ORCID Profile
0000-0003-0835-6864
Current Organisations
UNSW Sydney
,
School of Earth and Environmental Sciences, University of Queensland
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Publisher: Elsevier BV
Date: 03-2009
Publisher: Elsevier BV
Date: 09-2022
Publisher: University of Queensland Library
Date: 2022
DOI: 10.14264/D566C79
Publisher: MDPI AG
Date: 16-05-2019
DOI: 10.3390/JMSE7050151
Abstract: In Africa, several new seaport developments are being considered. In sedimentary environments, such port developments can have adverse impacts on the evolution of adjacent coastlines. To learn from past port engineering practice, we created a unique database containing the coastline evolution and characteristics of 130 existing African seaports. Whereas the systematic mapping of coastal impacts was previously h ered by data availability, innovative automated satellite image processing techniques have enabled us to intercompare ports at an unprecedented continental scale. We found large geographical differences with respect to the beach evolution. The total detected changes in the beach area between 1984 and 2018 totaled 44 km2, of which ca. 23 km2 is accretion and ca. 21 km2 is erosion. The top 10% “hotspot” ports account for more than 65% of these changes. These hotspots exhibit common characteristics, namely: they are located on open coastlines, have large alongshore sediment transport potential, and have large cross-shore breakwaters. Although these driving characteristics are well established in coastal engineering theory, our results indicate that the beaches adjacent to the existing seaports have been and remain seriously affected by these drivers. Our results can be used to inform beach maintenance strategies for existing seaports and to support planners and engineers to minimize long-term coastal impacts of port expansions and new port developments in Africa in the future.
Publisher: Elsevier BV
Date: 11-2021
Publisher: American Geophysical Union (AGU)
Date: 06-2021
DOI: 10.1029/2021JF006112
Abstract: Evaluating shoreline retreat rate (SRR) on different spatial‐temporal scales is critical for effective coastal management. Large‐scale evaluations typically rely on data‐driven methods such as Discrete Bayesian networks (BNs). However, these BNs require discretization of continuous variables which can lead to information loss. Here, we propose a new method, the Hybrid BN to incorporate continuous variables without discretization. Both Discrete and Hybrid BNs were developed and compared to evaluate large‐scale (continental scale) SRR in Australia, using Digital Earth Australia data set. These BNs used forcing parameters (e.g., waves, tide, sediment sink/source, and sea level rise [SLR]) and geomorphic settings (e.g., geomorphology, backshore profile, and surfzone slope) to predict SRR. Validation of the BNs showed that Hybrid BNs, which provide a more realistic assessment of the range of SRR, outperform in predicting continuous variables, when compared with Discrete BNs. However, Discrete and Hybrid BNs provide consistent qualitative findings for the SRR of Australia. Among forcing parameters, the sediment sink/source was found to be the most informative variable to indicate the shoreline retreat, followed by tide, SLR rate, and wave processes. In the scenario of an increased SLR rate, tropical tidal flats were predicted as the most at risk coasts in Australia. We found that BNs can reflect the impact of different factors on coastal evolution, and predict future shoreline change by exploring historical data. The performance of these models can be further improved when more data sets become available.
Location: Australia
No related grants have been discovered for Yongjing Mao.