> 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.

