## 1 Introduction  
Wetlands, sometimes described as \"the kidneys of the landscape\" [1, 2], play an important role in providing ecological services to both humans and wildlife. These include shelter, habitats, food, protection from catastrophic flooding, irrigation, and carbon sequestration [3, 4, 5]. However, wetlands have rapidly degraded globally due to intensifying anthropogenic activities, such as pollutant loading from agricultural practices and industrial wastewater discharge [6, 7]. The development of new, powerful, and data-based techniques is fundamental to urgent wetland restoration efforts. Rapidly developing computer technology, databases, and information/image analysis techniques have been applied to the planning and restoration of new and degraded wetlands. For example, [PERSON] [8] reviewed remote sensing techniques used in wetland management and found that analysis of satellite and aircraft imagery, combined with on-the-ground observations, allowed researchers to accurately and cost-effectively determine short-term changes and long-term trends in wetland vegetation and hydrology. [PERSON] et al. [9] used GIS (Geographic Information System) to optimize hydraulic analysis of macro- and micro-scale flow paths for wetland restoration, showing that this approach was capable of developing a useful representation of physical sites for further modeling. [PERSON] et al. [10] used remote sensing and GIS techniques to determine regions most suited for wetland preservation/restoration. This approach was especially helpful in guiding the conversion of farmland to wetland. However, although many studies have shown that hydrology is a critical factor in wetland restoration, the hydrodynamics and water quality characteristics of many wetlands are not clearly defined or understood due to limited datasets for numerical modeling. Traditional methods used to investigate the spatiotemporal characteristics of water quality are based on field measurements and laboratory analysis, but such data are often limited in scope while being time-consuming and costly to collect. Thus, various numerical models have been developed to study the hydrodynamic and environmental characteristics of wetlands and lakes. For instance, [PERSON] et al. [11] developed a two-dimensional depth-averaged model based on MIKE 21 to examine factors controlling flow in different wetland zones. This approach was capable of reproducing wetland flow distributions far more efficiently than field investigations could. [PERSON] et al. [12] studied the spatiotemporal distribution characteristics of wetland chemical oxygen demand using the MIKE 21 hydrodynamic and pollutant convection diffusion model. [PERSON] et al. [13] developed a mechanistic model for treating eutrophic water in free water surface constructed wetlands to simulate the removal of total suspended sediment and its relationship to phytoplankton and total phosphorus (TP). [PERSON] et al. [14] proposed an enhanced quasi-two-dimensional modeling strategy that can accurately simulate river and wetland dynamics for large wetland areas to better understand their hydrodynamics. [PERSON] and [PERSON] [15] established a two-dimensional hydrodynamic and water quality coupling model by using MIKE 21 to evaluate different engineering measures aimed at improving wetland water quality. Although such research has verified that numerical models improve the scientific understanding of water quality variations in different types of wetlands, few studies have reported on the spatiotemporal characteristics of water quality in highly channelized wetlands with many islands. In this study, we focused on the island-dominated Sanyang wetland in coastal China, using a two-dimensional hydrodynamic and water quality MIKE 21 model to investigate the unique hydrodynamic characteristics of this distinct setting and better define the area's spatiotemporal distribution of water quality.