- 1FUNDACION INSTITUTO DE HIDRAULICA AMBIENTAL, IHCANTABRIA, santander, Spain (cristina.prieto@unican.es)
- 2School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia
- 3Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- 4Dept. of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
- 5Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
- 6Dept. of Earth and Planetary Science, University of California, Berkeley, CA 94720, USA
Floods are among Earth's most widespread, frequent, and destructive natural hazards. In highly responsive
catchments, daily streamflow predictions will underestimate flood hazards. For example, peak flows occurring
on sub-daily timescales caused hundreds of fatalities and billions of Euros in damages in the devastating floods
in Germany in 2021 and Spain in 2024. Particularly in small and mesoscale catchments: 1) peak flows may
last only a few hours, so forecasts of daily flows can greatly underestimate flood peaks ; 2) A landscape's
responsiveness to precipitation depends critically on how wet it is; thus, it is essential to accurately model the
wetting and drying of the catchment, and hourly streamflow is needed to capture and understand the
hydrological processes in the rising limb of the hydrograph; and 3) the dominant processes affecting shortterm
predictions are not necessarily the same as those affecting streamflow at longer time scales. For example,
over longer time scales, predictions become more a question of mass balance, rather than dynamics and routing,
while the opposite is true for short-term predictions.
Thus, reliably assessing flood hazards requires understanding hydrologic responses at hourly time scales. But
paradoxically, hourly predictions have received relatively less focus. In this work we use a conceptual
hydrological model to obtain deterministic hourly predictions and estimate its uncertainty using a residual error
model. Case study catchments include hydrologically diverse catchments in Europe and the USA. We consider
bias, heteroscedasticity and autocorrelation by employing the Box-Cox transformation, autoregressive (AR)
and moving average models (ARMA) models. The log transformation was in general the most recommended
option, in combination with an AR3 model.
This work advances streamflow prediction by developing statistically rigorous methods for postprocessing the
residuals of conceptual models at the hourly time scale.
How to cite: Prieto, C., Kavetski, D., Fenicia, F., Kirchner, J., and Álvarez, C.: Towards achieving reliable probabilistic hydrological predictions at the hourly scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2946, https://doi.org/10.5194/egusphere-egu25-2946, 2025.
