Sydney
17.1 Evaporation from surface water

The reported volume for evaporation from surface water is 120,977 ML.

The following tables provide a summary and breakdown of evaporation from surface water in the Sydney region.

 1 Blue Mountains storages include Medlow Reservoir, Greaves Creek Reservoir, Upper Cascade Reservoir, Middle Cascade Reservoir and Lower Cascade Reservoir.

1 Data were calculated by the Bureau of Meteorology. Data for storages not listed in this disclosure note are not available. Where required, static surface area was used.

1 Data were calculated by the Bureau. Only the river sections for which river volumes have been calculated are included. Where required, static surface area was used.

The Bureau: National Climate Centre (NCC) daily climate grids (rainfall), geographical information system (GIS) layers; Australian Hydrological Geospatial Fabric (AHGF) waterbody feature class; Australian Water Resources Information System; New South Wales Office of Water: river channel cross-section and flow data from Hydstra and Computer Aided Improvements to River Operations (CAIRO) databases; New South Wales Office of Water: GIS layers; New South Wales Office of Water: GIS database, river channel sections included in the Sydney Connected Surface Water Store; New South Wales Office of Water: New South Wales waterbody polygon ArcSDE feature class.

The Bureau provided evaporation data for the river sections and the storages which are not managed by the Sydney Catchment Authority.

The potential evaporation estimate produced by the Australian Water Resources Assessment system Landscape model (AWRA-L) version 2.0.0 (Van Dijk 2010) was used to calculate evaporation from the surface water store. The AWRA-L model uses a modified version of the Penman-Monteith method to produce the potential evaporation. Daily AWRA-L potential evaporation grids were produced based on daily gridded climate data that were 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 datasets 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 average monthly surface area was calculated from daily storage levels and capacity tables. Where this data was not available a static surface area was produced from the AHGF. The surface area of the rivers was estimated using cross-section and long term average river level data and remains static in these calculations.

The evaporation at each waterbody was estimated from the proportionally weighted average of grid-pixels 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 reservoir storage level for storages where the relationship between storage level and surface area had been derived. The average monthly surface area was calculated from daily storage levels and capacity tables. Where this data was not available a static surface area was produced from the AHGF. The surface area of the rivers was estimated using cross-section and long term average river level data and remains static in these calculations.

•  The AWRA-L 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 will not capture changes that occur on a shorter timescale.

• The total surface area of the surface water store within the Sydney region included only the storages, urban storages, weirs and river sections listed in the supporting information.

Uncertainty was not quantified.

Measured pan evaporations were adjusted by a pan factor.

The evaporation volume is based on measured data. Estimated uncertainty based on measurement accuracy, professional judgment on the calculation method and organisational practice is +/– 10%.