Seasonal Streamflow Forecasts

Exceedance probability for Tuross River at Tuross Vale


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Product list for Tuross River at Tuross Vale


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Exceedance probability for Tuross River at Tuross Vale(  )

Exceedance Probability Summary
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
1019.15930.497
2011.31315.197
307.7369.410
405.4825.959
503.9883.980
602.7992.565
702.0211.684
801.2501.031
900.6220.512

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
176.572107.747
251.32679.207
338.85668.609
433.55860.298
529.64454.435
626.41446.768
724.15241.761
822.08037.389
920.38233.336
1019.15930.497
1117.98128.275
1216.69925.962
1315.65424.302
1414.71322.720
1514.01721.073
1613.44419.832
1712.77218.455
1812.19617.130
1911.69316.027
2011.31315.197
2110.94614.429
2210.48413.609
2310.04013.058
249.76212.287
259.32811.823
268.98511.434
278.60210.861
288.29710.422
297.9799.930
307.7369.410
317.5068.953
327.2748.507
336.9468.167
346.6967.842
356.4577.413
366.2357.083
376.0596.765
385.8496.479
395.6816.250
405.4825.959
415.3265.688
425.1965.493
435.0105.332
444.8575.118
454.6914.930
464.5474.727
474.3874.547
484.2414.320
494.0844.154
503.9883.980
513.8153.827
523.6793.677
533.5653.508
543.4653.351
553.3583.197
563.2353.017
573.0932.914
582.9952.812
592.8912.704
602.7992.565
612.6982.432
622.6212.338
632.5482.252
642.4762.150
652.3912.073
662.3181.996
672.2661.925
682.1761.836
692.1051.747
702.0211.684
711.9571.601
721.8731.537
731.8081.463
741.7221.399
751.6241.330
761.5601.271
771.4831.207
781.4261.151
791.3301.098
801.2501.031
811.1920.970
821.1210.914
831.0620.852
841.0050.803
850.9370.761
860.8800.707
870.8130.653
880.7610.612
890.6870.563
900.6220.512
910.5590.467
920.5050.415
930.4450.364
940.3730.306
950.2950.267
960.2260.224
970.1570.168
980.0730.125
990.0000.081


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 The historical reference is a probabilistic representation of the historical data.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