Seasonal Streamflow Forecasts

Exceedance probability for Tarcutta Creek at Old Borambola


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Exceedance probability for Tarcutta Creek at Old Borambola(  )

Exceedance Probability Summary
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
1025.31332.551
2018.08621.369
3013.83016.123
4010.93812.336
508.7229.716
606.9447.464
705.3945.787
803.9264.262
902.3442.711

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
164.51789.857
251.41165.777
342.78257.942
437.61452.202
534.79648.150
631.68143.200
729.56539.965
828.00137.102
926.40034.440
1025.31332.551
1124.51431.051
1223.59529.468
1322.59428.300
1421.85727.155
1521.08625.949
1620.41225.032
1719.70723.969
1819.05922.925
1918.57922.043
2018.08621.369
2117.59620.728
2217.08620.004
2316.54619.548
2416.09018.855
2515.76618.440
2615.34418.084
2714.98617.535
2814.58917.109
2914.22316.646
3013.83016.123
3113.49615.665
3213.18115.191
3312.89414.833
3412.62114.500
3512.38414.018
3612.08013.657
3711.82013.290
3811.48212.961
3911.22112.687
4010.93812.336
4110.69312.000
4210.43511.759
4310.23711.551
4410.02811.277
459.79611.033
469.60210.756
479.35610.519
489.15910.204
498.8919.965
508.7229.716
518.5419.492
528.3399.274
538.1699.009
548.0148.774
557.8388.517
567.6798.237
577.4538.067
587.3047.893
597.1307.712
606.9447.464
616.8057.234
626.6427.065
636.4856.905
646.3326.717
656.1586.569
666.0106.419
675.8426.280
685.6636.100
695.5255.917
705.3945.787
715.2925.610
725.1475.469
734.9945.306
744.8645.161
754.6805.002
764.5504.860
774.4034.708
784.2324.568
794.0724.435
803.9264.262
813.7804.101
823.6333.949
833.4483.778
843.3013.636
853.1533.513
863.0183.349
872.8393.182
882.6683.047
892.5072.887
902.3442.711
912.1812.547
922.0292.355
931.8202.152
941.5661.905
951.3481.730
961.1201.528
970.8771.235
980.6040.981
990.0920.684


About the exceedance probability 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 The historical reference is a probabilistic representation of the historical data.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. Further explanation of some technical terms is provided under the FAQ.


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