Benchmarking seasonal forecasting skill using river flow persistence in Irish catchments

Abstract

This study assesses the seasonal forecast skill of river flow persistence in 46 catchments representing a range of hydrogeological conditions across Ireland. Skill is evaluated against a climatology benchmark forecast and by examining correlations between predicted and observed flow anomalies. Forecasts perform best when initialized in drier summer months, 87% of which show greater skill relative to the benchmark at a 1-month horizon. Such skill declines as forecast horizon increases due to the longer time a catchment has to "forget" initial anomalous flow conditions and/or to be impacted by "new" events. Skill is related to physical catchment descriptors such as the baseflow index (correlation ρ = 0.86) and is greatest in permeable high-storage catchments. The distinct seasonal and spatial variations in persistence skill allow us to pinpoint when and where this method can provide a useful benchmark in the future development of more complex seasonal hydrological forecasting approaches in Ireland.

Dynamical–statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland

“…For example, Foran Quinn et al . (2021) assessed the seasonal forecast skill of persistence‐based methods applied to river flows in 46 Irish catchments. They found that skill was greatest when initialized in summer months in catchments with significant groundwater storage.…”

Quantifying pluriannual hydrological memory with Catchment Forgetting Curves

“…The concept of catchment memory is used with intra-annual objectives to qualify the predictability linked with initial hydrological states (e.g., Svensson, 2015;Bierkens and van Beek, 2009;van Dijk et al, 2013;Quinn et al, 2021). Studies have aimed to weigh the predictability linked with the past, and conveyed by hydrological states (or catchment memory), in relation to the predictability linked with future rainfall and temperatures for predicting discharge (Wood et al, 2016;Arnal et al, 2017).…”

Reply on RC2

“…Because we do not use any tracers in this study, we cannot check any hypothesis about water age and we will not discuss this topic further; catchment memory, as defined in this paper, describes the period of time during which we manage to detect a significant dependency between two signals: the past climatic inputs and the current ability of the catchment to transform precipitation into river flow. The scientific literature sometimes addresses catchment memory also through "flow persistence" (see e.g., Svensson, 2015;Quinn et al, 2021) or "flow predictability" (see e.g., Bierkens and van Beek, 2009;van Dijk et al, 2013). Compared with a description of water age, the ambition to physically understand the system is more limited and restricted to explaining catchment behavior from the perspective of an operational flow prediction model.…”