## 4 Study Case
### Data Configuration and Computing  
Based on the published data services, users can configure input data for the SWAT model in the workspace. As shown in Figure 12, the workspace provides a graphical data configuration method. Each graphical node corresponds to a data-processing method, and the arrows between nodes represent the logical sequence of data processing. The data configuration of this case mainly includes eight steps: (1) DEM data processing in the study area. Through image mosaic, projection, and clipping, the collected original DEM data are processed into DEM data of the study area. (2) Land-use-type data processing. Through data format conversion, clipping, land-type reclassification, and projection operation, the collected original land-use-type data were processed into land-use-type data within the study area. (3) Construction of land-use-type index table. Based on the data of land-use type output in Step 2, the index table of land-use type is constructed. (4) Soil data processing. Similar to Step 2, the collected soil data are processed into soil data within the study area. (5) Construction of soil physical property database. Based on the output of Step 4, the soil  
Figure 11: List of data-processing services in the data service container.  
Figure 10: Raw data of the rainfall station and its UDX description.  
physical property database is constructed. (6) Construction of soil-type index table. Based on the output of Step 5, the soil-type index table is constructed. (7) Construction of weather generator based on meteorological data. (8) Construction of daily meteorological data file. Each step consists of related data-processing nodes, each of which performs different data-processing operations to obtain different types of data resources. For example, Step 1 consists of three data-processing nodes (i.e., Mosaic, Projection, and Clip) that are bound to corresponding data-processing services. After the calculation of Step 1 is completed, the DEM data of the study area can be obtained, and the data can be directly used as the input of the SWAT model without additional processing. Similarly, the other steps are configuring the corresponding data.  
In this way, when all the data-processing nodes are bound to the corresponding data-processing service, the data-processing workflow becomes an executable data-computing solution. Then the workspace sends the computing solution to the data-computing engine, and the data can be calculated online. Additionally, from the dependency relationships between data-processing steps in Figure 12, it can be found that steps 1, 2, 4, 7, and 8 can be executed concurrently for data processing, while Step 3 must be run after Step 2 has been completed, Step 5 must be run after Step 4, and Step 6 must be run after Step 5.  
When the data-computing solution is completed, the input data of the SWAT model can be obtained. In this case, ArcSWAT (the plug-in of SWATmodel in ArcGIS) is used to simulate runoff. Figure 13 shows the simulation output of the SWAT model and its visual presentation. In this case, the output of the SWAT model is also described by UDX, and the average daily flow out of the river reach can be obtained by accessing the \"FLOW_OUTcms\" node of UDX, without reading the complex raw output file of the SWAT model. Users can then conduct relevant analysis based on the resulting data. In this case, a script is written to visually analyze the resulting data.  
Figure 12: Data configuration in the workspace.