Development of a monthly to seasonal forecast framework tailored to inland waterway transport in Central Europe

Abstract

Abstract. Traditionally, navigation-related forecasts in Central Europe cover short- to medium-range lead-times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead-time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead-times of several weeks up to several months ahead currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known Ensemble Streamflow Prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators as well as an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) As former studies for other regions of Central Europe indicate, also for relevant stations along the German waterways meteorological forcings dominate initial hydrological conditions in most cases already after the first forecast month. (2) Despite the predictive limitations on longer lead-times over Central Europe, this study reveals the existence of a valuable predictability of streamflow at monthly up to seasonal time-scales along Rhine, Upper Danube and Elbe, while the Elbe achieves the highest skill and value. (3) The more physically-based as well as the statistical approach are able to improve the predictive skills compared to climatology and the ESP-approach. The specific forecast skill highly depends on the forecast location, the lead-time and the season. (4) Currently, the statistical approach seems to be most skilful for the three waterways investigated. The lagged relationship between the monthly/seasonal streamflow and the climatic/oceanic variables vary between one month (e.g. local precipitation and temperature, soil moisture) up to six months (e.g. sea surface temperature). Besides improving the forecast methodology, especially by combining the individuals approaches, the focus is on developing useful forecast products on monthly to seasonal time-scale for waterway transport and to operationalize the related forecasting service.

Skilful seasonal forecasts of streamflow over Europe?

“…At longer lead times, historical meteorological information becomes as good as or better than Sys4 for seasonal streamflow forecasting over Europe. Crochemore et al (2016) and Meißner et al (2017) similarly found positive skill in the seasonal streamflow forecast (Sys4 forced hydrological model compared to an ESP) for the first month of lead time, after which the skill faded away for basins in France and central Europe, respectively. Additionally, on average over Europe and across all seasons, the CM-SSF is less reliable than the ESP for all lead times.…”

“…the February 2017 streamflow for a forecast made on 1 January 2017), etc. Monthly averages were selected for the analysis presented in this paper as it is a valuable aggregation time step for decision-makers for many water-related applications (as shown in the literature for applications such as, for example, navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management , irrigation (Chiew et al, 2003;Li et al, 2017) and hydropower (Hamlet et al, 2002)). …”

“…While climate-model-based seasonal streamflow forecasting experiments are more common outside of Europe, for example for the United States (Wood et al, 2002(Wood et al, , 2005Mo and Lettenmaier, 2014), Australia (Bennett et al, 2016), or Africa , they remain limited in Europe, with a few examples in France (Céron et al, 2010;Singla et al, 2012;Crochemore et al, 2016), in central Europe (Demirel et al, 2015;Meißner et al, 2017), in the United Kingdom Prudhomme et al, 2017) and at the global scale (Yuan et al, 2015a;Candogan Yossef et al, 2017). This is because, although the quality of seasonal climate forecasts has increased over the past decades, there remains limited skill in seasonal climate forecasts for the extra-tropics, particularly for the variables of interest for hydrology, notably precipitation and temperature (Arribas et al, 2010;DoblasReyes et al, 2013).…”

“…Translating the quality of a forecast into an added value for decision-making and incorporating new forecasting products into established decision-making chains are not easy tasks. This has been explored for many water-related applications, such as navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management Crochemore et al, 2017), irrigation (Chiew et al, 2003;Li et al, 2017), water resource management and hydropower (Hamlet et al, 2002), but seasonal streamflow forecasts have yet to be adopted by the flood preparedness community.…”

Moisture transport and Antarctic sea ice: Austral spring 2016 event

“…The stability maps approach used in this study is based on a methodology that was successfully applied to predict the monthly to seasonal streamflow of Elbe and Rhine rivers (Ionita et al, 2008(Ionita et al, , 2015Meißner et al, 2017). The basic idea of the stability maps is to identify regions with stable teleconnections (the correlation does not change on time) between 20 SIE averaged over specific regions (e.g.…”