Complex Coastline Research Articles

SRMA: a dual-branch parallel multi-scale attention network for remote sensing images sea-land segmentation

ABSTRACT The use of deep learning-based high resolution remote sensing image sea and land segmentation method has become prevalent in various fields such as environmental monitoring, resource assessment, and urban planning. However, the segmentation process faces challenges due to the complex coastline and the similarity in spectral colour difference between the sea and land in certain areas. The remote sensing images obtained from satellites often exhibit unclear boundaries between the sea and land, as well as intricate coastlines. Consequently, the segmentation of sea and land becomes a difficult task. The existing sea-land segmentation methods have limitations in accurately segmenting long and narrow waters, as well as areas with low differentiation between sea and land. In this paper, we propose a new sea-land segmentation method called SRMA (Swin-Res-Multi-Attention). SRMA performs parallel feature extraction using a dual-branch parallel: Swin-Transformer branch and ResNet branch. The combination of Swin-Transformer’s self-attention and ResNet’s residual design helps reduce the loss of feature information, thereby avoiding incorrect segmentation in regions with low distinctness. To improve overall stability, we introduce the MCA (Multiscale channel attention module) module, which combines feature information from different scales. The multi-scale pyramid design of MCA module ensures stability and improves segmentation accuracy in narrow waters. Experimental results on a public dataset demonstrate that our model outperforms existing methods. Additionally, we evaluate the model’s performance in complex scenarios by creating a new dataset that includes a complex urban water body and cloud interference. The results showcase the robustness and superiority of our model.

Advancing storm surge forecasting from scarce observation data: A causal-inference based Spatio-Temporal Graph Neural Network approach

Rapid and precise forecasting of storm surge in coastal regions is crucial for ensuring safety of coastal communities’ life and property. Yet, learning a data-driven forecasting model from observation data such as gauges and post-event reconnaissance remains challenging, due to the observation data scarcity and the real-world complexity. Recently, deep learning has received increasing attention, but existing deep learning approaches solely focus on individual site scenarios, ignoring the value of information contained in neighboring sites’ observations. In this study, we propose to integrate graph neural networks (GNN) and gated recurrent unit (GRU) to capture the spatial and temporal storm surge dependencies across multiple observation stations. GNN provides the unique capability to model non-Euclidean complex spatial relationship across observation stations, while GRU enhances the data efficiency of temporal dependency modeling. To account for the effect of complex coastline topography, the Liang–Kleeman information flow theory is employed to establish a causal-inference based graph edge scheme connecting multiple observation stations. The Causal-inference based Spatio-Temporal Graph Neural Network (CSTGNN) were trained and evaluated on 13-year observation data from 4 observation stations along Florida coastline. Experiments affirm the competence of CSTGNN, which outperformed six commonly used competitive baselines across different metrics and observation stations, under lead times up to six hours. Furthermore, benefits of capturing the spatial dependency and leveraging causal inference are also comprehensively examined. To conclude, we believe that this novel spatio-temporal forecasting framework can result in enhanced coastal resilience by its improved storm surge forecasting capability.

Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic

The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale.

Connecting Land and Sea Vertical Datums: A Data Evaluation for Developing Australia’s AUSHYDROID Model

Land and sea vertical datums have traditionally been separate reference levels, due to the different methodologies and observations used in their derivations. However, the need for a seamless connection of these datums has become important due to a wide range of applications in the coastal zone. This study evaluates existing methods, oceanographic and geodetic models and observations for the development of an operational model of coastal heights called AUSHYDROID. We use a complex study area in north west Australia to gain a new insight into how accurately the separation between the lowest astronomical tide (LAT) and a reference ellipsoid can be estimated from existing models, which could form the basis of a national AUSHYDROID model for Australia. Our results are the first attempt to use this combination of existing data in this location, suggesting that ellipsoidal heights of LAT can be estimated to an accuracy of ∼0.2 m at the coast, with the combination of DTU18MSS and FES2014b having the lowest RMS of 0.125 m. However, in some complex coastline areas such as bays and estuaries, the differences increase to >0.5 m so that additional tide gauge observations with GNSS levelling connections and improved models are needed in these regions.

Simulating dispersal in a complex coastal environment: the Eastern Shore Islands archipelago

Abstract The Eastern Shore Islands (ESI) archipelago on the Scotian Shelf supports a rich variety of biogenic habitats and associated diversity of coastal species. The unique and complex geometry of the ESI coastline has a significant impact on circulation and, correspondingly, influences the dispersal of nearshore organisms. For many coastal areas, the ability to accurately resolve the dispersal processes is contingent on the availability of oceanographic models that can resolve fine-scale coastal boundary conditions, including coastlines and bathymetric features. We applied a high-resolution ocean circulation model and Lagrangian particle tracking in the ESI to simulate dispersal of nearshore organisms. Our results revealed predominant southwest–northeast transport that was associated with a nearshore reversal flow. While transport among different zones of the study region is mainly determined by residual currents over the long term, tidal currents dominate patterns of particle dispersal over shorter time scales. An analysis of Lagrangian coherent structures found that they were consistently associated with the mouths of bays, demonstrating that the islands and associated oceanographic processes promote self-retention. These results highlight how complex coastlines and associated oceanographic processes promote retention and underline the need to resolve these fine-scale physical and oceanographic features when estimating biophysical dispersal in the coastal environment.

The applicability of ALARO and AROME wind-fields in multi-scale waves forecasting in open sea and coastal areas of the southern Baltic Sea

The main objective of the study was to implement the wave model in two independent systems fed respectively by wind fields with a 4 km resolution in the Baltic-wide area and a 2 km resolution in the shallow coastal regions of the southern Baltic for operational work. The study included the selection the best parameterization, which was carried out using measured data and model data from the verified SWAN-COSMO system. The chosen best parameterization of the model, including the time step, iteration, wind scaling, dispersion coefficients and swell formula, among others, allowed the identification of the optimal system that guarantees the most reliable predictions. The most reliable forecasts reproducing real conditions, both for the model powered by wind fields with a resolution of 4 km and 2 km, were obtained for the configuration characterized by the parameters: 30-min time step, 3 iterations, a wind scaling factor of 32 using DEBIAS equal to 0.89, coefficients related to local dissipation and cumulative of 2.8 × 10–6 and 3.5 × 10–5, respectively, and Ardhiun's swell formula of 1.20. The values of the correlation coefficient with both the measured data and the results of the comparative model for significant wave height and wave period, respectively, were no lower than: 0.95 and 0.73 (Petrobaltic) and 0.96 and 0.90 (Pomeranian Bay). Root mean square error (RMSE) values were <0.23 m and <0.20 s (Petrobaltic) and <0.20 m and <0.74 s (Pomeranian Bay) for significant wave height and mean wave period, respectively. Analysis of the forecast results showed some limitations in using the mesoscale model in coastal areas, and the solution was to use the high-resolution model in this zone. It was shown that the stand-alone high-resolution model (domain: southern Baltic Sea) obtained similar results to the same model system but nested in the mesoscale model (domain: Baltic Sea). In addition, this model showed shorter simulation computation time and represents an independent, less vulnerable to discontinuity in forecast generation (lack of assimilation of boundary and initial conditions from a lower resolution model). A noticeable improvement in the quality of forecasts was shown by the AROME wind-field-powered model, which, due to its high resolution of the computational grid and wind data, maps well the wave dynamics in areas with limited wave exposure (the Vistula Lagoon, the Puck Lagoon) or complex coastline (the Hel Split) and seabed (the shallows of the Pomeranian Gulf and the Słupsk Shoal. The coupled data from the ALARO and AROME models, processed internally at IMWM-NRI, provide continuity and independence from global models and indicate applicability in generating wave forecasts by other Baltic countries.

Estimation of Coastal Area and Lake Water Level Changes Using High-Resolution Altimetry

Radar satellite altimeters are widely used in offshore areas, whereas they are underutilized in coastal areas due to a number of interference factors. Altimeter satellite data can be used to summarize elevation information at 1 Hz for offshore areas, but for areas close to land, it is more effective to utilize imagery with a resolution of 20 Hz to provide a more detailed representation. The use of highresolution satellite altimeter data is expected to increase the amount of data available for hydrological data such as complex coastlines and small lakes. Therefore, in this study, we investigated the applicability of 20 Hz altimeter data in the Korean Peninsula. First, the accuracy was analyzed by comparing the 20 Hz altimeter data from the Jason-3 satellite with the Ulleungdo tide data. Second, we compared the 20 Hz altimeter data from the Sentinel-3A satellite with the water level data of Soyang Lake to see if it can be applied to land areas. In the case of inland lakes, the water level is estimated to be affected by the discharge volume due to heavy rainfall in summer, and it was determined that the satellite altimeter data can be utilized. Therefore, utilizing the data from this study is expected to improve the accuracy of hydrological analysis in coastal and lake environments.

THE COASTAL MACRO-VORTICES DYNAMICS: A CASE STUDY IN HONG KONG WATERS

Coastal circulations are basic information when considering sediment transport and water quality in the oceanic environment, particularly the macro-vortices dynamics which greatly affected the mass transfer and extended the residence time (Hasegawa et al., 2009). Large-scale macro-vortices could be formed in the lee or around natural obstacles such as islands and headlands during the interaction between geographical features and coastal circulations induced by tides and winds. Pearl River is the second largest river in China considering discharge, delivering 3.26 × 1011 m3 freshwater every year into the South China Sea (SCS) through eight outlets (Wu et al., 2016). Hong Kong (HK) is located downstream of the Pearl River Estuary (PRE), acting as a bridge between the PRE and the SCS, as shown in Figure 1. HK possesses more than 200 islands as well as narrow channels and complex coastlines, which have a great impact on shaping local flow structures. In addition, prevailing southwest winds blow during the summer, while stronger northeast winds gust during the winter. Embracing tides from the SCS and river discharge from the PRE, HK waters is an energetic and interesting area and worth a detailed investigation.

The Role of Density Currents and Gravity Waves in the Offshore Propagation of Convection over Sumatra

Abstract The Maritime Continent experiences some of the world’s most severe convective rainfall, with an intense diurnal cycle. A key feature is offshore propagation of convection overnight, having peaked over land during the evening. Existing hypotheses suggest this propagation is due to the nocturnal land breeze and environmental wind causing low-level convergence; and/or gravity waves triggering convection as they propagate. We use a convection-permitting configuration of the Met Office Unified Model over Sumatra to test these hypotheses, verifying against observations from the Japanese Years of the Maritime Continent field campaign. In selected case studies there is an organized squall line propagating with the land-breeze density current, possibly reinforced by convective cold pools, at ∼3 m s−1 to around 150–300 km offshore. Propagation at these speeds is also seen in a composite mean diurnal cycle. The density current is verified by observations, with offshore low-level wind and virtual potential temperature showing a rapid decrease consistent with a density current front, accompanied by rainfall. Gravity waves are identified in the model with a typical phase speed of 16 m s−1. They trigger isolated cells of convection, usually farther offshore and with much weaker precipitation than the squall line. Occasionally, the isolated convection may deepen and the rainfall intensify, if the gravity wave interacts with a substantial preexisting perturbation such as shallow cloud. The localized convection triggered by gravity waves does not generally propagate at the wave’s own speed, but this phenomenon may appear as propagation along a wave trajectory in a composite that averages over many days of the diurnal cycle. Significance Statement The intense convection experienced by the Maritime Continent causes high-impact weather in the form of heavy precipitation, which can trigger floods and landslides, endangering human life and infrastructure. The geography of the region, with many islands with complex coastlines and orography, means that the spatial and temporal distributions of convection are difficult to predict. This presents challenges for operational forecasters in the region and introduces biases in weather and climate models, which may propagate globally. A key feature of the convection is its diurnal cycle and associated propagation offshore overnight from the islands. Although this phenomenon has been often investigated, there is no strong consensus in the literature on the mechanism or combination of mechanisms responsible. Improving our knowledge of these mechanisms and how they are represented in a convection-permitting model will assist forecasters in understanding how and when the propagation of intense convective storms occurs, and allow model developers to improve biases in numerical weather prediction and climate models.

Assessing the coastal hazard of Medicane Ianos through ensemble modelling

Abstract. On 18 September 2020, Medicane Ianos hit the western coast of Greece, resulting in flooding and severe damage at several coastal locations. In this work, we aim at evaluating its impact on sea conditions and the associated uncertainty through the use of an ensemble of numerical simulations. We applied a coupled wave–current model to an unstructured mesh, representing the whole Mediterranean Sea, with a grid resolution increasing in the Ionian Sea along the cyclone path and the landfall area. To investigate the uncertainty in modelling sea levels and waves for such an intense event, we performed an ensemble of ocean simulations using several coarse (10 km) and high-resolution (2 km) meteorological forcings from different mesoscale models. The performance of the ocean and wave models was evaluated against observations retrieved from fixed monitoring stations and satellites. All model runs emphasized the occurrence of severe sea conditions along the cyclone path and at the coast. Due to the rugged and complex coastline, extreme sea levels are localized at specific coastal sites. However, numerical results show a large spread of the simulated sea conditions for both the sea level and waves, highlighting the large uncertainty in simulating this kind of extreme event. The multi-model and multi-physics approach allows us to assess how the uncertainty propagates from meteorological to ocean variables and the subsequent coastal impact. The ensemble mean and standard deviation were combined to prove the hazard scenarios of the potential impact of such an extreme event to be used in a flood risk management plan.

A Single-Column Method to Identify Sea and Land Breezes in Mesoscale-Resolving NWP Models

Abstract One of the most prominent mesoscale phenomena in the coastal zone is the sea-breeze/land-breeze circulation. The pattern and its implications for the weather in coastal areas are well described, and with mesoscale-resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of the land–sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes. Significance Statement A wind pattern that is frequently occurring in the coastal zone is the sea-breeze/land-breeze circulation, and the purpose of this study is to test a new method to automatically identify sea breezes and land breezes in weather forecasts. Knowing if a sea breeze or a land breeze is occurring is helpful for the operational weather forecaster in understanding the weather situation. It can also be used to study systematic model behavior, for example, errors in the forecast temperature during sea-breeze conditions. The method has been tested for seven coastal sites in Sweden and shows promising results both in case studies and multiyear statistics.

Coastex wave calculation — A cost-efficient method for computing high-resolution wave conditions in coastal regions

Accurate and reliable information about ocean surface waves is crucial for marine activities in coastal regions. In areas with complex coastlines, where fjords, islets and strong bathymetry gradients affect the wave field, a high-resolution computational grid is needed to generate model results that agree with observations. For traditional wave modelling this requires enormous computational power, often resulting in insufficient spatial resolution at the most complex fjord and coastal regions. We present an alternative method for coastal wave calculation, the Coastex Wave Calculation, where the wave field is calculated from a set of predefined sea states given by wave properties at an offshore position and the local wind field. We assess four different versions of the Coastex method, using wave observations from five measurement sites at the western coast of Norway, and two control runs using a traditional model setup on a high-resolution computational grid. For our study area we find that deviations between the results of the Coastex calculations and the control runs are small: At the most exposed location the mean error, the mean absolute error and the root mean square error of the significant wave height are in the range 0.06 m to 0.10 m, 0.26 to 0.28 m and 0.34 m to 0.36 m, respectively (Coastex) and 0.08 m to 0.10 m, 0.24 m to 0.26 m and 0.31 m to 0.33 m for the control runs. At the most sheltered location the mean error of the Coastex hindcast ranges from −0.07 m to −0.02 m, compared to −0.05 m for the control runs, the mean absolute error is 0.08 m and 0.09 m (Coastex) and 0.07 m (control runs) and the root mean square error is 0.11 m and 0.12 m (Coastex) and 0.10 m for the control runs.

A numerical study on the responses of coastal water quality to river runoff after heavy rainfall in the case of a complex coastline with two artificial islands

Heavy rainfall causes large volumes of freshwater and nutrient salts to flow from rivers into the sea. This leads to major changes in the ecological environment of estuarine waters in a short period of time. The responses of the estuarine hydro-environment become more complex with the concentrated construction of artificial structures, which is a major cause of marine ecological disasters. This paper considers the Tang Estuary in Qinhuangdao, where artificial structures (e.g., ports, artificial islands, and bridges) are concentrated, as a prototype with the weak tidal dynamic environment. A nested prototype of hydrodynamics and water quality was established using a Delft3D model, with coupled waves and flows. The model was used to simulate the spatial and ephemeral characteristics of the short-term responses of ecological environmental factors, such as dissolved inorganic nitrogen (DIN), nitrate-nitrogen (i.e., NO3-N), ammonium-nitrogen (NH4-N), and orthophosphate (i.e., PO4-P), to an episode of heavy rainfall in August 2022. The results showed that concentrations of DIN and NO3-N in the source areas remain high after the flooding process. The concentration recovered to normal values approximately 5.5 days after the flooding process. In contrast, NH4-N and PO4-P showed a response with ephemeral correspondence with the flooding process, and concentration recovery took only approximately 1 day. This paper proposes two short-term response modes of pollutant diffusion, which provide help in exploring the role of hydro-environmental changes in offshore algal hazards and the effect of permeable buildings on pollutant dispersion. This additionally provides possibilities with the forecasting of red tide and green tide, and for their prevention in the future.

Analysis of China’s Coastline Changes during 1990–2020

As the boundary between the sea and the land, information on the location and type of coastline constantly changes with environmental changes in coastal zones. Monitoring of coastline changes in long time series becomes important for the monitoring and assessment of the coastal zone environment. In this study, Landsat series images from five time periods (1990, 2000, 2010, 2015, and 2020) were selected for monitoring and analyzing the changes in coastline length, sea–land pattern, the index of coastline diversity, and fractal dimension characteristics. Our conclusions are as follows: (1) The lengths of the entire coastline and the artificial coastline of mainland China increased from 30,041.22 km and 10,022.49 km in 1990 to 32,977.34 km and 17,660.84 km in 2020, with annual change rates of 97.87 km/year and 254.61 km/year, respectively. From 1990 to 2020, the rate of natural coastline decreased from 66.68% to 42.29%, and the artificial coastline increased from 33.32% to 57.71%. (2) The length of natural coastline decreased from 20,018.73 km to 15,316.5 km; among the types of natural coastline, the length of sandy coastline and bedrock coastline decreased the most, at 2062.95 km and 1815.8 km, respectively. (3) The coastal zone of mainland China had a significant increase in land area, with a net increase of about 10,902.55 km2. (4) The index of coastline diversity continued to decrease, and the coastline structure tended to be simple. The fractal dimension of the mainland coastline was consistent with the trend of the length of the coastline, which basically shows an increasing trend. Therefore, the length of the mainland coastline and artificial coastline displayed an upward trend between 1990 and 2020, which also led to simpler coastline diversity and more complex coastline shapes. Since the first year of the SDGs (2015), the growth rate of the artificial coastline has decreased by 158.32 km/year compared with that between 2010 and 2015. In recent years, China has enacted a number of laws, regulations, and action plans to protect its coastline, and it has proposed that by 2020, the proportion of natural coastline will be no less than 35%. The rapid development of China’s coastal areas drives the construction of coastal zone cities but also creates a variety of challenges for the ecological environment of the coastal zone, and the management and sustainable use of the mainland coastline resources should be further strengthened.

Comparative Analysis of Forecasting Accuracy and Model Performance for Development of Coastal Wave Forecasting System Based on Unstructured Grid

We develop a coastal wave forecasting system by using the unstructured grid based on sea wind data of Global Data Assimilation and Prediction System. The verification is performed to examine the performance and accuracy of the wave model. Since the conventional grid has limited wave forecasting on complex coastlines and bathymetry, the unstructured grid system is applied for precise numerical simulation, and applicability for operational support is evaluated. Both grid systems show similar prediction trends in offshore and coastal areas, and the difference in prediction errors according to the grid system is not large. In addition, the applicability of the operational wave forecasting system is confirmed by dramatically reducing the model execution time of the unstructured grid under the same conditions.

Wave Transformation behind a Breakwater in Jukbyeon Port, Korea—A Comparison of TOMAWAC and ARTEMIS of the TELEMAC System

Severe shoreline erosions are commonly observed due to the side effects of breakwaters constructed to protect the habitat. These breakwaters can cause wave energy differences behind the structure due to diffraction, inducing longshore sediment transport and resulting in shoreline changes. Therefore, it is essential to correctly simulate the effect of wave transformation in the lee side of structures, but such studies reporting performance of models in the field have been relatively rare. In this study, two wave models of the TELEMAC system were used to investigate the accuracy of modeling the wave transformation effect in a lee area of a breakwater built to secure the harbor’s tranquility, near Jukbyeon Port in Korea, through comparisons with field observations. Two cases of wave conditions with different wave heights and directions were tested. In both cases, the TEL EMAC–ARTEMIS model had lower errors than TELEMAC–TOMAWAC at the onshore wave location, confirming that the phase-resolving ARTEMIS showed better performance in simulating the wave transformation than the phase-averaged TOMAWAC, as expected. However, ARTEMIS had slightly higher errors than TOMAWAC at the offshore location, probably due to the interference by reflected waves from the complex coastlines formed by the different coastal structures. The results also provide various implications learned from the numerical experiments, which can be usefully applied to engineering aspects, such as for the estimation of harbor tranquility.

Multi-scale imaging of 3-D electrical conductivity structure under the contiguous US constrains lateral variations in the upper mantle water content

The magnetotelluric (MT) component of the USArray survey is a unique data set that enables imaging the electrical conductivity distribution from the surface down to astenospheric depths. Here, we present a new 3-D electrical conductivity model (MECMUS-2022) derived by inverting data from 1291 USArray MT stations covering ∼80% of the contiguous United States on a quasi-regular 70-km grid. The inversion was performed using a novel multi-scale imaging approach that can consistently incorporate a large range of spatial scales and perform 3-D modeling directly in the spherical frame, completely avoiding the conventional flat-Earth assumption. Furthermore, the use of locally refined meshes allows us to take into account the complex coastline and mimic the natural resolution footprint of broadband MT transfer functions. We find conductivity variations that correlate with known continental structures such as due to the active tectonic processes within the western United States (e.g., Yellowstone hotspot, Basin and Range extension, and subduction of the Juan de Fuca slab) as well as the presence of deep roots beneath cratons. We further interpreted conductivity variations in terms of the upper mantle water content by coupling electrical conductivity with constrains on mantle thermo-chemical structure derived from the analysis of seismic data (in the form of P-to-s and S-to-p receiver functions). Our results suggest the existence of a relatively dry upper mantle beneath the United States, with a weak trend in the upper mantle water content from North to South.

A Study on Geo-morphological Analysis and Risk Assessment Method Using Geospatial Information

Rock platforms and TTP (breakwater) are dangerous environments commonly subject to tidal and high wave energy on the open coast. This paper is a study on risk assessment to provide risk information, which is a representative method for preventing coastal safety accidents. Risk assessment based on geo-morphological information was conducted for the Halmi-Halabi rock platform in Anmyeon-eup, Taean-gun, Chungcheongnam-do. As a result of the risk assessment in 16 directions, the risk was evaluated high in the NE, ENE, S, SSW, and W directions, where there are many sections with slopes exceeding ±20o, and the NW direction was the lowest. Geo-morphology on rock platform is central to understanding what makes one stretch of complex coastline more hazardous than another, and it can be used to create site-specific morphological risk item. In particular, it will be assisting coastal managers in an effort to reduce the number of injuries and drowning incidents by providing hazard information to assess the relative risk.

Construction of Tidal Information Database on the Southern Coast of Korea and Prediction of the Tide

The construction of the tidal information database study was conducted through numerical simulations for the southern coast of Korea. Because Korea has a complex coastline, the grid size of the numerical model was set to 0.1min(approx. 200m) to improve the accuracy of tidal prediction. The NAO.99jb, one of the ocean tide models, was employed in the study to increase the accuracy of the database. The numerical model applied to the database construction was calibrated and validated using observation data from the Korea hydrographic and oceanographic agency. To construct a high-resolution tidal information database, we conducted harmonic analysis for each computational grid point using numerical simulation results. In order to evaluate the tide and tidal current prediction performance of the tidal information database, numerical simulation results were used and compared. The results show that the predictive performance of the harmonic constant database is sufficient, so it can be used where rapid tidal prediction is required.

Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models

This study aimed to establish numerical models to replicate wind conditions for nearshore waters, sensitive to onshore topography, and to compare the characteristics of computational fluid dynamic (CFD) and mesoscale models. Vertical Doppler light detection and ranging (LiDAR) observation data were measured at an onshore site, which showed that wind conditions were affected by thermodynamic phenomena, such as land and sea breeze, and dynamical effects from neighboring onshore topography. The estimation accuracy of the CFD model depended on the height of the LiDAR data input. A height close to the target, such as the hub height of wind turbines, seemed appropriate as input data, considering that the accuracy of the wind speed shear replicated in a CFD numerical model may be uncertain. The mesoscale model replicated the wind through the thermodynamic effect and reliably estimated wind speed over nearshore waters without observation correction. Larger estimation errors were detected in the CFD model than in the mesoscale model, as the former could not account for thermodynamic effects. Wind conditions in water areas near complex coastlines may also be formed by thermodynamic factors, making analysis using a mesoscale model advantageous.