Seasonal streamflow forecasting by conditioning climatology with precipitation indices

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 longrange 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.

Skilful seasonal forecasts of streamflow over Europe?

“…What is a useful scientific finding does not automatically translate into usable information which will fit into any user's decision-making chain (Soares and Dessai, 2016). While several authors have already investigated the usability of seasonal streamflow forecasts for applications such as navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management Yuan et al, 2013;Crochemore et al, 2017), irrigation (Chiew et al, 2003;Li et al, 2017), water resource management and hydropower (Hamlet et al, 2002), its application to flood preparedness is still left mostly unexplored. One exception being Neumann et al (in review), who look at the use of the CM-SSF to predict the 2013/14 Thames basin floods.…”

“…This system, called extended streamflow prediction (ESP; i.e. note that ESP nowadays stands for ensemble streamflow prediction, although it refers to the same forecasting method), was developed by the United States National Weather Service (NWS) in the 1970s (Twedt et al, 1977;Day, 1985). The ESP forecasts are produced by forcing a hydrological model, initialized with the current IHC, with the observed historical meteorological time series available.…”

“…The ESP forecasts are produced by forcing a hydrological model, initialized with the current IHC, with the observed historical meteorological time series available. The output is an ensemble streamflow forecast (where each year of historical data is a streamflow trace) for the following season(s) (Twedt et al, 1977;Day, 1985). The quality of the ESP forecasts can be high in basins where the IHC dominate the surface hydrological cycle for several months (the exact forecast quality depending on the time of year and the basin's physiographic characteristics; Wood and Lettenmaier, 2008).…”

“…Translating the quality of a forecast into an added value for decision-making and incorporating new forecasting products into established decision-making chains are not easy tasks. This has been explored for many water-related applications, such as navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management Crochemore et al, 2017), irrigation (Chiew et al, 2003;Li et al, 2017), water resource management and hydropower (Hamlet et al, 2002), but seasonal streamflow forecasts have yet to be adopted by the flood preparedness community.…”

Benchmarking Ensemble Streamflow Prediction skill in the UK

“…US NWS and HOUK) and as a low cost forecast against which to benchmark potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems (e.g. Crochemore et al, 2017;Pappenberger et al, 2015;Thober et al, 2015;Wood et al, 2005).…”