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



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


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


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




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.




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.)


Supervising Scientist Report; 138


1 volume (various pagings) : illustrations, maps

File type






Copyright owner

Environment Australia



Parent handle


Citation address


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

52 Runoff coefficient versus conceptual rainfall-runoff modelling Runs 7 and 8 are the same except for the method used to estimate surface runoff. In Run 7, surface runoff is estimated using a runoff coefficient multiplied by rainfall while in Run 8 surface runoff is simulated using a conceptual rainfall-runoff model, with the soil capacity parameter optimised to produce the same total runoff as in Run 7. The results in table 5.2.3 indicate that the use of constant runoff coefficients results in a small overestimate (about 0.4%) of the storage capacity required. Overall effect of the various assumptions used by ERA Although the effect of each of the individual assumptions discussed above is quite small, in most cases the result is an underestimate of the required storage capacity and the cumulative effect of these assumptions could be significant. This is illustrated in the comparison of the results obtained in Runs 6 and 9. In Run 6, the ERA assumptions have been used within the framework of the water balance model described in this review whereas Run 9 uses the assumptions described in this report. The results in table 5.2.3 indicate that the combined effect of the use of ERAs assumptions may lead to a 10% underestimate of the storage capacity required for a given design criterion. If the pond volume proposed by ERA, 810,000 m3, is increased by 10%, the exceedence probability obtained from figure 5.2.1 is about 0.0002, ie 1 in 5000, over the life of the mine. There were, as noted in section 5.2.3, other differences in the modelling approach adopted by ERA in that, instead of performing a full Monte Carlo calculation of the response of the catchment to rainfall, ERA performed a Monte Carlo calculation of annual rainfall and then selected particular sequences of Wet seasons to simulate the response of the catchment. The pond capacity derived by ERA on this basis was only 706,000 m3. ERA then added additional capacity to ensure that adequate storage would be available in subsequent years but there was no clear indication of what exceedence probability was expected to be achieved using the final total capacity of 810,000 m3. 5.2.5 Use of pond evaporation rather than evaporation in the ventilation system Chiew and Wang (1999) investigated the demands on storage capacity as a function of time during the life of the Jabiluka project. As was the case for the sensitivity analysis described above, the full simulation procedure described in section 5.2.3 was not adopted since it would have been very demanding on computer time. Instead, the model was run using the actual rainfall and evaporation data recorded during the period September 1972 and August 1998. The results of these calculations are presented in figure 5.2.2. The plots show the annual rainfall in the Oenpelli record for the period and the accumulated water volume calculated by the model at the end of each month during the period. The data presented in figure 5.2.2 show that the largest volume of accumulated water calculated by the model for the period is 594,000 m3. A storage capacity of about 600,000 m3 would, therefore, have been required to avoid exceedence at any time during the 26-year period using the actual recorded rainfall. Note that this volume is much lower than would be required for, say, a 1:10,000 exceedence probability if one used the rainfall records for this period as the basis for a Monte Carlo simulation of the type described in section 5.2.3. The largest accumulated volume of water occurred in the fifth year of this simulation following a steady build-up during the previous four years. After this peak volume is reached, the maximum volume required decreases in subsequent years until, following the simulated tenth year of operation, the maximum capacity required in all years is less than half the maximum value. Indeed, on a number of occasions, the volume of stored water reduces to

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