the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Mar 2021
ArcticBeach v1.0: A physics-based parameterization of pan-Arctic coastline erosion
Abstract. In the Arctic, air temperatures are warming and sea ice is declining, resulting in larger waves and a longer open water season, all of which intensify the thaw and erosion of ice-rich coasts. This change in climate has been shown to increase the rate of Arctic coastal erosion, causing problems for industrial, military, and civil infrastructure as well as changes in nearshore biogeochemistry. Numerical models that reproduce historical and project future Arctic erosion rates are necessary to understand how further climate change will affect these problems, and no such model yet exists to simulate the physics of erosion on a pan-Arctic scale. We have coupled a bathystrophic storm surge model to a simplified physical erosion model of a partially frozen cliff and beach. This Arctic erosion model, called ArcticBeach v1.0, is a first step toward a parameterization of Arctic shoreline erosion for larger-scale models, which are not able to resolve the fine spatial scale (up to about 40 m) needed to capture shoreline erosion rates from years to decades. It is forced by wind speeds and directions, wave period and height, sea surface temperature, all of which are masked during times of sea ice cover near the coastline. Model tuning requires observed historical retreat rates (at least one value), as well as rough nearshore bathymetry. These parameters are already available on a pan-Arctic scale. The model is validated at two study sites at Drew Point (DP), Alaska, and Mamontovy Khayata (MK), Siberia, which are respectively located in the Beaufort and Laptev Seas, on different sides of the Arctic Ocean. Simulated cumulative retreat rates for DP and MK respectively (169 and 170 m) over the time periods studied at each site (2007–2016, and 1995–2018) are found to be within the same order of magnitude as observed cumulative retreat rates (172 and 120 m). Given the large differences in geomorphology and weather systems between the two study sites, this study provides a proof-of-concept that ArcticBeach v1.0 can be applied on very different partially frozen coastlines. ArcticBeach v1.0 provides a promising starting point to project the retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations. Further, this model can provide estimates of the flux of sediment from land to sea for Arctic nearshore biogeochemical studies, while leaving an opportunity for further development of modelling the physics of a partially frozen shoreline.
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CC1: 'Rigorous Error Analysis Not Performed', Jennifer Frederick, 12 May 2021
Summary
This manuscript describes a model for Arctic coastal erosion that is based on a simplified physical erosion model of a partially frozen cliff and beach, coupled to a storm surge model. It is presented as a first step toward a parameterization of pan-Arctic shoreline erosion at a coarse spatial scale for capturing erosion rates on the order of years to decades. It uses physical data as boundary conditions, such as wind speeds and directions, wave period and height, and sea surface temperature, as well as accounting for sea ice cover. The authors claim the new model provides a promising starting point to project the retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations.
General Summary of Comments
I do not recommend publication in its current form. The model presented (ArcticBeachv1.0) is under-developed and the authors have not shown that this model has any predictive skill that outperforms a random number generator (proof described in detail in my review). For transparency, I have also included the Python script which performs this analysis at the end of the detailed review (attached). My suggestion to the authors is further development of the model and resubmission for publication at a later date and after further collaboration and consultation with peers in this research field. One benefit of the model presented is its low computational cost. If the low computational cost can be maintained while improving its ability to robustly predict coastal retreat rates, this would represent a ground-breaking advance in the field!
The results summarized in Figure 4 show the modeled annual and cumulative retreat at Mamontovy Khayata (MK) and Drew Point (DP) vs observations at each site. At first glance, the modeled retreat looks poor, but an error analysis was not provided to quantify model performance. For any predictive model, a thorough analysis of model predictive skill is required to evaluate its performance and ability to make reliable, robust predictions. One of the simplest routines is to test model predictions against a random prediction. If the model has good predictive skill, it should outperform a prediction generated at random within a plausible range of possible outcomes. This is essentially like posing the null hypothesis and showing that the model can disprove the null hypothesis. In this case, the null hypothesis states that, ‘ArcticBeachv1.0 cannot predict the annual erosion rate any better than a random number generator can.’ If the ArcticBeachv1.0 model can predict annual erosion rate statistically significantly better than a random number generator, then it can rightfully claim predictive skill. My concern here for both locations is that, while there are a few years where modeled erosion matched observed erosion fairly well, there are also many years in this time series where the erosion is far outside of the running average. In these years, a model with high predictive skill should be able to reproduce the trend, if it has captured the correct physics. However, the ArcticBeachv1.0 model predictions end up under- or over-estimating the retreat, in the OPPOSITE direction just as many times as they estimate the retreat in the CORRECT direction (above or below the mean retreat).
The conclusion from the analysis for predictive skill (described in full detail below) shows that the ArcticBeachv1.0 model has no predictive skill at the DP location, and has inverse predictive skill at the MK location. Based on the error analysis, I disagree with the authors, as stated in the abstract, that the ArcticBeachv1.0 model provides a promising starting point to project the retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations. The results of this analysis at both locations indicate that the model in its current form is under-developed, and cannot be relied upon to provide robust and skillful predictions for coastal retreat rates in the Arctic more than a randomly generated number can (in the case of the DP location) nor can be relied to provide a prediction in the correct trend direction (in the case of the MK location).
Detailed Analysis
Please see the attached document for the detailed analysis.
- AC1: 'Reply on CC1', Rebecca Rolph, 25 Jun 2021
-
RC1: 'Comment on gmd-2021-28', Anonymous Referee #1, 12 May 2021
- AC2: 'Reply on RC1', Rebecca Rolph, 25 Jun 2021
-
RC2: 'a novel approach', Anonymous Referee #2, 12 Jun 2021
General description of the paper
First of all, thank you for allowing me to review the paper. The paper was well written, the problem statement and the solutions are explained in detail. The writers developed a simplified model for large scale modelling despite limited available measurements of the parameters. The authors coupled basically three major numerical modules with different physical processes like cliff and beach erosion with storm surge interactively. The models, albeit simplified, are based on real-world physics. The authors used mainly water level to calibrate the model. The other inputs of the forcing parameters like wind speed, wind temperature and water temperatures were taken from global models.
======================================
Major comments
Technical issues
[a] Uniform statistical distribution is used for sensitivity analysis. In Table: 1, a range of the most influential parameters are provided. The range for each environmental parameter is quite broad. Justification to apply uniform distribution is under question. Did the authors try any other distributions with central tendency?
[b] The authors explained the effect and importance of the ‘offset water level’ as a proxy for some excluded physical process. Section 4.2.1 might be the place where it may be explained how water level offset indirectly compensates or estimates the notch erosion mechanism [authors did mention that the process is excluded in line 66 and also in Section#1 citing the notch erosion mechanism is not so common] Was equation#1 used to indirectly calculate notch erosion since the equation covers the portion of the cliff that is in contact with warmer seawater? This can be one explanation of why the model works despite excluding the block failure by the wave-created-notch mechanism.
[c] Assessment of how the model is performing should be determined. As a proof of concept, the model makes a strong argument. However, the accuracy of the validation is still warranted.
[d] A flow chart may be included in ‘Chapter#2: Methods’ to describe the methodology concisely. For example, it is not clear from the descriptions when and where the erosion process was ‘not simulated’ in the model. As understood, two binary switches (on/off) exist in the model: (1) the open water season in the time domain and (2) collapsed but not-yet-eroded sediments on the beach in the space domain.
Comments in general
Introduction
The introduction is well written. The requirement to establish a pan-Arctic level model is explained. The authors explained sufficiently the requirement of a simple physics-based model and the benefits of such a computationally inexpensive model.
Methods
The conceptual models are explained in this section. The major numerical modules are erosion module comprising cliff and beach erosion based on thermal energy transfer from water to the cliff via convection and a quasi-steady storm surge model based on wind speed. The conductive heat transfer and solar radiation are not included in the model. The authors did not provide the explanation of excluding the other two heat transfer mechanism but it is reasonable to assume, the solar radiation is indirectly included in the seawater temperature inputs, whereas the effect of the conduction is ‘felt’ as time-lag which can be ignored when modelled for a long duration.
The authors correctly identified the problem of determining absolute water level at the toe of the cliffs and provided the detailed methodology of circumventing the issue and reaching a reasonable solution. A small description of the statistical method of Monte Carlo is also provided which might be elongated.
Results
Results are discussed by comparing the outputs of the model with the observations. However, the estimation of the accuracy is not determined. One of the model outcome anomalies is the underestimate of the erosion from 2002 to 2009 is identified, but authors need to provide a strong explanation of the deviation.
Grammar and Comprehension
The script is admirably laid out. It is recommended to re-write very few sentences ( marked in the attached pdf)
===================================
Recommendation
The journal paper is recommended to publish with minor modifications. The work provides a novel approach to simulate coastal erosion. This is one of the early efforts to understand Arctic coastal erosion on a global level. The authors chose to use simplified models in favour of lower computation expenses and it is reasonable to exclude some physical processes. The novelty of the work is the coupling of the modules, calibration of the coupled model with water level and application of the model in two different sites.
- AC3: 'Reply on RC2', Rebecca Rolph, 25 Jun 2021
-
RC3: 'Comment on gmd-2021-28', Anatoly Sinitsyn, 30 Jun 2021
Anatoly Sinitsyn,
June 27 2021, Trondheim
Review of the article "ArcticBeach v1.0: A physics-based parametrization of pan-Arctic coastal erosion" by Rolph et al. (2021)
The article presents a model for estimation of coastal dynamics at permafrost, ice-rich coastlines. More specifically, erosion rates at coastal bluff and beach are handled by the model. The model utilizes 1-D coastline erosion model of Kobayashi et al. (1999), bathystrophic storm surge model of Freeman et al. (1957), and empirical equations of Kriebel and Dean (1985) for estimating cross-shore sediment transport. The model is forced by historic hydrometeorological data (wind speed and sea ice concentration), and initialized by existing bathymetry of the case study locations. The model is validated by observed water level data. Sensitivity of the modelled retreat rates is accessed with the Monte Carlo approach. Modelled retreat rates are compared with observed rates for evaluation of the model performance. It was found that the water level plays critical role in defining retreat rates. The results demonstrate that the model is capable to reproduce retreat rates withing the same order of magnitude as the observed retreat rates. This is promising result justifying the model performance, and possibilities of application for crude assessments of coastal dynamics in relevant coastal settings.
The model developed by the authors looks definitely useful for the field of arctic coastal dynamics, and shall be considered as a very good step forward.
I have several, largely suggestive comments, which are presented in the attached file. Intension of these comments is to clarify some points in the text of the article and make it more suitable for engineering community, who is not necessary dealing with permafrost coastlines on a daily basis.
Main point of my comments are the following:
- Despite the title, the model is aiming to handle some, but not all, of the morphologies comprising pan-Arctic coastlines, i.e. ice-rich coastal bluffs/coasts. This limitation could be mentioned in the text otherwise the article might provide to a reader a hope on a generic model applicable to all Arctic coastlines, or a vision that the Arctic coasts are all ice-rich.
- As continuation of the previous comment, it looks natural, if such modelling attempt would aim to model or refer to a well-described coastal process such as thermal abrasion or thermal denudation, and to model a core component of such processes. If fact model do model components of such processes. This would help to compare the model results with direct field observations. One may object that it is just a sense of usage of a certain terminology, as the article is efficiently deals with the processes called thermal abrasion and thermal denudation. Still, due to the aforementioned points, the article looks somewhat detached from the body of literature describing the processes on the Artic coastlines.
- In motivation for the article, the authors refer to the challenges ice-rich coastlines cause to the infrastructure. It is known from the practice, that it is normal to avoid ice-rich coasts when designing new infrastructural projects. Yet, sometimes handling such coastal type cannot be avoided. Hence, in general terms, relevance of models handling ice-rich sediments for the infrastructure developments might be somewhat limited. Yet, applicability of such models can take place in certain cases with relevant coastal conditions.
- As continuation of the previous comment, in my opinion, such model and its further development may consider the needs biogeochemistry on equal footing as the needs of infrastructure.
Sincerely yours,
Anatoly Sinitsyn
- AC4: 'Reply on RC3', Rebecca Rolph, 07 Jul 2021
Status: closed
-
CC1: 'Rigorous Error Analysis Not Performed', Jennifer Frederick, 12 May 2021
Summary
This manuscript describes a model for Arctic coastal erosion that is based on a simplified physical erosion model of a partially frozen cliff and beach, coupled to a storm surge model. It is presented as a first step toward a parameterization of pan-Arctic shoreline erosion at a coarse spatial scale for capturing erosion rates on the order of years to decades. It uses physical data as boundary conditions, such as wind speeds and directions, wave period and height, and sea surface temperature, as well as accounting for sea ice cover. The authors claim the new model provides a promising starting point to project the retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations.
General Summary of Comments
I do not recommend publication in its current form. The model presented (ArcticBeachv1.0) is under-developed and the authors have not shown that this model has any predictive skill that outperforms a random number generator (proof described in detail in my review). For transparency, I have also included the Python script which performs this analysis at the end of the detailed review (attached). My suggestion to the authors is further development of the model and resubmission for publication at a later date and after further collaboration and consultation with peers in this research field. One benefit of the model presented is its low computational cost. If the low computational cost can be maintained while improving its ability to robustly predict coastal retreat rates, this would represent a ground-breaking advance in the field!
The results summarized in Figure 4 show the modeled annual and cumulative retreat at Mamontovy Khayata (MK) and Drew Point (DP) vs observations at each site. At first glance, the modeled retreat looks poor, but an error analysis was not provided to quantify model performance. For any predictive model, a thorough analysis of model predictive skill is required to evaluate its performance and ability to make reliable, robust predictions. One of the simplest routines is to test model predictions against a random prediction. If the model has good predictive skill, it should outperform a prediction generated at random within a plausible range of possible outcomes. This is essentially like posing the null hypothesis and showing that the model can disprove the null hypothesis. In this case, the null hypothesis states that, ‘ArcticBeachv1.0 cannot predict the annual erosion rate any better than a random number generator can.’ If the ArcticBeachv1.0 model can predict annual erosion rate statistically significantly better than a random number generator, then it can rightfully claim predictive skill. My concern here for both locations is that, while there are a few years where modeled erosion matched observed erosion fairly well, there are also many years in this time series where the erosion is far outside of the running average. In these years, a model with high predictive skill should be able to reproduce the trend, if it has captured the correct physics. However, the ArcticBeachv1.0 model predictions end up under- or over-estimating the retreat, in the OPPOSITE direction just as many times as they estimate the retreat in the CORRECT direction (above or below the mean retreat).
The conclusion from the analysis for predictive skill (described in full detail below) shows that the ArcticBeachv1.0 model has no predictive skill at the DP location, and has inverse predictive skill at the MK location. Based on the error analysis, I disagree with the authors, as stated in the abstract, that the ArcticBeachv1.0 model provides a promising starting point to project the retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations. The results of this analysis at both locations indicate that the model in its current form is under-developed, and cannot be relied upon to provide robust and skillful predictions for coastal retreat rates in the Arctic more than a randomly generated number can (in the case of the DP location) nor can be relied to provide a prediction in the correct trend direction (in the case of the MK location).
Detailed Analysis
Please see the attached document for the detailed analysis.
- AC1: 'Reply on CC1', Rebecca Rolph, 25 Jun 2021
-
RC1: 'Comment on gmd-2021-28', Anonymous Referee #1, 12 May 2021
- AC2: 'Reply on RC1', Rebecca Rolph, 25 Jun 2021
-
RC2: 'a novel approach', Anonymous Referee #2, 12 Jun 2021
General description of the paper
First of all, thank you for allowing me to review the paper. The paper was well written, the problem statement and the solutions are explained in detail. The writers developed a simplified model for large scale modelling despite limited available measurements of the parameters. The authors coupled basically three major numerical modules with different physical processes like cliff and beach erosion with storm surge interactively. The models, albeit simplified, are based on real-world physics. The authors used mainly water level to calibrate the model. The other inputs of the forcing parameters like wind speed, wind temperature and water temperatures were taken from global models.
======================================
Major comments
Technical issues
[a] Uniform statistical distribution is used for sensitivity analysis. In Table: 1, a range of the most influential parameters are provided. The range for each environmental parameter is quite broad. Justification to apply uniform distribution is under question. Did the authors try any other distributions with central tendency?
[b] The authors explained the effect and importance of the ‘offset water level’ as a proxy for some excluded physical process. Section 4.2.1 might be the place where it may be explained how water level offset indirectly compensates or estimates the notch erosion mechanism [authors did mention that the process is excluded in line 66 and also in Section#1 citing the notch erosion mechanism is not so common] Was equation#1 used to indirectly calculate notch erosion since the equation covers the portion of the cliff that is in contact with warmer seawater? This can be one explanation of why the model works despite excluding the block failure by the wave-created-notch mechanism.
[c] Assessment of how the model is performing should be determined. As a proof of concept, the model makes a strong argument. However, the accuracy of the validation is still warranted.
[d] A flow chart may be included in ‘Chapter#2: Methods’ to describe the methodology concisely. For example, it is not clear from the descriptions when and where the erosion process was ‘not simulated’ in the model. As understood, two binary switches (on/off) exist in the model: (1) the open water season in the time domain and (2) collapsed but not-yet-eroded sediments on the beach in the space domain.
Comments in general
Introduction
The introduction is well written. The requirement to establish a pan-Arctic level model is explained. The authors explained sufficiently the requirement of a simple physics-based model and the benefits of such a computationally inexpensive model.
Methods
The conceptual models are explained in this section. The major numerical modules are erosion module comprising cliff and beach erosion based on thermal energy transfer from water to the cliff via convection and a quasi-steady storm surge model based on wind speed. The conductive heat transfer and solar radiation are not included in the model. The authors did not provide the explanation of excluding the other two heat transfer mechanism but it is reasonable to assume, the solar radiation is indirectly included in the seawater temperature inputs, whereas the effect of the conduction is ‘felt’ as time-lag which can be ignored when modelled for a long duration.
The authors correctly identified the problem of determining absolute water level at the toe of the cliffs and provided the detailed methodology of circumventing the issue and reaching a reasonable solution. A small description of the statistical method of Monte Carlo is also provided which might be elongated.
Results
Results are discussed by comparing the outputs of the model with the observations. However, the estimation of the accuracy is not determined. One of the model outcome anomalies is the underestimate of the erosion from 2002 to 2009 is identified, but authors need to provide a strong explanation of the deviation.
Grammar and Comprehension
The script is admirably laid out. It is recommended to re-write very few sentences ( marked in the attached pdf)
===================================
Recommendation
The journal paper is recommended to publish with minor modifications. The work provides a novel approach to simulate coastal erosion. This is one of the early efforts to understand Arctic coastal erosion on a global level. The authors chose to use simplified models in favour of lower computation expenses and it is reasonable to exclude some physical processes. The novelty of the work is the coupling of the modules, calibration of the coupled model with water level and application of the model in two different sites.
- AC3: 'Reply on RC2', Rebecca Rolph, 25 Jun 2021
-
RC3: 'Comment on gmd-2021-28', Anatoly Sinitsyn, 30 Jun 2021
Anatoly Sinitsyn,
June 27 2021, Trondheim
Review of the article "ArcticBeach v1.0: A physics-based parametrization of pan-Arctic coastal erosion" by Rolph et al. (2021)
The article presents a model for estimation of coastal dynamics at permafrost, ice-rich coastlines. More specifically, erosion rates at coastal bluff and beach are handled by the model. The model utilizes 1-D coastline erosion model of Kobayashi et al. (1999), bathystrophic storm surge model of Freeman et al. (1957), and empirical equations of Kriebel and Dean (1985) for estimating cross-shore sediment transport. The model is forced by historic hydrometeorological data (wind speed and sea ice concentration), and initialized by existing bathymetry of the case study locations. The model is validated by observed water level data. Sensitivity of the modelled retreat rates is accessed with the Monte Carlo approach. Modelled retreat rates are compared with observed rates for evaluation of the model performance. It was found that the water level plays critical role in defining retreat rates. The results demonstrate that the model is capable to reproduce retreat rates withing the same order of magnitude as the observed retreat rates. This is promising result justifying the model performance, and possibilities of application for crude assessments of coastal dynamics in relevant coastal settings.
The model developed by the authors looks definitely useful for the field of arctic coastal dynamics, and shall be considered as a very good step forward.
I have several, largely suggestive comments, which are presented in the attached file. Intension of these comments is to clarify some points in the text of the article and make it more suitable for engineering community, who is not necessary dealing with permafrost coastlines on a daily basis.
Main point of my comments are the following:
- Despite the title, the model is aiming to handle some, but not all, of the morphologies comprising pan-Arctic coastlines, i.e. ice-rich coastal bluffs/coasts. This limitation could be mentioned in the text otherwise the article might provide to a reader a hope on a generic model applicable to all Arctic coastlines, or a vision that the Arctic coasts are all ice-rich.
- As continuation of the previous comment, it looks natural, if such modelling attempt would aim to model or refer to a well-described coastal process such as thermal abrasion or thermal denudation, and to model a core component of such processes. If fact model do model components of such processes. This would help to compare the model results with direct field observations. One may object that it is just a sense of usage of a certain terminology, as the article is efficiently deals with the processes called thermal abrasion and thermal denudation. Still, due to the aforementioned points, the article looks somewhat detached from the body of literature describing the processes on the Artic coastlines.
- In motivation for the article, the authors refer to the challenges ice-rich coastlines cause to the infrastructure. It is known from the practice, that it is normal to avoid ice-rich coasts when designing new infrastructural projects. Yet, sometimes handling such coastal type cannot be avoided. Hence, in general terms, relevance of models handling ice-rich sediments for the infrastructure developments might be somewhat limited. Yet, applicability of such models can take place in certain cases with relevant coastal conditions.
- As continuation of the previous comment, in my opinion, such model and its further development may consider the needs biogeochemistry on equal footing as the needs of infrastructure.
Sincerely yours,
Anatoly Sinitsyn
- AC4: 'Reply on RC3', Rebecca Rolph, 07 Jul 2021
Model code and software
ArcticBeach v1.0 Rolph, Rebecca https://doi.org/10.5281/zenodo.4486817
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Cited
4 citations as recorded by crossref.
- High-resolution bathymetry models for the Lena Delta and Kolyma Gulf coastal zones M. Fuchs et al. 10.5194/essd-14-2279-2022
- Degrading permafrost river catchments and their impact on Arctic Ocean nearshore processes P. Mann et al. 10.1007/s13280-021-01666-z
- A Process-Based Model for Arctic Coastal Erosion Driven by Thermodenudation and Thermoabrasion Combined and including Nearshore Morphodynamics M. Islam & R. Lubbad 10.3390/jmse10111602
- Increase in Arctic coastal erosion and its sensitivity to warming in the twenty-first century D. Nielsen et al. 10.1038/s41558-022-01281-0