Assessment of an ensemble seasonal streamflow forecasting system for Australia

Abstract

Abstract. Despite an increasing availability of skilful longrange 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 oceanland-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 climatologylike 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 rainfallrunoff 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.

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

“…However, employing EIOR requires information about the streamflow forecast to alleviate the risk of flooding.The recent advancements of the meteorological and hydrological forecast systems provide an unprecedented opportunity for employing flexible operation rules rather than fixed ones for reservoir systems [19][20][21]. Combining seasonal meteorological forecasts with hydrological models at continental-scale has provided several continental-scale seasonal hydro-meteorological forecasting systems [22][23][24], such as the European Flood Awareness System [25], the Australian Government Bureau of Meteorology Seasonal Streamflow Forecasts [26], and the National Hydrologic Ensemble Forecast Service, USA [27]. Several studies have demonstrated that a skillful streamflow forecast can enhance the efficiency of water allocation systems to manage the trade-off between hydropower, irrigation, municipal, and environmental services [28][29][30][31].…”

The Value of Using Hydrological Datasets for Water Allocation Decisions: Earth Observations, Hydrological Models, and Seasonal Forecasts

“…The forecasted datasets for determining the expected reservoir inflow were obtained from an experimental streamflow forecasting system called Forecast Guided Stochastic Scenarios (FoGSS), available for the time period 1982-2009 (Bennett et al, 2017). Two different datasets from FoGSS were used.…”

“…The streamflow forecasting system "Forecast Guided Stochastic Scenarios" (FoGSS) is an experimental forecasting system, which has been developed and tested for the Australian continent (Bennett et al, 2016(Bennett et al, , 2017Turner et al, 2017). FoGSS is an ensemble streamflow forecast in the form of monthly time series for a 12-month forecast horizon.…”

“…FoGSS is an experimental seasonal streamflow forecast ensemble for a 12 months horizon developed for the Australian continent (Bennett et al, 2016(Bennett et al, , 2017 . FoGSS post-processes climate forecasts, either derived from ESP or POAMA to force a monthly hydrological model.…”

Assessing two precipitation data sources at basins of special interest to hydropower production in Brazil

“…This is the case when using the Ensemble Streamflow Prediction (ESP) method, a widely applied technique to generate ensembles of possible future scenarios of streamflow over several weeks and months ahead. The method is based on using a continuous hydrological model to estimate initial hydrological conditions (using real-time meteorological data as input) and future meteorological forecasts (based on historical sequences of meteorological data) to obtain streamflow predictions several months ahead (see recent applications in, for instance, Crochemore et al, 2016;Bennett et al, 2017;Arnal et al, 2018;Harrigan et al, 2018). Reliable and consistent long-term historic meteorological data are therefore also crucial when running seasonal forecasting systems.…”