Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5669–5683, 2022
https://doi.org/10.5194/hess-26-5669-2022
Hydrol. Earth Syst. Sci., 26, 5669–5683, 2022
https://doi.org/10.5194/hess-26-5669-2022
Research article
10 Nov 2022
Research article | 10 Nov 2022

Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too

David McInerney et al.

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Cited articles

Bauer, D. F.: Constructing Confidence Sets Using Rank Statistics, J. Am. Stat. Assoc., 67, 687–690, https://doi.org/10.2307/2284469, 1972. 
Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate – a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. B Met., 57, 289–300, 1995. 
Boucher, M.-A. and Ramos, M.-H.: Ensemble Streamflow Forecasts for Hydropower Systems, in: Handbook of Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., Cloke, H. L., and Schaake, J. C., Springer Berlin Heidelberg, Berlin, Heidelberg, 1289–1306, ISBN 978-3-642-39924-4, 2019. 
Box, G. E. P. and Cox, D. R.: An analysis of transformations, J. Roy. Stat. Soc. B, 26, 211–252, 1964. 
Bureau of Meteorology: Long-range weather, climate and hydrology, http://www.bom.gov.au/climate/
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Short summary
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.