Dune systems in relation to rising seas

Beaches and associated dunes are constituted of unconsolidated materials, such as sand, and thus are low-strength land forms less robust than rocky cliffs (van der Meulen et al. 1991). It is estimated that 70% of sand-based coastlines in the world are presently subject to erosion (Bird 1985; Wind and Peerbolte 1993). However, natural dune systems are inclined to adjust after stress without permanent damage (Brown and McLachlin 2002), and when stabilized by plant cover they offer a first line of coastal defence against assault from wave action (Wind and Peerbolte 1993; Broadus 1993; De Ronde 1993). Natural self-sustaining dune systems interact with the sea and closely reflect changes in sea levels. At any given time no single sea level characterizes all oceans, that is, the resting position of the ocean surface, or geoid, is not uniformly elevated over the earth. Eustatic sea levels, free of influence from tides, waves and storms, thus vary from place to place as well as over time. Satellite altimetry, which permits more accurate as well as more numerous observations than older tide-gage methods of measuring sea levels, shows that the ocean is actually a spheroid modified by depressions and elevations. For example, in parts of the Indian Ocean sea levels are as much as 70 m lower than the global mean and in the North Atlantic 80 m higher (Carter 1988). Climate is governed by long-term periodic variations in the earth’s orbit that effect changes in solar radiation and, consequently, also in sea levels (Bartlein and Prentice 1989; Woodroffe 2002). As a result, ice ages repeatedly alternate with periods of interglacial warming in which ice masses contract and sea levels increase. Most of the time that has passed since the Cambrian period—approximately 500 million years—sea levels, although fluctuating on several timescales, have been higher than they are today. Because of the difficulties in documenting conditions so far in the distant past estimates of these sea levels vary considerably, but those shown in Fig. 13.1, based on different kinds of evidence, are representative of attempts at reconstruction (personal communication RA Rohde 2008).

Tsunami waveform inversion by numerical finite-elements Green’s functions

During the last few years, the steady increase in the quantity and quality of the data concerning tsunamis has led to an increasing interest in the inversion problem for tsunami data. This work addresses the usually ill-posed problem of the hydrodynamical inversion of tsunami tide-gage records to infer the initial sea perturbation. We use an inversion method for which the data space consists of a given number of waveforms and the model parameter space is represented by the values of the initial water elevation field at a given number of points. The forward model, i.e. the calculation of the synthetic tide-gage records from an initial water elevation field, is based on the linear shallow water equations and is simply solved by applying the appropriate Green’s functions to the known initial state. The inversion of tide-gage records to determine the initial state results in the least square inversion of a rectangular system of linear equations. When the inversions are unconstrained, we found that in order to attain good results, the dimension of the data space has to be much larger than that of the model space parameter. We also show that a large number of waveforms is not sufficient to ensure a good inversion if the corresponding stations do not have a good azimuthal coverage with respect to source directivity. To improve the inversions we use the available a priori information on the source, generally coming from the inversion of seismological data. In this paper we show how to implement very common information about a tsunamigenic seismic source, i.e. the earthquake source region, as a set of spatial constraints. The results are very satisfactory, since even a rough localisation of the source enables us to invert correctly the initial elevation field.

Reducing Trajectory Modeling Response Time In Texas

ABSTRACT Oil spill trajectory models are one of the few tools that can help response teams stay ahead of an oil slick, but the process of trajectory modeling requires many time-consuming steps. Hydrodynamic modeling, used to generate the current forecasts required by trajectory models, can consume hours of operator and computer run-time. The Texas General Land Office and the Texas Water Development Board have automated the daily operation of hydrodynamic models to produce current forecasts for two Texas bays. Each midnight, computers running the hydrodynamic models for Galveston Bay and Corpus Christi Bay download the latest water level measurements from a real time tide gage network. The models use this data to produce a 2-day forecast of the currents for the two bay systems. In the event of a spill, the trajectory modeling team retrieves the recently generated current forecasts for immediate input to the trajectory model. Model automation saves two hours in computer run-time alone for these two bays. Along similar lines for the offshore environment, Texas A&M University is developing an automated gridded wind forecasts and a wind-driven, shelf circulation (spectral) model to predict near-surface current patterns and velocities along the inner Texas shelf on an operational basis.