Jutta Thielen

Development of a European flood forecasting system

Abstract Recent advances in meteorological forecast skill now enable significantly improved estimates of precipitation quantity, timing and spatial distribution to be made up to 10 days ahead for model scales of 40 km in forecast mode. Here we outline a prototype methodology to downscale these precipitation estimates using regional Numerical Weather Prediction models to spatial scales appropriate to hydrological forecasting and then use these to drive high‐resolution scale (1 or 5 km grid scale) water balance and rainfall‐runoff models. The aim is to develop a European Flood Forecasting System (EFFS) and determine what flood forecast skill can be achieved for given basins, meteorological events and prediction products. The output from the system is a probabilistic assessment of n‐day ahead discharge exceedence risk (where n < 10) for the whole of Europe at 5 km resolution which may then be updated as the forecast lead time reduces. At each stage the discharge estimates can be used to drive detailed (25–100 m resolution) hydraulic models to estimate the flood inundation which may potentially occur. Initial results are presented from a prototype version of the system used to perform a hindcast of the January 1995 flooding events in NW‐Europe (Rhine, Meuse).

Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting

Abstract Under the auspices of the World Meteorological Organization, there are a number of international initiatives to promote the development and use of so-called ensemble prediction systems (EPS) for flood forecasting. The campaign to apply these meteorological techniques to flood forecasting raises important questions about how the probabilistic information these systems provide can be used for what in operational terms is typically a binary decision of whether or not to issue a flood warning. To explore these issues, we report on the results of a series of focus group discussions conducted with operational flood forecasters from across Europe on behalf of the European Flood Alert System. Working in small groups to simulate operational conditions, forecasters engaged in a series of carefully designed forecasting exercises using various different combinations of actual data from real events. Focus group data was supplemented by a follow-up questionnaire survey exploring how flood forecasters understand risk, uncertainty, and error. Results suggest that flood forecasters may not instinctively use ensemble predictions in the way that promoters of EPS perhaps think they should. The paper concludes by exploring the implications of these divergent ‘epistemic cultures’ for efforts to apply ensemble prediction techniques developed in the context of weather forecasting to the rather different one of flood forecasting.

Visualizing probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication

The aim of this article is to improve the communication of the probabilistic flood forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable. Copyright

Quality control, validation and user feedback of the European Flood Alert System (EFAS)

The quality control, validation and verification of the European Flood Alert System (EFAS) are described. EFAS is designed as a flood early warning system at pan-European scale, to complement national systems and provide flood warnings more than 2 days before a flood. On average 20–30 alerts per year are sent out to the EFAS partner network which consists of 24 National hydrological authorities responsible for transnational river basins. Quality control of the system includes the evaluation of the hits, misses and false alarms, showing that EFAS has more than 50% of the time hits. Furthermore, the skills of both the meteorological as well as the hydrological forecasts are evaluated, and are included here for a 10-year period. Next, end-user needs and feedback are systematically analysed. Suggested improvements, such as real-time river discharge updating, are currently implemented.

Uncertainty analysis of the rainfall runoff model LisFlood within the Generalized Likelihood Uncertainty Estimation (GLUE)

Abstract The uncertainty of the GIS based rainfall runoff model LisFlood has been investigated within the Generalized Likelihood Uncertainty Estimation (GLUE) framework. Multipliers for the saturated and unsaturated hydraulic conductivity, the porosity of the upper and lower soil layer, channel and overland flow roughness and the maximum percolation from upper to lower storages have been sampled within a Monte Carlo analysis from a uniform random distribution. With each parameter set the model has been computed with input for the 1995 flood event of the river Meuse situated in France, Belgium and The Netherlands. Eight gauging stations have been used for model evaluation by the Multicomponent Mapping (M x ) method. All parameters demonstrate equifinality and no parameter set could be classified as behavioural for all the evaluation datasets. However, the results of the prediction of uncertainty percentiles on the flow are very satisfactory and encouraging. The model did further show the capability to predict the uncertainty for estimating the exceedence of threshold levels, which can be used in flood warning decision making and river basin management.

Improving the evaluation of hydrological multi-model forecast performance in the upper danube catchment

Medium range flood forecasting activities, driven by various meteorological forecasts ranging from high resolution deterministic forecasts to low spatial resolution ensemble prediction systems, share a major challenge in the appropriateness and design of performance measures. In this paper possible limitations of some traditional hydrological and meteorological prediction quality and verification measures are identified. Some simple modifications are applied in order to circumvent the problem of the autocorrelation dominating river discharge time-series and in order to create a benchmark model enabling the decision makers to evaluate the forecast quality and the model quality. Although the performance period is quite short the advantage of a simple cost-loss function as a measure of forecast quality can be demonstrated.

Visualizing probabilistic flood forecast information

The aim of this article is to improve the communication of the probabilistic flood forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable. Copyright

Visualizing probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication: VISUALISING PROBABILISTIC FLOOD FORECAST INFORMATION

The aim of this article is to improve the communication of the probabilistic flood forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable. Copyright

Evaluation of a four-decade pan-European database of surface precipitation for river flow modeling

The ECMWF Re Analysis (ERA-40) refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 employing all state-of-the-art information and satellite data input presently available. A selection of the reanalysis atmospheric output data can potentially be used to run a hydrological model to simulate historic river flows for the whole of Europe. Once evaluated against observed time series of rainfall and river flow, the output would constitute an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and allowing a range of evaluation possibilities. Here, in order to separate the meteorological model performance from the hydrological model performance, the ERA-40 near-surface rainfall aggregates, which come as a by-product of the ECMWF NWP system, are evaluated against interpolated fields of observed surface precipitation. The ERA-40 rainfall fields consist of forecast data with a 36 hr lead time at midday and midnight and a 6 hr lead time at 6:00 and 18:00 UTC, allowing different combinations of lead and base times to compute daily rainfall aggregates. The evaluation of these aggregated precipitation fields against observed totals is relevant to the spin-up time of the ECMWF NWP system and the forecast reliability with increasing lead time. Interpolated fields of observed daily rainfall totals are provided by the Monitoring Agriculture with Remote Sensing (MARS) database (1990-2001) based at the European Commission Joint Research Centre (JRC).

Evaluation of river flow in Europe over the last four decades using ERA40

ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Global Circulation model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. Here, a selection of the ERA-40 atmospheric output data at the surface is used to force the catchment hydrological model LISFLOOD to simulate historic river flows for the whole of Europe on a 5km grid resolution. Once evaluated against observed rainfall and point flow data, the output constitutes an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and potential for a range of evaluation purposes. For example, statistical analyses could serve to detect flooding and/or drying frequency trends. Alternatively, the output dataset could be used to assess flood alert levels in a consistent and uniform manner at any point for any river in Europe. The set would also be extremely useful as a basis for scenario studies, investigating the impact of policy and decision making such as de/aforestation and water reservoir management on flow regimes. Further, in the context of the general scientific consensus of an expected increasing trend in extreme events, the database may serve as a resource of information for extrapolated future scenarios.