Over the last decade, weather and hence hydrological forecasts with lead time up to six-seven months (so called “seasonal” forecasts) have become increasingly available. One of the key intended purposes of these forecast products is to support water resources management, particularly for water supply or other operational objectives that require a lead time longer than decadal. However, examples of water agencies that have formally embedded these products in their operational practice are hardly reported to date. This is often traced back to the uncertainty and inaccuracy that affect these forecast products, particularly outside the tropics, and many have concluded that skill must increase before seasonal forecasts can be used in operational management (Jackson-Blake et al. 2021). However, a handful of simulation studies have recently suggested that forecasts value (that is, the enhancement of management performance when forecasts are integrated into decision-making procedures) may exceed their skill (that is, the ability to predict inflows correctly). This is an interesting perspective and calls for more studies to investigate the relationship between skill and value for water management, so to understand when and how value could be extracted from forecasts despite their limited skill.
With this motivation, in a previous simulation study (Penuela et al. 2020) we evaluated the potential of seasonal forecasts for improving the operations of a pumped-storage supply system in the UK. We found that the forecast value was only loosely related to skill, and that operational priorities (that is, the relative weight given to the two objectives of saving energy and reaching full capacity at the end of the filling season) and hydrological conditions (the initial reservoir storage and the overall inflow volume over the filling season) determined the forecast value more than its skill.
In this work, we use the same case study to further explore the skill-value relationship by comprehensively assessing and comparing ensemble of forecasts with different skill. First, we use a novel technique to generate synthetic ensembles of weather forecasts with similar characteristics to the original one (provided by the ECMWF seasonal forecasting systems SEAS5, in our case) and artificially increased skill – up to the ‘perfect’ forecast where all ensemble members coincide with observations. Second, for each synthetic ensemble, we generate the corresponding hydrological forecasts through a conceptual rainfall-runoff model. Last, through a nested optimisation-simulation procedure, we reconstruct the reservoir operations that would have resulted from (optimally) using those hydrological forecasts over a 11-years simulation period. We then compare resulting performances in terms of storage conservation and energy costs (the forecast ‘value’) as forecast skill increases. This helps us shed some more light on the skill-value relationship, and identify thresholds (if they exist) below which forecasts are not useful, or conversely, above which further improving skill does not significantly increase value.
Peñuela et al. (2020) Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK, HESS, 24. https://doi.org/10.5194/hess-24-6059-2020
Jackson-Blake et al. (2021) Opportunities for seasonal forecasting to support water management outside the tropics, HESSD, In review. https://doi.org/10.5194/hess-2021-443