Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Harm-Jan F Benninga,Tom H M Rientjes,Renata J Romanowicz,Martijn J Booij

doi:10.5194/hess-21-5273-2017

Abstract

Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.

Highlights

Accurate flood forecasting (Cloke and Pappenberger, 2009; Penning-Rowsell et al, 2000; Werner et al, 2005) and lowstreamflow forecasting (Demirel et al, 2013a; Fundel et al, 2013) are important in mitigating the negative effects of extreme events, by enabling early warning
With a data-based mechanistic (DBM) model, the performance was worse for this year (Kiczko et al, 2015)
This must be the result of measurement errors and/or human influence, because it is unlikely that in this period different hydrological processes were taking place that are not captured well by both the Hydrologiska Byråns Vattenbalansavdelning (HBV) and DBM models

Summary

Introduction

Accurate flood forecasting (Cloke and Pappenberger, 2009; Penning-Rowsell et al, 2000; Werner et al, 2005) and lowstreamflow forecasting (Demirel et al, 2013a; Fundel et al, 2013) are important in mitigating the negative effects of extreme events, by enabling early warning. Accurate forecasting is becoming increasingly more important, because the frequency and magnitude of low- and high-streamflow events are projected to increase in many areas in the world as a result of climate change (IPCC, 2014). Ensemble forecasts provide information on the possibility that an event will occur (Krzysztofowicz, 2001; Thielen et al, 2009) and allow a quantification of the forecast uncertainty (Krzysztofowicz, 2001; Zappa et al, 2011). Uncertainties in streamflow forecasts originate from the meteorological inputs, as well as from the hydrological model parameters, initial conditions and model structure (Bourdin and Stull, 2013; Cloke and Pappenberger, 2009; Demirel et al, 2013a; Zappa et al, 2011)

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Discussion

Conclusion