Environmental Research Letters (Jan 2023)
Hydrological regimes explain the seasonal predictability of streamflow extremes
Abstract
Advances in hydrological modeling and numerical weather forecasting have allowed hydro-climate services to provide accurate impact simulations and skillful forecasts that can drive decisions at the local scale. To enhance early warnings and long-term risk reduction actions, it is imperative to better understand the hydrological extremes and explore the drivers for their predictability. Here, we investigate the seasonal forecast skill of streamflow extremes over the pan-European domain, and further attribute the discrepancy in their predictability to the local river system memory as described by the hydrological regimes. Streamflow forecasts at about 35 400 basins, generated from the E-HYPE hydrological model driven with bias-adjusted ECMWF SEAS5 meteorological forcing input, are explored. Overall the results show adequate predictability for both hydrological extremes over Europe, despite the spatial variability in skill. The skill of high streamflow extreme deteriorates faster as a function of lead time than that of low extreme, with a positive skill persisting up to 12 and 20 weeks ahead for high and low extremes, respectively. A strong link between the predictability of extremes and the underlying local hydrological regime is identified through comparative analysis, indicating that systems of analogous river memory, e.g. fast or slow response to rainfall, can similarly predict the high and low streamflow extremes. The results improve our understanding of the geographical areas and periods, where the seasonal forecasts can timely provide information on very high and low streamflow conditions, including the drivers controlling their predictability. This consequently benefits regional and national organizations to embrace seasonal prediction systems and improve the capacity to act in order to reduce disaster risk and support climate adaptation.
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