## 4 Discussion
### Comparison with Other Forewarning Systems  
The aim of this study is mainly to provide a simple fast forewarning system for brown tide. A similar method was also studied by [PERSON] et al. [56] in their forewarning system, with the indicator of Chl-a by establishing a coupled physical-chemistry-eco-hydrodynamic module to simulate the growth, development, and decline of harmful algal bloom. The method for monitoring red tide and green tide has developed over many years, but brown tide has rarely been studied. Nowadays, satellite technology can enable the large-scale simultaneous monitoring of green tide [57]. During the bloom period in the Huanghai Sea, the satellite data of MERSI and HJ/CCD were applied to monitor green tide migration [58]. [PERSON] et al. [59] took advantage of satellites combined with a model to monitor the chlorophyll and presented the medium resolution continental shelf (MRCS) to forecast red tide events. [PERSON] et al. [60] established a numerical model using genetic programming and predicted the red tide event in Tolo Harbor in Hong Kong. [PERSON] et al. [61] presented a numerical model based on the HAMSOM model and the Lagrangian red-tide model. With the rapid development of computer technology and artificial intelligence, neural network models have gradually been applied to harmful algal bloom forecasting systems. [PERSON] et al. [62] applied modified three-layer back propagation to forecast Noctiluca scintillans-induced red tide, and it performed well in reality. Velo-Suarez and Gutierrez-Estrada [63] combined principal component analysis and a feed forward neural network to predict red tide bloom. However, the neural network model has a projecting defect of overdependence on the sample size, which may decrease the adaptability and tolerance. In recent years, harmful algal coupled monitoring and forecasting systems have drawn high levels of attention around the world [64]. Europe and some East Asian countries have distributed these systems, and the most important aim of these systems is to monitor for different kinds of algae [65].  
Figure 13: The distribution of _A. anophageferens_ cell distribution in July, August, and September 2021.  
For a fast forewarning system of brown tide in Qinhuangdao, a simple model, based on the 2D hydrodynamic and transport module to simulate the TN concentration, is adopted in the shallow coastal water where brown tides often occur in the summer season. A relationship between _A. anophageferens_ and TN obtained from field-measured data, combined with TN distribution from the transport module considering the decay, diffusion, and settling of TN, provided a fast forewarning system of brown tide in Qinhuangdao coastal water. This simple fast forewarning system of brown tide has been successfully used in Qinhuangdao and provided the local government with the ability to quickly conduct monitoring in the key areas of potential risk and take appropriate actions to mitigate brown tide. However, it has been proven by many previous studies that the vertical nutrient condition also plays a vital role in affecting harmful algal bloom [33]. In addition to nutrients, some other pressure variables, such as salinity, temperature, and the distribution of Chl-a, influence _A. anophageferens_ growth, migration, and spatiotemporal distribution [32]. Hence, a 3D-coupled model involving a hydrodynamic model, an ecological model, and a particle tracking model should be further established to simulate the real process of _A. anophageferens_-induced brown tide, which is expected to be developed in following studies to improve the forewarning system.