Benchmarking seasonal forecasting skill using river flow persistence in Irish catchments

Dáire Foran Quinn,Conor Murphy,Robert L Wilby,Tom Matthews,Ciaran Broderick,Saeed Golian,Seán Donegan,Shaun Harrigan

doi:10.1080/02626667.2021.1874612

Abstract

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.

Highlights

Seasonal hydrological forecasting (SHF) can play an important role in the operation and management of water resources by enhancing preparedness and informing decision-making (Wilby 2001, Wedgbrow et al 2002, Anghileri et al 2016, Tang et al 2016, Viel et al 2016, Prudhomme et al 2017, Dixon and Wilby 2019)
3.1.1 Predictor–forecast horizon period combination Across all catchments and predictor periods, persistence skill declines with increasing forecast horizon
Across all catchments and forecast horizons, annual average persistence skill declines as the duration of the predictor period increases

Summary

Introduction

Seasonal hydrological forecasting (SHF) can play an important role in the operation and management of water resources by enhancing preparedness and informing decision-making (Wilby 2001, Wedgbrow et al 2002, Anghileri et al 2016, Tang et al 2016, Viel et al 2016, Prudhomme et al 2017, Dixon and Wilby 2019). Skilful predictions of future streamflow weeks to months in advance can help reser voir managers balance flood-control safety (Amnatsan et al 2018) with water security during drought conditions (Watts et al 2012). Such forecasts can improve hydropower productivity (Hamlet et al 2002), agriculture (Mushtaq et al 2012), tourism (Fundel et al 2013) and inland water transport (Meißner et al 2017). SHF based on river flow persistence is straightforward to implement because the method uses the most recently observed flow anomaly as the forecasted anomaly

Results

Discussion

Conclusion