General requirements for data

Input data is specific to the component models that you use, but typically consists of climate, topography, land use, rainfall, and management practices. Examples of these are provided in Tables 1 and 2.

Table 1. Model calibration and validation (required data sets)

Base layers and data	Common data formats	Description and use
Observed flow data preferable at the same time-step as model	Time series	Typically, you can use daily time-step gauging station data (ML/day or m3/s) in the hydrological calibration/validation process, or as a substitute for modelled runoff as an observed flow time series at relevant nodes. You need to assess the length of time for which records are available, data quality and data gaps to determine how use the data sets are for calibration/validatio. It is preferable to have data sets that are at least 10 years in duration and that cover both wet and dry periods. When extracting gauged flow time series data from databases such as HYDSTRA (commercial software used across Australia for archiving gauge data), it is important to align the time bases for flow with other climate data sets. For example, SILO data is collected each day at 09:00am, so each data point is the total rainfall for the previous day. Therefore, when extracting flow data from HYDSTRA, ensure that the "end of day" option is selected, so that the flow data will align with the SILO rainfall data. It is important to understand the conventions used by your organisation as well as those that send you data - they may not be the same!
Observed water quality data	Time series	Data used in the water quality calibration/validation process. You need to assess the length of time for which records are available, data quality and data gaps to determine how useful the data sets are for calibration/validation. It is preferable to have data sets that cover both storm event and ambient conditions that include both wet and dry periods.
Existing reports	Report or spreadsheet	Existing reports for the region may assist in hydrology and water quality calibration process, eg. load estimation.

Table 2. Optional data sets

Optional data sets	Common data formats	Description and use
Visualisation layers (eg. roads, towns, soils, streams)	Polygons, polylines or points	You can add (drag and drop) additional layers into the Layer Manager and turn these layers on or off as required. These layers are not used in model development and are more a visualisation tool. The layers need to have the same projection and resolution as the DEM or sub-catchment map (but can have different extents).
Aerial and satellite imagery	Image	You can use aerial or satellite imagery to ensure the node-link network generated (either drawn manually or generated by Source) is correct. You can also use imagery to check the accuracy of the DEM-generated sub-catchment map. The layers need to have the same projection and resolution as the DEM or sub-catchment map (but can have different extents.
Local or relevant data on best management practices	Reports or spreadsheets	This includes information on locally-relevant best management practices that could be used when creating scenarios.
Existing hydrology and water quality reports and data	Relevant format	Existing reports for the region may help when you parameterise water quality models (eg. EMC/DWC derivation. For example, an existing IQQM model of a region that uses the Sacramento rainfall runoff model can be used to parameterise a Sacramento model in Source. Climate data sets may be used to speed up calibration of rainfall runoff models.

Note that all spatial data must use the same supported projections:

• Albers Equal Area Comical;
• Lambert Conic Conformal; or
• Universal Transverse Mercator (UTM);

The exception is SILO gridded climate data, which is formatted in a geographic coordinate system.

Table 3 summarises the minimum necessary and optional input data needed to create a catchment model using Source.

Table 3. Building models (required data sets)