Flood forecast errors and ensemble spread—A case study

Abstract

[1] Flood forecasts are generally associated with errors, which can be attributed to uncertainties in the meteorological forecasts and the hydrologic simulations, and ensemble spreads are usually considered capable of representing them. To quantify these two components of the total forecast errors and to compare these to ensemble spreads, an extended data set is used. Four years of operational flood forecasts at hourly time step with lead times up to 48 h are evaluated for 43 catchments in Austria and Germany. Catchment sizes range from 70 to 25,600 km 2 , elevations from 200 to 3800 m, and mean annual precipitation from 700 to 2000 mm. A combination of ECMWF and ALADIN ensemble forecasts are used as input in a semidistributed conceptual water balance model on an hourly time step. The results indicate that, for short lead times, the ratio of hydrological simulation error to precipitation forecast error is 1.2 to 2.7 with increasing catchment size from 100 to 10,000 km 2 . For long lead times the ratio of hydrological simulation error to precipitation forecast error decreases from 1.1 to 0.9 with increasing catchment size. Clear scaling relationships of the forecast error components with catchment area are found. A similar scaling is also found for ensemble spreads, which are shown to represent quantitatively the total forecast error when forecasting floods.

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…For example, ECWMF produces an ensemble of 51 different weather forecasts. The spread of such ensemble members is an indication of the uncertainty related to the meteorological model, the main source of uncertainty in flood forecasting systems (Buizza, 2008;Nester et al, 2012). However, in current operational flood forecasting systems, uncertainty is considered as the difference between observed and forecasted values.…”

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

“…For instance, Webster et al [221] concluded that the oods of Pakistan could have been predicted, reducing their negative consequences. Even when data are available, there are still uncertainties and errors in the prediction of oods [222]. Satellite technology has proven to be a useful source of data for prediction and assessment of ood-related topics, but may be somehow inaccessible in developing countries.…”

Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold

“…The correlation between the rainfall forecast error and flood forecast error is a challenging work since few literature has discussed the physical mechanism of the correlation [17,18]. Results are reasonable because the correlation is statistically significant through the case study of DHF reservoir, but the correlation between the variables of the rainfall forecast error and flood forecast error may not the same correlation between the modes of two failure modes, and hence, only the linear correlation is considered for the two forecast errors in the convenient method.…”

“…The latter enabled the computation of confidence intervals in a normalized space, and the results demoed the validity of the model. Nester et al [17] analyzed the scaling relationship with catchment area between the precipitation forecast error and hydrological simulation error. Yazdi et al [18] proposed a stochastic methodology based on Monte Carlo simulation and multivariate analysis to provide the uncertainty band of the rainfall-runoff model and to calculate the probability of the acceptable forecasts.…”