Tsunami simulation and detection using global navigation satellite system reflectometry

In this thesis, research for tsunami remote sensing using the Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs) is presented. Firstly, a process for simulating GNSS-R DDMs of a tsunami-dominated sea sur- face is described. In this method, the bistatic scattering Zavorotny-Voronovich (Z-V) model, the sea surface mean square slope model of Cox and Munk, and the tsunami- induced wind perturbation model are employed. The feasibility of the Cox and Munk model under a tsunami scenario is examined by comparing the Cox and Munk model- based scattering coefficient with the Jason-1 measurement. A good consistency be- tween these two results is obtained with a correlation coefficient of 0.93. After con- firming the applicability of the Cox and Munk model for a tsunami-dominated sea, this work provides the simulations of the scattering coefficient distribution and the corresponding DDMs of a fixed region of interest before and during the tsunami. Fur- thermore, by subtracting the simulation results that are free of tsunami from those with presence of tsunami, the tsunami-induced variations in scattering coefficients and DDMs can be clearly observed. Secondly, a scheme to detect tsunamis and estimate tsunami parameters from such tsunami-dominant sea surface DDMs is developed. As a first step, a procedure to de- termine tsunami-induced sea surface height anomalies (SSHAs) from DDMs is demon- strated and a tsunami detection precept is proposed. Subsequently, the tsunami parameters (wave amplitude, direction and speed of propagation, wavelength, and the tsunami source location) are estimated based upon the detected tsunami-induced SSHAs. In application, the sea surface scattering coefficients are unambiguously re- trieved by employing the spatial integration approach (SIA) and the dual-antenna technique. Next, the effective wind speed distribution can be restored from the scat- tering coefficients. Assuming all DDMs are of a tsunami-dominated sea surface, the tsunami-induced SSHAs can be derived with the knowledge of background wind speed distribution. In addition, the SSHA distribution resulting from the tsunami-free DDM (which is supposed to be zero) is considered as an error map introduced during the overall retrieving stage and is utilized to mitigate such errors from influencing sub- sequent SSHA results. In particular, a tsunami detection procedure is conducted to judge the SSHAs to be truly tsunami-induced or not through a fitting process, which makes it possible to decrease the false alarm. After this step, tsunami parameter estimation is proceeded based upon the fitted results in the former tsunami detec- tion procedure. Moreover, an additional method is proposed for estimating tsunami propagation velocity and is believed to be more desirable in real-world scenarios. The above-mentioned tsunami-dominated sea surface DDM simulation, tsunami detection precept and parameter estimation have been tested with simulated data based on the 2004 Sumatra-Andaman tsunami event.

Modelling the growth of Northern Cod

We develop a body size growth model of Northern cod (Gadus morhua) in Northwest Atlantic Fisheries Organization (NAFO) Divisions 2J3KL during 2009-2013. We use individual length-at-age data from the bottom trawl survey in these divisions during 2009–2013. We use the Von Bertalanffy (VonB) model extended to account for between-individual variations in growth, and variations that may be caused by the methods which fish are caught and sampled for length and age measurements. We assume between-individual variation in growth appears because individuals grow at a different rate (k), and they achieve different maximum sizes (l∞). We also included measurement error in length and age in our model since ignoring these errors can lead to biased estimates of the growth parameters. We use the structural errors-invariables (SEV) approach to estimate individual variation in growth, ageing error variation, and the true age distribution of the fish. Our results shows the existence of individual variation in growth and ME in age. According to the negative log likelihood ratio (NLLR) test, the best model indicated: 1) different growth patterns across divisions and years. 2) Between individual variation in growth is the same for the same division across years. 3) The ME in age and true age distribution are different for each year and division.