Xin Liu

Determination of the High Water Mark Height and its Location Along a Coastline

Xin Liu Conf2012
University of Melbourne
Supervisor (Academic)
Dr C Xia & Prof G Wright, Curtin University & Prof C Fraser, University of Melbourne
Supervisor (Industry)
Dr Lesley Arnold, Geospatial Frameworks
Coordinator for Smart City and Big Data group, Australasian Joint Research Centre for Building Information Modelling, Curtin University
Thesis Abstract

The High Water Mark (HWM) is an important cadastral boundary that separates land and water. It is also used as a baseline to facilitate coastal hazard management, from which land and infrastructure development is offset to ensure the protection of property from storm surge and sea level rise. However, the location of the HWM is difficult to define accurately due to the ambulatory nature of water and coastal morphology variations. Contemporary research has failed to develop an accurate method for HWM determination because continual changes in tidal levels, together with unimpeded wave runup and the erosion and accretion of shorelines, make it difficult to determine a unique position of the HWM. While traditional surveying techniques are accurate, they selectively record data at a given point in time, and surveying is expensive, not readily repeatable and may not take into account all relevant variables such as erosion and accretion.

In this research, a consistent and robust methodology is developed for the determination of the HWM over space and time. The methodology includes two main parts: determination of the HWM by integrating both water and land information, and assessment of HWM indicators in one evaluation system. It takes into account dynamic coastal processes, and the effect of swash or tide probability on the HWM. The methodology is validated using two coastal case study sites in Western Australia. These sites were selected to test the robustness of the methodology in two distinctly different coastal environments.

At the first stage, this research develops a new model to determine the position of the HWM based on the spatial continuity of swash probability (SCSP) or spatial continuity of tidal probability (SCTP) for a range of HWM indicators. The indicators include tidal datum-based HWMs, such as mean high water spring or mean higher high water, and a number of shoreline indicators, such as the dune toe and vegetation line. HWM indicators are extracted using object-oriented image analysis or Light Detection and Ranging (LiDAR) Digital Elevation Modelling, combined with tidal datum information. Field verified survey data are used to determine the swash heights and shoreline features, and provide confidence levels against which the swash height empirical model and feature extraction methods are validated. Calculations of inundation probability for HWM indicators are based solely on tide data for property management purposes; while swash heights are included for coastal hazard planning.

The results show that the accuracy of swash height calculations is compromised due to gaps that exist in wave data records. As a consequence, two methods are utilised to interpolate for gaps in the wave data records: the wavelet refined cubic spline method and the fractal method. The suitability of these data interpolation methods for bridging the wave record data gaps is examined. The interpolation results are compared to the traditional simple cubic spline interpolation method, which shows different interpolation methods should be applied according to the duration of the gap in the wave record data.
At the second stage of this research, all the HWM indicators, including the two new HWM indicators, SCSP and SCTP, are evaluated based on three criteria: precision, stability and inundation risk. These indicators are integrated into a Multi-Criteria Decision Making model to assist in the selection and decision process to define the most ideal HWM position. Research results show that the position of the dune toe is the most suitable indicator of the HWM for coastal hazards planning, and SCTP is the most ideal HWM for coastal property management purposes.

The results from this research have the potential for significant socio-economic benefits in terms of reducing coastal land ownership conflicts and in preventing potential damage to properties from poorly located land developments. This is because the methodology uses a data-driven model of the environment, which allows the HWM to be re-calculated consistently over time and with consideration for historical and present day coastal conditions.