Extending a Hydromorphodynamic Reduced Complexity Model with Riparian Vegetation Dynamics — ASN Events

Extending a Hydromorphodynamic Reduced Complexity Model with Riparian Vegetation Dynamics (#53)

Emilio Politti 1 , Walter Bertoldi 1 , Alex Henshaw 2
  1. University of Trento, Trento, ITALY, Italy
  2. School of Geography, Queen Mary University of London, London

Hydraulic and morphodynamic numerical models have great importance in river management and restoration, as well as in flood protection planning. However, the computational demand of these types of models makes them unsuitable for long term predictions of landscape evolution. Such limitations led to the formulation of reduced complexity hydro-morphodynamic models that, to some extent, trade numerical accuracy for execution speed. Nevertheless, in both reduced complexity and classic numerical models, vegetation successional processes and their interactions with the physical habitat are poorly represented. This research proposes an extension of Caesar-Lisflood, a reduced complexity model that simulates flow and sediment transport in response to hydrological inputs. The riparian vegetation extension mimics woody species establishment, growth and dieback as well as their feedback on fluvial processes. Vegetation processes are modeled by means of two types of submodels: one based on fuzzy logic and the other based on “classical” equations. The hydromorphodynamic and vegetation model components are seamlessly coupled and spatially explicit. The model is grid based and operates on variable time steps to meet the different time scales at which physical and biological processes occur. The objective of this paper is to present the model concept and the preliminary results of 5 year simulation. The simulation was performed using parameters inferred from literature and expert knowledge. The results showed how the model is able to replicate vegetation changes in response to periods of low and high disturbance and generate a final landscape visually similar to the observed one.

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