Storm conditions are considered the dominating erosional mechanism for the coastal zone. Morphological changes during storms are hard to measure due to energetic environmental conditions. Surveys are therefore mostly executed right after a storm on a local scale over a single or few storms [days to weeks]. The impact of a single storm might depend on the preceding sequence of storms. Here, a video camera system is deployed in the South-West of England at the beach of Porthtowan to observe and assess short-term storm impact and long-term recovery. The morphological change is observed with a state-of-the-art video-based depth estimation tool that is based on the linear dispersion relationship between depth and wave celerity (cBathy). This work shows the first application of this depth estimation tool in a highly energetic macrotidal environment. Within this application two sources of first-order inaccuracies are identified: 1) camera related issues on the camera boundaries and 2) fixed pixel location for all tidal elevations. These systematic inaccuracies are overcome by 1) an adaptive pixel collection scheme and camera boundary solution and 2) freely moving pixels. The solutions together result in a maximum RMS-error reduction of 60%. From October 2013 to February 2015 depths are hourly estimated during daylight. This period included, the 2013-2014 winter season which was the most energetic winter since wave records began. Inter-tidal beach surveys show 200 m3/m erosion while the sub-tidal video derived bathymetries show a sediment loss of around 20 m3/m. At the same time the sub-tidal (outer) bar changes from 3D to linear due to a significant increase in alongshore wave power during storm conditions. Complex-EOF based storm-by-storm impact reveals that the individual storm impact at Porthtowan can be described as a combined function of storm-integrated incident offshore wave power [P] and disequilibrium and that the tidal range has limited effect on the storm impact. The inter- and sub-tidal domain together gain volume over the 2013-2014 winter and the two domains show an interactive inverse behaviour indicating sediment exchange during relatively calm summer conditions. The inter-tidal domain shows accelerated accretion during more energetic conditions in fall 2014. The outer bar slowly migrated onshore until more energetic wave conditions activate the sub-tidal storm deposits and 3 dimensionality is reintroduced. The inter-tidal beach shows full recovery in November 2014, 8 months after the stormy winter.

Velocity measurements from drifter GPS records are assimilated and used in an ensemble-based inversion technique to extract the river bathymetry. The method is tested on a deep meandering reach and a shallow braided reach of the Kootenai River in Idaho, USA. The Regional Ocean Modeling System (ROMS) is used to model numerous statistically varied bathymetries to create an ensemble of hydrodynamic states. These states, the drifter observations, and the uncertainty of each are combined to form a cost function which is minimized to produce an estimated velocity ���eld. State augmentation is then used to relate the velocity ���eld to bathymetry. Our goals are to assess whether ROMS can accurately reproduce the Kootenai River ���ow to an extent that depth inversion is feasible, investigate if drifter paths are sensitive enough to bottom topography to make depth inversion possible, and to establish practical limitations of the present methodology. At both test sites, the depth inversion method produced an estimate of bathymetry that was more accurate and more skillful than the prior estimate. / Graduation date: 2013

Video based remote sensing techniques are well suited to collect spatially resolved wave images in the surf zone with breaking waves and dynamic bathymetric changes. An advanced video-based depth inversion method is developed to remotely survey bathymetry in the surf zone. The present method involves image processing of original wave image sequences, wave property estimation based on linear feature extraction from the processed image sequences, and is combined with a nonlinear depth inversion model. The original wave image sequences are processed through video image frame differencing and directional low-pass filtering schemes to remove wave-breaking-induced foam noise having high frequencies in the surf zone. The features of individual crest trajectories are extracted from the processed and rectified image sequences, i.e. processed image cross-shore timestacks, by tracking pixels of high intensity within an interrogation window of a Radon-transform-based line-detection algorithm. The wave celerity is computed using space-time information of the extracted trajectories of individual wave crests in the cross-shore timestack domain. The presented retrieval of nearshore bathymetry from video image sequences is based on a nonlinear depth inversion using the nonlinear shallow water wave theory. The nonlinear wave amplitude dispersion effects at the breaker points are determined by combining the nonlinear shallow water celerity equation with a wave breaker criterion, thereby computing water depths iteratively from the celerity measured from the video data. The water depths estimated at the breaker points present initial bathymetric anchor points. Bathymetric profiles in the surf zone are inverted by calculating wave heights dissipated after wave breaking with a wave dissipation model and wave heights shoaled before wave breaking with a wave shoaling model. The continuous wave amplitude dispersion effects are subtracted from the measured celerity profiles, resulting in nearshore bathymetric profiles. The nonlinear depth inversion derived bathymetric estimates from nearshore imagery match the measured values with a biased mean depth error of about +0.06m in the depth range of 0.1 to 3m. In addition, the wave height estimates by the depth inversion model are comparable to the in-situ measured wave heights with a biased mean wave height error of about +0.14m. The present depth inversion method based on optical remote-sensing supports coastal management, navigation, and amphibious operations.

Cette thèse présente une plateforme d'assimilation de données issues de l'imagerie vidéo et intégrée au modèle numérique d'évolution de profil de plage 1DBEACH. Le manque de jeux de données bathymétriques haute-fréquence est un des problèmes récurrents pour la modélisation morphodynamique littorale. Pourtant, des relevés topographiques réguliers sont nécessaires non seulement pour la validation de nos modèles hydro-sédimentaires mais aussi dans une perspective de prévision d'évolution morphologique de nos plages sableuses et d'évolution de la dynamique des courants de baïnes en temps réel. Les récents progrès dans le domaine de l'imagerie vidéo littorale ont permis d'envisager un moyen de suivi morphologique quasi-quotidien et bien moins coûteux que les traditionnelles campagnes de mesure. En effet, les images dérivées de la vidéo de type timex ou timestack rendent possible l'extraction de proxys bathymétriques qui permettent de caractériser et de reconstruire la morphologie de plage sous-jacente. Cependant, ces méthodes d'inversion bathymétrique directes sont limitées au cas linéaire et nécessitent, selon les conditions hydrodynamiques ambiantes, l'acquisition de données vidéo sur plusieurs heures voire plusieurs jours pour caractériser un état de plage. En réponse à ces différents points bloquants, ces travaux de thèse proposaient l'implémentation puis la validation de méthodes d'inversion bathymétrique basées sur l'assimilation dans notre modèle de différentes sources d'observations vidéo disponibles et complémentaires. A partir d'informations hétérogènes et non redondantes, ces méthodes permettent la reconstruction rapide et précise d'une morphologie de plage dans son intégralité pour ainsi bénéficier de relevés bathymétriques haute fréquence réguliers. / This thesis presents data-model assimilation techniques using video-derived beach information to improve the modelling of beach profile evolution.The acquisition of accurate and recurrent nearshore bathymetric data is a difficult and challenging task which limits our understanding of nearshore morphological changes. This is particularly true in the surf zone which exhibits the largest degree of morphological variability. In addition, surfzone bathymetric data are crucial from many perspectives such as numerical model validation, operational rip current prediction or real-time nearshore evolution modelling. In parallel, video imagery recently arose as a low-cost alternative to direct measurement in order to daily monitor beach morphological changes. Indeed, bathymetry proxies can be extracted from video-derived images such as timex or timestacks. These data can be then used to estimate underlying beach morphologies. However, simple linear depth inversion techniques still suffer from some restrictions and require up to a 3-day dataset to completely characterize a given beach morphology. As an alternative, this thesis presents and validates data-assimilation methods that combine multiple sources of available video-derived bathymetry proxies to provide a rapid, complete and accurate estimation of the underlying bathymetry and prevent from excessive information.