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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-89
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-89
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2020

06 Mar 2020

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This preprint is currently under review for the journal HESS.

Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

Andres Peñuela1, Christopher Hutton2, and Francesca Pianosi1,3 Andres Peñuela et al.
  • 1Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
  • 2Wessex Water Services Ltd, Bath, BA2 7WW, UK
  • 3Cabot Institute, University of Bristol, BS8 1UH, UK

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimisation system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past ten years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the Continuous Ranked Probability Skill Score) as well as other factors such as bias correction, the decision maker priorities, hydrological conditions and level of uncertainty consideration. We find that some of these factors control the forecast value much more strongly than the forecast skill. For the (realistic) scenario where the decision-maker prioritises water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow predictions (ESP), which are more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference not only in terms of skill but also in terms of value.

Andres Peñuela et al.

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Andres Peñuela et al.

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