Coastal hazards, such as a tsunamis and storm surges, are a critical threat to coastal communities that lead to significant loss of lives and properties. To mitigate their impact, event-driven water level changes should be properly monitored. A tide gauge is one of the conventional water level measurement sensors. Still, alternative measurement systems can be needed to compensate for the role of tide gauge for contingency (e.g., broken and absence, etc.). Global Navigation Satellite System (GNSS) is an emerging water level measurement sensor that processes multipath signals reflected by the water surface that is referred to as GNSS-Reflectometry (GNSS-R). In this study, we adopted the GNSS-R technique to monitor tsunamis and storm surges by analyzing event-driven water level changes. To detect the extreme change of water level, enhanced GNSS-R data processing methods were applied which included the utilization of multi-band GNSS signals, determination of optimal processing window, and Kalman filtering for height rate determination. The impact of coastal hazards on water level retrievals was assessed by computing the confidence level of retrieval (CLR) that was computed based on probability of dominant peak representing the roughness of the water surface. The proposed approach was validated by two tsunami events, induced by 2012 Haida Gwaii earthquake and 2015 Chile earthquake, and two storm surge events, induced by 2017 Hurricane Harvey and occurred in Alaska in 2019. The proposed method successfully retrieved the water levels during the storm surge in both cases with the high correlation coefficients with the nearby tide gauge, 0.944, 0.933, 0.987, and 0.957, respectively. In addition, CLRs of four events are distinctive to the type of coastal events. It is confirmed that the tsunami causes the CLR deduction, while for the storm surges, GNSS-R keep high CLR during the event. These results are possibly used as an indicator of each event in terms of storm surge level and tsunami arrival time. This study shows that the proposed approach of GNSS-R based water level retrieval is feasible to monitor coastal hazards that are tsunamis and storm surges, and it can be a promising tool for investigating the coastal hazards to mitigate their impact and for a better decision making.

Coastal communities in low-lying regions are increasingly vulnerable to severe flooding triggered by high surges and large waves. The sea level rise resulting from climate change induces shorelines to encroach onto coastal land, further exacerbating the flooding damage in coastal areas. Thus, it is necessary to implement coastal structures to mitigate the influence of extreme flooding events on coastal communities. Seawalls, submerged breakwaters, and mangrove forests have been widely constructed worldwide to attenuate wave overflows and their impact on near-coast regions. However, studies on the comprehensive inter-comparison of the protective performance of each measure against flooding events to provide guidelines in coastal design and planning have yet to be limited. Therefore, experimental and numerical models were conducted to investigate the efficiency of natural (mangrove forests) and man-made (seawall and submerged breakwater) coastal structures in reducing the forces, pressures, and hydrodynamics generated by overflows in constructed environments.

Korean beaches have suffered erosion due to coastal development. Recently, the South Korean government has initiated efforts to restore the dune system for a beach on the west coast of Korea. Coastal sand dunes serve as natural barriers against extreme waves caused by climate change and rising sea levels. To design effective artificial sand dunes, understanding the erosion and restoration mechanisms of coastal dunes is crucial. Hence, the COastal sand Dune Experiment in Korea (CODEK), a large-scale two-dimensional movable bed experiment, was conducted to investigate these mechanisms. This study focuses on observing wave run-up affected by dune erosion, particularly the dune scarp during a storm. Wave run-up is a vital parameter for assessing the vulnerability of coastal beaches, dune systems, and structures to dynamic wave action. The aim of this study is to analyze the wave run-up on the dune scarp based on experimental results and propose ways to enhance prediction accuracy using an existing empirical formula.