zentrum Geesthacht (now called
Hereon) (UHH-HZG, Germany). A detailed overview of the
different models is provided in Appendix D (descriptions,
Supplementary Materials) and E (table overview, Supplementary
Materials). Both large domain models (covering the entire
Northwest European shelf) and small domain models (covering
2.g. only the English Channel or the Southern North Sea) were
applied to the exercise.

The different models have varying degrees of complexity with
respect to the processes they represent. Not all use the same external
nutrient inputs (Table 1) or have the same number of plankton
functional groups (Appendix E; Supplementary Table 2). Besides
internal model differences the simulations used in this exercise also
differ in spatial resolution (Appendix E; Supplementary Table 2)
and coverage (Figure 1). Only the SMHI model domain includes the
“ull Baltic Sea, all other domains have an open boundary with the
Baltic in the Belt Sea region. Table 1 shows the nutrients that are
used as inputs in the different models. Note that even if a model
does not use input for a certain nutrient, the dynamics of this
nutrient are usually still part of the model’s internal dynamics
(Appendix E; Supplementary Table 2).

TABLE 1 Overview of the nutrients used by each model from the supplied riverine inpı.*

MIRO&CO | RBINS (BE)
MARS3D-
Ifremer (FR)
MANGA4

Uni-Hamburg
ECOHAM
/HZG (DE)
GPM | Uni-Oldenburg
(DE)
DFLOW-FW Deltares (NL)
GETM-JRC-
) JRC (ZU)
ERSEM
GETM-ERSEM-
Cefas (UK) l
BFM
NEMO-SCOBI SMHI (SE) | x
BY) mm
Aere “Uni” stands for “University of”. Q stands for fresh water discharge

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van Leeuwen et al.

(built on an agreed baseline and acceptable deviation thereof),
.ncludes the definition of a so-called “reference status” related to
marine conditions undisturbed by anthropogenic inputs. As there
are no suitable areas undisturbed by anthropogenic pressures that
can serve as a reference area for the North-East Atlantic, an
alternative method is to define a “historic” reference that
tepresents a pre-eutrophic state (EC, 2003). Since observations
{from such a period are lacking, these conditions will have to be
estimated using ecosystem models. The objective of this study is
‚herefore to define a common baseline representing pre-eutrophic
conditions. This is a significant step forward towards science-based
and coherent thresholds for marine eutrophication management,
and away from previous thresholds based on expert judgment and
national modelling efforts, and applied to nationally set
assessment areas.

The pre-eutrophic conditions shown in this study are not
identical to pristine conditions (i.e. complete removal of
anthropogenic influences and Europe largely covered in woods,
see Billen & Garnier (1997)). In a previous study, Desmit et al.
2018) reported that the N:P ratio averaged across the coastal
North-East Atlantic would be lowered from -35 moIN molP“*
under current conditions to -11 molN molP* under pristine
conditions, which would foster diatoms and reduce the impact of
Phaeocystis globosa in the southern bight of the North Sea. Here, the
N:P ratio shows a decrease of similar magnitude, with a N:P ratio of
55-25 molN molP* in the current state and of 30-15 moIlN molP“
in the pre-eutrophic state, suggesting that pre-eutrophic conditions
may be sufficient to induce desirable phytoplankton community
structures. Although most coastal systems of the North Sea are P-
limited in the spring rather than N-limited, the high N:P ratios in
many coastal areas would argue against further P reduction
measures without accompanying N reductions as the N:P ratio
shapes the structure of the phytoplankton communities (Cloern,
2001; Conley et al., 2009). Therefore, any shift in this ratio should be
applied to improve the phytoplankton community structure, and
not foster undesirable species (Radach and Moll, 1990; Prins
et al., 2012).

10.3389/fmars.2023.1129951

The observations used in the weighting method were obtained
(rom the COMPEAT tool built by OSPAR, that draws on the ICES
marine database (Supplementary Materials Figure S$1). Even though
N and P are the main driving nutrients of primary production in
shelf seas and therefore constitute the basis of our understanding of
eutrophication processes, measurements of N and P concentrations
in many areas (for example the Channel Well Mixed Tidally
Influenced; CWMTI, Figure 5) are severely limited in number.
This complicates comparison with model results offering more
temporal and spatial resolution, and increases uncertainty. Figure
51 in the supplementary materials highlights the spatial bias of the
observations (available mainly in near-coastal zones), while the
annual results (Figures S9-S15, Appendix F) highlight issues with
temporal observational coverage. It is therefore not surprising that
cost function results for the individual models (Appendix E)
regularly show moderate or poor results. To have a good fit, the
model results, averaged over all simulated years and the entire
assessment area, should compare well with limited observations,
mainly taken in the coastal zone during daylight hours in fair
weather conditions. This discrepancy does not diminish the validity
of observations, rather it highlights both their importance and their
limitations (Skogen et al., 2021). In order to improve the applied
method more observations in time and space are needed. Thereby,
models can indicate where measurements are most needed to
improve spatial and temporal coverage with respect to the
processes being measured (Ferrarin et al., 2021).

Within this exercise the added value of the satellite data for Chl
has been clear, ensuring weighting factors for Chl in all assessment
areas. The integration of in-situ and EO Chl data is an optimal
remote sensing approach to provide water surface properties in
coastal regions with high temporal and spatial resolution (Arabi
et al., 2020). As such, Chl in-situ measurements are also vital, and
more are needed. Note that satellites observe wavelength, and that a
mathematical model is applied to derive surface chlorophyll a data
from these. By including observations in the weighted results, the
related observational uncertainties (due to limited data availability)
are also imported. Improved observational coverage could
mitigate this.

4.2 Importance of observational data
4.3 Level of confidence In model outputs
Observational data are of prime importance for the applied
approach: as input data, as validation data and as data used for
weighting different model results in the ensemble. This immediately
highlights issues with both avajilability of observations and their
:‚emporal and spatial resolution.

Chlorophyll a levels encompass diatom contributions, but these
phytoplankton can only grow when silicate is available for them to
build their cell walls. In the applied riverine inputs database
information on silicate is lacking for Belgian, Danish, Irish and
Spanish rivers. Total nitrogen data is lacking for British and Spanish
rivers. Without realistic values for these nutrient inputs the models
will invariably struggle to reproduce observed concentrations in the
adjacent coastal zones. More coordinated riverine monitoring
within Europe and subsequent central storage of sample data for
zasy access could address these issues.

“rontiers in Marıne © zie.17e

All models used in this exercise have been extensively validated,
and their respective cost functions are shown in Appendix E. Some
models generally underestimate mean concentrations while others
averestimate them: the ensemble approach ensures a balanced
response. For chlorophyll a it is apparent that two models
overestimate Chl concentrations (Deltares, SMHI) while the
others show a consistent underestimation. In river plume areas
Chl tends to be underestimated by nearly every model (Appendix
P). The scatter plot of modelled weighted-average Chl P90 versus
winter DIN in each area displays a linear relationship (results not
shown, r” > 0.81). The slope of this relationship is 0.34 [ıg Chl/umol
N, which is significantly lower than the slopes obtained from long
time series on the Belgian and Dutch continental shelves, displaying
respective values of 0.6 and 1.2 fg Chl/tuumol N (Desmit et al., 2015).

frontiersin.org

‚an Leeuwen et al.

10.3389/fmars.2023.1129951

5 HS

En
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N:P ratio

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:IGURE 10

difference between the ensemble results (unweighted) for the current state (CS) and the pre-eutrophic state (historic scenario, HS) for the N:P ratio
‘'A), net primary production (B) and near bed oxygen levels (C). Green colours indicate areas where the pre-eutrophic levels were lower than those
3f the current state. Note the changing colour bar scale, for O» the scale starts at 1%.

zedfield
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Current state
8 SCHPMI v MPM

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250

AUGURE 11

Model results of winter total phosphorus concentration (Ptot) as a function of winter total nitrogen concentration (Ntot) in each area for each
model. (A) current conditions, (B) pre-eutrophic conditions (both 2009-2014). The colours indicate the modelled net primary production (NPP) for
:he corresponding areas and models. The line indicates the global ocean Redfield ratio (N:P = 16). Only some outlying results have been named, for
area acronyms see Appendix C.

"rontiers in Marine Science

frontiersin.org

* frontiers | Frontiers in Marine Science

TYPE Original Research
PUBLISHED 15 May 2023
DO! 10.3389/fmars.2023.1129951

D Check for updates ı

Deriving pre-eutrophic
conditions from an ensemble
model approach for the
North-West European seas

OPEN ACCESS
<DITED BY
Alessandro Bergamasco,

1stitute of Marine Science, National
kesearch Council (CNR), Italy
<EVIEWED BY

1ugian Yu,

ong Kong University of Science and
‚echnology (Guangzhou), China
Maximilian Berthold,
Mount Allison University, Canada
Justus Van Beusekom,

lelmholtz Centre for Materials and Coastal
kesearch (HZG), Germany
'CORRESPONDENCE
Sonja M. van Leeuwen
53 sonja.van.leeuwen@nioz.nl
RECEIVED 22 December 2022
ACCEPTED 13 March 2023
DUBLISHED 15 May 2023

Sonja M. van Leeuwen*, Hermann-J. Lenhart“, Theo C. Prins®,
Anouk Blauw*, Xavier Desmit*, Liam Fernand®,

Rene Friedland“, Onur Kerimoglu®, Genevieve Lacroix*,
Annelotte van der Linden, Alain Lefebvre®,

Johan van der Molen*, Martin Plus®, Itzel Ruvalcaba Baroni*®,
Tiago Silva®, Christoph Stegert*““*, Tineke A. Troost*

and Lauriane Vilmin*

Department of Coastal Systems, Netherlands Institute for Sea Research (NIOZ), Texel, Netherlands,
Department of Informatiks, Scientific Computing Group, Hamburg University, Hamburg, Germany,
Department of Marine and Coastal Systems, Deltares, Delft, Netherlands, *Operational Directorate
Natural Environment, Royal Belgian Institute for Natural Sciences (RBINS), Brussels, Belgium, °Centre
or Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, United Kingdom, Joint
tesearch Centre, Directorate D — Sustainable Resources, Ispra, Italy, 7Physical Oceanography and
nstrumentation, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany, SInstitute
‘or Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of
Oldenburg, Oldenburg, Germany, °DYNECO-PELAGOS Laboratory, Institut Francais de Recherche
0ur l’Exploitation de la Mer (IFREMER), Plouzane, France, !*Department of Research and
Development, Oceanography, Swedish Meteorological and Hydrological Institute,

Norrköping, Sweden, Institute of Coastal Systems: Analysis and Modelling, Helmholtz-Zentrum
dereon, Geesthacht, Germany, *?Operational Modelling Group, Bundesamt für Seeschifffahrt und
Aydroagraphie. Hamburg, Germany

CITATION

van Leeuwen SM, Lenhart H-J, Prins TC,
3lauw A, Desmit X, Fernand L, Friedland R,
ferimoglu O, Lacroix G, van der Linden A,
„‚efebvre A, van der Molen J, Plus M,
Auvalcaba Baroni |, Silva T, Stegert C,
Troost TA and Vilmin L (2023) Deriving
ore-eutrophic conditions from an
ansemble model approach for the North-
West European seas.

Front. Mar. Sci. 10:1129951.

doi: 10.3389/fmars.2023.1129951
ZOPYRIGHT

‚an der Molen, Plus, Ruvalcaba Baroni, Silva,
Stegert, Troost and Vilmin. This is an open-
access article distributed under the terms of
'he Creative Commons Attribution License
CC BY). The use, distribution or
‚eproduction in other forums is permitted,
orovided the original author(s) and the
copyright owner(s) are credited and that
‘he original publication in this journal is
zited, in accordance with accepted
academic practice. No use, distribution or
‚eproduction is permitted which does not
comply with these terms.

The pre-eutrophic state of marine waters is generally not well known,
czomplicating target setting for management measures to combat
autrophication. We present results from an OSPAR ICG-EMO model
assessment to simulate the pre-eutrophic state of North-East Atlantic marine
waters. Using an ecosystem model ensemble combined with an observation-
based weighting method we derive sophisticated estimates for key
autrophication indicators. Eight modelling centres applied the same riverine
nutrient loads, atmospheric nutrient deposition rates and boundary conditions to
heir specific model set-up to ensure comparability. The pre-eutrophic state was
defined as a historic scenario of estimated nutrient inputs (riverine, atmospheric)
at around the year 1900, before the invention and widespread use of industrial
'ertilizers. The period 2009-2014 was used by all participants to simulate both
‘he current state of eutrophication and the pre-eutrophic scenario, to ensure
hat differences are solely due to the changes in nutrient inputs between the
scenarios. Mean values were reported for winter dissolved inorganic nutrients
and total nutrients (nitrogen, phosphorus) and the nitrogen to phosphorus ratio,
and for growing season chlorophyll, chlorophyll 90° percentile, near-bed
oxygen minimum and net phytoplankton production on the level of the
OSPAR assessment areas. Results showed distinctly lower nutrient
concentrations and nitrogen to phosphorus ratio’s in coastal areas under pre-

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van Leeuwen et al.

Two model systems (JRC and SMHI) were suffering from quite high
RMSD values, although these were mostly caused by just a few areas
(mainly small estuarine/coastal areas with high inputs and
‚.nsufficient spatial resolution in the models).

2.4 Assessment areas

As models differed in their spatial resolution and domain coverage,
evaluation and comparison to observations were applied per area. Here,
we use the areas as defined by OSPAR for use in the 4” application of
:he Common procedure (COMP4, Enserink et al, 2019; OSPAR,
2022b). The areas are based on the eco-hydrodynamic regimes as
defined by van Leeuwen et al. (2015), refined in the JMP-EUNOSAT
project (Enserink et al., 2019) and by the OSPAR contracting parties

10.3389/fmars.2023.1129951

(OSPAR, 2022a). The area delineation is given in Appendix C, and can
be seen in Figure 3. Model results were included in the ensemble mean
for an assessment area if the model domain covered 80% or more of the
area. As a result, different assessment areas were covered by different
numbers of models (Figure 3). For two areas model results have been
'ncluded despite insufficient domain coverage: the Atlantic area (ATL,
UHH-HZG 59.5% coverage) and the Northern North Sea area (NNS,
SMHI 78% coverage). The areas included for the individual models are
visible in Supplementary Figures 5-7 in Appendix F. Areas with the
highest model coverage are the river plumes in the Southern Bight of
the North Sea (Scheldt plume 1 and 2, Meuse plume, Rhine plume,
‚nset) and those in the northern part of the English Channel.
Maximum number of models contributing is 7, as Cefas was unable
to provide results for the pre-eutrophic scenario due to technical issues.
However, Cefas did contribute to the harmonization effort,

# models

ır
50

mn
rd
5 |
D&D
ö,
Do 55

=
RT

x
SLPM

„TI
A)

1
5PM

<C
RHPM
MPM
P
sCHPM:
Ds
CAP

10
5 d
Longitude [deg. E]
ATL: Atlantic

LCTI: Channel Coastal Shelf
Tidally Influenced

CWMTI: Channel Well Mixed
Tidally Influenced

ELPM: Elbe Plume

ENS: Eastern North Sea
HPM: Humber plume

‚U

MPM: Meuse Plume
NNS: Northern North Sea
RHPM: Rhine Plume
SCHPMI1: Scheldt Plume 1
SCHPM2: Scheldt Plume 2
SNS: Southern North Sea
SPM: Seine Plume

THPM: Thames plume

“IGURE 3
umber of models per assessment area, that have been included in the calculation of area means.

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van Leeuwen et al.

rivers. For P loads, E-HYPE percentage results were used for most
vers, but for some rivers alternative estimates were used. Due to
‚arge differences between E-HYPE and the finer scale catchment
models, and uncertainty in the E-HYPE P load estimation
‚Donnelly et al., 2013; Stegert et al., 2021), its historic P load
percentages were replaced for rivers where alternative, more
detailed information was available, as follows. The Danish
authorities, basing their estimates on Timmermann et al. (2021)
set their pre-eutrophic P loads at 36% of current day loads for all
Danish rivers. The German authorities opted to use the MONERIS
results for P loads in German rivers. In bilateral negotiations with
‚he Netherlands, Dutch riverine P loads of rivers arriving through
German territory (or severely interlaced with such rivers) were
adjusted to reflect the MONERIS results (Rhine, Meuse, Lake
IJssel). Note that E-HYPE historical nutrient levels were not used,
only the E-HYPE estimate of the percentage change in riverine
nutrients compared to current day loads. E-HYPE coastal areas
were then linked to actual rivers, and the CS and HS riverine loads
were derived from the observation-based ICG-EMO riverine
database for 2006-2014, using 100% and the reduction percentage
estimates, respectively. The reduction percentages are shown in
Figure 4, while Appendix A provides the same information as a
table. No change was applied to rivers with pre-eutrophic loads
higher than current loads (mainly Scottish rivers north of Inverness
where populations have declined), in order to preserve reduction
effects from other rivers. River freshwater discharges were kept at
current day levels and therefore are equal to those of the simulated
period: this choice was made to allow for easier definition of
(achievable) nutrient reductions in the current situation.
Estimates of atmospheric nitrogen deposition rates around 1900
were calculated based on the trends in TOxN and NH; emissions

Historic Scenario N levels

Historic level
3$ „urrent day
100

10.3389/fmars.2023.1129951

estimated by Schöpp et al. (2003) over Europe, including its
marginal seas. These trends were then used to estimate the
spatially resolved nitrogen deposition rate estimates by EMEP
\2020) for the years 1890-1900 following the method of Große
et al. (2016). Table 2 shows the current and pre-eutrophic
atmospheric deposition rates estimated by Schöpp et al. (2003),
and their ratio. These historic/current ratios are applied to current
deposition fields from EMEP to estimate historic atmospheric
deposition rates. Atmospheric phosphorous deposition rates were
deemed negligible, both in the current state and historical scenario.

For the nutrient inputs across model open boundaries, we
assumed that boundaries to the open sea were sufficiently far
away from riverine sources to not be affected by nutrient
reductions, and these were kept the same for CS and HS. For the
Baltic boundary a different approach was taken, as the Baltic is
highly eutrophic. As such, the pre-eutrophic boundary should
reflect the historic nutrient status at the Darss sill and Drogden
sill. Reduction percentages for nutrients at these locations were
derived from a long model simulation (1850 - 2008) with the
ERGOM model provided by Thomas Neumann (IOW, Germany).
The resulting historic percentages (compared to current-day loads)
are given in Table 3.

2.7 Weighted ensemble method

As models have varying skills in different areas, and variables,
we applied the weighted ensemble approach of Almroth and Skogen
2010) to calculate ensemble averages. This method uses
observations to determine a model’s skill in representing a certain
variable in a certain area, and assigns appropriate weights to the

. . . Historic level
Historic Scenario P levels % of current day

E
a
1
50

rh
Pe

u
3
U
Longitude [deg. LE

1Ur

P
k
vv
Longitude [deg.

FIGURE 4
?re-eutrophic riverine loads as percentages of current day loads. Left: pre-eutrophic N loads, right: pre-eutrophic P loads.

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