Articles | Volume 19, issue 10
https://doi.org/10.5194/bg-19-2741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-2741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 01 Jun 2022
Modeling interactions between tides, storm surges, and river discharges in the Kapuas River delta
Joko Sampurno
CORRESPONDING AUTHOR
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Department of Physics, Fakultas MIPA, Universitas Tanjungpura, Pontianak, 78124, Indonesia
Valentin Vallaeys
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Randy Ardianto
Pontianak Maritime Meteorological Station, Pontianak, 78111, Indonesia
Emmanuel Hanert
Earth and Life Institute (ELI), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Institute of Mechanics, Materials and Civil Engineering (iMMC), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
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Cited articles
Badan Informasi Geospasial: Batimetri Nasional,
https://tanahair.indonesia.go.id/demnas/#/batnas (last access: 14 July 2021), 2018a.
Badan Informasi Geospasial: Seamless Digital Elevation Model Nasional (DEMNAS),
https://tanahair.indonesia.go.id/demnas/#/ (last access: 1 January 2022), 2018b.
Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., Mentaschi, L., and Feyen, L.:
More meteorological events that drive compound coastal flooding are projected under climate change, Commun. Earth Environ., 11, 1–11, https://doi.org/10.1038/s43247-020-00044-z, 2020.
Bilskie, M. V. and Hagen, S. C.:
Defining Flood Zone Transitions in Low-Gradient Coastal Regions, Geophys. Res. Lett., 45, 2761–2770, https://doi.org/10.1002/2018GL077524, 2018.
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B.:
Copernicus Global Land Cover Layers – Collection 2, Remote Sens.-Basel, 12, 1044, https://doi.org/10.3390/RS12061044, 2020.
Chassignet, E. P., Hurlburt, H. E., Smedstad, O. M., Halliwell, G. R., Hogan, P. J., Wallcraft, A. J., Baraille, R., and Bleck, R.:
The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system, J. Marine Syst., 65, 60–83, https://doi.org/10.1016/J.JMARSYS.2005.09.016, 2007.
World Meteorological Organization: Coastal Flooding Forecast Strengthened in Indonesia,
https://public.wmo.int/en/media/news/coastal-flooding-forecast-strengthened-indonesia (last access: 3 April 2021), 2019.
Codiga, D. L.:
Unified Tidal Analysis and Prediction, Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, 59 pp., 2011.
Cotton, W. R., Bryan, G., and van den Heever, S. C.:
Cumulonimbus Clouds and Severe Convective Storms, in: International Geophysics Book series, Vol. 44: Storm and Cloud Dynamics, edited by: Cotton, W. R. and Anthes, R. A., Academic Press, 455–592, https://doi.org/10.1016/S0074-6142(08)60548-3, 1992.
Deb, M. and Ferreira, C. M.:
Potential impacts of the Sunderban mangrove degradation on future coastal flooding in Bangladesh, J. Hydro-Environ. Res., 17, 30–46, https://doi.org/10.1016/j.jher.2016.11.005, 2017.
Egbert, G. D. and Erofeeva, S. Y.:
Efficient inverse modeling of barotropic ocean tides, J. Atmos. Ocean. Tech., 19, 183–204, 2002.
GFDRR (Global Facility for Disaster Reduction and Recovery): Think Hazard – Indonesia,
https://thinkhazard.org/en/report/116-indonesia, last access: 8 January 2022.
Giloy, N., Hamdi, Y., Bardet, L., Garnier, E., and Duluc, C. M.:
Quantifying historic skew surges: an example for the Dunkirk Area, France, Nat. Hazards, 98, 869–893, https://doi.org/10.1007/s11069-018-3527-1, 2019.
Godin, G.:
The resolution of tidal constituents, Int. Hydrogr. Rev., 47, https://journals.lib.unb.ca/index.php/ihr/article/view/23916 (last access: 30 May 2022), 2015.
Goltenboth, F., Timotius, K. H., Milan, P. P., and Margraf, J.:
Ecology of insular Southeast Asia: the Indonesian archipelago, edited by: Goltenboth, F., Timotius, K., Milan, P., and Margraf, J., Elsevier B. V., https://doi.org/10.1016/B978-0-444-52739-4.X5000-1, 2006.
Gourgue, O., Baeyens, W., Chen, M. S., de Brauwere, A., de Brye, B., Deleersnijder, E., Elskens, M., and Legat, V.:
A depth-averaged two-dimensional sediment transport model for environmental studies in the Scheldt Estuary and tidal river network, J. Marine Syst., 128, 27–39, https://doi.org/10.1016/j.jmarsys.2013.03.014, 2013.
Hashimoto, H. and Park, K.:
Two-dimensional urban flood simulation: Fukuoka flood disaster in 1999, WIT Trans. Ecol. Envir., 118, 59–67, https://doi.org/10.2495/FRIAR080061, 2008.
Herdman, L., Erikson, L., and Barnard, P.:
Storm surge propagation and flooding in small tidal rivers during events of mixed coastal and fluvial influence, J. Mar. Sci. Eng., 6, 158, https://doi.org/10.3390/JMSE6040158, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.:
The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hidayat, H., Hoekman, D., Vissers, M., Hossain, M. M., Teuling, A., and Haryani, G.:
Inundation Frequency Mapping of The Upper Kapuas Wetlands Using Radar Imagery, in: International Conference on Ecohydrology (ICE), Yogyakarta, Indonesia,
10–12 November 2014, 250–257, 2014.
Höffken, J., Vafeidis, A. T., MacPherson, L. R., and Dangendorf, S.:
Effects of the Temporal Variability of Storm Surges on Coastal Flooding, Front. Mar. Sci., 7, 98, https://doi.org/10.3389/FMARS.2020.00098, 2020.
Huybrechts, N., Villaret, C., and Hervouet, J.-M.:
Comparison between 2D and 3D modelling of sediment transport: application to the dune evolution, in: River Flow 2010, Braunschweig, Germany,
8–10 September 2010, 887–894, 2010.
Kästner, K., Hoitink, A. J. F., Vermeulen, B., Geertsema, T. J., and Ningsih, N. S.:
Distributary channels in the fluvial to tidal transition zone, J. Geophys. Res.-Earth, 122, 696–710, https://doi.org/10.1002/2016JF004075, 2017.
Kästner, K., Hoitink, A. J. F., Torfs, P. J. J. F., Vermeulen, B., Ningsih, N. S., and Pramulya, M.:
Prerequisites for Accurate Monitoring of River Discharge Based on Fixed-Location Velocity Measurements, Water Resour. Res., 54, 1058–1076, https://doi.org/10.1002/2017WR020990, 2018.
Kästner, K., Hoitink, A. J. F., Torfs, P. J. J. F., Deleersnijder, E., and Ningsih, N. S.:
Propagation of tides along a river with a sloping bed, J. Fluid Mech., 872, 39–73, https://doi.org/10.1017/JFM.2019.331, 2019.
Kästner, K. and Hoitink, A. J. F.:
Flow and Suspended Sediment Division at Two Highly Asymmetric Bifurcations in a River Delta: Implications for Channel Stability, J. Geophys. Res.-Earth, 124, 2358–2380, https://doi.org/10.1029/2018JF004994, 2019.
Lambrechts, J., Humphrey, C., McKinna, L., Gourge, O., Fabricius, K. E., Mehta, A. J., Lewis, S., and Wolanski, E.:
Importance of wave-induced bed liquefaction in the fine sediment budget of Cleveland Bay, Great Barrier Reef, Estuar. Coast. Shelf S., 89, 154–162, https://doi.org/10.1016/j.ecss.2010.06.009, 2010.
Le, H.-A., Lambrechts, J., Ortleb, S., Gratiot, N., Deleersnijder, E., and Soares-Frazão, S.:
An implicit wetting-drying algorithm for the discontinuous Galerkin method: application to the Tonle Sap, Mekong River Basin, Environ. Fluid Mech., 20, 923–951, https://doi.org/10.1007/s10652-019-09732-7, 2020.
Li, C. and Busari, A. O.:
Hybrid modeling of flows over submerged prismatic vegetation with different areal densities, Eng. Appl. Comp. Fluid, 13, 493–505, https://doi.org/10.1080/19942060.2019.1610501, 2019.
MacKinnon, K., Hatta, G., Mangalik, A., and Halim, H.:
The ecology of Kalimantan, The Ecology of Indonesia Series, Vol. III, Oxford University Press, ISBN 0-945971-73-7, 1996.
Madrosid:
Cerita Warga, Detik-detik Banjir Rob Melanda Kota Pontianak, Trib. Pontianak, https://pontianak.tribunnews.com/2018/12/29/cerita-warga-detik-detik-banjir-rob-melanda-kota-pontianak
(last access: 5 April 2021), 2018.
Moftakhari, H. R., AghaKouchak, A., Sanders, B. F., Feldman, D. L., Sweet, W., Matthew, R. A., and Luke, A.:
Increased nuisance flooding along the coasts of the United States due to sea level rise: Past and future, Geophys. Res. Lett., 42, 9846–9852, https://doi.org/10.1002/2015GL066072, 2015.
Moon, I. J., Ginis, I., Hara, T., and Thomas, B.:
A Physics-Based Parameterization of Air–Sea Momentum Flux at High Wind Speeds and Its Impact on Hurricane Intensity Predictions, Mon. Weather Rev., 135, 2869–2878, https://doi.org/10.1175/MWR3432.1, 2007.
Néelz, S.:
Desktop review of 2D hydraulic modelling packages, Environment Agency, Bristol, 2009.
Olbert, A. I., Comer, J., Nash, S., and Hartnett, M.:
High-resolution multi-scale modelling of coastal flooding due to tides, storm surges and rivers inflows. A Cork City example, Coast. Eng., 121, 278–296, https://doi.org/10.1016/j.coastaleng.2016.12.006, 2017.
Patel, D. P., Ramirez, J. A., Srivastava, P. K., Bray, M., and Han, D.:
Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: a case application of the new HEC-RAS 5, Nat. Hazards, 89, 93–130, https://doi.org/10.1007/s11069-017-2956-6, 2017.
Pemerintah Kota Pontianak: Kondisi Geografis Kota Pontianak,
https://www.pontianakkota.go.id/tentang/geografis, last access: 5 April 2021.
OpenStreetMap contributors: Planet dump, https://planet.osm.org, last access: 3 April 2021.
Pham Van, C., de Brye, B., Deleersnijder, E., F Hoitink, A. J., Sassi, M., Spinewine, B., Hidayat, H., and Soares-Frazão, S.:
Simulations of the flow in the Mahakam river-lake-delta system, Indonesia, Environ. Fluid Mech., 16, 603–633, https://doi.org/10.1007/s10652-016-9445-4, 2016.
Pusat Riset Kelautan, Kementerian Kelautan dan Perikanan RI: Prediksi Pasang Surut,
https://pusriskel.litbang.kkp.go.id/index.php/en/data/prediksi-pasang-surut, last access: 5 April 2021.
Remacle, J. F. and Lambrechts, J.:
Fast and robust mesh generation on the sphere – Application to coastal domains, Comput. Aided Design, 103, 14–23, https://doi.org/10.1016/j.cad.2018.03.002, 2018.
Sampurno, J.: Observed Kapuas River water level in 2018, Zenodo [data set], https://doi.org/10.5281/zenodo.5809647, 2021.
Santiago-Collazo, F. L., Bilskie, M. V., and Hagen, S. C.:
A comprehensive review of compound inundation models in low-gradient coastal watersheds, Environ. Model. Softw., 119, 166–181, https://doi.org/10.1016/J.ENVSOFT.2019.06.002, 2019.
SLIM contributors: SLIM code, GitHub [code], https://git.immc.ucl.ac.be/slim/slim/-/wikis/home, last access: 30 May 2022.
Smith, S. D. and Banke, E. G.:
Variation of the sea surface drag coefficient with wind speed, Q. J. Roc. Meteor. Soc., 101, 665–673, https://doi.org/10.1002/QJ.49710142920, 1975.
Spicer, P., Huguenard, K., Ross, L., and Rickard, L. N.:
High-Frequency Tide-Surge-River Interaction in Estuaries: Causes and Implications for Coastal Flooding, J. Geophys. Res.-Oceans, 124, 9517–9530, https://doi.org/10.1029/2019JC015466, 2019.
Twilley, R. R., Bentley, S. J., Chen, Q., Edmonds, D. A., Hagen, S. C., Lam, N. S. N., Willson, C. S., Xu, K., Braud, D. W., Hampton Peele, R., and McCall, A.:
Co-evolution of wetland landscapes, flooding, and human settlement in the Mississippi River Delta Plain, Sustain. Sci., 11, 711–731, https://doi.org/10.1007/s11625-016-0374-4, 2016.
U.S. Army Corps of Engineers Hydrologic Engineering Center: The Hydrologic Engineering Center's River Analysis System (HEC-RAS),
https://www.hec.usace.army.mil/software/hec-ras/, last access: 8 January 2022.
Vallaeys, V., Kärnä, T., Delandmeter, P., Lambrechts, J., Baptista, A. M., Deleersnijder, E., and Hanert, E.:
Discontinuous Galerkin modeling of the Columbia River's coupled estuary-plume dynamics, Ocean Model., 124, 111–124, https://doi.org/10.1016/j.ocemod.2018.02.004, 2018.
Vitousek, S., Barnard, P. L., Fletcher, C. H., Frazer, N., Erikson, L., and Storlazzi, C. D.:
Doubling of coastal flooding frequency within decades due to sea-level rise, Sci. Rep.-UK, 7, 1–9, https://doi.org/10.1038/s41598-017-01362-7, 2017.
Vu, T. T. and Ranzi, R.:
Flood risk assessment and coping capacity of floods in central Vietnam, J. Hydro-Environ. Res., 14, 44–60, https://doi.org/10.1016/j.jher.2016.06.001, 2017.
Wahyu, A., Kuntoro, A., and Yamashita, T.:
Annual and Seasonal Discharge Responses to Forest/Land Cover Changes and Climate Variations in Kapuas River Basin, Indonesia, J. Int. Dev. Coop., 16, 81–100, https://doi.org/10.15027/29807, 2010.
Wells, J. A., Wilson, K. A., Abram, N. K., Nunn, M., Gaveau, D. L. A., Runting, R. K., Tarniati, N., Mengersen, K. L., and Meijaard, E.:
Rising floodwaters: Mapping impacts and perceptions of flooding in Indonesian Borneo, Environ. Res., 11, 064016, https://doi.org/10.1088/1748-9326/11/6/064016, 2016.
Wu, H., Adler, R. F., Tian, Y., Huffman, G. J., Li, H., and Wang, J.:
Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model, Water Resour. Res., 50, 2693–2717, https://doi.org/10.1002/2013WR014710, 2014.
Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series (R package version 0.4-0), The R Foundation for Statistical Computing, Vienna, Austria, https://cran.r-project.org/web/packages/hydroGOF/index.html (last access: 30 May 2022), 2020.
Zhang, J. and Liu, H.:
Modeling of waves overtopping and flooding in the coastal reach by a non-hydrostatic model, in: Procedia IUTAM, 126–130, https://doi.org/10.1016/j.piutam.2017.09.019, 2017.
Zijl, F., Verlaan, M., and Gerritsen, H.:
Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction, Ocean Dynam., 63, 823–847, https://doi.org/10.1007/s10236-013-0624-2, 2013.
Short summary
This study is the first assessment to evaluate the interactions between river discharges, tides, and storm surges and how they can drive compound flooding in the Kapuas River delta. We successfully created a realistic hydrodynamic model whose domain covers the land–sea continuum using a wetting–drying algorithm in a data-scarce environment. We then proposed a new method to delineate compound flooding hazard zones along the river channels based on the maximum water level profiles.
This study is the first assessment to evaluate the interactions between river discharges, tides,...
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