Quote from: http://dspace.library.uu.nl/handle/1874/373787

  Hydromorphological data for many estuaries worldwide is scarce and usually limited to offshore tidal amplitude and remotely-sensed imagery. In many projects, information about morphology and intertidal area is needed to assess the effects of human interventions and rising sea-level on the natural depth distribution and on changing habitats. Habitat area depends on the spatial pattern of intertidal area, inundation time, peak flow velocities and salinity. While numerical models can reproduce these spatial patterns fairly well, their data need and computational costs are high and for each case a new model must be developed. Here, we present a Python tool that includes a comprehensive set of relations that predicts the hydrodynamics, bed elevation and the patterns of channels and bars in mere seconds. Predictions are based on a combination of empirical relations derived from natural estuaries, including a novel predictor for cross-sectional depth distributions, which is dependent on the along-channel width profile. Flow velocity, an important habitat characteristic, is calculated with a new correlation between depth below high water level and peak tidal flow velocity, which was based on spatial numerical modelling. Salinity is calculated from estuarine geometry and flow conditions. The tool only requires an along-channel width profile and tidal amplitude, making it useful for quick assessments, for example of potential habitat in ecology, when only remotely-sensed imagery is available.