Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment

Bogner, Konrad; Kalaš, Milan

doi:10.1002/asl.180

Cited by 61 publications

(40 citation statements)

References 28 publications

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“…Recent research results from HEPEX are encouraging and demonstrate the potential benefit of probabilistic weather forecasts over deterministic ones for flood forecasting in large river basins Buizza, 2008;Bogner and Kalas, 2008;Gebhardt et al, 2008;Marty et al, 2008;Pappenberger et al, 2008b;Schalk et al, 2008;Tucci et al, 2008;Zappa et al, 2008). Roulin (2007) demonstrated that EPS-based flood forecasting can also be valuable for small river basins, while advances in limited area EPS modelling may provide even better quantitative precipitation estimates also for small basins (Marsigli et al, 2001;Marsigli et al, 2005;Tibaldi et al, 2006).…”

Section: J Thielen Et Al: Efas -Concept and Developmentmentioning

confidence: 99%

“…A detail case study of the EFAS performance during the spring floods in 2006 in Slovakia, for the Morava River (tributary to the Danube), has been recently published by Kalas et al (2008), and a case study on the performance of the system during the Elbe floods by . For the 2002 Danube floods, EFAS re-forecasts were produced and Bogner and Kalas (2008) have been looking into novel post-processing methods to remove forecast biases in the outputs and thus produce more reliable results with a reduced, but more meaningful, spread. The analysis of the EFAS performance during the flood events in Romania from 22-25th October 2007 has been recently finalized (Thielen et al, submitted).…”

Section: × ×mentioning

confidence: 99%

“…Historic floods affected the Elbe and the Danube river basins in 2002(Brázdil et al, 2005Yiou et al, 2006;Toothill, 2002). In 2005, widespread and repeated flooding was again observed in several tributaries to the Danube river basin, particularly in Switzerland and Austria and in the lower Danube countries (Romania and Bulgaria).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The European Flood Alert System – Part 1: Concept and development

Thielen

Bartholmes

Ramos

et al. 2009

Hydrol. Earth Syst. Sci.

488

345

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Abstract. This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with mediumrange and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3-10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the systems set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts.

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Section: J Thielen Et Al: Efas -Concept and Developmentmentioning

confidence: 99%

Section: × ×mentioning

confidence: 99%

See 1 more Smart Citation

The European Flood Alert System – Part 1: Concept and development

Thielen

Bartholmes

Ramos

et al. 2009

Hydrol. Earth Syst. Sci.

488

345

View full text Add to dashboard Cite

show abstract

“…In the work of Bogner and Kalas [16], an error-correcting method was developed combining wavelet transformations (e.g., Beylkin and Saito [17], Chou and Wang [18]) and Vector AutoRegressive Models with eXogeneousinput (Wave-VARX). The idea was to incorporate not only the most recent information of the error in the correction model, but also information with time lags of several hours and days.…”

Section: Error Correctionmentioning

confidence: 99%

Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Bogner

Liechti

Zappa

2016

Water

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Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase "post-processing" in meteorology and hydrology, in this paper, it is regarded as a method to correct model outputs (predictions) based on meteorological (1) observed input data, (2) deterministic forecasts (single time series) and (3) ensemble forecasts (multiple time series) and to derive predictive uncertainties. So far, the majority of the research has been related to floods, how to remove bias and improve the forecast accuracy and how to minimize dispersion errors. Given that global changes are driving climatic forces, there is an urgent need to improve the quality of low-flow predictions, as well, even in regions that are normally less prone to drought. For several catchments in Switzerland, different post-processing methods were tested with respect to low stream flow and flooding conditions. The complexity of the applied procedures ranged from simple AR processes to more complex methodologies combining wavelet transformations and Quantile Regression Neural Networks (QRNN) and included the derivation of predictive uncertainties. Furthermore, various verification methods were tested in order to quantify the possible improvements that could be gained by applying these post-processing procedures based on different stream flow conditions. Preliminary results indicate that there is no single best method, but with an increase of complexity, a significant improvement of the quality of the predictions can be achieved.

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“…This is relatively easy to implement but neither represents the physical processes nor updates the ''memory'' associated with state variables such as soil moisture storages. Various error correction approaches, including artificial neural network (ANN) models [Anctil et al, 2003;Shamseldin and O'Connor, 2001], parametric simple linear (PSL) models [Goswami et al, 2005], autoregressive moving average (ARMA) models [Broersen, 2007], and ARMA with exogenous input (ARMAX) models [Bogner and Kalas, 2008], have been used to update predicted discharge. State and parameter updating schemes are more popular approaches as they attribute the error in predicted discharge to individual components within the rainfall-runoff process, which is more process based, and the uncertainty reduction in states and parameters is more beneficial for forecasting purposes.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

Ryu

Western

et al. 2013

Water Resources Research

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[1] General filtering approaches in hydrologic data assimilation, such as the ensemble Kalman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the streamflow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and streamflow forecasting.

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Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment

Cited by 61 publications

References 28 publications

The European Flood Alert System – Part 1: Concept and development

The European Flood Alert System – Part 1: Concept and development

Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

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