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Quantitative real-time observations of a tsunami have been limited to deep-water, pressure-sensor observations of changes in the sea surface elevation and observations of sea level fluctuations at the coast, which are essentially point measurements. Constrained by these data, models have been used for predictions and warning of the arrival of a tsunami, but to date no detailed verification of flow patterns nor area measurements have been possible. Here we present unique HF-radar area observations of the tsunami signal seen in current velocities as the wave train approaches the coast. Networks of coastal HF-radars are now routinely observing surface currents in many countries and we report clear results from five HF radar sites spanning a distance of 8,200 km on two continents following the magnitude 9.0 earthquake off Sendai, Japan, on 11 March 2011. We confirm the tsunami signal with three different methodologies and compare the currents observed with coastal sea level fluctuations at tide gauges. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends on the bathymetry: the wider the shallow continental shelf, the greater this time. Data from these and other radars around the Pacific rim can be used to further develop radar as an important tool to aid in tsunami observation and warning as well as post-processing comparisons between observation and model predictions. [ABSTRACT FROM AUTHOR]

Sediment plumes, released to the Bering Sea from the delta front of the Yukon River, Alaska, are initiated mainly by glacier-melt sediment runoffs in the glacierized regions of the Yukon River drainage basin. The surface sediment plumes are extended around the fan-shaped Yukon River delta, which is followed by the northwestward dispersion. During continuous measurements of the Yukon River discharge and sediment load, behaviors of the sediment plumes were explored by shipboard and coastal observations in the Bering Sea. At the high river sediment load of ca. 2500 kg/s, the plume partially plunged into the sea bottom layer. The plunging probably originated in the nepheloid-layer formation from the flocculation of river-suspended sediment, of which more than 90% wt. is silt and clay (grain size d < 63 μm). In order to numerically obtain the area of the surface sediment plumes, a satellite image analysis was performed by using three near-infrared bands in MODIS/Aqua or MODIS/Terra. The plume area was significantly correlated (R2 = 0.735, p < 0.01) to the sediment load averaged for the two days with time lags of 20 days and 21 days to the date of a certain satellite image. Hence, the dispersion of plume-suspended sediment appears to be controlled by the sediment runoff events in the Yukon River rather than the northward "Alaskan Coastal Water". [ABSTRACT FROM AUTHOR]

The green mussel (Perna viridis) is one of the most commercially-important cultured species along the coast of Thailand. In this study, a suitable aquaculture site-selection model (SASSM) was developed to identify the most suitable areas in the inner part of the Gulf of Thailand (InnerGoT) for green mussel culture. Satellite-derived chlorophyll-a (Chl-a) and hydrodynamic model outputs for sea surface temperature (SST), salinity, maximum water current (MWC), and bathymetry between 2018 and 2019 were used as input to the SASSM. The results show that suitability scores in mussel aquaculture areas were lowest (1–3) during the Southwest (SW) monsoon, rainy season (July–August), and highest (6–7) during the Northeast (NE) monsoon, cold season (November–December). Moderate suitability scores (4–5) were obtained during the monsoon transition from the NE monsoon to the SW monsoon, summer (April–May). The study area was further divided into three zones: the western, central, and eastern regions. The western and eastern parts showed high suitability scores (5–7) while the central zone exhibited low suitability scores (2–4). The model results show a similar pattern to the actual mussel production in the study area. Seasonal events (i.e., flood and dry seasons) were incorporated into the model to examine the seasonal effects on the suitable mussel aquaculture areas. The suitability scores during the SW monsoon in 2018 were more sensitive to changes in SST and salinity relative to 2019. The higher freshwater discharge and lower temperature in 2018 relative to 2019 resulted in the accrual of suitable aquaculture areas. This pattern is consistent with the productions of the green mussel, where higher production was recorded in 2018 (2002.5 t) than in 2019 (410.8 t). However, correlations among atmospheric (air temperature, rainfall, and wind) and oceanographic factors (SST and MWC) were significant in the western and central regions, suggesting that the suitability of green mussel aquaculture in these regions is vulnerable to environmental disturbances. Thus, the SASSM can be a powerful tool in providing useful information on spatial management for marine aquaculture in environmentally-dynamic coastal systems. [ABSTRACT FROM AUTHOR]

To examine skipjack tuna's habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen's kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm. [ABSTRACT FROM AUTHOR]

The assessment of extreme weather events on suitable sites for aquaculture could help in establishing sustainable coastal environmental resource management. Japanese scallop culture is an economically important marine farming activity in the coastal communities of Shandong, China and Funka Bay, Japan. In this study, we improved the suitable aquaculture site-selection model (SASSM) by using Geostationary Ocean Color Imager (GOCI) data instead of Moderate Resolution Imaging Spectroradiometer (MODIS) data, as a complementary source for higher temporal and spatial resolution data that are useful for monitoring fine-scale coastal and oceanic processes. We also applied the newly developed SASSM to the Japanese scallop production site along the Shandong coast. Finally, we analyzed the correlations between environmental factors (chlorophyll a concentration, sea surface temperature (SST), and total suspended sediment), meteorological factors (precipitation, temperature, and wind), and climatic events (winter East Asian monsoon (EAM) and El Niño/La Niña Southern Oscillation), and the impacts of climate events on suitable zones for scallop aquaculture. The new SASSM maps show that GOCI products have the potential for oceanographic investigations in Shandong, China and Funka Bay, Japan. Our results highlighted higher aquaculture site suitability for scallop in Funka Bay than in Shandong coast. During the winter with a strong EAM (2011), the suitable area for Japanese scallop aquaculture increased. Conversely, in the winter during a strong El Niño (2016), we found fewer areas that were highly suitable for scallop aquaculture in Funka Bay. SST was extremely low in Funka Bay during spring and summer 2017, which caused fewer highly suitable areas (scores of 7 and 8) for scallop aquaculture relative to other years. These findings suggest that extreme climatic events significantly impact the availability of suitable sites for marine farming and thus, should be considered in the development and design of coastal aquaculture sites. [ABSTRACT FROM AUTHOR]

Radiometric calibration utilizing the Moon as a reference source is termed as lunar calibration. It is a useful method for evaluating the performance of optical sensors onboard satellites orbiting the Earth. Lunar calibration provides sufficient radiometric calibration opportunities without requiring any special equipment, and is suitable for nano/microsatellites. This study applies lunar calibration to a multispectral sensor, Ocean Observation Camera (OOC), on board a microsatellite named Rapid International Scientific Experiment Satellite. Simulating the brightness of the Moon based on the RObotic Lunar Observatory and SELENE/Spectrum Profiler models, sensitivity degradation was proven to be negligible in any of the four spectral bands of the OOC with the sensor temperature correction. A bluing trend in the OOC's sensor sensitivity was revealed, indicating a shorter observation wavelength shows larger irradiance. Comparing the top-of-atmosphere reflectance of Railroad Valley Playa with the Radiometric Calibration Network dataset revealed that the derived calibration parameter from the lunar calibration was valid for correcting the bluing trend in the visible range. Although the lunar and vicarious calibration parameters for the infrared band were unexpectedly inconsistent, lunar calibration could potentially contribute toward estimating the contaminated background radiance in the Earth observation images.