[Dataset] Predictions and observations of delta morphology using the quantitative Galloway triangle
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.
Steps to reproduce
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.
National Science Foundation