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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 dataCommon data formatsDescription and use
Observed flow data preferable at the same time-step as modelTime 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 dataTime seriesData 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 reportsReport or spreadsheetExisting reports for the region may assist in hydrology and water quality calibration process, eg. load estimation.
Table 2. Optional data sets
Optional data setsCommon data formatsDescription and use
Visualisation layers (eg. roads, towns, soils, streams)Polygons, polylines or pointsYou 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 imageryImage

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 practicesReports or spreadsheetsThis includes information on locally-relevant best management practices that could be used when creating scenarios.
Existing hydrology and water quality reports and dataRelevant 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)

Base layers and dataCommon data formatsDescription and use
Digital Elevation Model (DEM)Grid

A pit-filled DEM is used to compute sub-catchment boundaries and node-link networks. Source can automatically generate sub-catchment boundaries according to a user specified minimum drainage area (stream threshold) and flow gauging station positions. Selecting a small minimum sub-catchment area value will generate a large number of sub-catchments. This will increase the size of the project and run time.

Sub-catchment
map
Grid

A sub-catchment map can be used in place of a DEM. This defines the sub-catchment boundaries within Source. You then need to draw the node-link network for the catchment.

Functional Unit
(FU) map
Grid

A functional unit (FU) map divides the sub-catchments into areas of similar hydrological behaviour or response (eg. land use). Source uses FU maps to assign functional unit areas. The FU map needs to have the same projection and resolution as the DEM or sub-catchment map (but it can have different extents provided the FU map at least covers the extent of the sub-catchments defined by the modeller).

Gauging station
nodes
Point

Optionally, a shape file or ASCII text file that lists the gauging station coordinates, and an identifier such as gauge name or number that is used to define gauging station nodes. The coordinates of the gauges need to be in the same projection as the DEM or sub-catchment map.

Incorporating the location of gauging stations as nodes can be particularly useful when calibrating the rainfall runoff model at a gauge.

Rainfall and
PET data
Grid or time-series

Rainfall and potential evapotranspiration (PET) time series are used as inputs to the rainfall-runoff models. The most commonly-used files are SILO daily rainfall and PET ASCII grids. Using a daily ASCII grid format allows you to update the rainfall data at a later stage and re-run the model. Table 39 shows the format of the input data required. If local data is available, you can also attach your own rainfall data files to rainfall runoff models for each FU within a sub-catchment.

Point source
data (if storages
are to be
modelled)
Time series

Outflow and/or constituent data. The time-series needs to have the same time-step, and should be run for at least the same duration, as the climate or flow inputs to the model.

   
   
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