Territory Stories

Assessment of the Jabiluka Project : report of the Supervising Scientist to the World Heritage Committee

Details:

Title

Assessment of the Jabiluka Project : report of the Supervising Scientist to the World Heritage Committee

Creator

Johnston, A.; Prendergast, J. B.; Bridgewater, Peter

Collection

E-Publications; E-Books; PublicationNT; Supervising Scientist Report; 138

Date

1999

Location

Alligator Rivers Region

Table of contents

Main report--Appendix 2 of the Main Report. Submission to the Mission of the World Heritage Committee by some Australian Scientists ... --Attachment A. Johnston A. and Needham S. 1999. Protection of the environment near the Ranger uranium mine--Attachment B. Bureau of Meteorology 1999. Hydrometeorological analysis relevant to Jabiluka--Attachment C. Jones, R.N., Hennessy, K.J. and Abbs, D.J. 1999. Climate change analysis relevant to Jabiluka--Attachment D. Chiew, F and Wang, Q.J. 1999. Hydrological anaysis relevant to surface water storage at Jabiluka--Attachment E. Kalf, F. and Dudgeon, C. 1999. Analysis of long term groundwater dispersal of contaminants from proposed Jabiluka mine tailings repositories--Appendix 2 of Attachment E. Simulation of leaching on non-reactive and radionuclide contaminants from proposed Jabiluka silo banks.

Language

English

Subject

Uranium mill tailings - Environmental aspects - Northern Territory - Alligator Rivers Region; Environmental impact analysis - Northern Territory - Jabiluka; Uranium mines and mining - Environmental aspects - Northern Territory - Jabiluka; Jabiluka - Environmental aspects

Publisher name

Environment Australia

Place of publication

Canberra (A.C.T.)

Series

Supervising Scientist Report; 138

Format

1 volume (various pagings) : illustrations, maps

File type

application/pdf

ISBN

642243417

Use

Copyright

Copyright owner

Environment Australia

License

https://www.legislation.gov.au/Details/C2019C00042

Parent handle

https://hdl.handle.net/10070/264982

Citation address

https://hdl.handle.net/10070/462402

Related items

https://hdl.handle.net/10070/462403; https://hdl.handle.net/10070/462400; https://hdl.handle.net/10070/462405; https://hdl.handle.net/10070/462406; https://hdl.handle.net/10070/462408; https://hdl.handle.net/10070/462409; https://hdl.handle.net/10070/462411

Page content

51 data results in a higher storage capacity estimate because the rainfall in some months can be significantly greater than the monthly rainfall calculated as a proportion of the annual rainfall using a typical distribution through the year. The results in table 5.2.3 indicate that the use of a typical distribution to proportion the annual rainfall to individual months rather than actual or simulated monthly rainfall results in a 1.7% underestimate of the required storage capacity. Table 5.2.3 Largest storage capacity required as a function of the assumptions used in the hydrology model Rainfall data Pan evaporation data Ventilation loss Runoff estimation Largest storage (m3) Monthly simulation 1 Actual data Actual data Same for each month Runoff coefficient 582,944 2 Actual data Long-term monthly average Same for each month Runoff coefficient 565,939 3 Fixed distribution of annual rainfall Actual data Same for each month Runoff coefficient 573,145 4 Actual data Actual data Lower in Wet season Runoff coefficient 589,744 5 Actual data Actual data (Hattons pan factor) Same for each month Runoff coefficient 568,502 6 Actual data Long-term monthly average (Hattons pan factor) Same for each month Runoff coefficient 540,962 Daily simulation 7 Actual data Actual data Same for each month Runoff coefficient 591,188 8 Actual data Actual data Same for each month Conceptual storages 587,515 9 Actual data Actual data Lower in Wet season Conceptual storages 593,812 Constant ventilation loss versus smaller ventilation loss in the Wet season Runs 1 and 4 differ only in the ventilation loss calculations. Run 1 uses a constant ventilation loss throughout the year (as in the ERA approach) while Run 4 takes into account that the ventilation losses would be lower in the Wet season than in the Dry season. The assumption of constant ventilation system losses throughout the year leads to a 1.2% underestimate of the required storage capacity. Pan evaporation coefficients Run 1 uses the pan evaporation factors recommended in section 3.3 while Run 5 uses the pan factors given by Hatton (1997) and adopted by ERA. The factors are the same except for two months. The estimate of the storage capacity required is about 2.5% higher when the recommended pan factors are used. Daily versus monthly simulation Runs 1 and 7 differ only in the simulation time step. The use of a larger time step is expected to give rise to an underestimate of the required storage capacity because inputs to the system (rainfall) can vary rapidly on a daily basis but outputs or losses (evaporative losses and mill consumption) are fairly constant on a daily basis. The results in table 5.2.3 indicate that the use of a monthly time step may result in a 1.4% underestimate of the required storage capacity.