Development of a web-based modelling framework for harmful algal blooms transport simulation using open-source technologies.

Desktop-based modelling packages presented typical limitations in interactive simulation. This study presents a web-based modelling framework that fully consolidated the simulation work-flow into a WebGIS application, providing a one-step solution for HABs transport simulation within an intuitive and interactive modelling environment. An improved Lagrangian particle-tracking scheme was proposed using fractional Brownian motion technique. The presented model was devoted to quickly forecast the transport pathways in both temporal and spatial dimensions, and evaluate the approximate trends and qualitative understanding of HABs development in data-poor situations. The web modelling platform was developed using multiple open-source JavaScript libraries. The developed WebGIS application provides user-friendly interfaces to prepare inputs, configure simulation settings, visualize, analyse, and validate simulation results within the same framework. The feasibility, capacity, and advantage of the proposed framework were tested and evaluated in a real-world application of red tide transport simulation in the Qinhuangdao coastal waters. The model results showed qualitative agreement with the red tide observed from remote sensing. Our experimental results demonstrated that the developed web-based modelling prototype would present a useful performance for study cases related to HABs transport simulation.

Variation of Suspended Particles in the Bottom Layer of the East China Sea with Data from Seafloor Observatory.

The in situ scattering and transmissometry laser (LISST-100X), equipped with an acoustic wave and current (AWAC) meter and conductivity, temperature, and depth (CTD) instruments over the seabed in the East China Sea, was used to monitor the variation in suspended particles in the bottom sea layer, including particle size distribution (PSD) and volume concentration. The power law approximation was tested to describe the variability in PSD based on the field data. The results show that the power law was robust in processing continuous data, accompanied with the same optimal reference particle size (~63 µm) and little change in the corresponding exponent (~3.4) in both periods. Suspended particles were divided into three types: macroflocs (>133 µm), microflocs (36-133 µm), and single grains (<36 µm). Particle sizes were coarse during the two seasons, with macroflocs representing more than 60% of all the suspended particles, especially in February, when the particle size spectra were usually open-ended. Results from the harmonic analysis method indicate that tidal-induced resuspension and advection are the major reasons for the diurnal dynamics of sediments. Due to the tidal asymmetry in the region, we only found one mode in volume concentration at the moment of maximum velocity. However, the ratios of macroflocs were bimodal, with maximum floods and ebbs in one tidal cycle in February, when the higher mode at the maximum ebbs may be contributed to by the flocculation of finer particles considering the decreasing ratios of finer particles. Due to the enhanced stratification and the clean barrier built up by the Taiwan Warm Current in the southeast corner, the significant daily variation in suspended particles observed in February weakened in September. The influence of waves was uncertain, although the correlation coefficient between significant wave height and volume concentration was about 64% in February.

A Data Quality Control Method for Seafloor Observatories: The Application of Observed Time Series Data in the East China Sea.

With the construction and deployment of seafloor observatories around the world, massive amounts of oceanographic measurement data were gathered and transmitted to data centers. The increase in the amount of observed data not only provides support for marine scientific research but also raises the requirements for data quality control, as scientists must ensure that their research outcomes come from high-quality data. In this paper, we first analyzed and defined data quality problems occurring in the East China Sea Seafloor Observatory System (ECSSOS). We then proposed a method to detect and repair the data quality problems of seafloor observatories. Incorporating data statistics and expert knowledge from domain specialists, the proposed method consists of three parts: a general pretest to preprocess data and provide a router for further processing, data outlier detection methods to label suspect data points, and a data interpolation method to fill up missing and suspect data. The autoregressive integrated moving average (ARIMA) model was improved and applied to seafloor observatory data quality control by using a sliding window and cleaning the input modeling data. Furthermore, a quality control flag system was also proposed and applied to describe data quality control results and processing procedure information. The real observed data in ECSSOS were used to implement and test the proposed method. The results demonstrated that the proposed method performed effectively at detecting and repairing data quality problems for seafloor observatory data.