Articles | Volume 21, issue 3
Hydrol. Earth Syst. Sci., 21, 1573–1591, 2017

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 21, 1573–1591, 2017

Research article 14 Mar 2017

Research article | 14 Mar 2017

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Louise Crochemore1,a, Maria-Helena Ramos1, Florian Pappenberger2, and Charles Perrin1 Louise Crochemore et al.
  • 1IRSTEA, Catchment Hydrology Research Group, UR HBAN, Antony, France
  • 2ECMWF, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, UK
  • anow at: Hydrology Research Unit, Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden

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.

Short summary
The use of general circulation model outputs for streamflow forecasting has developed in the last decade. In parallel, traditional streamflow forecasting is commonly based on historical data. This study investigates the impact of conditioning historical data based on circulation model precipitation forecasts on seasonal streamflow forecast quality. Results highlighted a trade-off between the sharpness and reliability of forecasts.