Earth Surface Dynamics (Sep 2018)
Morphology of bar-built estuaries: empirical relation between planform shape and depth distribution
Abstract
Fluvial–tidal transitions in estuaries are used as major shipping fairways and are characterised by complex bar and channel patterns with a large biodiversity. Habitat suitability assessment and the study of interactions between morphology and ecology therefore require bathymetric data. While imagery offers data of planform estuary dimensions, only for a few natural estuaries are bathymetries available. Here we study the empirical relation between along-channel planform geometry, obtained as the outline from imagery, and hypsometry, which characterises the distribution of along-channel and cross-channel bed levels. We fitted the original function of Strahler (1952) to bathymetric data along four natural estuaries. Comparison to planform estuary shape shows that hypsometry is concave at narrow sections with large channels, while complex bar morphology results in more convex hypsometry. We found an empirical relation between the hypsometric function shape and the degree to which the estuary width deviates from an ideal convergent estuary, which is calculated from river width and mouth width. This implies that the occurring bed-level distributions depend on inherited Holocene topography and lithology. Our new empirical function predicts hypsometry and along-channel variation in intertidal and subtidal width. A combination with the tidal amplitude allows for an estimate of inundation duration. The validation of the results on available bathymetry shows that predictions of intertidal and subtidal area are accurate within a factor of 2 for estuaries of different size and character. Locations with major human influence deviate from the general trends because dredging, dumping, land reclamation and other engineering measures cause local deviations from the expected bed-level distributions. The bathymetry predictor can be used to characterise and predict estuarine subtidal and intertidal morphology in data-poor environments.