Skilful seasonal forecasts of streamflow over Europe?

Abstract

Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0

“…The GloFAS-Seasonal real-time forecasts are implemented and run operationally on the ECMWF computing facilities using ecFlow (Bahra, 2011;ECMWF, 2012), an ECMWF work package used to run large numbers of programmes with dependencies on each other and on time. An ecFlow suite is a collection of tasks and scheduling instructions with a user interface allowing for the interaction and monitoring of the suite, the code behind it, and the output.…”

“…the ability of the forecast to predict that weekly averaged river flow will fall in the upper 80th or lower 20th percentile of climatology using a climatology of historical observations as a benchmark. This can be referred to as the potential usefulness of the forecasts and is of particular importance for decisionmaking purposes (Arnal et al, 2018). Another key aspect of probabilistic forecasts to consider is their reliability, which indicates the agreement between forecast probabilities and the observed frequency of events.…”

“…A number of continental-scale seasonal hydrometeorological forecasting systems have begun to emerge around the globe over the past decade (Yuan et al, 2015a), using seasonal meteorological forecasts as input to hydrological models to produce forecasts of hydrological variables. These include the European Flood Awareness System (EFAS; Arnal et al, 2018;Cloke et al, 2013), the European Service for Water Indicators in Climate Change Adapta-3328 R. tion (SWICCA; Copernicus, 2018b), the Australian Government Bureau of Meteorology Seasonal Streamflow Forecasts (Bennett et al, 2017;BoM, 2018), and the USA's National Hydrologic Ensemble Forecast Service (HEFS; Demargne et al, 2014;Emerton et al, 2016). There are also various ongoing research efforts using seasonal hydro-meteorological forecasting systems for forecast applications and research purposes at regional Bennett et al, 2016;Crochemore et al, 2016;Meißner et al, 2017;Mo et al, 2014;Prudhomme et al, 2017;Wood et al, 2002Wood et al, , 2005Yuan et al, 2013) and global (Candogan Yossef et al, 2017;Yuan et al, 2015b) scales.…”

What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices

“…EA summer and autumn anomalies have been forecasted with a lead time of 1 to 2 months (Iglesias et al, 2014). Given that the seasonal predictability of the indices of atmospheric oscillations is often higher than that of hydrometeorological variables such as rainfall (Dunstone et al, 2018) and streamflow (Arnal et al, 2018), seasonal flood risk outlooks could potentially be developed through the integration of statistical models with dynamical seasonal forecasts of large-scale atmospheric oscillations. Further value could be added to the forecasts of indices of climate variability by combining them with information on the resulting flood losses, thereby enabling the seasonal forecasting of those socioeconomic impacts of floods.…”

“…While these systems have greatly improved our capability of forecasting hydrometeorological variables by producing predictions of flood magnitudes with increasing lead times (Emerton et al, 2016), there is still a gap in translating flood events into impact information, such as the economic damage of floods (Dottori et al, 2017). For instance, in Europe, while EFAS provides streamflow forecasts with a 7‐month lead time (Arnal et al, 2018), flood impact forecasts are only available through this system with a lead time up to 10 days (Dottori et al, 2017). If impact‐based forecasting information was available through such climate services at seasonal lead times, this could offer a great window of opportunity for implementing early action and risk transfer mechanisms (Michel‐Kerjan & Kunreuther, 2011) to address emerging flood risks.…”