Monitoring sediment impact on flood hazards

Alejandro Dussaillant (UKCEH), Matthew Perks (NU), Nick Everard (UKCEH)

 

Apply for this PhD here https://applyto.newcastle.ac.uk/ using application studentship code FLOOD247. Please contact Caspar Hewett (caspar.hewett@newcastle.ac.uk) if you have any questions about the application process. 

 

 

Rationale: 

Fine sediment (< 2 mm) is an essential, naturally occurring component of freshwater ecosystems, critical for habitat heterogeneity and ecosystem functioning. Yet the persistence of elevated levels can lead to downstream sedimentation with associated impacts on channel capacity. This may lead to reduced conveyance of flood flows resulting in the increased occurrence of out-of-bank flows and flood occurrence. However, there are significant gaps in our knowledge of both the fine timing and magnitude of sediment transfer processes occurring and the subsequent changes to channel and floodplain morphology in low-energy fluvial environments. In this project we will tackle the issue of a dearth of data representing both the transfer of fine sediment through river networks, and the subsequent impacts on channel and floodplain capacity using remote sensing workflows for UK and Chilean rivers. In the UK, systematic assessment of fine sediment fluxes is achieved through infrequent (often monthly) sampling conducted by the competent authorities (e.g. Environment Agency). Given the highly episodic nature of fine sediment transfer, tools for more accurate characterisation of sediment fluxes (both magnitude and timing) are required for assessing impacts on depositional environments. Similarly, cross-sectional information is infrequently acquired leading to uncertainties in flow routing where adjustments occur.

 

Methodology: 

In order to improve both the characterisation of sediment transfer processes, and to enable quantification of morphological change, this project will: 

i) develop and apply machine learning techniques to quantify suspended sediment concentrations using imagery acquired from a) fixed cameras, b) uncrewed aerial systems (UASs), and c) very high resolution satellite constellations (e.g. Manfreda et al., in review) complemented with low-cost turbidimeters (ongoing collaborations with Prof Wouter Buytaert at Imperial College and Dr Felipe Aguilar at Universidad de Aysén, Chile).

ii) develop, refine, and adopt techniques for characterisation of channel bathymetry from UAS platforms. This will be derived from a) indirect reconstruction based on optical reflectance characteristics of imagery, b) reconstruction based on structure from motion multi-view stereo (SfM-MVS) and application of refraction correction methods (Eltner et al., 2021), and c) application of airborne ground penetrating radar (GPR). 

Harmonization of these methods will facilitate the assessment of morphological changes and enable identification of the material in flux. Following acquisition of this information, the project will seek to assess the impacts of suspended sediment transport events on morphological conditions, and explicitly assess how this affects the routing of flood flows using LISFLOOD-FP 8.1 (Sharifian et al., 2023).

 

Location: 
Hosted at UK Centre for Ecology and Hydrology, degree awarded by University of Newcastle
Background Reading: 
  1. Eltner, A., Bertalan, L., Grundmann, J., Perks, M.T. & Lotsari, E. (2021) Hydro-morphological mapping of river reaches using videos captured with UAS. Earth Surface Processes and Landforms, 46(14), 2773–2787. https://doi.org/10.1002/esp.5205
  2. Manfreda, S., Miglino, D., Saddi, KC., Jomaa, S., Etner, A., Perks, MT, Peña-Haro, S., Bogaard, T., van Emmerik, THM., Mariani, S, Maddock, I, Tauro, F., Grimaldi, S., Zeng, Y., Gonçalves, G. Strelnikova, D., Bussettini, M., Marchetti, G., Lastoria, B., Su, B., Rode, M. Advancing hydrological monitoring using image-based techniques: challenges and opportunities. https://doi.org/10.31223/X50M5H

  3. Sharifian, MK., Kesserwani, G., Chowdhury, AA., Neal, J., Bates, P. (2023) LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations, Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023

 

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