Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

Abstract

The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfallrunoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

“…This is especially true in complex terrain where orographic effects are important and can lead to large systematic errors (Jasper et al, 2002;Pappenberger et al, 2005). An increase in NWP model resolution has improved weather forecasts greatly over the past few decades, but rainfall forecasts remain heavily dependent on subgrid-scale parameterizations of the precipitation-forming processes and are subject to additional sources of uncertainty.…”

“…Further, even high-resolution parameter information requires some degree of lumping, because the scale of measurement is naturally larger than the actual scale of heterogeneity in the watershed. This lumping results in effective parameter values only loosely associated with local watershed characteristics, making it very difficult to make suitable estimates of parameter values without calibration (Pappenberger et al, 2005). Parameter uncertainty is by no means a problem only of distributed models, however.…”

“…Carpenter and Georgakakos (2006) used a Monte Carlo sampling framework to account for both parametric and radar-rainfall uncertainty. Pappenberger et al (2005) took this approach one step further, using ECMWF ensemble weather forecasts with the GLUE framework to cascade model uncertainty from the weather model through a rainfall-runoff model and finally through an inundation model. According to Krzysztofowicz's (2001) definition, none of the above-cited work constitutes a truly probabilistic forecast.…”

Ensemble Flood Prediction by Cascading the Uncertainty From Rainfall to Runoff Short-Term Prediction

“…Still, it is difficult to make accurate prior estimates of optimal parameter values that result in a good model performance. The values required by the models seem to have weak association with basin physical condition 2) . Limited understanding of parameter relationship and inadequate information of basin physical condition to estimate the parameter may affect the accuracy.…”

Ensemble flood forecasting in Africa: a feasibility study in the Juba–Shabelle river basin

“…Thus it is not surprising that the application of probabilistic rainfall forecasts from ensemble prediction systems (EPSs) has gained in importance within the hydrological community leading to the development of hydrological ensemble prediction systems (HEPS) (Buizza, 2008;Pappenberger et al, 2008a,b;. This increased use of EPS is due to its ability to account for uncertainties related to future meteorological conditions, with lead times of up to 2 weeks, which is well beyond the limits of possibility of deterministic models, and results in more skilful forecasts (Pappenberger et al, 2005;Thielen et al, 2009b).…”