Ensemble forecasting using TIGGE for the July–September 2008 floods in the Upper Huai catchment: a case study

He, Yi; Wetterhall, F.; Bao, Hongjun; Cloke, Hannah; Li, Zhijia; Pappenberger, Florian; Hu, Yuzhong; Manful, Desmond; Huang, Yin

doi:10.1002/asl.270

Cited by 55 publications

(23 citation statements)

References 12 publications

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“…This was the same composition procedure adopted by He et al (2010) and is one of the suggestions of Park et al (2008a,b). With this assumption, it is expected that the ensemble with more members will have a great influence on the results, but the investigation about multiple ways of combining forecasts is beyond the scope of the present work, and so we adopted the same procedure already tested by the cited authors.…”

Section: Hindcasting Experiments Setupmentioning

confidence: 98%

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Fan

Schwanenberg

Collischonn

et al. 2015

Journal of Hydrology: Regional Studies

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a b s t r a c tStudy region: This paper describes a major ensemble-forecasts verification effort for inflows of three large-scale river basins of Brazil: Upper São Francisco, Doce, and Tocantins Rivers. Study focus: In experimental scenarios, inflow forecasts were generated forcing one hydrological model with quantitative precipitation forecasts (QPF) from three selected models of the TIGGE database. This study provides information on the regional ensemble performance and also evaluates how different QPF models respond for the different basins and what happens with the use of combined QPF in a greater ensemble. New hydrological insights for the region: This work presents one of the first extensive efforts to evaluate ensemble forecasts for large-scale basins in South America using TIGGE archive data. Results from these scenarios provide validation criteria and confirm that ensemble forecasts depend on the particular EPS used to run the hydrological model and on the basin studied. Furthermore, the use of the Super Ensemble seems to be a good strategy in terms of performance and robustness. The importance of the TIGGE database is also highlighted.

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Section: Hindcasting Experiments Setupmentioning

confidence: 98%

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Fan

Schwanenberg

Collischonn

et al. 2015

Journal of Hydrology: Regional Studies

View full text Add to dashboard Cite

show abstract

“…Most scores for dichotomous evaluation are based on contingency tables, which include four Peirce's skill score (PSS, Eq. 3) (Hanssen and Kuipers, 1965) has been calculated for each station, taking the 90th percentile as threshold values (i.e., the 90th percentile from the sorted observations and from the sorted simulated values to discriminate each corresponding data series). PSS = hits hits + misses − false alarms false alarms + correct negatives…”

Section: Evaluation Of the Hydrological Modelingmentioning

confidence: 99%

“…Several flood forecasting systems are based on observed river level, while future values are extrapolated through river routing models or by coupling observed rainfall fields into hydrological models. The extension of the forecast horizon beyond the response time of a river basin is enabled by the use of numerical weather predictions (NWPs) as input to hydrological-hydraulic models (e.g., He et al, 2010;Hopson and Webster, 2010;Paiva et al, 2012;Thiemig et al, 2010). Recent review articles by Cloke and Pappenberger (2009) and by Alfieri et al (2012a) showed the strong potential of using ensemble NWPs to further extend the forecasting horizon in early warning systems.…”

Section: Introductionmentioning

confidence: 99%

GloFAS – global ensemble streamflow forecasting and flood early warning

Alfieri

Burek

Dutra

et al. 2013

Hydrol. Earth Syst. Sci.

460

298

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Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

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“…Other sources of (Goswami et al, 2007). When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts is better retained and accounted for (He et al, 2009(He et al, , 2010). An ensemble of weather forecasts can be used on catchment hydrology and provide improved early flood forecasting as some of the uncertainties can be quantified .…”

Section: Introductionmentioning

confidence: 99%

Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

Bao

Liang

et al. 2011

Adv. Geosci.

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Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km 2 ) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

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Ensemble forecasting using TIGGE for the July–September 2008 floods in the Upper Huai catchment: a case study

Cited by 55 publications

References 12 publications

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

GloFAS – global ensemble streamflow forecasting and flood early warning

Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

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