Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event J. Dietrich, S. Trepte, Y. Wang, A. H. Schumann, F. Voß, F. B. Hesser, M. Denhard To cite this version: J. Dietrich, S. Trepte, Y. Wang, A. H. Schumann, F. Voß, et al.. Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event. Nonlinear Processes in Geophysics, European Geosciences Union (EGU), 2008, 15 (2), pp.275-286. hal-00302980 HAL Id: hal-00302980 https://hal.archives-ouvertes.fr/hal-00302980 Submitted on 19 Mar 2008 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Nonlin. Processes Geophys., 15, 275–286, 2008 www.nonlin-processes-geophys.net/15/275/2008/ Nonlinear Processes © Author(s) 2008. This work is licensed in Geophysics under a Creative Commons License. Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event J. Dietrich1, S. Trepte2, Y. Wang1, A. H. Schumann1, F. Voß1,*, F. B. Hesser1,**, and M. Denhard2 1Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr University Bochum, Bochum, Germany 2Deutscher Wetterdienst DWD (German National Weather Service), Offenbach, Germany *now at: Center for Environmental Systems Research, University of Kassel, Kassel, Germany **now at: Bundesanstalt fur¨ Wasserbau (Federal Waterways Engineering and Research Institute), Hamburg, Germany Received: 24 August 2007 – Revised: 3 December 2007 – Accepted: 13 February 2008 – Published: 19 March 2008 Abstract. Flood forecasts are essential to issue reliable flood tems would have predicted a high probability of an extreme warnings and to initiate flood control measures on time. The flood event, if they would already have been operational in accuracy and the lead time of the predictions for head waters 2002. COSMO-LEPS showed a reasonably good perfor- primarily depend on the meteorological forecasts. Ensemble mance within a lead time of 2 to 3 days. Some of the deter- forecasts are a means of framing the uncertainty of the po- ministic very short-range forecast initializations were able to tential future development of the hydro-meteorological situ- predict the dynamics of the event, but others underpredicted ation. rainfall. Thus a lagged average ensemble approach is sug- This contribution presents a flood management strategy gested. The findings from the case study support the often based on probabilistic hydrological forecasts driven by op- proposed added value of ensemble forecasts and their proba- erational meteorological ensemble prediction systems. The bilistic evaluation for flood management decisions. meteorological ensemble forecasts are transformed into dis- charge ensemble forecasts by a rainfall-runoff model. Ex- ceedance probabilities for critical discharge values and prob- abilistic maps of inundation areas can be computed and pre- 1 Introduction sented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system Uncertainties in flood forecasting mainly result from incom- integrates ensemble forecasts with different spatial resolution plete knowledge of the further meteorological development and different lead times. The hydrological models are con- and from uncertainties of hydrological and hydraulic mod- trolled in an adaptive way, mainly depending on the lead time elling. Known and knowable sources of uncertainty are the of the forecast, the expected magnitude of the flood event and availability and quality of input data, respectively initial and the availability of measured data. boundary conditions for the models, as well as model pa- The aforementioned flood forecast techniques have been rameters and model structure. Inaccurate human interaction applied to a case study. The Mulde River Basin (South- and technical problems may also affect the output of a flood Eastern Germany, Czech Republic) has often been affected prediction chain. The highly nonlinear behaviour of the at- by severe flood events including local flash floods. Hind- mospheric system and the land-atmosphere interaction adds casts for the large scale extreme flood in August 2002 have unknowable sources of uncertainty. Thus a perfect weather been computed using meteorological predictions from both forecast is impossible (Lorenz, 1963). Resulting from these the COSMO-LEPS ensemble prediction system and the de- uncertainties it is not possible to issue a perfect flood fore- terministic COSMO-DE local model. The temporal evolu- cast. tion of a) the meteorological forecast uncertainty and b) the During the last decades modelling and forecasting tech- probability of exceeding flood alert levels is discussed. Re- niques evolved from a deterministic towards a probabilis- sults from the hindcast simulations demonstrate, that the sys- tic paradigm. Uncertainty estimation in forecasting aims at framing the possible future development, admitting and com- Correspondence to: J. Dietrich municating the imperfection of the forecast. Exceedance ([email protected]) probabilities for threshold values (e.g. critical discharge Published by Copernicus Publications on behalf of the European Geosciences Union and the American Geophysical Union. 276 J. Dietrich et al.: Ensemble based operational flood management Global Prediction Deterministic local model COSMO-EU Systems Driving Driving COSMO-DE: m=1, +21 h, 2.8 km Force Force Driving Force Meso-scale Ensemble Systems COSMO-DE Lagged 2002 Observati ons Assimilation COSMO-LEPS: m=16, +5 d, 10 km Average-Ensemble 2006 radar, gauges 2005 SRNWP-PEPS: m=17, +2 d, 7 km m=7, +21 h, 2.8 km Superensemble (m=19): PEPS (m=17), COSMO-LEPS (median), COSMO-DE Calibration, Bayesian Assimilation Observations Model Average (BMA) radar, gauges Probabilistic weather scenario: reduced multi-model 2007 ensemble, pdf, model average, m approx. 10 Fig. 1. Meteorological ensembles from different sources used for an ensemble-based operational flood risk management strategy. The number of ensemble members is denoted by m, the other values indicate the lead time and the horizontal spatial resolution of the ensemble prediction systems. levels causing inundation) can be provided to flood managers a lot of information, which is not useful for decision ori- and decision makers. ented operational flood management. Ensemble techniques Ensemble techniques are suitable methods for producing aiming at the representation of hydrological uncertainty with probabilistic forecasts (Anderson, 1996; Kalnay, 2002; Toth only a few (in the dimension of 10) members still have to be et al., 2003). In the context of flood management, ensembles developed. Methods of combining the simulations of mul- are a group of alternative scenarios of possible future de- tiple models are subject of current research (e.g. Bayesian velopment of the hydro-meteorological situation. Different Model Averaging, Hoeting et al., 1999; Raftery et. al., 2005; types of ensembles can be classified according to the gener- Sloughter et al., 2006). Recently Ajami et al. (2007) pre- ating mechanisms (for meteorological as well as for hydro- sented a study about the computation and combination of logical applications): hydrological multi-model ensembles to account for model structure uncertainty. They list relevant sources in this field. – single system ensembles: perturbation of initial and Interdisciplinary studies dealing with the probabilistic as- boundary conditions, different convection schemes sessment of the flood forecast chain have been published (physically based ensembles), perturbation of model pa- e.g. by Krzysztofowicz (2002), Apel et al. (2004) and Pap- rameters; penberger et al. (2005). – multiple systems or multi-model ensembles (“poor man The participatory HEPEX project (Hydrological Ensem- ensembles”): combination of simulations from different ble Prediction Experiment, Schaake et al., 2007) integrates models (e.g. Georgakakos et al, 2004); meteorologists, hydrologists and users in order to promote the development of ensemble streamflow forecast systems. – lagged average ensembles: combination of current fore- In Europe, the probabilistic Flood Alert System (EFAS) is casts with forecasts from earlier model runs (Hoffman under development (Thielen et al., 2008). EFAS aims to pro- and Kalnay, 1983). vide flood information for the medium to long-range at large Meteorological ensemble prediction systems (EPS) have be- scale river basins being relevant for decisions at national or come operational. Buizza et al. (2005) compare three global EU level. EPS. The development of hydrological applications of en- Probabilistic forecast systems require the computation of semble forecasts has started in the late 1990-ies and is sub- a large number of model runs within a time frame of a few ject of ongoing research (e.g. de Roo et al., 2003; Mullusky hours. Here computational resources still constrain the pos- et al.,