Frequently Asked Questions
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 Who will use Seasonal Streamflow Forecasts?

Seasonal Streamflow Forecasts have the potential to help water managers and users make betterinformed decisions on:
 seasonal water allocation outlooks
 reservoir operations
 environmental flow management
 water markets
 drought response strategies, including setting supply restrictions.
 How do I interpret the tercile forecast?

Terciles are defined by arranging the historical data in ascending order (lowest to highest) and then partitioning the data into three equal sized groups, for example if there were 60 years of record each group would contain 20 years. An example of these steps is shown in the figure below. The lower third of the data values are defined as the lower tercile, the middle third of the values are the middle tercile and the upper third of the values are the upper tercile. For example, if you had 100 data values the lower tercile would contain the 1st33rd data values, the middle tercile the 34th67th values and the upper tercile the 68th100th values.
In our forecasts the lower tercile is called low flow, the middle tercile is near median flow and the upper tercile is high flow. The values that partition the historical data into terciles are applied to the forecast distribution (shown in the upper right of the figure below). The percentage of the forecast that is in each tercile is then displayed in a pie chart (shown in the left hand side of the figure below) and indicates the likelihood that the streamflow for the next 3 months will fall within each of these tercile ranges.
 What is the difference between a sitebased forecast and a total catchment inflow forecast?

A sitebased forecast uses streamflow data recorded at a gauging station. A total catchment inflow forecast uses estimated streamflow data based on knowledge of the measured streamflow, change in storage level, modelling of releases and regulated flow.
 Why are the pie chart forecasts on the tercile summary map faded or grey?

The pie charts are displayed differently to indicate the skill scores of the site forecasts for each season. Forecasts with moderate to high skill are represented by a coloured pie chart. The forecasts with low skill for a particular season are displayed as a faded or transparent pie chart on the tercile summary map. The forecast is still provided. Site forecasts with very low skill or skill the same as the historical reference forecasts are replaced with a grey scale pie split into equal thirds representing the historical reference. For these very low skilled site forecasts the historical reference forecasts are used and the BJP forecasts have been removed.
For example, in the tercile summary map below the site forecast for Darbalara is displayed as a coloured pie chart as it has moderate to high skill for that season. The forecast for the Kiewa catchment is displayed as a faded pie chart to represent that it has a low skill score. The very low skill forecast for Dohertys is shown as a grey pie chart.
 Why are the forecasts not shown on some of the products?

The forecasts are not shown for sites that have a very low skill score for that season. A very low skill score essentially means the forecast will not exceed the skill of a historical reference forecast. For this reason the BJP forecasts have been removed for particular seasons.
 Are the Seasonal Streamflow Forecasts related to the Bureau's Seasonal Climate Outlooks?

The statistical technique used for the Seasonal Streamflow Forecasts is different to the dynamical (physics based) technique used for the Climate Outlooks. The climate outlook provides forecasts across the whole of Australia. The Seasonal Streamflow Forecasts are provided at the catchment scale and the predictors and modelling have been developed to maximise the skill at the catchment scale. The seasonal climate outlook predicts the likelihood of rainfall in the coming three months. In future, rainfall forecasts from the Bureau’s climate outlooks will be used to drive an improved technique for producing Seasonal Streamflow Forecasts.
The amount of runoff and streamflow from a catchment is very dependent on the rainfall and catchment conditions such as soil moisture. It is possible to record high flow from a catchment that receives median rainfall when the catchment is saturated. It is also possible to record low flow from a catchment that receives median rainfall if the catchment is very dry. Rainfall intensity and duration can also influence soil moisture and runoff. Some catchments need heavy rainfall to produce runoff. A season with many rain days and low daily rainfall totals may record a high threemonthly rainfall total but still not provide high runoff.
 How can high streamflows be recorded when a catchment receives below average rainfall?

Seasonal streamflows at the forecast sites are a function of several factors, including initial catchment conditions, such as soil moisture, antecedent streamflows due to some level of autocorrelation in the streamflow series, and the characteristics of catchment rainfall events during the season. Even though rainfall was below average during April to June 2011, most forecast locations reported above median streamflows during April to July as a result of above to very much above average catchment soil moisture and high antecedent streamflows.
 How do I interpret the historical reference and forecast probability distribution graphs?

The left hand side of the graph displays various historical streamflow data trends as an accumulated total over three months. The accumulated streamflow starts at 0 at the beginning of the forecast period, which is identified in the graph title e.g. Mar 2010  May 2010. The minimum, maximum and average are derived from years when the total threemonthly flows have the minimum, maximum and average respectively.
The right hand side of the graph displays the probability distributions for the forecast and the historical reference. The forecast distribution is constructed from 5000 forecast ensemble members generated by the BJP model, with each member having the same probability of occurring. This distribution is a summary of the frequencies of the members in terms of their forecast magnitudes (or streamflow volumes).
The forecast distribution is shown as a shaded area and as a histogram. This histogram is obtained by splitting the range of forecast data into equalsized bins. For each bin the number of points from the data set that fall into each bin is counted. The normalised frequency that is displayed is the number of counts in the bin divided by the number of forecasts times the bin width. For this normalisation, the area (or integral) under the histogram is scaled to one. This normalisation results in a histogram with relative frequencies, that is most akin to the probability density function. This allows a direct comparison with the historical reference distribution.
 How do I interpret the exceedance probability forecast?

The exceedance probabilities are defined separately for each forecast location, for each three month period. Firstly, the historical streamflow data for a particular site and a particular three month period are arranged in descending order (highest to lowest). Then the rank value is converted to an exceedance probability from 0% to 100%. The forecast exceedance probability indicates the likelihood that a particular streamflow value will be exceeded. These steps are shown in the figure below.
An exceedance probability forecast is shown in the image below for the site Acheron River at Taggerty. As an example, the forecast likelihood of exceeding a streamflow volume of about 100 GL is 80% (indicated by the red arrows). Also, as another example, there is a 20% chance that a streamflow volume of 185 GL will be exceeded (indicated by the black arrows).
 How do I interpret the historical and probability distribution graph and the historical and exceedance probability graph?

The left hand side of the graphs below display various historical streamflow data trends as an accumulated total over three months. The accumulated streamflow starts at 0 at the beginning of the forecast period, which is identified in the graph title e.g. Jan 2010  Mar 2010. The information on the left hand side of the graphs has the same y axis as the right hand side of the graphs, meaning that historical data values, such as the average, can be read across from the left hand side to the right hand side.
For example, the streamflow volume for the same three month period in the previous year (January  March 2009) is 16 GL and is shown circled on the left hand side of the first graph below. Following this streamflow volume value across to the right hand side of the graph, shown by the red line, you can see that the majority of the forecast distribution is higher than this value. Therefore the forecast is indicating that the streamflow is likely to be greater than the streamflow for the same three month period in the previous year.
For example, the 10 year average streamflow volume for the three months January to March is 47 GL and is shown circled on the left hand side of the graph below. Following this streamflow volume value across to the right hand side of the graph, shown by the red line, you can see that the forecast exceedance probability for this value is about 28%.
 How do I interpret the monthly streamflow boxplot?

A boxplot is also known as a boxandwhisker diagram. This boxplot represents the monthly streamflow historical data. The time period of the historical data used is given in the legend e.g. Jan 1900  Dec 2010. The monthly observed streamflow data for the last 12 months is represented by the pink dots and is joined by a dotted line.
The box extends from the 25th percentile (lower quartile) to the 75th percentile (upper quartile), with a line at the median (50th percentile). Percentiles are values that divide a set of observations into 100 equal parts. The distance of the 75th percentile minus the 25th percentile is the interquartile range (IQR), which is the height of each box. The whiskers extend from the box and are represented by the bottom and top bars at the end of the blue dotted lines. The value of the top whisker is the highest data value still within the 75th percentile value plus 1.5 multiplied by IQR. The value of the bottom whisker is the lowest data value still within the 25th percentile value minus 1.5 multiplied by IQR. The plus (+) symbols represent outliers or extreme values. Outliers are data points that fall outside of the range of whiskers.
 How are the skill score categories defined?

The following definitions can be used in relation to the hindcast RMSEP skill score:
 0 is considered to be a forecast with no skill (equivalent skill to predicting using historical averages or historical reference)
 less than 5 is considered to be a forecast with very low skill
 515 is considered to be a forecast with low skill
 1530 is considered to be a forecast with moderate skill
 greater than 30 is considered to be a forecast with high skill
A forecast with zero skill gives no additional information when compared with the historical reference.
 How do I interpret the model validation graphs?

The forecast quantiles and observations versus forecast median graph compares forecasts with observed data. The grey diagonal line shows a 1:1 relationship between the forecast median and the forecast or observed value. The forecast [0.10, 0.90] quantile range should generally increase with forecast median and be consistent with the observed values. The forecast quantile range shows how wide or narrow the forecast is.
In the forecast quantiles and observations versus year plots, the pink dots indicate the observed value, the dark blue lines are the interquartile range, and the light blue lines are the 5th to 95th percentile range. There should not be any obvious trend with time in the relationship between the forecasts and the observed values.
 Why are climate indices used to predict streamflow?

The Seasonal Streamflow Forecasts rely on a modelling approach that was developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) under the Water Information Research and Development Alliance (WIRADA). This research describes how climatic conditions influence streamflow forecasts. The following paragraphs are edited excerpts from (Robertson, D.E. and Q.J. Wang 2009).
There are two main sources of predictability in Australian streamflows; initial catchment conditions and climate indices. Strong serial correlations in streamflows arise due to soil and groundwater storages extending the time between the incidence of rainfall and any resulting streamflow. Thus, indicators of initial catchment conditions may be good predictors of future streamflows. Future rainfall and climate also influence future streamflows. Many indices of largescale climate anomalies, such as the Southern Oscillation Index, Indian Ocean Dipole Mode Index and the Antarctic Oscillation show significant concurrent and lagged correlations with rainfall and streamflows, and therefore may be useful predictors of streamflows too.
A method has been developed to select predictors of streamflows for the BJP modelling approach to seasonal streamflow prediction. The predictor selection method seeks to identify reliable predictors that produce skilful predictions. An important outcome should be that the selected predictors are consistent with our understanding of the physical hydrological and climate systems. The method selects predictors that give the largest improvement in prediction accuracy and are supported by statistical evidence.
The majority of climate indices used in the BJP modelling system are related to sea surface temperature, but there are several related to atmospheric pressure and one related to upper winds.
More climate information is available at Australian climate influences.
 Are other catchments being modelled using BJP?

Several catchments are being investigated across different climate regimes to test the applicability of the BJP model across Australia. The map below shows the catchments that are currently being investigated. The service will be expanded to more catchments across Australia based on user need, data availability and considering when the modelling is providing the appropriate skill.