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( Sep 2014 )

Exceedance Probability Summary
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
1023.28716.695
2017.6269.952
3013.5316.832
4010.7064.730
508.3223.404
606.4932.375
704.8511.683
803.3721.127
901.9080.641

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
141.87340.758
236.51132.558
333.02429.416
430.29926.881
528.70925.040
627.48922.542
726.20420.836
825.07019.286
924.27017.787
1023.28716.695
1122.59515.813
1221.74614.865
1321.09114.165
1420.59113.480
1520.02212.749
1619.57512.183
1719.08411.541
1818.61910.907
1918.09710.366
2017.6269.952
2117.0239.561
2216.6629.137
2316.1878.848
2415.8558.437
2515.4358.185
2615.0827.973
2714.7107.656
2814.2947.410
2913.9867.131
3013.5316.832
3113.1976.566
3212.9256.303
3312.6126.100
3412.2915.905
3512.0655.643
3611.7465.440
3711.4675.242
3811.2025.061
3910.9504.916
4010.7064.730
4110.3794.554
4210.1454.427
439.8334.321
449.6194.179
459.3634.054
469.1493.917
478.9203.795
488.7233.640
498.5383.525
508.3223.404
518.1303.296
527.9203.191
537.7013.070
547.5062.957
557.3522.845
567.2112.713
577.0272.637
586.8262.561
596.6622.480
606.4932.375
616.3312.274
626.1212.202
635.9472.135
645.8082.055
655.6561.995
665.4881.933
675.3191.877
685.1451.806
694.9961.734
704.8511.683
714.7261.615
724.6121.561
734.4761.500
744.3251.446
754.1541.388
764.0061.336
773.8401.282
783.6931.233
793.5251.186
803.3721.127
813.2411.072
823.0591.022
832.9480.966
842.7970.920
852.6520.881
862.5000.830
872.3530.779
882.2190.739
892.0670.692
901.9080.641
911.7890.595
921.6580.543
931.4850.489
941.3050.427
951.1580.384
961.0140.336
970.8240.271
980.6410.219
990.3600.162


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 The historical reference is a probabilistic representation of the historical data.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