Seasonal Streamflow Forecasts

Probability distribution for Tuross River at Tuross Vale


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Product list for Tuross River at Tuross Vale



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Probability distribution for Tuross River at Tuross Vale ( May 2014 )

Basic Statistics
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
25% Quartile4.3931.900
Median9.1425.258
Mean15.21712.885
75% Quartile18.69114.226
Interquartile Range14.29712.326

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
198.197108.153
274.10980.678
362.62770.510
454.47762.536
549.38956.902
643.99949.506
740.77444.647
838.18740.377
935.97636.386
1034.14133.568
1132.44431.347
1231.16929.017
1329.26027.334
1428.19925.720
1526.88524.027
1625.62022.742
1724.71221.308
1823.75919.917
1922.97718.751
2022.14917.869
2121.26617.047
2220.58116.166
2319.82915.571
2419.17614.733
2518.69714.226
2617.84313.800
2717.32513.170
2816.83512.685
2916.36212.138
3015.95311.558
3115.51511.045
3215.06910.542
3314.60610.157
3414.1179.788
3513.6849.298
3613.3018.919
3712.9088.552
3812.4628.220
3912.0807.955
4011.7417.615
4111.5037.297
4211.2527.068
4310.8956.878
4410.6476.624
4510.4426.401
4610.1686.158
479.8495.943
489.6345.670
499.4245.469
509.1425.258
518.9105.071
528.6464.888
538.4134.680
548.2064.487
558.0314.297
567.7784.073
577.5633.944
587.3253.817
597.1773.680
606.9993.505
616.7993.337
626.5693.217
636.3923.107
646.2042.976
656.0502.876
665.8852.776
675.6832.685
685.5302.568
695.3662.452
705.2072.370
715.0742.261
724.8652.175
734.7022.077
744.5621.992
754.3921.899
764.2221.819
774.0441.733
783.8941.656
793.7571.584
803.5801.492
813.4371.408
823.2751.330
833.1301.245
842.9841.175
852.8391.117
862.7041.040
872.5580.963
882.4210.903
892.2970.834
902.1640.759
912.0120.692
921.8670.616
931.7210.539
941.5170.450
951.3310.390
961.1640.324
970.9870.235
980.7210.165
990.4670.091


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 The historical reference is a probabilistic representation of the historical data.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