Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

Abstract

Abstract. Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month.The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60 %, in case a forecast user is able to take preventive action based on the forecast.

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

“…However, most studies focused either on flood forecasts (e.g. Alfieri et al, 2014;Bürger et al, 2009;Komma et al, 2007;Olsson and Lindström, 2008;Roulin and Vannitsem, 2005;Thielen et al, 2009;Zappa et al, 2011) or low-streamflow forecasts (Demirel et al, 2013a;Fundel et al, 2013). Studies on non-specific ensemble streamflowforecasting systems (Bennett et al, 2014;Demargne et al, 2010;Renner et al, 2009;Verkade et al, 2013) did not evaluate the performance for different streamflow categories (i.e.…”

“…Accurate flood forecasting 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).…”

Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

“…The platform does not provide official warnings, but is thought of as an information platform for a broad user group (about 500 registered users as of December 2015). The evaluated forecasts concerning the drought.ch application relate to the Thur River (Fundel et al [68] and Joerg-Hess et al [69]) and have been running since 2011, and the archived forecast outcomes are first evaluated here. …”

A Tri-National program for estimating the link between snow resources and hydrological droughts

“…The model GSSHA (Gridded Surface/Subsurface Hydrologic Analysis; Downer and Ogden, 2004) is being widely applied by the project partner Czech Technical University in Prague (David et al, 2013;Strouhal and David, 2013), whereas the model PREVAH (Viviroli et al, 2009) is well established in Switzerland (Fundel et al, 2013, for a drought-forecasting application). In particular, the first application of both models in the Caucasus will provide a potential to improve the parameterization of the model components in different mountainous catchments across Europe.…”