Routing of Numerical Weather Predictions Through a Rainfall-Runoff Model

Hlavčová, Kamila; Szolgay, Ján; Kubes, R.; Kohnová, Silvia; Zvolenský, Marcel

doi:10.1007/1-4020-4902-1_8

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The second objective of this study is to evaluate the capability of the runoff ensemble forecasts to represent the total forecast error as a function of lead time. Ensemble forecasts have been considered a suitable tool for quantifying and communicating the uncertainties of forecasts [see e.g., Hlavcova et al , 2006; Demeritt et al , 2007], as the spread of the ensemble members can be used as a measure of forecast uncertainty [ Buizza , 2003]. In the studies of Johnell et al [2007], which is based on ECMWF ensemble forecasts and Jaun and Ahrens [2009], which is based on downscaled ECMWF ensemble forecasts, forecast errors increased with increasing ensemble spread and with increasing runoff for all catchments.…”

Section: Discussionmentioning

confidence: 99%

Flood forecast errors and ensemble spread—A case study

Nester

Komma

Viglione

et al. 2012

Water Resources Research

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[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.

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Section: Discussionmentioning

confidence: 99%

Flood forecast errors and ensemble spread—A case study

Nester

Komma

Viglione

et al. 2012

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Ensemble forecasts have been used for quantifying and communicating the uncertainties of forecasts (see e.g., Demeritt et al, 2007;Hlavcova et al, 2006) using the spread of the ensemble members as a measure of forecast uncertainty (Buizza, 2003). To account for small scale spatial uncertainty, the AL-ADIN forecasts are spatially shifted in both West-East and North-South directions to produce 25 pseudo-ensembles.…”

Section: Forecasting and Data Assimilationmentioning

confidence: 99%

“…Further downstream the Danube, models are in use in Slovakia (Hlavcova et al, 2006), Hungary (Balint et al, 2006;Csík et al, 2007) and Romania (Matreata et al, 2013). The models are operated by the hydrological offices or dedicated forecasting services in each country or state, depending on the responsibilities assigned by the respective constitutions.…”

Section: Introductionmentioning

confidence: 99%

Real time flood forecasting in the Upper Danube basin

Nester

Komma

Blöschl

2016

Journal of Hydrology and Hydromechanics

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This paper reports on experience with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of hydrological and hydrodynamic components, and involves precipitation forecasts. The model parameters were estimated based on the dominant processes concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the model performance indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. During the 2013 flood, there was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts. It is suggested that the human forecaster plays an essential role in interpreting the model results and, if needed, adjusting them before issuing the forecasts to the general public.

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“…Several authors agreed that, compared to traditional deterministic forecasting, the additional information provided by EPS should help improve forecasting quality and provide flood forecasts with valuable information, but were less clear about exactly what that information was or how useful it might be for their operational purposes (Palmer, 2002;Legg and Mylne, 2004;Hlavcova et al, 2006). And, in some cases of medium-range meteorological forecasts, ensemble gave a clear flood signal up to 4 days in advance, but it has a restricted application for using EPSs effectively in flood forecasting systems on a small catchment scale because it needs localized higher accuracy in terms of rainfall prediction.…”

Section: Introductionmentioning

confidence: 99%