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Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by hydrometeorological agencies. The most obvious advantages of HEPS are that more of the uncertainty in the modelling system can be assessed; and that ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the technical aspects of the model systems themselves. However, in this paper we argue that there are other areas of HEPS that need urgent attention; such as assessment of the full uncertainty in the forecast chain, multimodel approaches, robust forecast skill assessment and further collaboration and knowledge exchange between operational forecasters and the model development community. In light of limited resources we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement in operational HEPS.

[1] Topography is an important control on hydrological processes. One approach to quantify this control is the topographic ln(a/tanβ) index. This index has become widely used in hydrology, but it utilizes a relatively small portion of the information contained in a digital elevation model (DEM). One potentially important feature not considered in the implementation of the ln(a/tanβ) index is the enhancement or impedance of local drainage by downslope topography. This effect could be important in some terrain for controlling hydraulic gradients. We propose a new way of estimating the hydraulic gradient by calculating how far downhill (Ld, [m]) a parcel of water must move in order to lose a certain amount of potential energy (d, [m]). Expressed as a gradient, tanαd = d/Ld, values tend to be lower on concave slope profiles and higher on convex slope profiles compared with the local gradient, tanβ. We argue that the parameter d controls the deviation of hydraulic gradient from surface slope. While we determine this subjectively, landscape relief, DEM resolution, and soil transmissivity should be considered at the selection of d. We found the downslope index values to be less affected by changes in DEM resolution than local slope. Three applications are presented where the new index is shown to be useful for hydrological, geomorphological, and biogeochemical applications.