Assessment of spatial and temporal variations of high water mark indicators
The high water mark (HWM) is commonly used as a boundary for coastal management and planning. Due to the dynamic nature of the coastal environment, the determination of HWM can be difficult and may vary based on the indicators unique to the location. Using remote-sensing image analysis techniques, t...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/26589 |
| _version_ | 1848752029715922944 |
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| author | Liu, Xin Xia, Jianhong (Cecilia) Kuhn, Michael Wright, Graeme Arnold, Lesley |
| author_facet | Liu, Xin Xia, Jianhong (Cecilia) Kuhn, Michael Wright, Graeme Arnold, Lesley |
| author_sort | Liu, Xin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The high water mark (HWM) is commonly used as a boundary for coastal management and planning. Due to the dynamic nature of the coastal environment, the determination of HWM can be difficult and may vary based on the indicators unique to the location. Using remote-sensing image analysis techniques, this study evaluates the spatial and temporal variation of HWM based on several indicators. These include vegetation lines, frontal dune toe, mean high water spring (MHWS)/mean higher high water (MHHW), and high water lines (HWL). Other linear boundaries defined by agencies for various applications are also used as indicators. For improved coastal property management, this study also uses an enhanced Spatial Continuity of the Swash Probability (SCSP) model as a HWM indicator by excluding the runup parameter regarding the Spatial Continuity of Tide Probability (SCTP). In order to better account for sudden shape changes, the extended instead of the simple Hausdorff distance has been used to measure the seasonal variation of HWM position. Monte Carlo simulation of DEM data and Fractal Dimension (FD) techniques were used to examine spatial uncertainties due to both the precision of input data and the processing techniques used. Two case study areas in Western Australia with varying coastal conditions have been selected to evaluate the approach. These are Coogee Beach in South Fremantle and Cooke Point in Port Hedland. Results for both study areas indicate that spatial variations of HWM due to seasonal changes are about one order of magnitude larger than variations due to uncertainties in the input data. This behaviour, while present at both study areas, is more significant at Coogee Beach having a sandy beach with high wave energy. |
| first_indexed | 2025-11-14T08:02:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-26589 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:02:08Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-265892017-09-13T15:41:02Z Assessment of spatial and temporal variations of high water mark indicators Liu, Xin Xia, Jianhong (Cecilia) Kuhn, Michael Wright, Graeme Arnold, Lesley The high water mark (HWM) is commonly used as a boundary for coastal management and planning. Due to the dynamic nature of the coastal environment, the determination of HWM can be difficult and may vary based on the indicators unique to the location. Using remote-sensing image analysis techniques, this study evaluates the spatial and temporal variation of HWM based on several indicators. These include vegetation lines, frontal dune toe, mean high water spring (MHWS)/mean higher high water (MHHW), and high water lines (HWL). Other linear boundaries defined by agencies for various applications are also used as indicators. For improved coastal property management, this study also uses an enhanced Spatial Continuity of the Swash Probability (SCSP) model as a HWM indicator by excluding the runup parameter regarding the Spatial Continuity of Tide Probability (SCTP). In order to better account for sudden shape changes, the extended instead of the simple Hausdorff distance has been used to measure the seasonal variation of HWM position. Monte Carlo simulation of DEM data and Fractal Dimension (FD) techniques were used to examine spatial uncertainties due to both the precision of input data and the processing techniques used. Two case study areas in Western Australia with varying coastal conditions have been selected to evaluate the approach. These are Coogee Beach in South Fremantle and Cooke Point in Port Hedland. Results for both study areas indicate that spatial variations of HWM due to seasonal changes are about one order of magnitude larger than variations due to uncertainties in the input data. This behaviour, while present at both study areas, is more significant at Coogee Beach having a sandy beach with high wave energy. 2013 Journal Article http://hdl.handle.net/20.500.11937/26589 10.1016/j.ocecoaman.2013.09.009 Elsevier restricted |
| spellingShingle | Liu, Xin Xia, Jianhong (Cecilia) Kuhn, Michael Wright, Graeme Arnold, Lesley Assessment of spatial and temporal variations of high water mark indicators |
| title | Assessment of spatial and temporal variations of high water mark indicators |
| title_full | Assessment of spatial and temporal variations of high water mark indicators |
| title_fullStr | Assessment of spatial and temporal variations of high water mark indicators |
| title_full_unstemmed | Assessment of spatial and temporal variations of high water mark indicators |
| title_short | Assessment of spatial and temporal variations of high water mark indicators |
| title_sort | assessment of spatial and temporal variations of high water mark indicators |
| url | http://hdl.handle.net/20.500.11937/26589 |