Bathymetry fusion using multiple-point geostatistics: Novelty and challenges in representing non-stationary bedforms

  • Authors:

  • Sanjeev Kumar Jha;Gregoire Mariethoz;Bryce F. J. Kelly

  • Affiliations:

  • Connected Waters Initiative Research Centre, University of New South Wales, Sydney, NSW, Australia and National Centre for Groundwater Research & Training, Australia;Connected Waters Initiative Research Centre, University of New South Wales, Sydney, NSW, Australia and National Centre for Groundwater Research & Training, Australia;Connected Waters Initiative Research Centre, University of New South Wales, Sydney, NSW, Australia and National Centre for Groundwater Research & Training, Australia

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

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Abstract

In large rivers, complex sediment dynamics cause rapid changes in the position and shape of bed deposits. Regular monitoring of changes in river bed geometry is essential for assessing the nature of morphological change and associated bed load during low, high, and medium flow conditions. We demonstrate the application of Direct Sampling (DS) for patching partial river morphological surveys to generate complete maps of the river morphology, by incorporating prior knowledge from bathymetry data collected in different seasons at collocated or adjacent reaches. This novel approach is based on multiple-point statistics (MPS), which uses a training image (TI) to provide prior statistical and architectural constraining data. In this study high and low resolution bathymetry data from a reach of the Mississippi river have been used. High-resolution measurements were conducted using Multi-beam-echo-sounder (MBES), which provides very detailed bed geometry at high spatial resolution. These measurements cannot be acquired at intervals frequent enough to characterize the rapid sedimentological processes. Low resolution bathymetry data can be obtained at frequent intervals but at sparse locations, by installing depth measuring sensors on boats passing the study reach several times a week. The DS method is used to simulate the high resolution bathymetry at the frequency of the low-resolution data. In the simulations, the method uses the bed geometry information contained in the MBES high-resolution surveys, the local information contained in the boat-borne low-resolution measurements, and provides an updated bathymetry map with quantified uncertainty.