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 ( Jan 2012 )

Basic Statistics
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
25% Quartile5.7543.200
Median10.0097.050
Mean12.85711.126
75% Quartile16.80113.985
Interquartile Range11.04710.785

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
152.33170.133
243.77750.826
338.35544.684
434.84140.212
532.62537.067
630.86433.232
729.67730.727
828.41528.512
926.97426.451
1025.66724.988
1124.59023.825
1223.88422.597
1323.08521.690
1422.41920.800
1521.66519.862
1621.02919.148
1720.37918.319
1819.88917.504
1919.31416.815
2018.93016.287
2118.48415.785
2217.99515.217
2317.58914.860
2417.25614.316
2516.81013.989
2616.43613.709
2716.08113.276
2815.76912.940
2915.37012.575
3015.03812.162
3114.71911.800
3214.40511.424
3314.14911.140
3413.80310.876
3513.49310.493
3613.21610.206
3712.9829.914
3812.7689.652
3912.5399.433
4012.2949.153
4111.9888.885
4211.7798.692
4311.5638.526
4411.3038.306
4511.0478.110
4610.7997.887
4710.6137.697
4810.3997.444
4910.2017.251
5010.0097.050
519.8286.869
529.6926.693
539.4886.479
549.3236.289
559.1546.081
568.9855.853
578.7765.715
588.5835.574
598.4255.426
608.2905.224
618.1005.036
627.9634.898
637.7704.768
647.5864.614
657.4304.492
667.2464.369
677.1074.255
686.9414.107
696.7813.956
706.5893.849
716.4523.703
726.2903.587
736.0993.452
745.9513.332
755.7533.200
765.5863.083
775.3972.956
785.2182.840
795.0812.729
804.9342.585
814.7822.450
824.5992.323
834.4032.180
844.2472.060
854.0311.957
863.8591.819
873.7191.678
883.5171.564
893.3311.429
903.1331.279
912.8451.139
922.6160.976
932.3620.802
942.1030.590
951.8840.439
961.6240.264
971.2310.009
980.8800.000
990.2130.000


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 The historical reference is a probabilistic representation of the historical data.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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