the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Phytoplankton Retention Mechanisms in Estuaries: A Case Study of the Elbe Estuary
Abstract.
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RC1: 'Comment on egusphere-2023-2231', Anonymous Referee #1, 02 Nov 2023
General Comments:
The article predominantly emphasizes numerical aspects, as evident from their consistent use of the term 'particles' throughout the text, as opposed to 'phytoplankton' as stated in the title. I would advise the authors to be cautious about this choice of terminology. If their intention is to underscore the biological aspects of their research, the paper requires further clarification in three main areas:
- Terminology: Instead of referring to 'particles,' using 'phytoplankton cells' would better align with the biological focus of their study.
- Model Validation: Including aspects related to model validation, such as capturing the seasonal cycle, the duration of bloom events, the spatial distribution of blooms in relation to distance from the estuary, and other relevant parameters, would enhance the biological relevance of their research and provide a more comprehensive understanding of the dynamics at play.
- The aspect that phytoplankton cells survive in the dry grid cell (without water) needs to be justified. Otherwise, this should be corrected in the method, post-processing (without redoing all the tests) – these cells can be excluded from the final count, and further conclusions should be corrected.
Another crucial aspect to consider is that the authors apply the physical outputs derived from another model to drive the passive movement of their particles. In my view, it would be valuable to compare their retention areas and the vertical distribution (represented as violins) with the physical aspects governing lateral movement. Specifically, exploring the impact of tides on distance from the estuary, tide amplitude, and current structure could provide deeper insights into why the oldest cells tend to remain close to the coast.
By examining these additional physical factors in relation to the behavior of phytoplankton cells, the study could offer a more holistic perspective on the dynamics of the ecosystem and help elucidate the mechanisms that influence cell retention and distribution. This approach would enhance the overall quality and relevance of the research.
In general, it is clear that the authors are modelers. All biological parts should be reviewed by a specialist in phytoplankton biology. In my opinion, the paper contains some interesting novel approaches, such as considering a variety of different phytoplankton species with their varying reproduction rates and vertical migrations together. However, results need further exploration, focusing on the physics of the current, and justifying questionable model parameterization choices. I believe the authors should put more effort into this work for publication. Thus, I propose a major revision.
Specific Comments:
- Page 2, lines 30-35: It appears that the author may be conflating two distinct diel migration behaviors observed in planktonic species. One type of diel migration is exhibited by phytoplankton, which is primarily driven by the availability of sunlight for photosynthesis. This behavior is solely dependent on the sun's position in the sky, as phytoplankton are primary producers that rely on light for their metabolic processes. On the other hand, carnivorous planktonic species, like certain zooplankton and dinoflagellates, exhibit a different diel migration pattern. Their vertical movements are not directly driven by the sun but are instead motivated by the distribution of their prey, mainly phytoplankton, which, in turn, is influenced by sunlight-driven photosynthesis. These species engage in diel migration as a survival strategy, often to avoid predators or to exploit variations in food availability. In this context, it is essential to emphasize the distinction between these two types of diel migration patterns to provide a more accurate and biologically informed account of the behaviors of planktonic organisms. Recognizing the ecological drivers behind these migrations is crucial for a comprehensive understanding of aquatic ecosystems.
- Page 2, line 44: Please correct the reference "St. Lawrence Estuary while() (Kimmerer et al., 2014)."
- Page 4, lines 83-84: What is the spatial resolution of the three-dimensional unstructured grid used to represent the Elbe estuary in this model, and how does it vary within the dataset?
- Page 5 lines 107-110: The statement, "A particle starts its life with a light budget of 28 days, and each minute below 1m reduces this budget by one minute, while the opposite applies when they are above 1m. Children of light-limited parents inherit the remaining light budget of their parents," should be supported by relevant laboratory studies or evidence. Additionally, the terminology used, such as "children" and "parents" for phytoplankton, might be confusing and should be rephrased for clarity.
- Page 5 lines 118-122: The statement that "particles become stranded when the current grid cell becomes dry, and once this cell is rewetted, all stranded particles resuspend and are able to move again" should be justified based on ecological principles and the behavior of phytoplankton. It's important to explain the reasoning behind this choice, as phytoplankton typically cannot survive when completely dry.
- Page 6, line 150: Please provide an explanation for the choice of population doubling times in idealized conditions ranging from 40 to 404 days. This choice should be based on scientific rationale and may require further clarification.
- Page 7, Section "Results": Before analyzing the retention success, it's advisable to perform some form of model validation. Consider whether your model or specific scenarios with their parameters successfully reproduce the seasonal cycle of phytoplankton, including the duration of bloom events and the number of particles over distance from the North Sea. Model validation is crucial to ensure the reliability of your results.
- Page 7, line 171: Please clarify the intention behind looking at the state of phytoplankton after one year in terms of estimating areas where they "successfully retain."
- Page 7, line 175: In the statement, "to successfully retain," please provide a clear definition or criteria for what constitutes successful retention in the context of your study.
- Page 7, line 177: When stating "approximately 3 months," consider providing supporting evidence or references to confirm the accuracy of this time frame based on relevant observations or studies.
- Page 9, Figure 4: The positive depths shown in Figure 4 may be related to tidal oscillations. It would be valuable to describe the tide variabilities or free surface level variability in the site section to help explain these depth variations. Page 9, line 196: Please clarify which tests or scenarios were chosen to be plotted on Figure 5. Explain whether this is an average over all the tests conducted and provide justification for this choice.
- Page 9, line 195: The statement regarding the parameterization of drifting particles as phytoplankton and their tendency to strand near riverbanks should be approached with caution. Phytoplankton typically cannot survive away from water. To provide a more accurate assessment of phytoplankton behavior, consider excluding particles that become stranded in dry grid cells and correlating their behavior with currents over the coasts and tides, as these factors are usually lower near the coasts, favoring retention.
- Page 10, Figure 5: If possible, mark the important sites labeled as "a," "b," "c," etc., on Figure 1 to provide a clearer reference for readers.
- Page 10, lines 210-220: Please cite relevant observations or studies where phytoplankton survival without water is documented to support the statement made in this section. If it cannot be supported, all conclusions about retention in tidal flats should be rewritten.
- Conclusion section is very poor and need to be revised.
Kindly note that the combined text incorporates both the general and specific comments for improved clarity.
Citation: https://doi.org/10.5194/egusphere-2023-2231-RC1 -
AC1: 'Reply on RC1', Laurin Steidle, 10 Jan 2024
First of all, thank you for providing such helpful feedback and taking the time to present all your questions in detail.
> 1. Terminology: Instead of referring to 'particles,' using 'phytoplankton
cells' would better align with the biological focus of their study.We had a similar discussion when we were writing the paper, so we particularly
value this input.
In the end, we chose "particle" to emphasise the abstraction that takes place in
our model when representing a phytoplankton cell, as our model is of course not
able to fully capture their behaviour and dynamics.
Ideally, therefore, these two concepts should not be confused by the reader.
However, as you suggested, the term "particle" might be more confusing,
especially for non-modeling readers, as it obscures the biological implications,
and we have changed the term "particle" to "phytoplankton cell" where
appropriate, as you suggested.E.g. we changed the paragraph introducing the concept of a lagrangian model to
read:[...] [Eulerian models] lack temporal consistency, meaning that the life
history and trajectory of a phytoplankton cell cannot be tracked.
Previous modeling studies have attempted to overcome this problem using a
Lagrangian approach.
A Lagrangian model does not try to track e.g. concentrations at fixed
positions, but rather follows the motion of individual particles that
can be used to represent e.g. water parcels or organisms.
Their ability to resolve the interactions of individual phytoplankton cells
or aggregates with the bathymetry, e.g. through settling or stranding,
while maintaining temporal consistency, is essential for investigating
retention mechanisms.
> 2. Model Validation: Including aspects related to model validation, such as
capturing the seasonal cycle, the duration of bloom events, the spatial
distribution of blooms in relation to distance from the estuary, and other
relevant parameters, would enhance the biological relevance of their research
and provide a more comprehensive understanding of the dynamics at play.We agree that model validation is important, but it seems to us that there may
be a misunderstanding about the type of model presented, its implications and
purpose.Our model can be interpreted (in part) as a post-processing or further analysis
of the model presented by Pein et al. (2021).
The model of Pein et al. is a Eulerian model that captures both hydrodynamics
and biology. The biology is modelled using ECOSMO. This model includes several
planktonic compartments (diatoms, flagellates, cyanobacteria and two zooplankton
compartments) and has been successfully applied in several areas (Schrum et al.,
(2006), Daewel and Schrum (2013)).
In the model of the Elbe estuary that we use, it is calibrated and validated
with observational data from both long-term measuring stations and cruises that
take transects in the centre of the channel.
The Pein et al. model is able to predict population dynamics at the
concentration level reasonably well. It captures both the seasonal cycle, i.e.
the bloom events, and the spatial distribution.Our model does not attempt to predict population dynamics.
We use our model to shed light on physical processes that are largely ignored in
Eulerian models such as that of Pein et al. (2021) - in particular, the process
of stranding and other interactions with the bathymetry.
Our focus is therefore, in a sense, to help understand the loss term induced by
outwashing to high salinity waters or dry shores.
These processes are not directly represented in the differential equation of the
Eulerian model that predicts ecosystem dynamics and therefore can not be easily
studied with such models.
For this purpose, we have chosen a Lagrangian approach, which allows us to model
phytoplankton stranding in a simple and (computationally) inexpensive way, with
a temporal consistency that is crucial for modelling the processes studied and
that cannot be achieved with Eulerian models.Because of this narrow focus, we have simplified the biological processes as
much as possible to allow for high interpretability of our results.
We agree that it would be desirable for our model to predict the full ecosystem
dynamics, as this would potentially improve the interpretability of the effects
of the processes studied, i.e. to make quantitative rather than just qualitative
predictions.
However, not only would this greatly increase the cost of building, running and
evaluating such models, it is not currently possible due to technical
constraints and lack of calibration and validation data, neither in our
Lagrangian model nor in any other model to our knowledge.We this paragraph to emphasize the already validated hydrodynamical and
ecostsystem model on which our study is based:Nevertheless, there are sophisticated estuarine models that are able to
reproduce the complex dynamics of estuaries reasonably well. This
includes currents and water levels on the physical side, but also
chlorophyll concentrations and other biologically driven properties
(Pein et al. (2021), Schoel et al (2014)).
However, these are Eulerian models.
This means that they are based on a fixed grid and calculate the
concentration of a tracer, such as phytoplankton, at each grid cell.
This makes it difficult to study concepts such as retention times, as they
lack temporal consistency, meaning that the life history and trajectory
of a phytoplankton cell cannot be tracked.
Previous modeling studies have attempted to overcome this problem using a
Lagrangian approach.
A Lagrangian model does not try to track e.g. concentrations at fixed
positions, but rather follows the motion of individual particles that
can be used to represent e.g. water parcels or organisms.
The ability to resolve the interactions of individual phytoplankton cells or
aggregates with the bathymetry, e.g. through settling or stranding,
while maintaining temporal consistency, is essential for investigating
retention mechanisms.and the following section in the "model limitations" section:
In this study, we aimed to thoroughly investigate different possible
retention mechanisms in a complex Lagrangian model system with a highly
resolved bathymetry.
Due to this computational and spatial complexity, the complexity of the
biological particle properties needed to remain simple to keep
computational cost manageable and due to a lack of high resolution
validation data.
Our model design does not resolve more complex ecosystem dynamics such as
nutrient limitation and grazing by higher trophic levels.
The Lagrangian model is performed offline, meaning it is not coupled to the
Eulerian model that calculates the hydrodynamics and is performed after
the fact.
Therefore, modeling the advection and dispersal of changes in concentration
fields e.g. nutrients due to growth or remineralization was not easily
possible.
Future modeling efforts could couple the Lagrangian model to a Eulerian
model that disperses changes in concentrations fields by biotic activity
throughout the model domain. [...]
> 3. The aspect that phytoplankton cells survive in the dry grid cell (without
water) needs to be justified. Otherwise, this should be corrected in the method,
post-processing (without redoing all the tests) – these cells can be excluded
from the final count, and further conclusions should be corrected.We agree that pythoplankon cannot survive indefinitely in dry conditions.
To contextualise their ability to survive, we would like to highlight two
things:First, the areas where phytoplankton strand are typically frequently flooded by
the tide.
The vast majority of phytoplankton in our model are stranded for less than one
tidal cycle, i.e. less than 12 hours.
Secondly, cells that are considered 'dry' by the model are not necessarily
devoid of water.
The cell resolution in these areas is typically between 50 and 100m.
Cells are considered dry if the water level falls below 0.1m over the majority
of their area.
Therefore, a lot of sub-resolution structure can be expected.
These include sand ripples, tidal creeks or small pools that hold water where
phytoplankton could survive for several days before drying out.
In addition, the low marsh that surrounds most of the estuary contains a lot of
vegetation, typically tall reeds.
This is thought to improve the survivability of the phytoplankton around it by
increasing soil moisture long enough for most cells to survive through a tidal
cycle.The stranding and resuspension of phytoplankton and microphytobenthos has been
shown to be an important process for primary production under eastuarine
conditions (Carlson et al (1984), De Jonge et al (1992), Kromkamp et al (1995),
Savelli et al (2019)).
While this process is particularly well established for microphytobenthos
(Savelli et al), Semcheski et al (2016) showed that the distinction between
'phyoplankton' and 'microphytobenthos' is fuzzy with a large overlap.To our knowledge, no study has investigated the survival of stranded
phytoplankton under estuarine conditions.
We therefore tested a range of parameter choices before publication and have now
added a sensitivity analysis in the appendix to show that time to dry-out is not
a particularly sensitive parameter.
Testing parameters from 1 to 30 days showed no regime shift in our results.
We chose the 7 day cut-off because we felt it was a reasonable time frame under
the conditions observed in the tidal marshes, and there were no observational
data to suggest a better choice.We have also added the following first paragraphs and adjusted the second in the
methods section to better contextualise this choice for the reader:We consider phytoplankton cells that are stranded out of the water by the
receding tide, and lie dry for more than 7 consecutive days to be dead
and remove them.
Note that these dry cells are not necessarily completely devoid of water,
but are considered dry if the majority of its area has a water level
below 0.1 m.
Additionally, in nature these areas typically contain small sub-resolution
structures like tidal ripples or small puddles and vegetation.
There are currently no studies investigating the time range for survival of
stranded phytoplankton on tidal-flats or marshes in estuaries.
Therefore, we performed a sensitivity analysis to determine the effect
of this parameter on the retention success of the phytoplankton
population (see appendix section A).We include a settling and resuspension model to represent tidal stranding
and phytoplankton cells settling on the bed of the estuary. Stranding
phytoplankton and microphytobenthos have been shown on several occasions
to be a major driver of estuarine primary production (Carlson et al.,
1984; De Jonge and Van Beuselom, 1992; Kromkamp et al., 1995; Savelli et
al.,
2019). Phytoplankton cells become stranded when the current grid cell
becomes dry and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is flooded again, all the
stranded phytoplankton cells are resuspended and able to move again.
> Page 2, lines 30-35: It appears that the author may be conflating two distinct
diel migration behaviors observed in planktonic species. One type of diel
migration is exhibited by phytoplankton, which is primarily driven by the
availability of sunlight for photosynthesis. This behavior is solely dependent
on the sun's position in the sky, as phytoplankton are primary producers that
rely on light for their metabolic processes. On the other hand, carnivorous
planktonic species, like certain zooplankton and dinoflagellates, exhibit a
different diel migration pattern. Their vertical movements are not directly
driven by the sun but are instead motivated by the distribution of their prey,
mainly phytoplankton, which, in turn, is influenced by sunlight-driven
photosynthesis. These species engage in diel migration as a survival strategy,
often to avoid predators or to exploit variations in food availability. In this
context, it is essential to emphasize the distinction between these two types of
diel migration patterns to provide a more accurate and biologically informed
account of the behaviors of planktonic organisms. Recognizing the ecological
drivers behind these migrations is crucial for a comprehensive understanding of
aquatic ecosystems.
We agree that the reason why diel migration is beneficial for autotrophs,
mixotrophs and heterotrophs is different.
As we study phytoplankton, we focus on autotrophic and mixotrophic plankton.
Therefore, all model organisms benefit from diel migration by maximising light
capture while potentially avoiding grazing, while the mixotrophs may
additionally benefit by following food or nutrient sources.
In all cases, however, the consequence remains the same: an upward movement
during the day and a downward movement at night.While there may be two reasons for the diurnal migration, whatever the cause,
the purpose of this paper is to examine the effect of this migration on
retention.We changed the mentioned paragraph to make this clearer. It now reads:
Diel vertical migration is a process where organisms move up and down in the
water column in response to the sun. This
movement may favors retention by allowing plankton to reduce the time in the
faster downstream currents at the water surface.
A study by Anderson and Stolzenbach (1985) showed that diel migrating
dinoflagellates were able to out compete other
non-motile phytoplankton in an embayment environment and even compensate for
outwashing losses through reproduction
increasing their abundance. However, this also implies that the growing part
of the population is somehow retaining their
position. If the regrowing population is also continuously drifting
downstream they will not able to sustain their population
in that area and ultimately die out due to unfavorable salinity conditions
in marine waters (Admiraal, 1976; von Alvensleben
et al., 2016; Jiang et al., 2020). The presence of diel migration has mostly
been demonstrated for motile phytoplankton such
as dinoflagellates (Hall et al., 2015; Crawford and Purdie, 1991; Hall and
Paerl, 2011) and zooplankton species (Kimmerer
et al., 2002). While the motivation for diel migration for autotrophic,
mixotrophic, and heterotrophic differs, the consequence
remains the same, an upward movement during the day and a downward movement
during the night.
> Page 4, lines 83-84: What is the spatial resolution of the three-dimensional
unstructured grid used to represent the Elbe estuary in this model, and how does
it vary within the dataset?We added more detailed information on the gridding of the model domain in the
methods sections as requested. It now reads:The unstructured mesh is three-dimensional and consists of 32k nodes using
terrain-following coordinates based on the LSC2 technique (Zhang et al.,
2016) for the vertical grid, allowing a maximum number of 20 levels.
Regions with depths less than 2 m are resolved by only one vertical level.
The bathymetric data were provided by the German Federal Maritime and
Hydrographic Agency (Bundesamt fuer Seeschifffahrt und Hydrographie,
BSH) and the German Waterways Agency (Wasserstraßen- und Schiffahrtsamt,
WSA) with a horizontal resolution of 50 m in the German Bight, 10 m in
the Elbe estuary and 5 m in the Hamburg port \cite{Stanev2019}. [...]
The model provides us with a node-based mesh containing a range of
information [...] and a dynamically varying spacial resolution with
distance between nodes ranging from 5 to 1400 m with a median distance
of approximately 75 m
> Page 5 lines 107-110: The statement, "A particle starts its life with a light
budget of 28 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies when they are above 1m. Children of light-limited
parents inherit the remaining light budget of their parents," should be
supported by relevant laboratory studies or evidence. Additionally, the
terminology used, such as "children" and "parents" for phytoplankton, might be
confusing and should be rephrased for clarity.We changed the paragraph as suggested to better explain the choice and avoiding
the term "children" and "parents". We also fixed a typo incorrectly stating the
light budget used in the model in this section.
The mentioned paragraph now reads:
Phytoplankton cells will also die if they are light-limited for 14 days.
This value is based on measurements presented in (Walter et al., 2017)
which imply
that the majority of phytoplankton is dead after 14 days of light
limitation. A sensitivity analysis for this parameter is presented
in sec. B suggesting no strong influence on the retention success. They are
considered light-limited below a depth of 1m based
on SPM data presented in (Stanev et al., 2019). The initial batch of
phytoplankton cells starts their life with a full light budget
of 14 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies if they are above 1m.
When a cell splits both inherit the same remaining light budget.
> Page 5 lines 118-122: The statement that "particles become stranded when the
current grid cell becomes dry, and once this cell is rewetted, all stranded
particles resuspend and are able to move again" should be justified based on
ecological principles and the behavior of phytoplankton. It's important to
explain the reasoning behind this choice, as phytoplankton typically cannot
survive when completely dry.We justified this choice in our answer to 3.) above as requested.
In short grid cells are typically not completely dry and phytoplankton cells
typically rewettet in less then 12 hours.
We added a paragraph in the paper to reflect our arguments and updated the
mentioned paragraph as also presented in our response to 3.).
It now reads:We consider phytoplankton cells that are stranded out of the water by the
receding tide, and lie dry for more than 7 consecutive days to be dead
and remove them.
Note that these dry cells are not necessarily completely devoid of water,
but are considered dry if the majority of its area has a water level
below 0.1 m.
Additionally, in nature these areas typically contain small sub-resolution
structures like tidal ripples or small puddles and vegetation.
There are currently no studies investigating the time range for survival of
stranded phytoplankton on tidal-flats or marshes in andTherefore, we
performed a sensitivity analysis to determine the effect of this
parameter on the retention success of the phytoplankton population (see
appendix section A).We include a settling and resuspension model to represent tidal stranding
and phytoplankton cells settling on the bed of the estuary. Stranding
phytoplankton and microphytobenthos have been shown on several occasions
to be a major driver of estuarine primary production (Carlson et al.,
1984; De Jonge and Van Beuselom, 1992; Kromkamp et al., 1995; Savelli et
al.,
2019). Phytoplankton cells become stranded when the current grid cell
becomes dry and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is flooded again, all the
stranded phytoplankton cells are resuspended and able to move again.> Page 6, line 150: Please provide an explanation for the choice of population
doubling times in idealized conditions ranging from 40 to 404 days. This choice
should be based on scientific rationale and may require further clarification.Under ideal conditions, phytoplankton doubling times are much lower than the
range tested in our model, with doubling times of less than one day.
These ideal cases are of course rare, as phytoplankton are almost always
strongly limited in nature, e.g. by light or nutrient availability.In our study we are examining the impact of a range of physical drivers, most
importantly losses due to outwashing of phytoplankton and are trying to decouple
the biological drivers as much as possible to achieve a better interpretability
of the results.
Hence, we chose our doubling times not to accuratley represent fission rates
observed in nature but such that they allow us to estimate the losses due to
physical drivers, which in our case are light limitation, outwashing to the
shores and to the sea.
The presented doubling times in our study can be interpreted as potential
average net-doubling-times in the presence of predation and mortality, nutrient
availability.
We are not trying to representing the ecosystem dynamics by natural growth and
mortality of phytoplankton as this is already done in the cited study Pein et
al. (2021) where they include a full ecosystem model but lack the possibility to
represent the process (e.g. stranding) simulated and studied here.We added a comment to clarify this to the mention paragraph. It now reads:
Each vertical velocity is examined for a range of different reproduction
rates, expressed as population doubling times ranging from 40 to 404
days with a logarithmic scaling.
In the following, we will use reproduction rate to refer to the prescribed
population growth rate under idealized conditions and use growth rate
whenever we describe population growth in nature.
The prescribed population growth rate can be interpreted as potential
average net-doubling-times in the presence of predation and mortality,
nutrient availability while testing the effect of outwashing.
> Page 7, Section "Results": Before analyzing the retention success, it's
advisable to perform some form of model validation. Consider whether your model
or specific scenarios with their parameters successfully reproduce the seasonal
cycle of phytoplankton, including the duration of bloom events and the number of
particles over distance from the North Sea. Model validation is crucial to
ensure the reliability of your results.This request is similar to point 2.) where we explained why this model does not
attempt to predict population dynamics.
We agree that model validation is important to ensure the reliability of model
results, which is why we use the hydrodynamics of an ecosystem model with
validated population dynamics.
However, to our knowledge, no observational studies have been conducted to
investigate the mechanism of phytoplankton retention under estuarine conditions
and spatial distribution at finer scales.
In fact, the lack of field studies was the main motivation for this modelling
study, as we try to emphasise the importance of these processes and suggest that
such experiments should be carried out.
Quantifying the importance of these processes in the field is essential before
they can be added to the current state of the art models to better represent
phytoplankton losses, which are currently fitted to observational data mainly
using natural mortality and grazing parameters.We have added the following paragraph, as previously stated in our response to
2.) above:We added this paragraph to emphasise the already validated hydrodynamic and
ecosystem model on which our study is based:[...] there are sophisticated estuarine models that are able to reproduce
the complex dynamics of estuaries reasonably well. This includes
currents and water levels on the physical side, but also chlorophyll
concentrations and other biologically driven properties (Pein et al.
(2021), Schoel et al (2014)).
However, these are Eulerian models.
This means that they are based on a fixed grid and calculate the
concentration of a tracer, such as phytoplankton, at each grid cell.
This makes it difficult to study concepts such as retention times, as they
lack temporal consistency, meaning that the life history and trajectory
of a phytoplankton cell cannot be tracked.
[...]and the following section in the "model limitations" section:
In this study, we aimed to thoroughly investigate different possible
retention mechanisms in a complex Lagrangian model system with a highly
resolved bathymetry.
Due to this computational and spatial complexity, the complexity of the
biological particle properties needed to remain simple to keep
computational cost manageable and due to a lack of high resolution
validation data.
Our model design does not resolve more complex ecosystem dynamics such as
nutrient limitation and grazing by higher trophic levels.
The Lagrangian model is performed offline, meaning it is not coupled to the
Eulerian model that calculates the hydrodynamics and is performed after
the fact.
Therefore, modeling the advection and dispersal of changes in concentration
fields e.g. nutrients due to growth or remineralization was not easily
possible.
Future modeling efforts could couple the Lagrangian model to a Eulerian
model that disperses changes in concentrations fields by biotic activity
throughout the model domain. [...]And further emphasised the point that this study suggest and shall work as a
foundation for future field measurements in the outlook. It now reads:Our results clearly suggest the importance of tidal flats and shallow areas
along the river banks for the persistence of primary production in the
Elbe estuary. However, their effect can currently not be quantified due
to the lack of validation data.
Chlorophyll data with a sufficient temporal and spacial resolution is only
gathered in the center of the river. Future monitoring efforts should
therefore also include data along the river shores on tidal flats or
shore-to-shore to quantify the effect of potential future changes by
dredging, diking or restoration attempts.
Frequently stranded plankton have been shown to be essential to the survival
of populations in our model. However, data on their ability to survive
under these conditions are scarce. Our results suggest that these
conditions may be as important as their ability to quickly regrow under
more favorable conditions, and we suggest further research on plankton
survivability when stranded.
> Page 7, line 171: Please clarify the intention behind looking at the state of
phytoplankton after one year in terms of estimating areas where they
"successfully retain."This is a reference to our 'retention metric' defined on line 158ff.
Conceptually, we consider a population to be successfully maintained if it shows
long-term growth.
We consider one year to be a reasonable "long-term" time frame for this, firstly
because it is much longer than the typical outwash period (see newly added
Figure 6) of up to 3 weeks, and secondly because it represents all the major
seasonal cycles, in particular the upstream seasonal runoff cycle and the
downstream seasonal and tidal cycles.We have modified the "retention metric" paragraph as you suggested to reflect
the reasoning presented here.
It now reads:Conceptually,
we consider a population to be successfully retained if it is able to
sustain itself long term or even shows growth. Practically,
this is evaluated by comparing the population size at the end of the year to
the size after release. The choice of one year is
considered reasonable because it covers the full seasonal cycle and is also
much longer than the average exit or flushing time
of the estuary (see fig. 6).
and added a paragraph to the outlook:Our hydrodynamics data set was limited to the year 2012. Therefore, we were
not able to study different release times with the same methodology.
While we do not expect the general dynamics to change, future research
could examine the effect of varying discharge throughout the seasons on
retention and could address the very long term success (>1 year) of the
population,
as it affected by inter-annual variability and climate change.> Page 7, line 177: When stating "approximately 3 months," consider providing
supporting evidence or references to confirm the accuracy of this time frame
based on relevant observations or studies.This is not based on other studies but is a reference to our results presented
in fig. 3 where the break even point between physically induced loses and growth
lies in between 81-101 days. which are approximatly 3 months.
The mentioned paragraph now references this:Our simulations show that the population is able to successfully retains
itself under certain conditions. Passively drifting
phytoplankton is able to sustain themselves in the estuary if they have a
reproduction rate that doubles their population size
within approximately 3 months (see fig. 3)
> Page 9, Figure 4: The positive depths shown in Figure 4 may be related to
tidal oscillations. It would be valuable to describe the tide variabilities or
free surface level variability in the site section to help explain these depth
variations.Yes, the positive values are caused by tidal oscillations that lift
phytoplankton cells into areas where they become stranded during ebb tides.
We have added a contextualising comment to the tidal range to make this clearer
to the reader:Fig. 4 compares two box plots showing the average water depth at
the location of each phytoplankton cell between those cells that remained
alive for less than three months (short-living) and
for more than three months (long-living). Depth is measured relative to the
current water surface. Therefore, a value greater
than zero indicates that the phytoplankton cell is stranded on the shore
during ebb tide. For reference, the water level varies
on average by about 5 m due to the tides. (Stanev et al., 2019; Schöl et
al., 2014).
> Page 9, line 196: Please clarify which tests or scenarios were chosen to be
plotted on Figure 5. Explain whether this is an average over all the tests
conducted and provide justification for this choice.Since we have no reason to favour or emphasise any particular case, we use an
average calculated over all cases, or tests as you call them here.
Furthermore, all cases follow the same spatial pattern when plotted
individually, with no significant shift in the structure of the average age map,
as they all rely on stranding processes to retain themselves, as shown in Figure
4.In order to make this clear to the reader, we have modified the referenced
paragraph, which now reads
We moreover analyzed the horizontal spacial distribution of long and short-
living phytoplankton in fig. 5. To do this, we
divide the model domain into equally sized hexagons. The color of each
hexagon indicates the average age of the phytoplankton
cells within it calculated across all cases. Note, that the spatial age
structure is similar for all cases. Hexagons with a yellow
color indicate an average age of over three months. These yellow areas are
mainly found along the river banks in shallow
waters or tidal flats.
> Page 9, line 195: The statement regarding the parameterization of drifting
particles as phytoplankton and their tendency to strand near riverbanks should
be approached with caution. Phytoplankton typically cannot survive away from
water. To provide a more accurate assessment of phytoplankton behavior, consider
excluding particles that become stranded in dry grid cells and correlating their
behavior with currents over the coasts and tides, as these factors are usually
lower near the coasts, favoring retention.As discussed in our response to point 3), we do indeed remove
particles/phytoplankton aggregates that become stranded after a period of time,
and cells flagged as 'dry' have a lot of sub-resolution structure. Perhaps a
better name for this scihsm flag would have been 'not-flooded' as these cells
are in most cases quite moist.Regarding your comment after "To provide [...]", we are not quite sure what you
are referring to.
If you are asking how the currents are calculated and how they affect the
movement of the phytoplankton: The trajectory of a phytoplankton is driven by
currents and tides. They move almost instantaneously with the currents and
follow them, ignoring diffusion for the moment.They are therefore correlated.
Advection and diffusion were calculated by Pein et al. (2021) using SCHISM
solving the Navier-Stokes equations, and their behaviour, which in our case is a
kind of vertical motion, is added by us.
This implies that we represent the currents and tides along the coast as
accurately as possible in our model resolution. This is discussed in more detail
in the Pein paper, where the validation process with tides and currents is
shown.
The currents in shallow water are also slower in our model than in deeper water,
as you suggested. This is mainly due to friction between the water layer and the
river or sea bed.Alternativly, if you are referring to the inclusion of a dynamic behaviour:
During the early conceptual development of this model we also considered
including a vertical migration process of the phytoplankton depending on their
velocity, e.g. that they move up and down depending on their speed relative to
the coast.However, we couldn't find any observations showing that pytoplankton exhibit
such a migration behaviour or any other behaviour that would suggest that they
are somehow able to feel their speed, only their acceleration.
One could consider acceleration as a driver for migratory behaviour.
However, we did not find any study showing that phytoplankton also exhibit
acceleration-dependent migratory behaviour either.
We therefore decided to include only light-dependent migration, i.e. moving up
and down with the sun.A phytoplankton cell is moved by three processes: Advection (currents influenced
by tides), Diffusion (e.g. turbulence) and their behavior.
> Page 10, Figure 5: If possible, mark the important sites labeled as "a," "b,"
"c," etc., on Figure 1 to provide a clearer reference for readers.The areas labelled in Figure 5 are not visible in Figure 1.
Figure 1 only shows the port area, which is the far right part of Figure 5.
We have added a comment to the figure description to help the reader to align
these to maps.
> Page 10, lines 210-220: Please cite relevant observations or studies where
phytoplankton survival without water is documented to support the statement made
in this section. If it cannot be supported, all conclusions about retention in
tidal flats should be rewritten.We added citations to relevant observations or studies where phytoplankton
survival without water is documented to support the statement made in this
section and as described in our response to 3.)In short they are:
[...]. Stranding phytoplankton and microphytobenthos have been shown on
several occasions to be a major driver of
estuarine primary production (Carlson et al., 1984; De Jonge and Van
Beuselom, 1992; Kromkamp et al., 1995; Savelli et al.,2019).
> Conclusion section is very poor and need to be revised.We reworked the conlusion as suggested and it now reads:
In this study, we investigated the role of different retention strategies
for phytoplankton organisms to persist in an estuarine
environment. We showed that stranding in shallow nearshore areas is
essential for phytoplankton retention, and that phyto-
plankton that do not strand are rapidly washed away. Our model simulations
suggest that growth rates much lower than those
observed in nature may be sufficient for populations to prevent their
decline due to outwashing, implying that stranding may
be sufficient to maintain the population. Moreover, buoyancy and strong diel
vertical migration enhance retention within the
estuary. These results highlight the importance of shallow nearshore areas
in maintaining the productivity of estuarine ecosys-
tems. Our results suggest that current state-of-the-art models of estuarine
ecosystems may overlook an important process and
emphasize the need for informed ecosystem-based management to avoid the
degradation of estuarine ecosystems by dredging
and diking activities.Citation: https://doi.org/10.5194/egusphere-2023-2231-AC1 -
AC4: 'Reply on RC1', Laurin Steidle, 10 Jan 2024
Sorry, but it seems to have broken the formatting of my previous reply.
I have attached it as a text file to this reply for better readability.
I hope this makes it easier for you to read.Additionally, we attached figures added to the manuscript showing the sensitivity analysis that you requested.
We also added two figures showing flushing times as suggested by RC2 that we attached here.- Figure "retention_success_sa_dryout_1_day.png" and ""retention_success_sa_dryout_14_day.png"" show a sensitivity analysis for mortality due to stranding i.e. drying out showing the retention success similar to fig. 3 (relative pop. changes) with a threshold of 1 and 14 days without resuspension compared to the 7 days in fig 3 before phytoplankton are culled
- Figure "retention_success_sa_light_deficit_7_days.png" and "retention_success_sa_light_deficit_28_days.png" shows a sensitivity analysis for mortality due to light limitation showing the retention success similar to fig. 3 with a light deficit threshold of 7 and 28 days compared to the 14 days in fig 3 before phytoplankton are culled
- Figure "flush_times_hexbin.png" shows a hex-bin heatmap of average exiting times of the Elbe estuary with Hamburgs port area, as shown in \ref{fig:bathymetry}, in the bottom right without reproduction, light-limitation, stranding and settling on the riverbed. Colors indicate the time of a phytoplankton cell or water parcel to reach the 20 PSU isohaline from its origin hexagon.
- Figure "average_salinity_with_20PSU_isohaline.png" shows a salinity map of the Elbe estuary with Hamburgs port area, as shown in fig. 1, in the bottom right.
Salinity is averaged in depth and over the whole year. 20 PSU isohaline is marked with a black line. that this plotted area has been extended downstream compared to fig. 5. Further note that the color map has been capped at 25 PSU for better visibility in low salinity areas.
-
RC2: 'Comment on egusphere-2023-2231', Anonymous Referee #2, 03 Nov 2023
This paper studies the retention mechanisms of phytoplankton in an estuarine environment. The paper is clearly written and the results are of much interest. However, since this work is strongly based in model simulations, I miss a lot of information about how the numerical simulations have been performed. Also, there is a lack of sensitivity analysis of the results when the parameters are changed.
Thus, before the paper can be accepted the author should provide details (probably including appendixes):
- On the numerical velocity fields: boundary conditions, resolution, vertical components, explicit bathymetry, etc...
- On the Lagrangian transport model: interpolation schemes, use of eddy diffusion?, sticking of particles to land, etc... You may also compute other Lagrangian quantities to show like exit times, retention times, or accumulation zones (vertical and horizontal).
- On the "population dynamics": how particles divide, die (is it a Gillespie simulation)? number of particles, density, etc...
- Provide a sensitivity analysis of the many parameters of the model, in particular those concerning phytoplankton demography like mortality, reproduction rates, etc...
Citation: https://doi.org/10.5194/egusphere-2023-2231-RC2 -
AC2: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
> On the numerical velocity fields: boundary conditions, resolution, vertical
components, explicit bathymetry, etc...
We added the requested information to the description of the hydrological in the
introduction. It now reads:
We use the hydrodynamic data generated by the latest SCHISM model of the
Elbe estuary (Pein et al., 2021) from the
weir at Geesthacht to the North Sea, including several side channels and the
port area (see figure 2). SCHISM solves the
Reynolds-averaged Navier-Stokes equations on unstructured meshes assuming
hydrostatic conditions with a time step of 60 s.
The unstructured mesh is three-dimensional and consists of 32k nodes using
terrain-following coordinates based on the LSC2
technique (Zhang et al., 2016) for the vertical grid, allowing a maximum
number of 20 levels. Regions with depths less than 2 m
are resolved by only one vertical level. The bathymetric data were provided
by the German Federal Maritime and Hydrographic
Agency (Bundesamt fuer Seeschifffahrt und Hydrographie, BSH) and the German
Waterways Agency (Wasserstraßen- und
Schiffahrtsamt, WSA) with a horizontal resolution of 50 m in the German
Bight, 10 m in the Elbe estuary and 5 m in the
Hamburg port Stanev et al. (2019). The boundary conditions on the seaward
side include sea surface elevation, horizontal
currents, salinity and temperature Stanev et al. (2019) and discharge and
temperature from the Elbe river on the land-ward side.
Atmospheric forcing includes wind, air temperature, precipitation, shortwave
and longwave radiation Stanev et al. (2019).
Model validation is based on tide gauge stations and long-term stationary
measurements of salinity, water temperature, and
horizontal currents. Biochemical variables, including chlorophyll, are based
on long-term measurements at the Seemannshöft
and Grauerort stations Pein et al. (2021). The model provides us with a
node-based mesh containing a range of information such
as water velocity, salinity, water level and dispersion. The year
represented in that dataset is 2012 with a temporal resolution
of 1 hour and a dynamically varying spacial resolution with distance between
nodes ranging from 5 to 1400 m with a median
distance of approximately 75 m.
> On the Lagrangian transport model: interpolation schemes, use of eddy
diffusion?, sticking of particles to land, etc... You may also compute other
Lagrangian quantities to show like exit times, retention times, or accumulation
zones (vertical and horizontal).
We modified the section describing the interpolation scheme, use of eddy
diffusion and how they stick to the land to be clearer.
Thank you very much for the suggestion to include "exit-times". We added a
figure showing average exit times in the results.The section describing eddy diffusivity now reads:
Phytoplankton cells are not only advected but also diffused based on eddy
diffusivity which is crucial
to represent tidal-pumping processes. Diffusion was modeled using a random
walk using a random number generator with a
normal distribution. Horizontally the standard distribution of the random
walk is set to 0.1 ms−1. The vertical displacement of
a phytoplankton cell ∂z i is calculated by
∂zi = K′_v (z_i(n))∂t + N (0, 2K_v (z_i)) (2)
based on Yamazaki et al. (2014) where zi is the vertical position of the
phytoplankton cell, K′_v is the vertical eddy diffusivity gradient,
K_v is the vertical eddy diffusivity and N is the normal
distribution. The term based K′_v is needed to avoid
phytoplankton accumulation on the top and bottom of the water column from
the hydrodynamic model output.The section describing the interpolation scheme now reads:
Flow velocities, like any other hydrodynamic data, were
interpolated linearly in time, linearly in space on the vertical axis and on
the horizontal axis using barycentric coordinates, with175
the exception of water velocity in the bottom model cell, where logarithmic
vertical interpolation is used.The section describing sticking to land now reads:
Phytoplankton cells become stranded when the current grid cell becomes dry
and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is
becomes flooded again all stranded phytoplankton cells resuspend and are
able to move again
> On the "population dynamics": how particles divide, die (is it a Gillespie
simulation)? number of particles, density, etc...
We do not use a Gillespie simulation. Phytoplankton cells die independent off
the presence of other cell but only based on enviromental conditions.
Hence we do not need to enforce a mass balance as required in a Gillespie
simulation.
We updated the paragraph describing the particle division, mortality and the
paragraph describing particle density.The section regarding reproduction now reads:
Reproduction is represented as a fission process, where each phytoplankton
cell has a probability to split effectively produc-
ing a copy. This is a novel feature [...]
We perform multiple
simulations for a range of reproduction rates, implemented as a fission
probability evaluated every minute, that are constant
over the lifetime of the cell. While a fixed reproduction rate is a
simplification that does not allow for more realistic simulation115
of the population dynamics of a particular species, it does allow us to
investigate the general mechanisms that enable plankton
retention.The section regarding mortality now reads:
Mortality is induced by one of three processes: high salinity, when they dry
out while stranded, or due to long term lightlimitation. When particles cells are exposed to high salinity water above
20PSU, a mortality probability of 0.5% per minute is
applied removing dead phytoplankton cells from the simulation (see salinity
map in fig. C1 ).
[...] # reasoning
We consider phytoplankton cells that are
stranded out of the water by the receding tide, and lie dry for more than 7
consecutive days to be dead and remove them.
[...] # reasoning
Phytoplankton cells will also die if they are light-limited for 14 days.
This value is based on measurements presented in (Walter et al., 2017)
[...] # reasoning
They are considered light-limited below a depth of 1m based
on SPM data presented in (Stanev et al., 2019). The initial batch of
phytoplankton cells starts their life with a full light budget
of 14 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies if they are above 1m.
When a cell splits both inherit the same remaining light budget.The section on particle counts now reads:
In both sets of experiments, we release 10,000 individuals representing the
studied phytoplankton population at the be-
ginning of the year. This results in over 1 billion individual particles
simulated for each case with approximately 1 million
particles active simultaneously counted over all cases over 500,000 time
steps. This corresponds approximately to a one to one
ratio of simulated phytoplankton cells to mesh nodes in the hydrodynamic
model at each time step
> Provide a sensitivity analysis of the many parameters of the model, in
particular those concerning phytoplankton demography like mortality,
reproduction rates, etc...
We have added a sensitivity analysis for the mortality conditions of
phytoplankton dry-out and light limitation to the sensitivity analysis already
performed for growth rates and vertical velocities in the appendix.
Varying these parameters changes the break-even point of growth and loss as
expected but no regime shift occurs and the observed trends remain the same.Citation: https://doi.org/10.5194/egusphere-2023-2231-AC2 -
AC3: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
It seems to have broken the formatting of my previous reply.
I have attached it as a text file to this reply for better readability.
I hope this makes it easier to read.Additionally, we attached figures added to the manuscript that you requested.
These are the sensitivity analysis and flush times.- Figure "retention_success_sa_dryout_1_day.png" and ""retention_success_sa_dryout_14_day.png"" show a sensitivity analysis for mortality due to stranding i.e. drying out showing the retention success similar to fig. 3 (relative pop. changes) with a threshold of 1 and 14 days without resuspension compared to the 7 days in fig 3 before phytoplankton are culled
- Figure "retention_success_sa_light_deficit_7_days.png" and "retention_success_sa_light_deficit_28_days.png" shows a sensitivity analysis for mortality due to light limitation showing the retention success similar to fig. 3 with a light deficit threshold of 7 and 28 days compared to the 14 days in fig 3 before phytoplankton are culled
- Figure "flush_times_hexbin.png" shows a hex-bin heatmap of average exiting times of the Elbe estuary with Hamburgs port area, as shown in \ref{fig:bathymetry}, in the bottom right without reproduction, light-limitation, stranding and settling on the riverbed. Colors indicate the time of a phytoplankton cell or water parcel to reach the 20 PSU isohaline from its origin hexagon.
Thank you particularly for this suggestion! - Figure "average_salinity_with_20PSU_isohaline.png" shows a salinity map of the Elbe estuary with Hamburgs port area, as shown in fig. 1, in the bottom right.
Salinity is averaged in depth and over the whole year. 20 PSU isohaline is marked with a black line. that this plotted area has been extended downstream compared to fig. 5. Further note that the color map has been capped at 25 PSU for better visibility in low salinity areas.
-
AC2: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2231', Anonymous Referee #1, 02 Nov 2023
General Comments:
The article predominantly emphasizes numerical aspects, as evident from their consistent use of the term 'particles' throughout the text, as opposed to 'phytoplankton' as stated in the title. I would advise the authors to be cautious about this choice of terminology. If their intention is to underscore the biological aspects of their research, the paper requires further clarification in three main areas:
- Terminology: Instead of referring to 'particles,' using 'phytoplankton cells' would better align with the biological focus of their study.
- Model Validation: Including aspects related to model validation, such as capturing the seasonal cycle, the duration of bloom events, the spatial distribution of blooms in relation to distance from the estuary, and other relevant parameters, would enhance the biological relevance of their research and provide a more comprehensive understanding of the dynamics at play.
- The aspect that phytoplankton cells survive in the dry grid cell (without water) needs to be justified. Otherwise, this should be corrected in the method, post-processing (without redoing all the tests) – these cells can be excluded from the final count, and further conclusions should be corrected.
Another crucial aspect to consider is that the authors apply the physical outputs derived from another model to drive the passive movement of their particles. In my view, it would be valuable to compare their retention areas and the vertical distribution (represented as violins) with the physical aspects governing lateral movement. Specifically, exploring the impact of tides on distance from the estuary, tide amplitude, and current structure could provide deeper insights into why the oldest cells tend to remain close to the coast.
By examining these additional physical factors in relation to the behavior of phytoplankton cells, the study could offer a more holistic perspective on the dynamics of the ecosystem and help elucidate the mechanisms that influence cell retention and distribution. This approach would enhance the overall quality and relevance of the research.
In general, it is clear that the authors are modelers. All biological parts should be reviewed by a specialist in phytoplankton biology. In my opinion, the paper contains some interesting novel approaches, such as considering a variety of different phytoplankton species with their varying reproduction rates and vertical migrations together. However, results need further exploration, focusing on the physics of the current, and justifying questionable model parameterization choices. I believe the authors should put more effort into this work for publication. Thus, I propose a major revision.
Specific Comments:
- Page 2, lines 30-35: It appears that the author may be conflating two distinct diel migration behaviors observed in planktonic species. One type of diel migration is exhibited by phytoplankton, which is primarily driven by the availability of sunlight for photosynthesis. This behavior is solely dependent on the sun's position in the sky, as phytoplankton are primary producers that rely on light for their metabolic processes. On the other hand, carnivorous planktonic species, like certain zooplankton and dinoflagellates, exhibit a different diel migration pattern. Their vertical movements are not directly driven by the sun but are instead motivated by the distribution of their prey, mainly phytoplankton, which, in turn, is influenced by sunlight-driven photosynthesis. These species engage in diel migration as a survival strategy, often to avoid predators or to exploit variations in food availability. In this context, it is essential to emphasize the distinction between these two types of diel migration patterns to provide a more accurate and biologically informed account of the behaviors of planktonic organisms. Recognizing the ecological drivers behind these migrations is crucial for a comprehensive understanding of aquatic ecosystems.
- Page 2, line 44: Please correct the reference "St. Lawrence Estuary while() (Kimmerer et al., 2014)."
- Page 4, lines 83-84: What is the spatial resolution of the three-dimensional unstructured grid used to represent the Elbe estuary in this model, and how does it vary within the dataset?
- Page 5 lines 107-110: The statement, "A particle starts its life with a light budget of 28 days, and each minute below 1m reduces this budget by one minute, while the opposite applies when they are above 1m. Children of light-limited parents inherit the remaining light budget of their parents," should be supported by relevant laboratory studies or evidence. Additionally, the terminology used, such as "children" and "parents" for phytoplankton, might be confusing and should be rephrased for clarity.
- Page 5 lines 118-122: The statement that "particles become stranded when the current grid cell becomes dry, and once this cell is rewetted, all stranded particles resuspend and are able to move again" should be justified based on ecological principles and the behavior of phytoplankton. It's important to explain the reasoning behind this choice, as phytoplankton typically cannot survive when completely dry.
- Page 6, line 150: Please provide an explanation for the choice of population doubling times in idealized conditions ranging from 40 to 404 days. This choice should be based on scientific rationale and may require further clarification.
- Page 7, Section "Results": Before analyzing the retention success, it's advisable to perform some form of model validation. Consider whether your model or specific scenarios with their parameters successfully reproduce the seasonal cycle of phytoplankton, including the duration of bloom events and the number of particles over distance from the North Sea. Model validation is crucial to ensure the reliability of your results.
- Page 7, line 171: Please clarify the intention behind looking at the state of phytoplankton after one year in terms of estimating areas where they "successfully retain."
- Page 7, line 175: In the statement, "to successfully retain," please provide a clear definition or criteria for what constitutes successful retention in the context of your study.
- Page 7, line 177: When stating "approximately 3 months," consider providing supporting evidence or references to confirm the accuracy of this time frame based on relevant observations or studies.
- Page 9, Figure 4: The positive depths shown in Figure 4 may be related to tidal oscillations. It would be valuable to describe the tide variabilities or free surface level variability in the site section to help explain these depth variations. Page 9, line 196: Please clarify which tests or scenarios were chosen to be plotted on Figure 5. Explain whether this is an average over all the tests conducted and provide justification for this choice.
- Page 9, line 195: The statement regarding the parameterization of drifting particles as phytoplankton and their tendency to strand near riverbanks should be approached with caution. Phytoplankton typically cannot survive away from water. To provide a more accurate assessment of phytoplankton behavior, consider excluding particles that become stranded in dry grid cells and correlating their behavior with currents over the coasts and tides, as these factors are usually lower near the coasts, favoring retention.
- Page 10, Figure 5: If possible, mark the important sites labeled as "a," "b," "c," etc., on Figure 1 to provide a clearer reference for readers.
- Page 10, lines 210-220: Please cite relevant observations or studies where phytoplankton survival without water is documented to support the statement made in this section. If it cannot be supported, all conclusions about retention in tidal flats should be rewritten.
- Conclusion section is very poor and need to be revised.
Kindly note that the combined text incorporates both the general and specific comments for improved clarity.
Citation: https://doi.org/10.5194/egusphere-2023-2231-RC1 -
AC1: 'Reply on RC1', Laurin Steidle, 10 Jan 2024
First of all, thank you for providing such helpful feedback and taking the time to present all your questions in detail.
> 1. Terminology: Instead of referring to 'particles,' using 'phytoplankton
cells' would better align with the biological focus of their study.We had a similar discussion when we were writing the paper, so we particularly
value this input.
In the end, we chose "particle" to emphasise the abstraction that takes place in
our model when representing a phytoplankton cell, as our model is of course not
able to fully capture their behaviour and dynamics.
Ideally, therefore, these two concepts should not be confused by the reader.
However, as you suggested, the term "particle" might be more confusing,
especially for non-modeling readers, as it obscures the biological implications,
and we have changed the term "particle" to "phytoplankton cell" where
appropriate, as you suggested.E.g. we changed the paragraph introducing the concept of a lagrangian model to
read:[...] [Eulerian models] lack temporal consistency, meaning that the life
history and trajectory of a phytoplankton cell cannot be tracked.
Previous modeling studies have attempted to overcome this problem using a
Lagrangian approach.
A Lagrangian model does not try to track e.g. concentrations at fixed
positions, but rather follows the motion of individual particles that
can be used to represent e.g. water parcels or organisms.
Their ability to resolve the interactions of individual phytoplankton cells
or aggregates with the bathymetry, e.g. through settling or stranding,
while maintaining temporal consistency, is essential for investigating
retention mechanisms.
> 2. Model Validation: Including aspects related to model validation, such as
capturing the seasonal cycle, the duration of bloom events, the spatial
distribution of blooms in relation to distance from the estuary, and other
relevant parameters, would enhance the biological relevance of their research
and provide a more comprehensive understanding of the dynamics at play.We agree that model validation is important, but it seems to us that there may
be a misunderstanding about the type of model presented, its implications and
purpose.Our model can be interpreted (in part) as a post-processing or further analysis
of the model presented by Pein et al. (2021).
The model of Pein et al. is a Eulerian model that captures both hydrodynamics
and biology. The biology is modelled using ECOSMO. This model includes several
planktonic compartments (diatoms, flagellates, cyanobacteria and two zooplankton
compartments) and has been successfully applied in several areas (Schrum et al.,
(2006), Daewel and Schrum (2013)).
In the model of the Elbe estuary that we use, it is calibrated and validated
with observational data from both long-term measuring stations and cruises that
take transects in the centre of the channel.
The Pein et al. model is able to predict population dynamics at the
concentration level reasonably well. It captures both the seasonal cycle, i.e.
the bloom events, and the spatial distribution.Our model does not attempt to predict population dynamics.
We use our model to shed light on physical processes that are largely ignored in
Eulerian models such as that of Pein et al. (2021) - in particular, the process
of stranding and other interactions with the bathymetry.
Our focus is therefore, in a sense, to help understand the loss term induced by
outwashing to high salinity waters or dry shores.
These processes are not directly represented in the differential equation of the
Eulerian model that predicts ecosystem dynamics and therefore can not be easily
studied with such models.
For this purpose, we have chosen a Lagrangian approach, which allows us to model
phytoplankton stranding in a simple and (computationally) inexpensive way, with
a temporal consistency that is crucial for modelling the processes studied and
that cannot be achieved with Eulerian models.Because of this narrow focus, we have simplified the biological processes as
much as possible to allow for high interpretability of our results.
We agree that it would be desirable for our model to predict the full ecosystem
dynamics, as this would potentially improve the interpretability of the effects
of the processes studied, i.e. to make quantitative rather than just qualitative
predictions.
However, not only would this greatly increase the cost of building, running and
evaluating such models, it is not currently possible due to technical
constraints and lack of calibration and validation data, neither in our
Lagrangian model nor in any other model to our knowledge.We this paragraph to emphasize the already validated hydrodynamical and
ecostsystem model on which our study is based:Nevertheless, there are sophisticated estuarine models that are able to
reproduce the complex dynamics of estuaries reasonably well. This
includes currents and water levels on the physical side, but also
chlorophyll concentrations and other biologically driven properties
(Pein et al. (2021), Schoel et al (2014)).
However, these are Eulerian models.
This means that they are based on a fixed grid and calculate the
concentration of a tracer, such as phytoplankton, at each grid cell.
This makes it difficult to study concepts such as retention times, as they
lack temporal consistency, meaning that the life history and trajectory
of a phytoplankton cell cannot be tracked.
Previous modeling studies have attempted to overcome this problem using a
Lagrangian approach.
A Lagrangian model does not try to track e.g. concentrations at fixed
positions, but rather follows the motion of individual particles that
can be used to represent e.g. water parcels or organisms.
The ability to resolve the interactions of individual phytoplankton cells or
aggregates with the bathymetry, e.g. through settling or stranding,
while maintaining temporal consistency, is essential for investigating
retention mechanisms.and the following section in the "model limitations" section:
In this study, we aimed to thoroughly investigate different possible
retention mechanisms in a complex Lagrangian model system with a highly
resolved bathymetry.
Due to this computational and spatial complexity, the complexity of the
biological particle properties needed to remain simple to keep
computational cost manageable and due to a lack of high resolution
validation data.
Our model design does not resolve more complex ecosystem dynamics such as
nutrient limitation and grazing by higher trophic levels.
The Lagrangian model is performed offline, meaning it is not coupled to the
Eulerian model that calculates the hydrodynamics and is performed after
the fact.
Therefore, modeling the advection and dispersal of changes in concentration
fields e.g. nutrients due to growth or remineralization was not easily
possible.
Future modeling efforts could couple the Lagrangian model to a Eulerian
model that disperses changes in concentrations fields by biotic activity
throughout the model domain. [...]
> 3. The aspect that phytoplankton cells survive in the dry grid cell (without
water) needs to be justified. Otherwise, this should be corrected in the method,
post-processing (without redoing all the tests) – these cells can be excluded
from the final count, and further conclusions should be corrected.We agree that pythoplankon cannot survive indefinitely in dry conditions.
To contextualise their ability to survive, we would like to highlight two
things:First, the areas where phytoplankton strand are typically frequently flooded by
the tide.
The vast majority of phytoplankton in our model are stranded for less than one
tidal cycle, i.e. less than 12 hours.
Secondly, cells that are considered 'dry' by the model are not necessarily
devoid of water.
The cell resolution in these areas is typically between 50 and 100m.
Cells are considered dry if the water level falls below 0.1m over the majority
of their area.
Therefore, a lot of sub-resolution structure can be expected.
These include sand ripples, tidal creeks or small pools that hold water where
phytoplankton could survive for several days before drying out.
In addition, the low marsh that surrounds most of the estuary contains a lot of
vegetation, typically tall reeds.
This is thought to improve the survivability of the phytoplankton around it by
increasing soil moisture long enough for most cells to survive through a tidal
cycle.The stranding and resuspension of phytoplankton and microphytobenthos has been
shown to be an important process for primary production under eastuarine
conditions (Carlson et al (1984), De Jonge et al (1992), Kromkamp et al (1995),
Savelli et al (2019)).
While this process is particularly well established for microphytobenthos
(Savelli et al), Semcheski et al (2016) showed that the distinction between
'phyoplankton' and 'microphytobenthos' is fuzzy with a large overlap.To our knowledge, no study has investigated the survival of stranded
phytoplankton under estuarine conditions.
We therefore tested a range of parameter choices before publication and have now
added a sensitivity analysis in the appendix to show that time to dry-out is not
a particularly sensitive parameter.
Testing parameters from 1 to 30 days showed no regime shift in our results.
We chose the 7 day cut-off because we felt it was a reasonable time frame under
the conditions observed in the tidal marshes, and there were no observational
data to suggest a better choice.We have also added the following first paragraphs and adjusted the second in the
methods section to better contextualise this choice for the reader:We consider phytoplankton cells that are stranded out of the water by the
receding tide, and lie dry for more than 7 consecutive days to be dead
and remove them.
Note that these dry cells are not necessarily completely devoid of water,
but are considered dry if the majority of its area has a water level
below 0.1 m.
Additionally, in nature these areas typically contain small sub-resolution
structures like tidal ripples or small puddles and vegetation.
There are currently no studies investigating the time range for survival of
stranded phytoplankton on tidal-flats or marshes in estuaries.
Therefore, we performed a sensitivity analysis to determine the effect
of this parameter on the retention success of the phytoplankton
population (see appendix section A).We include a settling and resuspension model to represent tidal stranding
and phytoplankton cells settling on the bed of the estuary. Stranding
phytoplankton and microphytobenthos have been shown on several occasions
to be a major driver of estuarine primary production (Carlson et al.,
1984; De Jonge and Van Beuselom, 1992; Kromkamp et al., 1995; Savelli et
al.,
2019). Phytoplankton cells become stranded when the current grid cell
becomes dry and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is flooded again, all the
stranded phytoplankton cells are resuspended and able to move again.
> Page 2, lines 30-35: It appears that the author may be conflating two distinct
diel migration behaviors observed in planktonic species. One type of diel
migration is exhibited by phytoplankton, which is primarily driven by the
availability of sunlight for photosynthesis. This behavior is solely dependent
on the sun's position in the sky, as phytoplankton are primary producers that
rely on light for their metabolic processes. On the other hand, carnivorous
planktonic species, like certain zooplankton and dinoflagellates, exhibit a
different diel migration pattern. Their vertical movements are not directly
driven by the sun but are instead motivated by the distribution of their prey,
mainly phytoplankton, which, in turn, is influenced by sunlight-driven
photosynthesis. These species engage in diel migration as a survival strategy,
often to avoid predators or to exploit variations in food availability. In this
context, it is essential to emphasize the distinction between these two types of
diel migration patterns to provide a more accurate and biologically informed
account of the behaviors of planktonic organisms. Recognizing the ecological
drivers behind these migrations is crucial for a comprehensive understanding of
aquatic ecosystems.
We agree that the reason why diel migration is beneficial for autotrophs,
mixotrophs and heterotrophs is different.
As we study phytoplankton, we focus on autotrophic and mixotrophic plankton.
Therefore, all model organisms benefit from diel migration by maximising light
capture while potentially avoiding grazing, while the mixotrophs may
additionally benefit by following food or nutrient sources.
In all cases, however, the consequence remains the same: an upward movement
during the day and a downward movement at night.While there may be two reasons for the diurnal migration, whatever the cause,
the purpose of this paper is to examine the effect of this migration on
retention.We changed the mentioned paragraph to make this clearer. It now reads:
Diel vertical migration is a process where organisms move up and down in the
water column in response to the sun. This
movement may favors retention by allowing plankton to reduce the time in the
faster downstream currents at the water surface.
A study by Anderson and Stolzenbach (1985) showed that diel migrating
dinoflagellates were able to out compete other
non-motile phytoplankton in an embayment environment and even compensate for
outwashing losses through reproduction
increasing their abundance. However, this also implies that the growing part
of the population is somehow retaining their
position. If the regrowing population is also continuously drifting
downstream they will not able to sustain their population
in that area and ultimately die out due to unfavorable salinity conditions
in marine waters (Admiraal, 1976; von Alvensleben
et al., 2016; Jiang et al., 2020). The presence of diel migration has mostly
been demonstrated for motile phytoplankton such
as dinoflagellates (Hall et al., 2015; Crawford and Purdie, 1991; Hall and
Paerl, 2011) and zooplankton species (Kimmerer
et al., 2002). While the motivation for diel migration for autotrophic,
mixotrophic, and heterotrophic differs, the consequence
remains the same, an upward movement during the day and a downward movement
during the night.
> Page 4, lines 83-84: What is the spatial resolution of the three-dimensional
unstructured grid used to represent the Elbe estuary in this model, and how does
it vary within the dataset?We added more detailed information on the gridding of the model domain in the
methods sections as requested. It now reads:The unstructured mesh is three-dimensional and consists of 32k nodes using
terrain-following coordinates based on the LSC2 technique (Zhang et al.,
2016) for the vertical grid, allowing a maximum number of 20 levels.
Regions with depths less than 2 m are resolved by only one vertical level.
The bathymetric data were provided by the German Federal Maritime and
Hydrographic Agency (Bundesamt fuer Seeschifffahrt und Hydrographie,
BSH) and the German Waterways Agency (Wasserstraßen- und Schiffahrtsamt,
WSA) with a horizontal resolution of 50 m in the German Bight, 10 m in
the Elbe estuary and 5 m in the Hamburg port \cite{Stanev2019}. [...]
The model provides us with a node-based mesh containing a range of
information [...] and a dynamically varying spacial resolution with
distance between nodes ranging from 5 to 1400 m with a median distance
of approximately 75 m
> Page 5 lines 107-110: The statement, "A particle starts its life with a light
budget of 28 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies when they are above 1m. Children of light-limited
parents inherit the remaining light budget of their parents," should be
supported by relevant laboratory studies or evidence. Additionally, the
terminology used, such as "children" and "parents" for phytoplankton, might be
confusing and should be rephrased for clarity.We changed the paragraph as suggested to better explain the choice and avoiding
the term "children" and "parents". We also fixed a typo incorrectly stating the
light budget used in the model in this section.
The mentioned paragraph now reads:
Phytoplankton cells will also die if they are light-limited for 14 days.
This value is based on measurements presented in (Walter et al., 2017)
which imply
that the majority of phytoplankton is dead after 14 days of light
limitation. A sensitivity analysis for this parameter is presented
in sec. B suggesting no strong influence on the retention success. They are
considered light-limited below a depth of 1m based
on SPM data presented in (Stanev et al., 2019). The initial batch of
phytoplankton cells starts their life with a full light budget
of 14 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies if they are above 1m.
When a cell splits both inherit the same remaining light budget.
> Page 5 lines 118-122: The statement that "particles become stranded when the
current grid cell becomes dry, and once this cell is rewetted, all stranded
particles resuspend and are able to move again" should be justified based on
ecological principles and the behavior of phytoplankton. It's important to
explain the reasoning behind this choice, as phytoplankton typically cannot
survive when completely dry.We justified this choice in our answer to 3.) above as requested.
In short grid cells are typically not completely dry and phytoplankton cells
typically rewettet in less then 12 hours.
We added a paragraph in the paper to reflect our arguments and updated the
mentioned paragraph as also presented in our response to 3.).
It now reads:We consider phytoplankton cells that are stranded out of the water by the
receding tide, and lie dry for more than 7 consecutive days to be dead
and remove them.
Note that these dry cells are not necessarily completely devoid of water,
but are considered dry if the majority of its area has a water level
below 0.1 m.
Additionally, in nature these areas typically contain small sub-resolution
structures like tidal ripples or small puddles and vegetation.
There are currently no studies investigating the time range for survival of
stranded phytoplankton on tidal-flats or marshes in andTherefore, we
performed a sensitivity analysis to determine the effect of this
parameter on the retention success of the phytoplankton population (see
appendix section A).We include a settling and resuspension model to represent tidal stranding
and phytoplankton cells settling on the bed of the estuary. Stranding
phytoplankton and microphytobenthos have been shown on several occasions
to be a major driver of estuarine primary production (Carlson et al.,
1984; De Jonge and Van Beuselom, 1992; Kromkamp et al., 1995; Savelli et
al.,
2019). Phytoplankton cells become stranded when the current grid cell
becomes dry and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is flooded again, all the
stranded phytoplankton cells are resuspended and able to move again.> Page 6, line 150: Please provide an explanation for the choice of population
doubling times in idealized conditions ranging from 40 to 404 days. This choice
should be based on scientific rationale and may require further clarification.Under ideal conditions, phytoplankton doubling times are much lower than the
range tested in our model, with doubling times of less than one day.
These ideal cases are of course rare, as phytoplankton are almost always
strongly limited in nature, e.g. by light or nutrient availability.In our study we are examining the impact of a range of physical drivers, most
importantly losses due to outwashing of phytoplankton and are trying to decouple
the biological drivers as much as possible to achieve a better interpretability
of the results.
Hence, we chose our doubling times not to accuratley represent fission rates
observed in nature but such that they allow us to estimate the losses due to
physical drivers, which in our case are light limitation, outwashing to the
shores and to the sea.
The presented doubling times in our study can be interpreted as potential
average net-doubling-times in the presence of predation and mortality, nutrient
availability.
We are not trying to representing the ecosystem dynamics by natural growth and
mortality of phytoplankton as this is already done in the cited study Pein et
al. (2021) where they include a full ecosystem model but lack the possibility to
represent the process (e.g. stranding) simulated and studied here.We added a comment to clarify this to the mention paragraph. It now reads:
Each vertical velocity is examined for a range of different reproduction
rates, expressed as population doubling times ranging from 40 to 404
days with a logarithmic scaling.
In the following, we will use reproduction rate to refer to the prescribed
population growth rate under idealized conditions and use growth rate
whenever we describe population growth in nature.
The prescribed population growth rate can be interpreted as potential
average net-doubling-times in the presence of predation and mortality,
nutrient availability while testing the effect of outwashing.
> Page 7, Section "Results": Before analyzing the retention success, it's
advisable to perform some form of model validation. Consider whether your model
or specific scenarios with their parameters successfully reproduce the seasonal
cycle of phytoplankton, including the duration of bloom events and the number of
particles over distance from the North Sea. Model validation is crucial to
ensure the reliability of your results.This request is similar to point 2.) where we explained why this model does not
attempt to predict population dynamics.
We agree that model validation is important to ensure the reliability of model
results, which is why we use the hydrodynamics of an ecosystem model with
validated population dynamics.
However, to our knowledge, no observational studies have been conducted to
investigate the mechanism of phytoplankton retention under estuarine conditions
and spatial distribution at finer scales.
In fact, the lack of field studies was the main motivation for this modelling
study, as we try to emphasise the importance of these processes and suggest that
such experiments should be carried out.
Quantifying the importance of these processes in the field is essential before
they can be added to the current state of the art models to better represent
phytoplankton losses, which are currently fitted to observational data mainly
using natural mortality and grazing parameters.We have added the following paragraph, as previously stated in our response to
2.) above:We added this paragraph to emphasise the already validated hydrodynamic and
ecosystem model on which our study is based:[...] there are sophisticated estuarine models that are able to reproduce
the complex dynamics of estuaries reasonably well. This includes
currents and water levels on the physical side, but also chlorophyll
concentrations and other biologically driven properties (Pein et al.
(2021), Schoel et al (2014)).
However, these are Eulerian models.
This means that they are based on a fixed grid and calculate the
concentration of a tracer, such as phytoplankton, at each grid cell.
This makes it difficult to study concepts such as retention times, as they
lack temporal consistency, meaning that the life history and trajectory
of a phytoplankton cell cannot be tracked.
[...]and the following section in the "model limitations" section:
In this study, we aimed to thoroughly investigate different possible
retention mechanisms in a complex Lagrangian model system with a highly
resolved bathymetry.
Due to this computational and spatial complexity, the complexity of the
biological particle properties needed to remain simple to keep
computational cost manageable and due to a lack of high resolution
validation data.
Our model design does not resolve more complex ecosystem dynamics such as
nutrient limitation and grazing by higher trophic levels.
The Lagrangian model is performed offline, meaning it is not coupled to the
Eulerian model that calculates the hydrodynamics and is performed after
the fact.
Therefore, modeling the advection and dispersal of changes in concentration
fields e.g. nutrients due to growth or remineralization was not easily
possible.
Future modeling efforts could couple the Lagrangian model to a Eulerian
model that disperses changes in concentrations fields by biotic activity
throughout the model domain. [...]And further emphasised the point that this study suggest and shall work as a
foundation for future field measurements in the outlook. It now reads:Our results clearly suggest the importance of tidal flats and shallow areas
along the river banks for the persistence of primary production in the
Elbe estuary. However, their effect can currently not be quantified due
to the lack of validation data.
Chlorophyll data with a sufficient temporal and spacial resolution is only
gathered in the center of the river. Future monitoring efforts should
therefore also include data along the river shores on tidal flats or
shore-to-shore to quantify the effect of potential future changes by
dredging, diking or restoration attempts.
Frequently stranded plankton have been shown to be essential to the survival
of populations in our model. However, data on their ability to survive
under these conditions are scarce. Our results suggest that these
conditions may be as important as their ability to quickly regrow under
more favorable conditions, and we suggest further research on plankton
survivability when stranded.
> Page 7, line 171: Please clarify the intention behind looking at the state of
phytoplankton after one year in terms of estimating areas where they
"successfully retain."This is a reference to our 'retention metric' defined on line 158ff.
Conceptually, we consider a population to be successfully maintained if it shows
long-term growth.
We consider one year to be a reasonable "long-term" time frame for this, firstly
because it is much longer than the typical outwash period (see newly added
Figure 6) of up to 3 weeks, and secondly because it represents all the major
seasonal cycles, in particular the upstream seasonal runoff cycle and the
downstream seasonal and tidal cycles.We have modified the "retention metric" paragraph as you suggested to reflect
the reasoning presented here.
It now reads:Conceptually,
we consider a population to be successfully retained if it is able to
sustain itself long term or even shows growth. Practically,
this is evaluated by comparing the population size at the end of the year to
the size after release. The choice of one year is
considered reasonable because it covers the full seasonal cycle and is also
much longer than the average exit or flushing time
of the estuary (see fig. 6).
and added a paragraph to the outlook:Our hydrodynamics data set was limited to the year 2012. Therefore, we were
not able to study different release times with the same methodology.
While we do not expect the general dynamics to change, future research
could examine the effect of varying discharge throughout the seasons on
retention and could address the very long term success (>1 year) of the
population,
as it affected by inter-annual variability and climate change.> Page 7, line 177: When stating "approximately 3 months," consider providing
supporting evidence or references to confirm the accuracy of this time frame
based on relevant observations or studies.This is not based on other studies but is a reference to our results presented
in fig. 3 where the break even point between physically induced loses and growth
lies in between 81-101 days. which are approximatly 3 months.
The mentioned paragraph now references this:Our simulations show that the population is able to successfully retains
itself under certain conditions. Passively drifting
phytoplankton is able to sustain themselves in the estuary if they have a
reproduction rate that doubles their population size
within approximately 3 months (see fig. 3)
> Page 9, Figure 4: The positive depths shown in Figure 4 may be related to
tidal oscillations. It would be valuable to describe the tide variabilities or
free surface level variability in the site section to help explain these depth
variations.Yes, the positive values are caused by tidal oscillations that lift
phytoplankton cells into areas where they become stranded during ebb tides.
We have added a contextualising comment to the tidal range to make this clearer
to the reader:Fig. 4 compares two box plots showing the average water depth at
the location of each phytoplankton cell between those cells that remained
alive for less than three months (short-living) and
for more than three months (long-living). Depth is measured relative to the
current water surface. Therefore, a value greater
than zero indicates that the phytoplankton cell is stranded on the shore
during ebb tide. For reference, the water level varies
on average by about 5 m due to the tides. (Stanev et al., 2019; Schöl et
al., 2014).
> Page 9, line 196: Please clarify which tests or scenarios were chosen to be
plotted on Figure 5. Explain whether this is an average over all the tests
conducted and provide justification for this choice.Since we have no reason to favour or emphasise any particular case, we use an
average calculated over all cases, or tests as you call them here.
Furthermore, all cases follow the same spatial pattern when plotted
individually, with no significant shift in the structure of the average age map,
as they all rely on stranding processes to retain themselves, as shown in Figure
4.In order to make this clear to the reader, we have modified the referenced
paragraph, which now reads
We moreover analyzed the horizontal spacial distribution of long and short-
living phytoplankton in fig. 5. To do this, we
divide the model domain into equally sized hexagons. The color of each
hexagon indicates the average age of the phytoplankton
cells within it calculated across all cases. Note, that the spatial age
structure is similar for all cases. Hexagons with a yellow
color indicate an average age of over three months. These yellow areas are
mainly found along the river banks in shallow
waters or tidal flats.
> Page 9, line 195: The statement regarding the parameterization of drifting
particles as phytoplankton and their tendency to strand near riverbanks should
be approached with caution. Phytoplankton typically cannot survive away from
water. To provide a more accurate assessment of phytoplankton behavior, consider
excluding particles that become stranded in dry grid cells and correlating their
behavior with currents over the coasts and tides, as these factors are usually
lower near the coasts, favoring retention.As discussed in our response to point 3), we do indeed remove
particles/phytoplankton aggregates that become stranded after a period of time,
and cells flagged as 'dry' have a lot of sub-resolution structure. Perhaps a
better name for this scihsm flag would have been 'not-flooded' as these cells
are in most cases quite moist.Regarding your comment after "To provide [...]", we are not quite sure what you
are referring to.
If you are asking how the currents are calculated and how they affect the
movement of the phytoplankton: The trajectory of a phytoplankton is driven by
currents and tides. They move almost instantaneously with the currents and
follow them, ignoring diffusion for the moment.They are therefore correlated.
Advection and diffusion were calculated by Pein et al. (2021) using SCHISM
solving the Navier-Stokes equations, and their behaviour, which in our case is a
kind of vertical motion, is added by us.
This implies that we represent the currents and tides along the coast as
accurately as possible in our model resolution. This is discussed in more detail
in the Pein paper, where the validation process with tides and currents is
shown.
The currents in shallow water are also slower in our model than in deeper water,
as you suggested. This is mainly due to friction between the water layer and the
river or sea bed.Alternativly, if you are referring to the inclusion of a dynamic behaviour:
During the early conceptual development of this model we also considered
including a vertical migration process of the phytoplankton depending on their
velocity, e.g. that they move up and down depending on their speed relative to
the coast.However, we couldn't find any observations showing that pytoplankton exhibit
such a migration behaviour or any other behaviour that would suggest that they
are somehow able to feel their speed, only their acceleration.
One could consider acceleration as a driver for migratory behaviour.
However, we did not find any study showing that phytoplankton also exhibit
acceleration-dependent migratory behaviour either.
We therefore decided to include only light-dependent migration, i.e. moving up
and down with the sun.A phytoplankton cell is moved by three processes: Advection (currents influenced
by tides), Diffusion (e.g. turbulence) and their behavior.
> Page 10, Figure 5: If possible, mark the important sites labeled as "a," "b,"
"c," etc., on Figure 1 to provide a clearer reference for readers.The areas labelled in Figure 5 are not visible in Figure 1.
Figure 1 only shows the port area, which is the far right part of Figure 5.
We have added a comment to the figure description to help the reader to align
these to maps.
> Page 10, lines 210-220: Please cite relevant observations or studies where
phytoplankton survival without water is documented to support the statement made
in this section. If it cannot be supported, all conclusions about retention in
tidal flats should be rewritten.We added citations to relevant observations or studies where phytoplankton
survival without water is documented to support the statement made in this
section and as described in our response to 3.)In short they are:
[...]. Stranding phytoplankton and microphytobenthos have been shown on
several occasions to be a major driver of
estuarine primary production (Carlson et al., 1984; De Jonge and Van
Beuselom, 1992; Kromkamp et al., 1995; Savelli et al.,2019).
> Conclusion section is very poor and need to be revised.We reworked the conlusion as suggested and it now reads:
In this study, we investigated the role of different retention strategies
for phytoplankton organisms to persist in an estuarine
environment. We showed that stranding in shallow nearshore areas is
essential for phytoplankton retention, and that phyto-
plankton that do not strand are rapidly washed away. Our model simulations
suggest that growth rates much lower than those
observed in nature may be sufficient for populations to prevent their
decline due to outwashing, implying that stranding may
be sufficient to maintain the population. Moreover, buoyancy and strong diel
vertical migration enhance retention within the
estuary. These results highlight the importance of shallow nearshore areas
in maintaining the productivity of estuarine ecosys-
tems. Our results suggest that current state-of-the-art models of estuarine
ecosystems may overlook an important process and
emphasize the need for informed ecosystem-based management to avoid the
degradation of estuarine ecosystems by dredging
and diking activities.Citation: https://doi.org/10.5194/egusphere-2023-2231-AC1 -
AC4: 'Reply on RC1', Laurin Steidle, 10 Jan 2024
Sorry, but it seems to have broken the formatting of my previous reply.
I have attached it as a text file to this reply for better readability.
I hope this makes it easier for you to read.Additionally, we attached figures added to the manuscript showing the sensitivity analysis that you requested.
We also added two figures showing flushing times as suggested by RC2 that we attached here.- Figure "retention_success_sa_dryout_1_day.png" and ""retention_success_sa_dryout_14_day.png"" show a sensitivity analysis for mortality due to stranding i.e. drying out showing the retention success similar to fig. 3 (relative pop. changes) with a threshold of 1 and 14 days without resuspension compared to the 7 days in fig 3 before phytoplankton are culled
- Figure "retention_success_sa_light_deficit_7_days.png" and "retention_success_sa_light_deficit_28_days.png" shows a sensitivity analysis for mortality due to light limitation showing the retention success similar to fig. 3 with a light deficit threshold of 7 and 28 days compared to the 14 days in fig 3 before phytoplankton are culled
- Figure "flush_times_hexbin.png" shows a hex-bin heatmap of average exiting times of the Elbe estuary with Hamburgs port area, as shown in \ref{fig:bathymetry}, in the bottom right without reproduction, light-limitation, stranding and settling on the riverbed. Colors indicate the time of a phytoplankton cell or water parcel to reach the 20 PSU isohaline from its origin hexagon.
- Figure "average_salinity_with_20PSU_isohaline.png" shows a salinity map of the Elbe estuary with Hamburgs port area, as shown in fig. 1, in the bottom right.
Salinity is averaged in depth and over the whole year. 20 PSU isohaline is marked with a black line. that this plotted area has been extended downstream compared to fig. 5. Further note that the color map has been capped at 25 PSU for better visibility in low salinity areas.
-
RC2: 'Comment on egusphere-2023-2231', Anonymous Referee #2, 03 Nov 2023
This paper studies the retention mechanisms of phytoplankton in an estuarine environment. The paper is clearly written and the results are of much interest. However, since this work is strongly based in model simulations, I miss a lot of information about how the numerical simulations have been performed. Also, there is a lack of sensitivity analysis of the results when the parameters are changed.
Thus, before the paper can be accepted the author should provide details (probably including appendixes):
- On the numerical velocity fields: boundary conditions, resolution, vertical components, explicit bathymetry, etc...
- On the Lagrangian transport model: interpolation schemes, use of eddy diffusion?, sticking of particles to land, etc... You may also compute other Lagrangian quantities to show like exit times, retention times, or accumulation zones (vertical and horizontal).
- On the "population dynamics": how particles divide, die (is it a Gillespie simulation)? number of particles, density, etc...
- Provide a sensitivity analysis of the many parameters of the model, in particular those concerning phytoplankton demography like mortality, reproduction rates, etc...
Citation: https://doi.org/10.5194/egusphere-2023-2231-RC2 -
AC2: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
> On the numerical velocity fields: boundary conditions, resolution, vertical
components, explicit bathymetry, etc...
We added the requested information to the description of the hydrological in the
introduction. It now reads:
We use the hydrodynamic data generated by the latest SCHISM model of the
Elbe estuary (Pein et al., 2021) from the
weir at Geesthacht to the North Sea, including several side channels and the
port area (see figure 2). SCHISM solves the
Reynolds-averaged Navier-Stokes equations on unstructured meshes assuming
hydrostatic conditions with a time step of 60 s.
The unstructured mesh is three-dimensional and consists of 32k nodes using
terrain-following coordinates based on the LSC2
technique (Zhang et al., 2016) for the vertical grid, allowing a maximum
number of 20 levels. Regions with depths less than 2 m
are resolved by only one vertical level. The bathymetric data were provided
by the German Federal Maritime and Hydrographic
Agency (Bundesamt fuer Seeschifffahrt und Hydrographie, BSH) and the German
Waterways Agency (Wasserstraßen- und
Schiffahrtsamt, WSA) with a horizontal resolution of 50 m in the German
Bight, 10 m in the Elbe estuary and 5 m in the
Hamburg port Stanev et al. (2019). The boundary conditions on the seaward
side include sea surface elevation, horizontal
currents, salinity and temperature Stanev et al. (2019) and discharge and
temperature from the Elbe river on the land-ward side.
Atmospheric forcing includes wind, air temperature, precipitation, shortwave
and longwave radiation Stanev et al. (2019).
Model validation is based on tide gauge stations and long-term stationary
measurements of salinity, water temperature, and
horizontal currents. Biochemical variables, including chlorophyll, are based
on long-term measurements at the Seemannshöft
and Grauerort stations Pein et al. (2021). The model provides us with a
node-based mesh containing a range of information such
as water velocity, salinity, water level and dispersion. The year
represented in that dataset is 2012 with a temporal resolution
of 1 hour and a dynamically varying spacial resolution with distance between
nodes ranging from 5 to 1400 m with a median
distance of approximately 75 m.
> On the Lagrangian transport model: interpolation schemes, use of eddy
diffusion?, sticking of particles to land, etc... You may also compute other
Lagrangian quantities to show like exit times, retention times, or accumulation
zones (vertical and horizontal).
We modified the section describing the interpolation scheme, use of eddy
diffusion and how they stick to the land to be clearer.
Thank you very much for the suggestion to include "exit-times". We added a
figure showing average exit times in the results.The section describing eddy diffusivity now reads:
Phytoplankton cells are not only advected but also diffused based on eddy
diffusivity which is crucial
to represent tidal-pumping processes. Diffusion was modeled using a random
walk using a random number generator with a
normal distribution. Horizontally the standard distribution of the random
walk is set to 0.1 ms−1. The vertical displacement of
a phytoplankton cell ∂z i is calculated by
∂zi = K′_v (z_i(n))∂t + N (0, 2K_v (z_i)) (2)
based on Yamazaki et al. (2014) where zi is the vertical position of the
phytoplankton cell, K′_v is the vertical eddy diffusivity gradient,
K_v is the vertical eddy diffusivity and N is the normal
distribution. The term based K′_v is needed to avoid
phytoplankton accumulation on the top and bottom of the water column from
the hydrodynamic model output.The section describing the interpolation scheme now reads:
Flow velocities, like any other hydrodynamic data, were
interpolated linearly in time, linearly in space on the vertical axis and on
the horizontal axis using barycentric coordinates, with175
the exception of water velocity in the bottom model cell, where logarithmic
vertical interpolation is used.The section describing sticking to land now reads:
Phytoplankton cells become stranded when the current grid cell becomes dry
and stay in place until they are resuspended
or dry-out. They are not allowed to move from wet cells to dry cells, by the
random walk diffusion applied to all phytoplankton
cells. A grid cell is considered dry based on the flag given in the SCHISM
hydrodynamic model output. Once this cell is
becomes flooded again all stranded phytoplankton cells resuspend and are
able to move again
> On the "population dynamics": how particles divide, die (is it a Gillespie
simulation)? number of particles, density, etc...
We do not use a Gillespie simulation. Phytoplankton cells die independent off
the presence of other cell but only based on enviromental conditions.
Hence we do not need to enforce a mass balance as required in a Gillespie
simulation.
We updated the paragraph describing the particle division, mortality and the
paragraph describing particle density.The section regarding reproduction now reads:
Reproduction is represented as a fission process, where each phytoplankton
cell has a probability to split effectively produc-
ing a copy. This is a novel feature [...]
We perform multiple
simulations for a range of reproduction rates, implemented as a fission
probability evaluated every minute, that are constant
over the lifetime of the cell. While a fixed reproduction rate is a
simplification that does not allow for more realistic simulation115
of the population dynamics of a particular species, it does allow us to
investigate the general mechanisms that enable plankton
retention.The section regarding mortality now reads:
Mortality is induced by one of three processes: high salinity, when they dry
out while stranded, or due to long term lightlimitation. When particles cells are exposed to high salinity water above
20PSU, a mortality probability of 0.5% per minute is
applied removing dead phytoplankton cells from the simulation (see salinity
map in fig. C1 ).
[...] # reasoning
We consider phytoplankton cells that are
stranded out of the water by the receding tide, and lie dry for more than 7
consecutive days to be dead and remove them.
[...] # reasoning
Phytoplankton cells will also die if they are light-limited for 14 days.
This value is based on measurements presented in (Walter et al., 2017)
[...] # reasoning
They are considered light-limited below a depth of 1m based
on SPM data presented in (Stanev et al., 2019). The initial batch of
phytoplankton cells starts their life with a full light budget
of 14 days, and each minute below 1m reduces this budget by one minute,
while the opposite applies if they are above 1m.
When a cell splits both inherit the same remaining light budget.The section on particle counts now reads:
In both sets of experiments, we release 10,000 individuals representing the
studied phytoplankton population at the be-
ginning of the year. This results in over 1 billion individual particles
simulated for each case with approximately 1 million
particles active simultaneously counted over all cases over 500,000 time
steps. This corresponds approximately to a one to one
ratio of simulated phytoplankton cells to mesh nodes in the hydrodynamic
model at each time step
> Provide a sensitivity analysis of the many parameters of the model, in
particular those concerning phytoplankton demography like mortality,
reproduction rates, etc...
We have added a sensitivity analysis for the mortality conditions of
phytoplankton dry-out and light limitation to the sensitivity analysis already
performed for growth rates and vertical velocities in the appendix.
Varying these parameters changes the break-even point of growth and loss as
expected but no regime shift occurs and the observed trends remain the same.Citation: https://doi.org/10.5194/egusphere-2023-2231-AC2 -
AC3: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
It seems to have broken the formatting of my previous reply.
I have attached it as a text file to this reply for better readability.
I hope this makes it easier to read.Additionally, we attached figures added to the manuscript that you requested.
These are the sensitivity analysis and flush times.- Figure "retention_success_sa_dryout_1_day.png" and ""retention_success_sa_dryout_14_day.png"" show a sensitivity analysis for mortality due to stranding i.e. drying out showing the retention success similar to fig. 3 (relative pop. changes) with a threshold of 1 and 14 days without resuspension compared to the 7 days in fig 3 before phytoplankton are culled
- Figure "retention_success_sa_light_deficit_7_days.png" and "retention_success_sa_light_deficit_28_days.png" shows a sensitivity analysis for mortality due to light limitation showing the retention success similar to fig. 3 with a light deficit threshold of 7 and 28 days compared to the 14 days in fig 3 before phytoplankton are culled
- Figure "flush_times_hexbin.png" shows a hex-bin heatmap of average exiting times of the Elbe estuary with Hamburgs port area, as shown in \ref{fig:bathymetry}, in the bottom right without reproduction, light-limitation, stranding and settling on the riverbed. Colors indicate the time of a phytoplankton cell or water parcel to reach the 20 PSU isohaline from its origin hexagon.
Thank you particularly for this suggestion! - Figure "average_salinity_with_20PSU_isohaline.png" shows a salinity map of the Elbe estuary with Hamburgs port area, as shown in fig. 1, in the bottom right.
Salinity is averaged in depth and over the whole year. 20 PSU isohaline is marked with a black line. that this plotted area has been extended downstream compared to fig. 5. Further note that the color map has been capped at 25 PSU for better visibility in low salinity areas.
-
AC2: 'Reply on RC2', Laurin Steidle, 10 Jan 2024
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