ORCID Profile
0000-0003-2101-3858
Current Organisation
University of Southampton
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Publisher: Springer Science and Business Media LLC
Date: 07-02-2020
DOI: 10.1038/S41598-020-59018-Y
Abstract: Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than in idual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.
Publisher: Copernicus GmbH
Date: 15-11-2016
DOI: 10.5194/GMD-2016-264
Abstract: Abstract. Modelling coastal morphological changes at decadal to centennial time scales is required to support sustainable coastal management world-wide. One approach involves coupling of landform-specific components (e.g. cliff, beach, dunes, estuaries, etc.) that have been independently developed. An alternative, and novel approach explored in this paper is to capture the essential characteristics of the landform-specific models using a common spatial representation within an appropriate software-environment. In the proposed Coastal Modelling Environment (CoastalME), change in coastal morphology is formulated by means of dynamically linked raster and geometrical objects. A grid of raster cells provides the data structure for representing quasi-3D spatial heterogeneity and sediment conservation. Other geometrical objects (lines, areas and volumes) that are consistent with, and derived from, the raster structure represent a library of coastal elements (e.g. shoreline, beach profiles and estuary volumes) as required by different landform-specific models. As a proof-of-concept, we illustrate the potential capabilities of CoastalME by integrating a cliff-beach model. We verify that CoastalME can reproduce behaviours of each in idual landform-specific model. Their integration within the framework of CoastalME reveals behaviours that emerge from landforms interaction and which have not previously been captured, such as the influence of the regional bathymetry on the local alongshore sediment transport gradient. This is the first step of the framework development, which provides an alternative to directly coupling existing models.
Publisher: Elsevier BV
Date: 2022
Publisher: Copernicus GmbH
Date: 17-07-2017
Abstract: Abstract. The ability to model morphological changes on complex, multi-landform coasts over decadal to centennial timescales is essential for sustainable coastal management worldwide. One approach involves coupling of landform-specific simulation models (e.g. cliffs, beaches, dunes and estuaries) that have been independently developed. An alternative, novel approach explored in this paper is to capture the essential characteristics of the landform-specific models using a common spatial representation within an appropriate software framework. This avoid the problems that result from the model-coupling approach due to between-model differences in the conceptualizations of geometries, volumes and locations of sediment. In the proposed framework, the Coastal Modelling Environment (CoastalME), change in coastal morphology is represented by means of dynamically linked raster and geometrical objects. A grid of raster cells provides the data structure for representing quasi-3-D spatial heterogeneity and sediment conservation. Other geometrical objects (lines, areas and volumes) that are consistent with, and derived from, the raster structure represent a library of coastal elements (e.g. shoreline, beach profiles and estuary volumes) as required by different landform-specific models. As a proof-of-concept, we illustrate the capabilities of an initial version of CoastalME by integrating a cliff–beach model and two wave propagation approaches. We verify that CoastalME can reproduce behaviours of the component landform-specific models. Additionally, the integration of these component models within the CoastalME framework reveals behaviours that emerge from the interaction of landforms, which have not previously been captured, such as the influence of the regional bathymetry on the local alongshore sediment-transport gradient and the effect on coastal change on an undefended coastal segment and on sediment bypassing of coastal structures.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Ian Townend.