the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Jun 2022
Detecting most effective cleanup locations using network theory to reduce marine plastic debris: A case study in the Galapagos Marine Reserve
Abstract. The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique biodiversity. Unfortunately, the islands are polluted by marine plastic debris and the island authorities face the challenge to effectively remove plastic from its shorelines due to limited resources. To optimise efforts, we have identified the most effective cleanup locations on the Galapagos Islands using network theory. A network is constructed from a Lagrangian simulation describing the flow of macroplastic between the various islands within the Galapagos Marine Reserve, where the nodes represent locations along the coastline and the edges the likelihood for plastic to travel from one location and beach at another. We have found four network centralities that provide the best coastline ranking to optimise the cleanup effort based on various impact metrics. In particular locations with a high retention rate are favourable for cleanup. The results indicate that using the most effective centrality for finding cleanup locations is a good strategy for heavily polluted regions if the distribution of marine plastic debris on the coastlines is unknown and limited cleanup resources are available.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Status: closed
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CC1: 'Comment on egusphere-2022-426', Noam Vogt-Vincent, 16 Jun 2022
Firstly, I think this is a great manuscript and I enjoyed reading it – a novel idea with potentially very useful results for those involved in marine debris management efforts on Galapagos (and other remote islands if this methodology were implemented elsewhere). The effectiveness of the clean-up strategies proposed in this manuscript will depend on the veracity of the assumptions a significant proportion of debris undergoes resuspension, but this is clearly stated in the conclusion. So overall, I think this manuscript will be valuable for island managers, and researchers working in this field. I do have some technical questions/requests for clarification though:
- I wonder whether you carried out a sensitivity analysis to test whether you released sufficient particles? With such high resolution hydrodynamical data forcing your particle-tracking, significant dispersion can occur over a 60-day integration time with an original separation of <4km (e.g. the off-diagonal cells in the transition matrix are ‘noisy’, and this is probably why). This in itself is not an issue since practitioners will probably not be using your transition matrix, but it would be nice to know how robust, say, Figure 7 is to particle number. I’m guessing that you were limited to 700k particles due to storage (since you were saving particle positions with a very high output frequency) but, if it is tractable, a quick sensitivity test might provide some assurance.
- I’m struggling to completely follow section 2.5. If I understand correctly, the equation on line 162 models the distribution of debris on coastlines in the limit of 100% resuspension. You simulate clean-up as removing the outgoing nodes of a clean-up target cell. You then say that “particles will accumulate at the target cleanup node”, but how can particles accumulate if you’re constantly removing them?
- It’s also not clear to me why you based your definition of steady state on the number of particles in the ocean – does the vector vtnot reach steady state?
- I’m also finding the “benefit” metric quite difficult to follow. “Zero connectivity between different nodes”, to me, implies that 100% of resuspended debris enters the ocean, but I don’t think this is what you meant?
- I’m not 100% convinced by your sensitivity tests to the initial macroplastic distribution (3.3). You’ve tested how robust your method is to uncertainty in the initial distribution by using a completely random initial distribution, i.e. assuming that the mass of plastic on beaches is completely decorrelated across length scales L > 4km. Is this realistic? Your result that the efficacy of node rankings remains the same with a random initial distribution is not surprising to me, since mesoscale ocean structures are much larger than 4km so will on average still see a ‘uniform’ distribution of resuspended debris. But given that van Sebille et al. (2019) showed that most debris incident to Galapagos arrives from the East, and that there could be large-scale effects from wind shadows, wake eddies, etc., I’d have expected that there would be some large-scale structure in the distribution of debris. I wonder if a more realistic way to model an uncertain initial macroplastic distribution might be by generating perlin noise with a wavelength larger than 4km (e.g. maybe the length scale of an island).
- This is not a criticism, but I wonder to what extent using a time-mean transition matrix affects your results. For instance, if we have sites A, B and C each releasing 1 unit of debris per time-step with probability P(A→B)=1/6 and P(B→C)=1/6, the probability of transition P(A→C) is 1/36 (over two timesteps). After 12 time-steps, 1/3 units would have been transported from A→C. But if these transition probabilities were time-varying and turned out to be P(A→B)= P(B→C)=1 during time-steps 1-2 and 0 otherwise, 1 unit would have been transported A→C – 3x more than the time-mean case, even though P(A→B)= P(B→C)=1/6 in both cases when averaged over 12 time-steps. This is obviously an artificially bad case and I completely accept that this goes beyond the scope of your study, but I didn’t see a mention of this in your discussion of limitations so I was wondering whether you thought this was a limitation (or if there is evidence that this time variability in the transition matrix is not important).
Citation: https://doi.org/10.5194/egusphere-2022-426-CC1 -
AC1: 'Reply on CC1', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC1-supplement.pdf
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RC1: 'Comment on egusphere-2022-426', Anonymous Referee #1, 01 Jul 2022
Review for : Detecting most effective cleanup locations using network theory to reduce marine plastic debris: A case study in the Galapagos Marine Reserve
The present work proposes a new methodology for improving beach clean up, to reduce marine plastic debris. The case study is dedicated to the Galapagos islands and the methodology is using a network to select the optimum criteria for the beach clean up purposes.
The manuscript is of very good quality, well organized with no specific structure problem or methodological problem. Although quite dense for the part explaining the criteria, it is possible for non specialist to understand the goal of the selected criteria.
In summary, The overall quality of this work is up to the expected standards and to my point of view can be published as it is ( after some typo and minor possible mistakes). I emphasize the fact that such decision (to my experience of reviewer) is very rare. I therefore thank the authors for having so well prepared their work before submission.
However, in order to improve the manuscript, or give ideas for new works, I wish to share some questions or comments :
- a very basic but tricky one to start with : could the authors imagine what the results could be with an irregular grid with a strong refinement close to the coast, or with a high resolution regular grid of , let say, 300 m in resolution on the horizontal ? I reckon that this point could be discussed using some literature. My main concern is that , the present study does not prove that the results are not strongly dependent on the OGCM resolution used. If it were, the overall methodology might provide different scores in terms of criteria ?
- the beaching probability: despite the fact that this has been already published , I am still very skeptical about the tuning of this probability. I am pretty sure that replacing this rather “adamant” formula by a probability calculated on the average wind direction versus the coastline layout in the area would be more realistic...Of course, I understand that there has to be a beaching time-scale of some sort to compensate the lack of grid resolution, but just days, regardless of general weather conditions in the area is strange to me. This is of course just my point of view, but this is what we can see when deploying drifters in general. Of course, this is complicated as the results can be different when looking at different scales, from large to very local scale (at the scale of a beach for example), but for a statistical approach, using regional winds might be ok.
- A remark concerning the overall network method: according to some literature, connectivity and oceanic distance can be calculated out of the Lagrangian model “quite” simply, I mean without using a complex machinery. Therefore, it could have been interesting to show some comparisons with some basic diagnostics (overall stranding rate / source identification / oceanographic distance) , to better prove the real added value of this complex work presented here. In other words, one might think that rather simple diagnostics could deliver nearly the same message or present similar general results , with a computational cost or calculation cost quite reduced. I understand that the authors might reply that their diagnostics are way refined and accurate, which is probably true, but the fact remains that, in the end, when it comes to mobilizing a cleaning team to go on site, maybe simple diagnostics can already deliver the necessary information for the management? (ok I am teasing a bit here, but never underestimate the robustness of simplicity ..!)
- Concerning the seeding of the particles, the random seeding is one interesting case , but when targeting real cases, I am quite surprised that the authors have not tried to perform test cases for which the seeding was increased after heavy rains. If so, they would have put themselves in the position of delivering a real connectivity between islands with identified real sources, i.e. the beaches receiving inland waste through rivers or waste management pipes just after the rain events ?
- In addition, I think that, as the final goal is to deliver advice for where and when to go for beach cleaning, one missing part can be the part looking at the correlation between the weather conditions and the stranding or accumulation close to the shore. This is a temporal advice that can be more efficient than the cleaning frequency advice that can be deduced from the present work. Indeed, as mentioned in their introduction, waste distribution is highly heterogeneous, and one cause is the high variability of regional to local weather conditions. Therefore, one could think that the general cleaning plan could be quite different if the weather conditions variations were selected as one of the key parameter.
Citation: https://doi.org/10.5194/egusphere-2022-426-RC1 -
AC2: 'Reply on RC1', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC2-supplement.pdf
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AC2: 'Reply on RC1', Stefanie Ypma, 30 Aug 2022
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RC2: 'Comment on egusphere-2022-426', Anonymous Referee #2, 19 Jul 2022
This manuscript presents an interesting statistical analysis of the connectivity patterns in the Galapagos to infer the coastal locations with a highest probability of accumulating marine macroplastics. In particular, the authors combine concepts from the graph theory, I.e, centrality derived metrics, and a hydrodynamical model to provide a map with the coastlines of the Galapagos Marine Reserve classified according to the optimization in removing marine litter.
Some of the findings are: provide a methodology to cleanup strategy that can be applied even when there are not data about the distribution of macroplastics; among the centralities metrics, the retention rate provide the most useful information to localize regions for cleanup; it is more effective in terms of removing macroplastic to clean at sink points (coastlines with large positive Source-Sink index values: high SSI_sink values) than at source locations.
In general, the authors present a interesting work aimed at improving the Lagrangian identification of coastal locations with high probability to find macriplastic advected by the ocean flow. The strategy adopted by the authors, i.e. the use of outputs of a hydrodynamical model and the methodology employed through the network theory is technically sound, turning out to be appropriate for the scope of this work. This is a good piece of work which could be of interest to some OS readers. However there are some weakness in the manuscript an a revision has to be addressed before publication. An improvement of the methodology description is strongly recommended before a new submission. Some aspects related to the methodology should be better supported and discussed more appropriately in the context of previous literature. The structure and organization of the Introduction section lack in clarity, where sentences are repeated. I think the paper could be considered for publication after major revision. The main issues that need to be clarified by the authors are listed bellow.
1. I am not sure if the size of macroplastics (>0.5 cm) allows the macroplastic particles to be considered as Lagrangian particles? I think that to compute Lagrangian particle trajectories one needs to assume that the particle has to instantaneously follow or to be totally constrained to the motion of the fluid flow. Even for particles with finite size, the Maxey-Riley approximation to resolve the equation of motion for inertial particles, assumes that particles are very small (small microplastics?).
2. One weakness of the manuscript is the description of the methodology. Some of the definitions are not clear. For example the definition of retention rate, the loss rate, etc. I think the authors can greatly improve the definition of these centrality metrics through mathematical expressions, i.e using equations. For example using the mathematical formalism based on connectivity probabilities between network nodes in the weighted network, starting from the definition of the transition matrix.
3. Have the authors considered that in temporal networks, as the analyzed here, also the synchronous arrivals at a node could impact on the network connectivity metrics? The “cumulated” implicit connectivity is based on adding up all the events of synchronous arrival (see Ser-Giacomi et al, 2021, PRE). However considering implicit connectivity could modify the resulting connectivity patterns
4. The resolution of the model is too coarse as to resolve submesoscale dynamics. Ignoring submesoscale motions is not a simple matter when it comes to surface material dispersion. It is well known that submesoscales are associated with vertical motion (an extreme case was documented via drifter measurements by D’Asaro et al., 2018 PNAS paper). The submesoscales cannot be removed from the analysis when their impact on the horizontal transport properties is substantial. They also generate high convergence zones, which could impact the connectivity probability between nodes. Please provide arguments to show that by dismissing small scale dynamics in the small region, the applicability of the results obtained here to the real ocean do not become very uncertain.
5. Numerical diffusion. The spatial and temporal resolution of the model (4km and one day) could originate large numerical diffusion in the computation of the Lagrangian particle trajectories. Note that assuming velocities of 0.6m/s we obtain that in one day (the temporal resolution) the particle could move around 50km, which is 12 times larger than the spatial resolution (~4km). This can be “fixed” by decreasing the time step in the Lagrangian integration scheme, but still some small scale dynamics is missing, and this could affect to the Lagrangian transport associated with the large scale structures.
6. One of the advantages of the methodology is that it can be used independently of whether there exists available macroplastic distribution data or not. However, a validation exercise could be beneficial, in particular to better support the conclusions.
7. In the introduction section, I found some long and complicated sentences that could be split . Lines 30-32. Lines 37-39.
8. The sentence in line 40: “[…] the connectivity can still, to a first order [...]” could be improved. The connectivity by its self does not inform about aggregation but rather some derived metrics, and under some assumptions. Please clarify it.
Citation: https://doi.org/10.5194/egusphere-2022-426-RC2 -
AC3: 'Reply on RC2', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC3-supplement.pdf
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AC3: 'Reply on RC2', Stefanie Ypma, 30 Aug 2022
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RC3: 'Comment on egusphere-2022-426', Anonymous Referee #3, 04 Aug 2022
Not being a modeller myself, i have reviewed this article to the best of my abilities.
I think this paper is very well-written, clear and concise. The quality of the analysis, figures and data interpretation is very high and the scientific quality, rigour and significance seem to be excellent. I, therefore, congratulate the authors on such an excellent manuscript. Really, no issues were found during my review, and i think the approach and the results presented here, represent a very promising development in the field of plastic pollution. Besides, the real-world applications to the selection of the best clean-up locations are really concrete (although field validation of this modeling exercise is currently missing, but strongly envisaged).
Maybe, i just have a minor comment for the author's consideration:
Given that as you say at lines 293-294 "locations with a high Retention Rate (Figure 7a), SSIsink (Figure 7d), betweenness centrality (Figure 7h) and PRin centrality (Figure 7i) are the most effective cleanup locations", why not combining all these 4 centralities in a single index to further restrict potential the list of potential clean-up locations to the "very" best ones? Assuming that clean-up capabilities of the Galapagos local municipality are restricted, this could be an effective way of further restricting/prioritising the best locations? (i.e. the ones having the best ranks in all 4 centralities). This index, could be potentially also added as a last panel to Fig. 7.Citation: https://doi.org/10.5194/egusphere-2022-426-RC3 -
AC4: 'Reply on RC3', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC4-supplement.pdf
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AC4: 'Reply on RC3', Stefanie Ypma, 30 Aug 2022
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2022-426', Noam Vogt-Vincent, 16 Jun 2022
Firstly, I think this is a great manuscript and I enjoyed reading it – a novel idea with potentially very useful results for those involved in marine debris management efforts on Galapagos (and other remote islands if this methodology were implemented elsewhere). The effectiveness of the clean-up strategies proposed in this manuscript will depend on the veracity of the assumptions a significant proportion of debris undergoes resuspension, but this is clearly stated in the conclusion. So overall, I think this manuscript will be valuable for island managers, and researchers working in this field. I do have some technical questions/requests for clarification though:
- I wonder whether you carried out a sensitivity analysis to test whether you released sufficient particles? With such high resolution hydrodynamical data forcing your particle-tracking, significant dispersion can occur over a 60-day integration time with an original separation of <4km (e.g. the off-diagonal cells in the transition matrix are ‘noisy’, and this is probably why). This in itself is not an issue since practitioners will probably not be using your transition matrix, but it would be nice to know how robust, say, Figure 7 is to particle number. I’m guessing that you were limited to 700k particles due to storage (since you were saving particle positions with a very high output frequency) but, if it is tractable, a quick sensitivity test might provide some assurance.
- I’m struggling to completely follow section 2.5. If I understand correctly, the equation on line 162 models the distribution of debris on coastlines in the limit of 100% resuspension. You simulate clean-up as removing the outgoing nodes of a clean-up target cell. You then say that “particles will accumulate at the target cleanup node”, but how can particles accumulate if you’re constantly removing them?
- It’s also not clear to me why you based your definition of steady state on the number of particles in the ocean – does the vector vtnot reach steady state?
- I’m also finding the “benefit” metric quite difficult to follow. “Zero connectivity between different nodes”, to me, implies that 100% of resuspended debris enters the ocean, but I don’t think this is what you meant?
- I’m not 100% convinced by your sensitivity tests to the initial macroplastic distribution (3.3). You’ve tested how robust your method is to uncertainty in the initial distribution by using a completely random initial distribution, i.e. assuming that the mass of plastic on beaches is completely decorrelated across length scales L > 4km. Is this realistic? Your result that the efficacy of node rankings remains the same with a random initial distribution is not surprising to me, since mesoscale ocean structures are much larger than 4km so will on average still see a ‘uniform’ distribution of resuspended debris. But given that van Sebille et al. (2019) showed that most debris incident to Galapagos arrives from the East, and that there could be large-scale effects from wind shadows, wake eddies, etc., I’d have expected that there would be some large-scale structure in the distribution of debris. I wonder if a more realistic way to model an uncertain initial macroplastic distribution might be by generating perlin noise with a wavelength larger than 4km (e.g. maybe the length scale of an island).
- This is not a criticism, but I wonder to what extent using a time-mean transition matrix affects your results. For instance, if we have sites A, B and C each releasing 1 unit of debris per time-step with probability P(A→B)=1/6 and P(B→C)=1/6, the probability of transition P(A→C) is 1/36 (over two timesteps). After 12 time-steps, 1/3 units would have been transported from A→C. But if these transition probabilities were time-varying and turned out to be P(A→B)= P(B→C)=1 during time-steps 1-2 and 0 otherwise, 1 unit would have been transported A→C – 3x more than the time-mean case, even though P(A→B)= P(B→C)=1/6 in both cases when averaged over 12 time-steps. This is obviously an artificially bad case and I completely accept that this goes beyond the scope of your study, but I didn’t see a mention of this in your discussion of limitations so I was wondering whether you thought this was a limitation (or if there is evidence that this time variability in the transition matrix is not important).
Citation: https://doi.org/10.5194/egusphere-2022-426-CC1 -
AC1: 'Reply on CC1', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC1-supplement.pdf
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RC1: 'Comment on egusphere-2022-426', Anonymous Referee #1, 01 Jul 2022
Review for : Detecting most effective cleanup locations using network theory to reduce marine plastic debris: A case study in the Galapagos Marine Reserve
The present work proposes a new methodology for improving beach clean up, to reduce marine plastic debris. The case study is dedicated to the Galapagos islands and the methodology is using a network to select the optimum criteria for the beach clean up purposes.
The manuscript is of very good quality, well organized with no specific structure problem or methodological problem. Although quite dense for the part explaining the criteria, it is possible for non specialist to understand the goal of the selected criteria.
In summary, The overall quality of this work is up to the expected standards and to my point of view can be published as it is ( after some typo and minor possible mistakes). I emphasize the fact that such decision (to my experience of reviewer) is very rare. I therefore thank the authors for having so well prepared their work before submission.
However, in order to improve the manuscript, or give ideas for new works, I wish to share some questions or comments :
- a very basic but tricky one to start with : could the authors imagine what the results could be with an irregular grid with a strong refinement close to the coast, or with a high resolution regular grid of , let say, 300 m in resolution on the horizontal ? I reckon that this point could be discussed using some literature. My main concern is that , the present study does not prove that the results are not strongly dependent on the OGCM resolution used. If it were, the overall methodology might provide different scores in terms of criteria ?
- the beaching probability: despite the fact that this has been already published , I am still very skeptical about the tuning of this probability. I am pretty sure that replacing this rather “adamant” formula by a probability calculated on the average wind direction versus the coastline layout in the area would be more realistic...Of course, I understand that there has to be a beaching time-scale of some sort to compensate the lack of grid resolution, but just days, regardless of general weather conditions in the area is strange to me. This is of course just my point of view, but this is what we can see when deploying drifters in general. Of course, this is complicated as the results can be different when looking at different scales, from large to very local scale (at the scale of a beach for example), but for a statistical approach, using regional winds might be ok.
- A remark concerning the overall network method: according to some literature, connectivity and oceanic distance can be calculated out of the Lagrangian model “quite” simply, I mean without using a complex machinery. Therefore, it could have been interesting to show some comparisons with some basic diagnostics (overall stranding rate / source identification / oceanographic distance) , to better prove the real added value of this complex work presented here. In other words, one might think that rather simple diagnostics could deliver nearly the same message or present similar general results , with a computational cost or calculation cost quite reduced. I understand that the authors might reply that their diagnostics are way refined and accurate, which is probably true, but the fact remains that, in the end, when it comes to mobilizing a cleaning team to go on site, maybe simple diagnostics can already deliver the necessary information for the management? (ok I am teasing a bit here, but never underestimate the robustness of simplicity ..!)
- Concerning the seeding of the particles, the random seeding is one interesting case , but when targeting real cases, I am quite surprised that the authors have not tried to perform test cases for which the seeding was increased after heavy rains. If so, they would have put themselves in the position of delivering a real connectivity between islands with identified real sources, i.e. the beaches receiving inland waste through rivers or waste management pipes just after the rain events ?
- In addition, I think that, as the final goal is to deliver advice for where and when to go for beach cleaning, one missing part can be the part looking at the correlation between the weather conditions and the stranding or accumulation close to the shore. This is a temporal advice that can be more efficient than the cleaning frequency advice that can be deduced from the present work. Indeed, as mentioned in their introduction, waste distribution is highly heterogeneous, and one cause is the high variability of regional to local weather conditions. Therefore, one could think that the general cleaning plan could be quite different if the weather conditions variations were selected as one of the key parameter.
Citation: https://doi.org/10.5194/egusphere-2022-426-RC1 -
AC2: 'Reply on RC1', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Stefanie Ypma, 30 Aug 2022
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RC2: 'Comment on egusphere-2022-426', Anonymous Referee #2, 19 Jul 2022
This manuscript presents an interesting statistical analysis of the connectivity patterns in the Galapagos to infer the coastal locations with a highest probability of accumulating marine macroplastics. In particular, the authors combine concepts from the graph theory, I.e, centrality derived metrics, and a hydrodynamical model to provide a map with the coastlines of the Galapagos Marine Reserve classified according to the optimization in removing marine litter.
Some of the findings are: provide a methodology to cleanup strategy that can be applied even when there are not data about the distribution of macroplastics; among the centralities metrics, the retention rate provide the most useful information to localize regions for cleanup; it is more effective in terms of removing macroplastic to clean at sink points (coastlines with large positive Source-Sink index values: high SSI_sink values) than at source locations.
In general, the authors present a interesting work aimed at improving the Lagrangian identification of coastal locations with high probability to find macriplastic advected by the ocean flow. The strategy adopted by the authors, i.e. the use of outputs of a hydrodynamical model and the methodology employed through the network theory is technically sound, turning out to be appropriate for the scope of this work. This is a good piece of work which could be of interest to some OS readers. However there are some weakness in the manuscript an a revision has to be addressed before publication. An improvement of the methodology description is strongly recommended before a new submission. Some aspects related to the methodology should be better supported and discussed more appropriately in the context of previous literature. The structure and organization of the Introduction section lack in clarity, where sentences are repeated. I think the paper could be considered for publication after major revision. The main issues that need to be clarified by the authors are listed bellow.
1. I am not sure if the size of macroplastics (>0.5 cm) allows the macroplastic particles to be considered as Lagrangian particles? I think that to compute Lagrangian particle trajectories one needs to assume that the particle has to instantaneously follow or to be totally constrained to the motion of the fluid flow. Even for particles with finite size, the Maxey-Riley approximation to resolve the equation of motion for inertial particles, assumes that particles are very small (small microplastics?).
2. One weakness of the manuscript is the description of the methodology. Some of the definitions are not clear. For example the definition of retention rate, the loss rate, etc. I think the authors can greatly improve the definition of these centrality metrics through mathematical expressions, i.e using equations. For example using the mathematical formalism based on connectivity probabilities between network nodes in the weighted network, starting from the definition of the transition matrix.
3. Have the authors considered that in temporal networks, as the analyzed here, also the synchronous arrivals at a node could impact on the network connectivity metrics? The “cumulated” implicit connectivity is based on adding up all the events of synchronous arrival (see Ser-Giacomi et al, 2021, PRE). However considering implicit connectivity could modify the resulting connectivity patterns
4. The resolution of the model is too coarse as to resolve submesoscale dynamics. Ignoring submesoscale motions is not a simple matter when it comes to surface material dispersion. It is well known that submesoscales are associated with vertical motion (an extreme case was documented via drifter measurements by D’Asaro et al., 2018 PNAS paper). The submesoscales cannot be removed from the analysis when their impact on the horizontal transport properties is substantial. They also generate high convergence zones, which could impact the connectivity probability between nodes. Please provide arguments to show that by dismissing small scale dynamics in the small region, the applicability of the results obtained here to the real ocean do not become very uncertain.
5. Numerical diffusion. The spatial and temporal resolution of the model (4km and one day) could originate large numerical diffusion in the computation of the Lagrangian particle trajectories. Note that assuming velocities of 0.6m/s we obtain that in one day (the temporal resolution) the particle could move around 50km, which is 12 times larger than the spatial resolution (~4km). This can be “fixed” by decreasing the time step in the Lagrangian integration scheme, but still some small scale dynamics is missing, and this could affect to the Lagrangian transport associated with the large scale structures.
6. One of the advantages of the methodology is that it can be used independently of whether there exists available macroplastic distribution data or not. However, a validation exercise could be beneficial, in particular to better support the conclusions.
7. In the introduction section, I found some long and complicated sentences that could be split . Lines 30-32. Lines 37-39.
8. The sentence in line 40: “[…] the connectivity can still, to a first order [...]” could be improved. The connectivity by its self does not inform about aggregation but rather some derived metrics, and under some assumptions. Please clarify it.
Citation: https://doi.org/10.5194/egusphere-2022-426-RC2 -
AC3: 'Reply on RC2', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Stefanie Ypma, 30 Aug 2022
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RC3: 'Comment on egusphere-2022-426', Anonymous Referee #3, 04 Aug 2022
Not being a modeller myself, i have reviewed this article to the best of my abilities.
I think this paper is very well-written, clear and concise. The quality of the analysis, figures and data interpretation is very high and the scientific quality, rigour and significance seem to be excellent. I, therefore, congratulate the authors on such an excellent manuscript. Really, no issues were found during my review, and i think the approach and the results presented here, represent a very promising development in the field of plastic pollution. Besides, the real-world applications to the selection of the best clean-up locations are really concrete (although field validation of this modeling exercise is currently missing, but strongly envisaged).
Maybe, i just have a minor comment for the author's consideration:
Given that as you say at lines 293-294 "locations with a high Retention Rate (Figure 7a), SSIsink (Figure 7d), betweenness centrality (Figure 7h) and PRin centrality (Figure 7i) are the most effective cleanup locations", why not combining all these 4 centralities in a single index to further restrict potential the list of potential clean-up locations to the "very" best ones? Assuming that clean-up capabilities of the Galapagos local municipality are restricted, this could be an effective way of further restricting/prioritising the best locations? (i.e. the ones having the best ranks in all 4 centralities). This index, could be potentially also added as a last panel to Fig. 7.Citation: https://doi.org/10.5194/egusphere-2022-426-RC3 -
AC4: 'Reply on RC3', Stefanie Ypma, 30 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-426/egusphere-2022-426-AC4-supplement.pdf
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AC4: 'Reply on RC3', Stefanie Ypma, 30 Aug 2022
Peer review completion
Journal article(s) based on this preprint
Data sets
Data and code for analysis and figures Stefanie L. Ypma, Q. Bohte, E. van Sebille https://github.com/OceanParcels/Galapagos_connectivity
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Quinten Bohte
Alexander Forryan
Alberto C. Naveira Garabato
Andy Donnelly
Erik van Sebille
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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