Murray-Darling Basin
42.1 Evaporation from storages
Supporting Information
The volumetric value for the line item for the 2010–11 year was 679,513 ML. The line item represents evaporation volumes from major surface water storages within the Murray–Darling Basin (MDB) region. The line item value applies only to the Southern Basin. Line item includes evaporation from Menindee Lake (242,027 ML), Hume Reservoir (228,973 ML), Dartmouth Reservoir (56,094 ML) and Lake Victoria (152,419 ML) within the MDB region.
Evaporation information for other storages is available (see line item 17.1). However, information is provided only for above four storages to be consistent with information provided in other storage line items (line line items 41.1, 41.3, 42.4, 42.5 and 42.6).
Quantification Approach
Data Source
Provided by
Method
The Priestly and Taylor method to estimate potential evaporation (as calculated by the WaterDyn model [Raupach 2008]) was used to estimate evaporation from the surface water assets. Monthly potential evaporation data produced by the Bureau were used, based on daily gridded climate data that is available on a 0.05 degree (approximately 5 km) national grid.
Potential evaporation is an estimate of the evaporative demand of the environment. The daily gridded climate data sets used to produce this estimate are generated by the Bureau and include downward solar irradiance, and maximum and minimum air temperature. The methods used to generate these gridded datasets are outlined in Jones et al. (2007).
The evaporation at each waterbody was estimated from the proportionally weighted average of grid-pixels cells that intersected each water feature. The volume was then estimated using the monthly average surface area of each waterbody. The surface area varied dynamically with changing level for storages where the relationship between storage level and surface area had been derived.
Assumptions, Limitations, Caveats and Approximations
The Priestly and Taylor model potential evaporation estimates are subject to approximations associated with interpolating the observation point input data to a national grid as described in Jones et al. (2007).
The dynamic storage surface areas calculated from the levels and storage rating tables represent a monthly average and therefore do not capture changes that occur on a shorter time-scale.