Abstract
Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.
Highlights
1.1 Approaches to seasonal streamflow forecastingNumerical prediction is valuable to proactively manage risks in areas such as hydropower, drinking water production and drought preparedness (Wilhite et al, 2000)
Hamlet and Lettenmaier (1999) selected past precipitation based on categories of El Niño– Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) to feed a hydrological model for streamflow forecasting, and, later on, for reservoir operation (Hamlet et al, 2002)
Once the historical sequences are selected, two cases can lead to a streamflow forecast ensemble: (a) the selected precipitation sequences are used as input to the hydrological model to generate a streamflow forecast ensemble or (b) the historical streamflows corresponding to the selected precipitation sequences are directly used as ensemble members to build a streamflow forecast ensemble
Summary
Numerical prediction is valuable to proactively manage risks in areas such as hydropower, drinking water production and drought preparedness (Wilhite et al, 2000). The conclusions of Shukla et al (2013) are quite consistent with these findings They found that the predictability of a forecast issued in July in France lies in the meteorological forcing for horizons longer than 3 months. Day (1985) introduced the ensemble streamflow prediction (ESP), which uses the climatology of meteorological forcings as input to a hydrological model previously initialised for the forecast date. This approach has been extensively used for research purposes and operationally in seasonal streamflow forecasting (Wang et al, 2011) and reservoir operations (Faber and Stedinger, 2001), among other fields. Some studies have shown that dynamical and statistical approaches can complement and benefit from each other (Block and Rajagopalan, 2009; Seibert and Trambauer, 2015)
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