Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Abstract

Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made -namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Skilful seasonal forecasts of streamflow over Europe?

“…the February 2017 streamflow for a forecast made on 1 January 2017), etc. Monthly averages were selected for the analysis presented in this paper as it is a valuable aggregation time step for decision-makers for many water-related applications (as shown in the literature for applications such as, for example, navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management , irrigation (Chiew et al, 2003;Li et al, 2017) and hydropower (Hamlet et al, 2002)). …”

“…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.…”

Assessment of an ensemble seasonal streamflow forecasting system for Australia

“…The prior removes the base case's negative skills in the Ranken catchment in JanuaryMarch, but also introduces negative skill in the drier months of June, August and December. We note that, on balance, this may have practical benefits: in another study (Turner et al, 2017) we show that FoGSS forecasts can benefit reservoir operations in cases where forecasts are not skilful in very dry months but positively or neutrally skilful at other times of the year. This is because the dry months contribute little to the annual inflow volume, so small positive bias in dry months (the cause of negative skill) does not have a strong influence on the value of forecasts.…”