Abstract
Abstract. Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.
Highlights
Recent years have seen a proliferation of experimental longrange ensemble streamflow forecasting systems, and, to a lesser extent, the operationalization of these systems as forecasting services that are available to water agencies and the public
Other seasonal forecasting systems generally have some combination of shortcomings with respect to stochastic scenarios: they may not produce reliable ensembles (e.g. Crochemore et al, 2016; Wood and Schaake, 2008); the ensembles may be biased with respect to climatology (e.g. Fundel et al, 2013; Wood and Schaake, 2008); and/or the forecasts may be less skilful than climatology for certain months or lead times (Yuan et al, 2013)
Historical rainfall forcings are sampled from observations from 1982 to 2009, using a leave-4-years-out cross-validation scheme. (The leave-4years-out scheme was chosen in part for computational convenience: it results in a forcing ensemble of 25 members, which divides evenly into 1000, the size of the forecast guided stochastic scenarios” (FoGSS) ensemble.) To produce a 1000-member ensemble, we run each historical rainfall sequence through the FoGSS hydrological and error models 40 times, using a different random seed at the start of each run
Summary
Recent years have seen a proliferation of experimental longrange ensemble streamflow forecasting systems (examples from this issue: Meißner et al, 2017; Beckers et al, 2016; Candogan Yossef et al, 2017; Bell et al, 2017; Greuell et al, 2016), and, to a lesser extent, the operationalization of these systems as forecasting services that are available to water agencies and the public. Resampled historical inflow sequences (termed stochastic scenarios in this paper) have some appeal for water agencies: they are unbiased, they are available as time series, they are easy to generate to long time horizons, and, presuming a long observation record is available from which to sample, the ensemble of inflows is inherently statistically reliable (either taken at individual months or when individual ensemble members are summed, e.g. to produce an ensemble of 6 months’ total inflow). Fundel et al, 2013; Wood and Schaake, 2008); and/or the forecasts may be less skilful than climatology for certain months or lead times (Yuan et al, 2013). Any of these can be a serious barrier to their use by water agencies to plan operations
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