Mangrove ecosystem properties regulate high water levels in a river delta

Pelckmans, Ignace; Belliard, Jean-Philippe; Dominguez-Granda, Luis E.; Slobbe, Cornelis; Temmerman, Stijn; Gourgue, Olivier

doi:https://doi.org/10.5194/egusphere-2023-428

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https://doi.org/10.5194/egusphere-2023-428

Preprints

22 May 2023

| 22 May 2023

Mangrove ecosystem properties regulate high water levels in a river delta

Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue

Abstract. Intertidal wetlands, such as mangroves in the tropics, are increasingly recognized for their role in nature-based mitigation of coastal flood risks. Yet it is still poorly understood how effective they are in attenuating the propagation of extreme sea levels through large (order of 100 km²) estuarine or deltaic systems, with complex geometry formed by networks of branching channels intertwined with mangrove and intertidal flat areas. Here, we present a delta-scale hydrodynamic modelling study, aiming to explicitly account for these complex landforms, for the case of the Guayas delta (Ecuador), the largest estuarine system at the Pacific coast of Latin America. Despite coping with data scarcity, our model accurately reproduces the observed propagation of high water levels during a spring tide. Further, based on a model sensitivity analysis, we show that high water levels are most sensitive to the mangrove platform elevation and degree of channelization, but to a much lesser extent to vegetation-induced friction. Mangroves with a lower surface elevation, lower vegetation density and higher degree of channelization all favour a more efficient flooding of the mangroves and therefore more effectively attenuate the high water levels in the deltaic channels. Our findings indicate that vast areas of channelized mangrove forests, rather than densely vegetated forests, are most effective for nature-based flood risk mitigation in a river delta.

Received: 08 Mar 2023 – Discussion started: 22 May 2023

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Journal article(s) based on this preprint

28 Sep 2023

Mangrove ecosystem properties regulate high water levels in a river delta

Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue

Nat. Hazards Earth Syst. Sci., 23, 3169–3183, https://doi.org/10.5194/nhess-23-3169-2023,https://doi.org/10.5194/nhess-23-3169-2023, 2023

Short summary

Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-428', Anonymous Referee #1, 20 Jun 2023

Sentinel-2 is known for providing fine spatial resolution multispectral images. However, the paper fails to address the spatial resolution discrepancy between the green and SWIR bands of Sentinel-2, which are 10 m and 20 m, respectively. This raises an important issue when calculating the Modified Normalized Difference Water Index (MNDWI) from Sentinel-2 images. Two options are available: (1) Upscaling the 10-m green band to 20 m and calculating MNDWI at 20-m resolution, which is simpler but sacrifices detailed information from the 10-m resolution. (2) Downscaling the 20-m SWIR band to 10 m using a pan-sharpening technique and calculating MNDWI at 10-m resolution, which is more complex but preserves the 10-m information and yields a finer result. However, neither of these methods is discussed or mentioned in the paper. It is essential to address this issue and provide a rationale for the chosen approach in calculating MNDWI from Sentinel-2 images.

Citation: https://doi.org/10.5194/egusphere-2023-428-RC1
- AC1: 'Comment on egusphere-2023-428', Ignace Pelckmans, 11 Jul 2023
  
  This is indeed not described in the manuscript or supplementary materials, but we agree that it is an important step in the procedure which should be described. In the methods section of the main manuscript our goal is to give an overview of the various techniques we used to overcome the data scarcity challenge. For the technical explanation of these methods, we refer to the supplementary materials. Therefore, we suggest adding to Supplementary section 3:
  
  The spatial resolution of the Green and SWIR bands of Sentinel 2 imagery have a spatial resolution of 10 and 20 m respectively. When calculating MNDWI, the resolution of the Green band was scaled to 20 m using an average-based aggregator. Consequently, the eventual spatial resolution of MNDWI was 20 m. As > 99 % of the mesh elements within the intertidal flats have a resolution coarser than 20 m, generating the intertidal topography at a spatial resolution of 20 m was considered sufficient.
  
  Citation: https://doi.org/10.5194/egusphere-2023-428-AC1
RC2:
'Comment on egusphere-2023-428', Anonymous Referee #2, 30 Jun 2023

Overall the quality of the manuscript is acceptable, and I would accept it with some minor revisions.
1. Please justify the choice: "We defined the mesh resolution as a function of the channel width" (L146). Citation required.
2. Cite the similar practice for " L149 we defined the mesh resolution based on the distance to the channel edge to ensure a smooth transition in mesh resolution from the channel into the mangroves."
3. L289 NSE (ME) 0.60 ± 0.32 is given; what was typically accepted range in the similar study or model perforomnce accepted for decision making .
4. How this study can be implemented to other data scarce costal regions of the world, a little discussion will be worthwhile.

Citation: https://doi.org/10.5194/egusphere-2023-428-RC2
- AC2: 'Reply on RC2', Ignace Pelckmans, 11 Jul 2023
  
  Comment 1:
  Please justify the choice: "We defined the mesh resolution as a function of the channel width" (L146). Citation required.
  We defined the narrower the channel, the finer the mesh resolution. This was to guarantee a minimum of 4 nodes per channel cross-section to ensure good connectivity within the channels. This means that for a 20 m wide channel, mesh resolution is as small as 5 m. For much wider channels (100s of meters) there is no need for such a fine resolution which would make the model computationally heavier. Hence, by expressing the mesh resolution in function of channel width we can guarantee channel connectivity without dramatically increasing the computation requirements. A similar approach is presented by Deb et al. 2022.
  In the main manuscript, we propose to adapt the statement on mesh resolution in function of mesh resolution (L146): Inside the channels dissecting the delta, we defined the mesh resolution as a function of the channel width, with a minimum of 4 nodes per channel cross-section to guarantee channel connectivity (Deb et al. 2022). The resulting mesh resolution ranges between 3 m and 100 m (Fig. 2).
  
  Comment 2:
  Cite the similar practice for " L149 we defined the mesh resolution based on the distance to the channel edge to ensure a smooth transition in mesh resolution from the channel into the mangroves."
  Similar to comment 1, we enforced this rule to guarantee a smooth transition between the channels with fine mesh resolution to the mangrove platform where mesh resolution is coarser. A similar approach is presented by Deb et al. 2022.
  In the main manuscript, we propose to add that we followed a similar practice as in Deb et al. 2022 (L149).
  
  Comment 3:
  L289 NSE (ME) 0.60 ± 0.32 is given; what was typically accepted range in the similar study or model perforomnce accepted for decision making.
  We acknowledge that we should refer to commonly accepted ranges of NSE model efficiency. In section 2.7, we already describe that ME > 0.65 are considered excellent (Allen et al. 2007) but we will extend this by referring to (Moriasi et al. 2007; Gori et al., 2020) who considered ME > 0.5 as acceptable. We propose to also refer to these references at the end of section 3.1 (L289).
  
  Comment 4:
  How this study can be implemented to other data scarce costal regions of the world, a little discussion will be worthwhile.
  I suggest adding this paragraph to section 4.4:
  28 million people living in developing or least developed economies are prone to coastal flooding due to tropical storms only (Edmonds et al. 2020). In addition, long-term climatic fluctuations such as ENSO can also lead to extreme sea level events and related coastal hazards, especially in developing countries in the tropics (Belliard et al. 2021; Reguero et al. 2015). Nevertheless, the majority of efforts in modelling high water levels in deltas and estuaries are concentrated in temperate regions (e.g. Stark et al. 2016; Smolders et al. 2015; Lawler et al. 2016, Sheng et al. 2021; Harrison et al. 2021) or in developed tropical countries (e.g. Zhang et al. 2012, Li et al. 2012, Liu et al. 2013; Dominicis et al. 2023) where abundant field data are available. Here, we demonstrate how freely available data can contribute to fill this gap in geographical coverage toward data-scarce regions. Delineating the channel- and mangrove extent and mapping unvegetated intertidal topography is based on freely- and globally available Sentinel 2 data, and scarcely spread bathymetric observations can be partly compensated by interpolating along channel coordinates. Nevertheless, there is still a need for vertically referenced water level observations (such as by tide gauges) to apply the waterline method and calibrate and validate simulated water levels. However, future developments in remote sensing such as the recent launch of the SWOT mission by NASA (Biancamaria et al. 2016) may contribute to globally mapping water surface elevations (and water surface slopes). Future research, supported by the presented methodology in this paper together with current and future availability of free global remote sensing data, should cover a wide variety of river deltas to further develop the potential of conserving wetlands as a nature-based solution for coastal flooding in river deltas.
  
  References
  Biancamaria, S., Lettenmaier D. P., and Pavelsky, T. M.: The SWOT mission and its capabilities for land hydrology, 117-147, 2016.
  Deb, M., Abdolali, A., Kirby, J. T., and Shi, F. : Hydrodynamic modeling of a complex salt marsh system: Importance of channel shoreline and bathymetric resolution. Coastal Engineering, 173, https://doi.org/10.1016/J.COASTALENG.2022.104094, 2022.
  Edmonds, D. A., Caldwell, R. L., Brondizio, E. S., and Siani, S. M. O.: Coastal flooding will disproportionately impact people on river deltas, Nat Commun, 11, 4741, https://doi.org/10.1038/s41467-020-18531-4, 2020.Harrison, L. M., Coulthard, T. J., Robins, P. E., and Lewis, M. J.: Sensitivity of Estuaries to Compound Flooding, Estuaries Coasts, 45, 1250–1269, https://doi.org/10.1007/s12237-021-00996-1, 2022.
  Lawler, S., Haddad, J., and Ferreira, C. M.: Sensitivity considerations and the impact of spatial scaling for storm surge modeling in wetlands of the Mid-Atlantic region, Ocean Coast. Manag., 134, 226–238, https://doi.org/10.1016/j.ocecoaman.2016.10.008, 2016.
  Reguero, B. G., Losada, I. J., Díaz-Simal, P., Méndez, F. J., and Beck, M. W.: Effects of Climate Change on Exposure to Coastal Flooding in Latin America and the Caribbean, Plos One, 10, e0133409, https://doi.org/10.1371/journal.pone.0133409, 2015.
  Sheng, Y. P., Rivera-Nieves, A. A., Zou, R., and Paramygin, V. A.: Role of wetlands in reducing structural loss is highly dependent on characteristics of storms and local wetland and structure conditions, Sci. Rep., 11, 5237, https://doi.org/10.1038/s41598-021-84701-z, 2021.
  
  Citation: https://doi.org/10.5194/egusphere-2023-428-AC2
AC1: 'Comment on egusphere-2023-428', Ignace Pelckmans, 11 Jul 2023

This is indeed not described in the manuscript or supplementary materials, but we agree that it is an important step in the procedure which should be described. In the methods section of the main manuscript our goal is to give an overview of the various techniques we used to overcome the data scarcity challenge. For the technical explanation of these methods, we refer to the supplementary materials. Therefore, we suggest adding to Supplementary section 3:

The spatial resolution of the Green and SWIR bands of Sentinel 2 imagery have a spatial resolution of 10 and 20 m respectively. When calculating MNDWI, the resolution of the Green band was scaled to 20 m using an average-based aggregator. Consequently, the eventual spatial resolution of MNDWI was 20 m. As > 99 % of the mesh elements within the intertidal flats have a resolution coarser than 20 m, generating the intertidal topography at a spatial resolution of 20 m was considered sufficient.

Citation: https://doi.org/10.5194/egusphere-2023-428-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-428', Anonymous Referee #1, 20 Jun 2023

Sentinel-2 is known for providing fine spatial resolution multispectral images. However, the paper fails to address the spatial resolution discrepancy between the green and SWIR bands of Sentinel-2, which are 10 m and 20 m, respectively. This raises an important issue when calculating the Modified Normalized Difference Water Index (MNDWI) from Sentinel-2 images. Two options are available: (1) Upscaling the 10-m green band to 20 m and calculating MNDWI at 20-m resolution, which is simpler but sacrifices detailed information from the 10-m resolution. (2) Downscaling the 20-m SWIR band to 10 m using a pan-sharpening technique and calculating MNDWI at 10-m resolution, which is more complex but preserves the 10-m information and yields a finer result. However, neither of these methods is discussed or mentioned in the paper. It is essential to address this issue and provide a rationale for the chosen approach in calculating MNDWI from Sentinel-2 images.

Citation: https://doi.org/10.5194/egusphere-2023-428-RC1
- AC1: 'Comment on egusphere-2023-428', Ignace Pelckmans, 11 Jul 2023
  
  This is indeed not described in the manuscript or supplementary materials, but we agree that it is an important step in the procedure which should be described. In the methods section of the main manuscript our goal is to give an overview of the various techniques we used to overcome the data scarcity challenge. For the technical explanation of these methods, we refer to the supplementary materials. Therefore, we suggest adding to Supplementary section 3:
  
  The spatial resolution of the Green and SWIR bands of Sentinel 2 imagery have a spatial resolution of 10 and 20 m respectively. When calculating MNDWI, the resolution of the Green band was scaled to 20 m using an average-based aggregator. Consequently, the eventual spatial resolution of MNDWI was 20 m. As > 99 % of the mesh elements within the intertidal flats have a resolution coarser than 20 m, generating the intertidal topography at a spatial resolution of 20 m was considered sufficient.
  
  Citation: https://doi.org/10.5194/egusphere-2023-428-AC1
RC2:
'Comment on egusphere-2023-428', Anonymous Referee #2, 30 Jun 2023

Overall the quality of the manuscript is acceptable, and I would accept it with some minor revisions.
1. Please justify the choice: "We defined the mesh resolution as a function of the channel width" (L146). Citation required.
2. Cite the similar practice for " L149 we defined the mesh resolution based on the distance to the channel edge to ensure a smooth transition in mesh resolution from the channel into the mangroves."
3. L289 NSE (ME) 0.60 ± 0.32 is given; what was typically accepted range in the similar study or model perforomnce accepted for decision making .
4. How this study can be implemented to other data scarce costal regions of the world, a little discussion will be worthwhile.

Citation: https://doi.org/10.5194/egusphere-2023-428-RC2
- AC2: 'Reply on RC2', Ignace Pelckmans, 11 Jul 2023
  
  Comment 1:
  Please justify the choice: "We defined the mesh resolution as a function of the channel width" (L146). Citation required.
  We defined the narrower the channel, the finer the mesh resolution. This was to guarantee a minimum of 4 nodes per channel cross-section to ensure good connectivity within the channels. This means that for a 20 m wide channel, mesh resolution is as small as 5 m. For much wider channels (100s of meters) there is no need for such a fine resolution which would make the model computationally heavier. Hence, by expressing the mesh resolution in function of channel width we can guarantee channel connectivity without dramatically increasing the computation requirements. A similar approach is presented by Deb et al. 2022.
  In the main manuscript, we propose to adapt the statement on mesh resolution in function of mesh resolution (L146): Inside the channels dissecting the delta, we defined the mesh resolution as a function of the channel width, with a minimum of 4 nodes per channel cross-section to guarantee channel connectivity (Deb et al. 2022). The resulting mesh resolution ranges between 3 m and 100 m (Fig. 2).
  
  Comment 2:
  Cite the similar practice for " L149 we defined the mesh resolution based on the distance to the channel edge to ensure a smooth transition in mesh resolution from the channel into the mangroves."
  Similar to comment 1, we enforced this rule to guarantee a smooth transition between the channels with fine mesh resolution to the mangrove platform where mesh resolution is coarser. A similar approach is presented by Deb et al. 2022.
  In the main manuscript, we propose to add that we followed a similar practice as in Deb et al. 2022 (L149).
  
  Comment 3:
  L289 NSE (ME) 0.60 ± 0.32 is given; what was typically accepted range in the similar study or model perforomnce accepted for decision making.
  We acknowledge that we should refer to commonly accepted ranges of NSE model efficiency. In section 2.7, we already describe that ME > 0.65 are considered excellent (Allen et al. 2007) but we will extend this by referring to (Moriasi et al. 2007; Gori et al., 2020) who considered ME > 0.5 as acceptable. We propose to also refer to these references at the end of section 3.1 (L289).
  
  Comment 4:
  How this study can be implemented to other data scarce costal regions of the world, a little discussion will be worthwhile.
  I suggest adding this paragraph to section 4.4:
  28 million people living in developing or least developed economies are prone to coastal flooding due to tropical storms only (Edmonds et al. 2020). In addition, long-term climatic fluctuations such as ENSO can also lead to extreme sea level events and related coastal hazards, especially in developing countries in the tropics (Belliard et al. 2021; Reguero et al. 2015). Nevertheless, the majority of efforts in modelling high water levels in deltas and estuaries are concentrated in temperate regions (e.g. Stark et al. 2016; Smolders et al. 2015; Lawler et al. 2016, Sheng et al. 2021; Harrison et al. 2021) or in developed tropical countries (e.g. Zhang et al. 2012, Li et al. 2012, Liu et al. 2013; Dominicis et al. 2023) where abundant field data are available. Here, we demonstrate how freely available data can contribute to fill this gap in geographical coverage toward data-scarce regions. Delineating the channel- and mangrove extent and mapping unvegetated intertidal topography is based on freely- and globally available Sentinel 2 data, and scarcely spread bathymetric observations can be partly compensated by interpolating along channel coordinates. Nevertheless, there is still a need for vertically referenced water level observations (such as by tide gauges) to apply the waterline method and calibrate and validate simulated water levels. However, future developments in remote sensing such as the recent launch of the SWOT mission by NASA (Biancamaria et al. 2016) may contribute to globally mapping water surface elevations (and water surface slopes). Future research, supported by the presented methodology in this paper together with current and future availability of free global remote sensing data, should cover a wide variety of river deltas to further develop the potential of conserving wetlands as a nature-based solution for coastal flooding in river deltas.
  
  References
  Biancamaria, S., Lettenmaier D. P., and Pavelsky, T. M.: The SWOT mission and its capabilities for land hydrology, 117-147, 2016.
  Deb, M., Abdolali, A., Kirby, J. T., and Shi, F. : Hydrodynamic modeling of a complex salt marsh system: Importance of channel shoreline and bathymetric resolution. Coastal Engineering, 173, https://doi.org/10.1016/J.COASTALENG.2022.104094, 2022.
  Edmonds, D. A., Caldwell, R. L., Brondizio, E. S., and Siani, S. M. O.: Coastal flooding will disproportionately impact people on river deltas, Nat Commun, 11, 4741, https://doi.org/10.1038/s41467-020-18531-4, 2020.Harrison, L. M., Coulthard, T. J., Robins, P. E., and Lewis, M. J.: Sensitivity of Estuaries to Compound Flooding, Estuaries Coasts, 45, 1250–1269, https://doi.org/10.1007/s12237-021-00996-1, 2022.
  Lawler, S., Haddad, J., and Ferreira, C. M.: Sensitivity considerations and the impact of spatial scaling for storm surge modeling in wetlands of the Mid-Atlantic region, Ocean Coast. Manag., 134, 226–238, https://doi.org/10.1016/j.ocecoaman.2016.10.008, 2016.
  Reguero, B. G., Losada, I. J., Díaz-Simal, P., Méndez, F. J., and Beck, M. W.: Effects of Climate Change on Exposure to Coastal Flooding in Latin America and the Caribbean, Plos One, 10, e0133409, https://doi.org/10.1371/journal.pone.0133409, 2015.
  Sheng, Y. P., Rivera-Nieves, A. A., Zou, R., and Paramygin, V. A.: Role of wetlands in reducing structural loss is highly dependent on characteristics of storms and local wetland and structure conditions, Sci. Rep., 11, 5237, https://doi.org/10.1038/s41598-021-84701-z, 2021.
  
  Citation: https://doi.org/10.5194/egusphere-2023-428-AC2
AC1: 'Comment on egusphere-2023-428', Ignace Pelckmans, 11 Jul 2023

This is indeed not described in the manuscript or supplementary materials, but we agree that it is an important step in the procedure which should be described. In the methods section of the main manuscript our goal is to give an overview of the various techniques we used to overcome the data scarcity challenge. For the technical explanation of these methods, we refer to the supplementary materials. Therefore, we suggest adding to Supplementary section 3:

The spatial resolution of the Green and SWIR bands of Sentinel 2 imagery have a spatial resolution of 10 and 20 m respectively. When calculating MNDWI, the resolution of the Green band was scaled to 20 m using an average-based aggregator. Consequently, the eventual spatial resolution of MNDWI was 20 m. As > 99 % of the mesh elements within the intertidal flats have a resolution coarser than 20 m, generating the intertidal topography at a spatial resolution of 20 m was considered sufficient.

Citation: https://doi.org/10.5194/egusphere-2023-428-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (15 Jul 2023) by Dhruvesh Patel

AR by Ignace Pelckmans on behalf of the Authors (17 Jul 2023) Author's response

EF by Polina Shvedko (18 Jul 2023) Manuscript Author's tracked changes

ED: Referee Nomination & Report Request started (18 Jul 2023) by Dhruvesh Patel

RR by Anonymous Referee #2 (24 Jul 2023)

RR by Anonymous Referee #3 (03 Aug 2023)

ED: Publish as is (16 Aug 2023) by Dhruvesh Patel

ED: Publish as is (24 Aug 2023) by Daniela Molinari (Executive editor)

AR by Ignace Pelckmans on behalf of the Authors (24 Aug 2023)

Journal article(s) based on this preprint

28 Sep 2023

Mangrove ecosystem properties regulate high water levels in a river delta

Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue

Nat. Hazards Earth Syst. Sci., 23, 3169–3183, https://doi.org/10.5194/nhess-23-3169-2023,https://doi.org/10.5194/nhess-23-3169-2023, 2023

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Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue

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Mangroves are increasingly recognised as a coastal protection against extreme sea levels. Their effectiveness in doing so, however, is still poorly understood as mangroves are typically located in tropical countries were data on mangrove vegetation and topography properties are typically scarce. Through a modelling study we identified the degree of channelisation and mangrove forest floor topography as the key properties in regulating high water levels in a tropical delta.


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Mangrove ecosystem properties regulate high water levels in a river delta

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