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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

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Copyright owner

Environment Australia



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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

48 The DMM (daily-monthly-mixed) algorithm (Wang & Nathan 1999) described in Appendix A of Chiew and Wang (1999) was used to generate daily rainfall data. The advantages of this algorithm are that it has a small number of parameters (six for each month) and is capable of reproducing key characteristic statistics simultaneously at the daily, monthly and annual time periods. To evaluate the DMM algorithm, statistics from 1000 years of generated daily rainfall data were compared with the statistics for the observed data. Table 5.2.2 summarises some of the daily statistics. The table shows that the generated data closely reproduce the observed statistics, including the skewness which is not used in the model fitting. Table 5.2.2 Comparison of key daily rainfall statistics in the generated and observed data Mean (mm) CV Skewness Observed Simulated Observed Simulated Observed Simulated All data 3.82 3.81 3.02 3.05 5.52 5.52 Jan 10.90 10.95 1.66 1.69 3.28 3.28 Feb 11.33 11.26 1.58 1.60 3.15 3.14 Mar 9.00 9.01 1.90 1.92 3.58 3.58 Apr 2.60 2.63 3.79 3.89 8.02 8.00 May 0.46 0.44 8.08 7.13 12.16 12.15 Jun 0.05 0.04 16.99 16.94 31.60 31.64 Jul 0.08 0.06 20.55 16.15 25.76 25.79 Aug 0.03 0.01 20.79 23.01 40.85 40.10 Sep 0.16 0.11 11.12 12.00 21.52 21.48 Oct 0.86 0.84 5.92 5.37 12.18 12.13 Nov 3.60 3.59 2.68 2.70 4.96 4.96 Dec 7.19 7.21 2.02 2.02 3.65 3.66 The monthly and annual statistics are presented and assessed in Chiew and Wang (1999). Overall the model reproduces the monthly statistics very well with minor unimportant variations in the Dry season. The annual mean and the coefficient of variation are almost exactly reproduced but a small positive skewness is produced in the generated data while the observed skewness is almost zero. This could tend to produce higher rainfalls in very wet years and is therefore conservative. 5.2.3 Estimation of required storage capacity under current climatic conditions Daily simulation of the storage water balance is given by LossesInflowsSS tt +=+1 where St is the present storage and St+1 is storage on the following day. The inflows into (runoff and mine dewatering) and losses from the storage (evaporation, mill requirement, ore wetdown and ventilation loss) were as described above. All the losses were subtracted from the storage as long as there was water in the retention pond. Fifty thousand runs were carried out, with each run simulating the daily storage water balance over a 30-year mine life, starting with an empty pond. The largest accumulated storage in each run gave an estimate of the storage capacity required such that the retention