[Dataset] Predictions and observations of delta morphology using the quantitative Galloway triangle

Published: 24 December 2022| Version 1 | DOI: 10.17632/j83n7hbvst.1
Contributors:
Juan Felipe Paniagua-Arroyave,

Description

We provide predictions of delta morphology with the quantitative Galloway triangle (Niehuis et al., 2020). We compare these predictions with delta morphology observations from a new methodology that uses the quantitative Galloway triangle.

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1. PREDICT RELATIVE SEDIMENT FLUXES AT DELTAS 1.1. Qriver Fluvial sediment fluxes (Qriver) were obtained from Cohen et al. (2014) in the Global Delta Change repository (https://github.com/jhnienhuis/GlobalDeltaChange) 1.2. Qwave Maximum potential sediment fluxes by waves were calculated following Nienhuis et al. (2015) and the implementation in the attached MATLAB function 'jfpa_Qwave.m'. Wave climate data were obtained from the WaveWatchIII model (Chawla et al., 2013) at https://jhnienhuis.users.earthengine.app/view/changing-shores. Data are in the folder 'Waves_WW3'. 1.3. Qtide Amplitudes of tidal fluxes, Qtide, were calculated following Nienhuis et al. (2018) and the implementation in the attached MATLAB function 'jfpa_MorphologyPred.m'. Tidal properties were obtained from the TPX model (Egbert and Erofeeva, 2002) at https://jhnienhuis.users.earthengine.app/view/changing-shores. Data are in the folder ''Tides_TPX. 2. MEASURE DELTA MORPHOLOGY We digitized the delta morphology features in Google Earth, in this order: (1) reference shoreline. (2) delta profile. (3) fluvial channel width. (4) left delta flank. (5) right delta flank. (6-onward) distributary mouth(s) channel(s) width(s) Data are in the folder 'Morphology_GE'. 3. QUANTIFY RELATIVE SEDIMENT FLUXES FROM DELTA MORPHOLOGY We used the function 'jfpa_MorphologyPred.m' within the script 'get_GallowayTernaryData.m' to calculate the fluxes by rivers, waves, and tides, both from predictions and observations. These values are included in the MS Excel spreadsheet 'Pani&Nienhuis_SupplTables.xlsx'. The data in this table are used to create the quantitative Galloway ternary diagram in Figs. 7 and 8 from the MATLAB routines 'Fig07_PredictionDeltaMorphology.m' and 'Fig08_ObservationsDeltaMorphology.m'. We also include routines to plot the ternary error (e_ter) and comparisons between predictions and observations of delta morphology and dominance ratios (cf. 'Fig09_TernaryErrors.m' and 'Fig10_PredictionVersusObservations'). REFERENCES Chawla, A., Spindler, D. M., & Tolman, H. L. (2013). Validation of a thirty year wave hindcast using the Climate Forecast System Reanalysis winds. Ocean Modelling, 70, 189–206. https://doi.org/10.1016/j.ocemod.2012.07.005. Egbert, G. D., & Erofeeva, S. Y. (2002). Efficient inverse modeling of barotropic ocean tides. Journal of Atmospheric and Oceanic Technology, 19(2), 183–204. https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2. Nienhuis, J. H., Ashton, A. D., & Giosan, L. (2015). What makes a delta wave-dominated? Geology, 43(6), 511–514. https://doi.org/10.1130/G36518.1. Nienhuis, J. H., Hoitink, A. J. F. T., & Törnqvist, T. E. (2018). Future Change to Tide-Influenced Deltas. Geophysical Research Letters, 45(8), 3499–3507. https://doi.org/10.1029/2018GL077638.

Institutions

Universidad EAFIT, Universiteit Utrecht Faculteit Geowetenschappen

Categories

Earth Sciences, Earth Surface Sediment Transport, Applied Computing in Earth Sciences, Coastal Landscape, Applied Geomorphology, Coastal Evolution

Funding

National Science Foundation

EAR-1810855

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