Seasonal Streamflow Forecasts

Probability distribution for Total Inflows to Lake Nillahcootie


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Product list for Total Inflows to Lake Nillahcootie


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Probability distribution for Total Inflows to Lake Nillahcootie( Oct 2014 )

Basic Statistics
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
25% Quartile3.1223.406
Median7.2089.775
Mean10.38515.303
75% Quartile14.85122.432
Interquartile Range11.72919.026

Exceedance Probability
Exceedance Prob. of
Streamflow (%)
Streamflow Forecast
(3 month total flow in GL)
Historical Reference
(3 month total flow in GL)
144.66969.568
239.56959.452
335.64355.486
432.65452.226
530.39849.812
628.87146.453
727.41144.090
826.16141.879
925.24939.670
1024.31038.009
1123.18036.629
1222.35735.104
1321.38233.946
1420.68932.784
1520.07331.506
1619.30530.492
1718.51429.307
1818.09428.102
1917.63627.043
2017.01926.211
2116.45525.408
2216.01724.517
2315.63723.896
2415.25922.995
2514.85222.433
2614.28021.950
2713.82521.219
2813.39120.641
2912.98119.974
3012.66519.245
3112.29518.583
3211.98317.918
3311.61217.397
3411.21716.887
3510.94316.196
3610.59615.649
3710.30915.110
3810.08614.613
399.79714.209
409.55913.684
419.30213.183
429.06512.817
438.80312.511
448.56712.097
458.33311.729
468.06211.322
477.80310.959
487.61910.492
497.45610.145
507.2089.775
516.9809.445
526.8059.119
536.5688.745
546.3668.395
556.1398.045
565.9677.631
575.8007.392
585.6607.153
595.4696.895
605.3106.564
615.1296.241
624.9716.011
634.8015.799
644.6445.544
654.4945.350
664.3325.154
674.1804.975
684.0344.745
693.9214.514
703.7904.351
713.6624.133
723.5273.962
733.3813.765
743.2613.593
753.1213.406
762.9873.243
772.8473.068
782.6762.911
792.5542.763
802.4162.575
812.2872.402
822.1882.241
832.0712.064
841.9331.920
851.8031.798
861.6871.637
871.5571.477
881.4311.352
891.3101.205
901.1881.048
911.0790.906
920.9770.745
930.8260.581
940.6880.391
950.5650.262
960.4010.120
970.2350.000
980.0590.000
990.0000.000


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 The historical reference is a probabilistic representation of the historical data.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