Fine-Scale Coastal Storm Surge Disaster Vulnerability and Risk Assessment Model: A Case Study of Laizhou Bay, China

Quantitative risk assessment of typhoon storm surge for multi-risk sources

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the "Mangkhut" as a case study, the method's feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the "Mangkhut" case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0-3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents' life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

Vulnerability Analysis of Coastal Zone Based on InVEST Model in Jiaozhou Bay, China

The coastal zone plays an essential part in maintaining the balance of the ecosystem and promoting the development of human society and economy. It is significant to assess the extent to which the Jiaozhou Bay coastal zone withstands floods and coastal erosion during storms. The coastal exposure index (CEI) of the Jiaozhou Bay in 1984, 2000 and 2019 was obtained by the coastal vulnerability model based on data including coastline, bathymetry and coastal terrain elevation. The spatial distribution and aggregation characteristics of CEI in Jiaozhou Bay were analyzed through spatial autocorrelation analysis. The results show that the north coast of Jiaozhou Bay is highly vulnerable, that is, prone to coastal erosion, while the south and east are less vulnerable, meaning that they can basically withstand natural disasters such as storm surges and floods. The CEI shows significant spatial autocorrelation, with little spatial heterogeneity. The type of coastline, elevation, distance to continental shelf and socio-economic development are the main factors that cause the north–south vulnerability differences in the Jiaozhou Bay coast. The results can identify the districts along the Jiaozhou Bay that are at greater risk of marine disasters, and provide scientific theoretical support for the coastal protection and sustainable development of the Jiaozhou Bay.

Establishing a Risk Assessment Framework for Marine Assets and Assessing Typhoon Lekima Storm Surge for the Laizhou Bay Coastal Area of the Bohai Sea, China

Effective risk assessment can reduce the economic losses and physical damage caused by marine dynamic processes, such as storm surges. Most risk assessments of marine disasters are based on regional parameters and discrete hazard grades. Targeted, multilevel, and multiangle risk assessments are urgently needed. This study focuses on specific types of affected infrastructure. We established a sensitivity matrix by considering the effects of different disaster causal factors on different types of affected infrastructure. Through this matrix, hazards, vulnerability, and emergency response and recovery capability were effectively combined in a risk assessment framework. We completed the risk calculation for multiple concurrent effects of disasters in areas with superimposed key infrastructure using complementary risk superposition. The hazard grade, vulnerability grade, and coefficient of emergency response and recovery capability were established based on the means of return period, characteristics of disaster distribution, types of affected infrastructure and disaster relief distance, and were continuous by solving functions, normal cumulative distributions, and analytic functions. On the basis of reasonable MIKE21 numerical simulation and abstract spatial distribution of vulnerable assets, we tested the rationality of the assessment system in the Lekima typhoon storm surge process. The results showed that the assessment system accurately reflected the risk of damage to the important infrastructure in terms of spatial distribution. Therefore, this risk assessment framework was suitable for the assessment of a marine dynamic disaster process in the lower Laizhou Bay coastal area of the Bohai Sea, China. Moreover, it provided a reference for disaster prevention and reduction, guided the way for decision making, and effectively reduced disaster losses.

Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals

Remote sensing (RS) technology, which can facilitate the sustainable management and development of fisheries, is easily accessible and exhibits high performance. It only requires the collection of sufficient information, establishment of databases and input of human and capital resources for analysis. However, many countries are unable to effectively ensure the sustainable development of marine fisheries due to technological limitations. The main challenge is the gap in the conditions for sustainable development between developed and developing countries. Therefore, this study applied the Web of Science database and geographic information systems to analyze the gaps in fisheries science in various countries over the past 10 years. Most studies have been conducted in the offshore marine areas of the northeastern United States of America. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. This study also found that research hotspots of satellite RS applications in fisheries were mainly conducted in (1) the northeastern sea area in the United States, (2) the high seas area of the North Atlantic Ocean, (3) the surrounding sea areas of France, Spain and Portugal, (4) the surrounding areas of the Indian Ocean and (5) the East China Sea, Yellow Sea and Bohai Bay sea areas to the north of Taiwan. A comparison of publications examining the three major oceans indicated that the Atlantic Ocean was the most extensively studied in terms of RS applications in fisheries, followed by the Indian Ocean, while the Pacific Ocean was less studied than the aforementioned two regions. In addition, all research hotspots were located in the Northern Hemisphere, indicating a lack of relevant studies from the Southern Hemisphere. The Atlantic Ocean and the Indian Ocean have been the subjects of many local in-depth studies; in the Pacific Ocean, the coastal areas have been abundantly investigated, while offshore local areas have only been sporadically addressed. Collaboration and partnership constitute an efficient approach for transferring skills and technology across countries. For the achievement of the sustainable development goals (SDGs) by 2030, research networks can be expanded to mitigate the research gaps and improve the sustainability of marine fisheries resources.

Coastal Flooding Risk Assessment Using a GIS-Based Spatial Multi-Criteria Decision Analysis Approach

Coastal areas are expected to be at a higher risk of flooding when climate change-induced sea-level rise (SLR) is combined with episodic rises in sea level. Flood susceptibility mapping (FSM), mostly based on statistical and machine learning methods, has been widely employed to mitigate flood risk; however, they neglect exposure and vulnerability assessment as the key components of flood risk. Flood risk assessment is often conducted by quantitative methods (e.g., probabilistic). Such assessment uses analytical and empirical techniques to construct the physical vulnerability curves of elements at risk, but the role of people’s capacity, depending on social vulnerability, remains limited. To address this gap, this study developed a semiquantitative method, based on the spatial multi-criteria decision analysis (SMCDA). The model combines two representative concentration pathway (RCP) scenarios: RCP 2.6 and RCP 8.5, and factors triggering coastal flooding in Bandar Abbas, Iran. It also employs an analytical hierarchy process (AHP) model to weight indicators of hazard, exposure, and social vulnerability components. Under the most extreme flooding scenario, 14.8% of flooded areas were identified as high and very high risk, mostly located in eastern, western, and partly in the middle of the City. The results of this study can be employed by decision-makers to apply appropriate risk reduction strategies in high-risk flooding zones.