Superposition of three sources of uncertainties in operational flood forecasting chains

Abstract

One of the less known aspects of operational flood forecasting systems in complex topographic areas is the way how the uncertainties of its components propagate and superpose when they are fed into a hydrological model. This paper describes an experimental framework for investigating the relative contribution of meteorological forcing uncertainties, initial conditions uncertainties and hydrological model parameter uncertainties in the realization of hydrological ensemble forecasts. Simulations were done for a representative small-scale basin of the Swiss Alps, the Verzasca river basin (186 km 2). For seven events in the time frame from June 2007 to November 2008 it was possible to quantify the uncertainty for a five-day forecast range yielded by inputs of an ensemble numerical weather prediction (NWP) model (COSMO-LEPS, 16 members), the uncertainty in real-time assimilation of weather radar precipitation fields expressed using an ensemble approach (REAL, 25 members), and the equifinal parameter realizations of the hydrological model adopted (PREVAH, 26 members). Combining the three kinds of uncertainty results in a hydrological ensemble of 10,400 members. Analyses of sub-samples from the ensemble provide insight in the contribution of each kind of uncertainty to the total uncertainty. The results confirm our expectations and show that for the operational simulation of peak-runoff events the hydrological model uncertainty is less pronounced than the uncertainty obtained by propagating radar precipitation fields (by a factor larger than 4 in our specific setup) and NWP forecasts through the hydrological model (by a factor larger than 10). The use of precipitation radar ensembles for generating ensembles of initial conditions shows that the uncertainty in initial conditions decays within the first 48 hours of the forecast. We also show that the total spread obtained when superposing two or more sources of uncertainty is larger than the cumulated spread of experiments when only one uncertainty source is propagated through the hydrological model. The full spread obtained from uncertainty superposition is growing non-linearly.