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Incorporating uncertainty into climate variability

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• In the Simulation toolbar, change the analysis option to Stochastic Analysis; and
• From the main toolbar, click Prepare to Run.
• Click Configure

The Stochastic Configuration dialogue opens (Figure 1). To begin the analysis:

• Define the Start and End years;
• Specify the number of replicates to run. The default is 20;
• Click OK; and
• In the Simulation toolbar, click Run. 

The Stochastic Analysis tool will automatically load the rainfall data that is being used by the scenario, and then stochastically generate rainfall replicates for each sub-catchment.

Figure 1. Stochastic analysis set up

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The Stochastic Analysis tool may take some time to complete, depending on the number of sub-catchments, replicates, number of recorded variables and type of rainfall data used in the scenario. Generally, the more complex the model or scenario, the longer the stochastic generation of rainfall data will be.

Image Modified To produce valid results when comparing statistics from the generated and historical data, the replicate length and the historical input data length must be the same. To properly capture sampling variability, you should also generate at least 100 replicates.

Once the Stochastic Analysis tool has finished, the modelled flow and constituent parameters appear in the Recording Manager and can be viewed by selecting the parameter of interest in the right-hand column of the Recording Manager.

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Although stochastic hydrology is a mature science, new stochastic models are continually being developed, usually with marginal improvements on previous models. The modelling approach for generating stochastic rainfall data has been selected because of available expertise, the model’s robustness, along with extensive and successful model testing using data from across Australia.

Image Modified A basic understanding of stochastic climate data is required to properly use stochastic data with hydrological and ecological models to quantify uncertainty in environmental systems associated with climate variability. The user should consider the following questions when designing a stochastic and hydrological modelling study:   Is there a need for stochastic simulations? Is the hydrological model used for the particular application appropriate/reliable? Are the historical data reliable (note that stochastic data do not improve poor records, but improve the design made with whatever reliable historical records that are available)? Is the stochastic and hydrological modelling methodology appropriate? Do the statistics in the stochastically-generated climate data match those of the historical data?

The Stochastic Analysis tool can take a long time to run, particularly when applied to large catchments and when you have specified many replicates (ie over 100). Such applications can take several hours to run. Therefore, when first designing a scenario with stochastically-generated rainfall data, keep the following in mind:

• Perform a "dummy" run with only a few replicates (< 20) to assess whether or not the stochastically-generated data is being generated properly;
• Be specific about what you need to record. If the flow that is coming out of the catchment outlet is of particular interest, then only record the catchment outflow when using the Stochastic Analysis tool. All other parameters and reporting options can be set to Record None. This will reduce the repeat run time and reduce the size of the generated output data; and
• Make note of how many years of data you want to generate climate data for. Although it is desirable to use as much data as possible, only use what you need to get a satisfactory model result.

Image Modified As a general rule, the input data should have at least 20 years of historical climate time-series data. As an absolute minimum, the stochastic analysis requires a climate record of 5 years. Longer data is used to improve the calibration of the stochastic model to the characteristics of the historical time series. In addition, the model needs to be configured with at least two distinct time series inputs.