Articles | Volume 27, issue 4
https://doi.org/10.5194/hess-27-873-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-873-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2023
Research article |
| 23 Feb 2023
| 23 Feb 2023
Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts
Richard Laugesen
CORRESPONDING AUTHOR
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Bureau of Meteorology, Canberra, ACT, Australia
Mark Thyer
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
David McInerney
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Dmitri Kavetski
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
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Short summary
Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
Forecasts may be valuable for user decisions, but current practice to quantify it has critical...
