Seasonal Streamflow Forecasts

Probability distribution for Tarcutta Creek at Old Borambola


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Product list for Tarcutta Creek at Old Borambola



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Probability distribution for Tarcutta Creek at Old Borambola ( Mar 2014 )

Basic Statistics
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
25% Quartile3.6475.002
Median6.9629.716
Mean10.03914.963
75% Quartile12.74618.435
Interquartile Range9.09913.433

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
152.45589.857
242.39065.777
335.44657.942
430.78952.202
528.42348.150
626.08243.200
724.27839.965
822.93837.102
921.72034.440
1020.72032.551
1119.99831.051
1219.22829.468
1318.46128.300
1417.81227.155
1517.21925.949
1616.62925.032
1716.04223.969
1815.61522.925
1915.15822.043
2014.65321.369
2114.30120.728
2213.86920.004
2313.46119.548
2413.09618.855
2512.75318.440
2612.41518.084
2712.11717.535
2811.85417.109
2911.56216.646
3011.23216.123
3110.95715.665
3210.66915.191
3310.47514.833
3410.21114.500
359.97214.018
369.75613.657
379.54913.290
389.30512.961
399.03712.687
408.82912.336
418.59612.000
428.40911.759
438.23211.551
448.06711.277
457.90211.033
467.66010.756
477.46610.519
487.30310.204
497.1339.965
506.9629.716
516.8499.492
526.6879.274
536.5479.009
546.3768.774
556.2508.517
566.0888.237
575.9288.067
585.7927.893
595.6497.712
605.5217.464
615.3687.234
625.2467.065
635.1116.905
644.9836.717
654.8366.569
664.7226.419
674.5736.280
684.4486.100
694.3335.917
704.2295.787
714.1105.610
723.9885.469
733.8885.306
743.7895.161
753.6465.002
763.5294.860
773.3994.708
783.2854.568
793.1614.435
803.0424.262
812.9014.101
822.7583.949
832.6423.778
842.5013.636
852.3773.513
862.2323.349
872.1203.182
881.9763.047
891.8492.887
901.7152.711
911.5822.547
921.4522.355
931.2782.152
941.1001.905
950.9081.730
960.7001.528
970.5111.235
980.2320.981
990.0000.684


About the probability distribution product


  1. The forecast is a simulation from the Bayesian Joint Probability (BJP) Model. The simulation comprises 5000 members.
  2. The forecast skill of the BJP model is different for different forecast periods of the year. Please look at the Model Validation results to assess model skill for this forecast period.
  3. The historical reference is a probabilistic representation of the historical data.
  4. The forecast location name is displayed in the graph title. Site forecast locations are followed by the Australian Water Resources Council (AWRC) river station number in brackets e.g. (405219).
  5. The streamflow data used for new forecasts and for verification is from realtime data sources which are not rigorously quality controlled.
  6. The historical reference plot and the historical data plots are derived from the available historical record. The legend shows the year associated with the start month of the forecast period.
  7. The RMSEP skill scores have been defined as less than 10 very low skill, 10-20 low skill, 20-40 moderate skill, greater than 40 high skill.
  8. The 25% quartile, also defined as the first quartile cuts off the lowest 25% of data. The 75% quartile, also defined as the third quartile cuts off the lowest 75%. The interquartile range, also called the middle fifty, is a measure of statistical dispersion, being equal to the difference between the third and first quartiles.
  9. Further explanation of some technical terms is provided under the FAQ.


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