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Development of estuary morphological models

Huthnance, J.M. ORCID: https://orcid.org/0000-0002-3682-2896; Karunarathna, G.; Lane, A.; Manning, A.J.; Norton, P.; Reeve, D.; Spearman, J.; Soulsby, R.L.; Townend, I.H.; Wright, A.. 2007 Development of estuary morphological models. Southend-on-Sea, Essex, The Institute of Mathematics and its Applications, 10pp.

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Abstract/Summary

The accuracy of process-based models decreases through the sequence water levels and currents to sediment transports and in turn to evolving morphology. Especially, the validity of longer-term (decadal) simulations is uncertain. The aim here is to develop, apply and compare models capable of indicating likely estuarine morphologies 50 years hence. From these should come estimates of associated changes in flood risks under various management and climate change scenarios. “Bottom-Up” process-based models, combined with Lagrangian particle-tracking, have been considered for accuracy and sensitivities to formulation and forcing conditions. A SHELL Application Framework aims to facilitate “Hybrid” coupling of 1-D hydrodynamic models and top-down (T-D) “regime” and physical constraints on estuary form. An ASMITA-type model (Stive et al. 1998) models sediment inputs and transports between aggregated estuarine elements (flats, channels, delta) to predict adaptation capacity to sea-level rise. An 'Inverse' hybrid model uses a diffusion-type T-D model equation to retrieve time-averaged “forcing” of recent morphological change as a basis for prediction. An Analytical Emulator is based on 1-D dynamical equations with simplifying assumptions. The various models have been applied to eight estuaries to give an 'ensemble' of predictions for 2050 morphologies for intercomparison.

Item Type: Publication - Report
Programmes: POL Programmes > Shallow coastal seas - function and impacts of change
Additional Keywords: estuary, model, morphology
NORA Subject Terms: Marine Sciences
Date made live: 21 Dec 2007 12:05 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/1164

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