## 4 Study Case
### Publication of SWAT-Related Data Service  
The SWAT (Soil and Water Assessment Tool) model is a distributed hydrological model developed by the Agricultural Research Center (USDA-ARS) of the United States Department of Agriculture [40]. It can continuously simulate hydrological and water-quality processes such as soil evapotranspiration, surface runoff, groundwater and surface interaction, and pollutant migration on a daily scale [41; 42]. The SWAT model has been widely used in the world for its powerful functions, among which the runoff simulation is one of the most basic and important functions [43; 44].  
The western part of Taihu Lake Basin, located in the northwest corner of Taihu Lake Basin in southeast China, with a total area of about 7722 km\({}^{2}\), is an important upstream area of Taihu Lake Basin and plays a key role in regulating the water quality and quantity of Taihu Lake [45]. In this section, the SWAT model is used to simulate the runoff process in this area, and the whole process of data sharing, computing, and application is demonstrated, so as to verify the feasibility of the geographic data-sharing and calculation framework proposed in this paper.  
The main input data for the runoff simulation using the SWAT model include DEM data of the study area, land-use-type data, soil-type data, and meteorological data (such as precipitation data, temperature data, relative humidity data, solar radiation data, wind speed data), etc. The data sources in this case are shown in Table 1.  
Obviously, these data sources come from different data-sharing institutions, and the data are organized and stored in different ways. In order to reduce the difficulty for users to use these data, we first use UDX to structurally describe the content of these data. As shown in Figure 10, the raw data of the rainfall station (stored in a text file) is described by UDX. From the UDX description, it is clear to see the data content information such as the station ID, station name, station coordinate, and station rainfall. Moreover, users only need to access UDX data nodes to obtain the corresponding data, without reading the original text file. In this way, the rainfall station data can be published as a data resource service.  
In order to drive the SWAT model, some data-processing methods need to be developed to process the collected data resources, so as to meet the execution requirements of the SWAT model. For example, using a clipping algorithm to clip the DEM data of the study area from the original DEM data, constructing land-use-type index tables and soil-type index tables, generating weather-generator data, etc. By publishing these data-processing methods as data-processing services, users only need to invoke these services to process data, which greatly improves the efficiency of preparing data for the SWAT model. Figure 11 shows some of the data-processing services published in this case. Since some open-source data-processing libraries (such as GDAL/OGR, GeoTools, proj4) provide common data-processing methods, such as data clipping, projection transformation, etc., in this case, these open-source libraries are directly packaged and published as corresponding data-processing services.  
\begin{table}
\begin{tabular}{c c} \hline
**Data Type** & **Data Source** \\ \hline DEM & Geospatial data cloud [13] \\ \hline Land-use data & High-precision land-use data of Tsinghua University [46] \\ \hline Soil data & HWSD Global Soil Database [47] \\ \hline Meteorological data & CMADSV1.0 (Chinese) [48] \\ \hline \end{tabular}
\end{table}
Table 1: Relevant data sources for a SWAT-based runoff simulation.