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Performance of multi-model ensemble combinations for flood forecasting

Zomerdijk, L. (2015) Performance of multi-model ensemble combinations for flood forecasting.

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Abstract:Flooding is becoming a serious issue in recent decades due to urban expansion and climate change. As a consequence of floods international interest in flood forecasts has increased in the last decades. Accurate forecasting in small mountainous catchment areas is often difficult due to the short lead times of precipitation forecasts. More accurate forecasting can be obtained with the use of ensemble flood forecasts instead of deterministic forecasts. Recently research has been done on multi-model ensemble (grand ensemble) forecasts. In grand ensemble forecasts the ensembles of different EPSs are combined to improve the performance of the forecast in comparison with a single EPS. However, techniques to combine the different EPSs need to be developed. This study has the aim to develop an ensemble flood forecasting system for Quzhou (East-China) for lead times of 1 to 10 days and to evaluate different combined Grand Ensemble flood forecasts. The lumped hydrological GR4J model is used to forecast flow with ensemble precipitation forecasts of 4 different weather centres (European Centre for Medium-Range Weather Forecasts (ECMWF); Chinese Meteorological Administration (CMA); UK Met Office (UKMO) and US National Centers for Environmental Prediction (NCEP)) as input. The EPSs of these centres have different ensemble sizes and each consists of 1 control forecast from where the other perturbed ensemble members are derived. The ensemble forecasts are bias corrected with the Quantile Mapping method and that resulted in an improvement of the forecasts. After bias correction the precipitation forecasts are used as input to the hydrological model. The GR4J model was already calibrated for the Quzhou river basin with the Nash Sutcliffe efficiency coefficient (NS). Since the NS is more sensitive to high flows the calibrated values from this previous study are used. To further improve the forecasts an updating procedure is used for the hydrological model that updates the initial conditions of the routing storage with discharge observations at one day before the forecast day. This resulted in an improvement of the NS value for all lead times especially for short lead times of 1-3 days. The flood forecasts are evaluated on three important components of skill: reliability, resolution and sharpness. Six different grand ensemble flood forecasts are constructed after the evaluation of the single model forecasts. There are two simple combinations used. The first is a combination of the members where the EPSs are not weighted, as a consequence EPSs with more ensembles have more influence on the grand ensemble. The second is a combination of the models where the models are weighted so that their influence on the grand ensemble is equally. Other combinations in this study are constructed with the simple grand ensembles using weighted contributions based on skills of the evaluated EPSs. As expected, evaluation of the flood forecasts show that skill decreases with lead time and with increasing exceedance threshold. Two recognizable components of the forecast error, the meteorological error and the hydrological model error both increase with lead time, with an increasing contribution of the meteorological error compared to the hydrological error with lead time. All forecasts have relatively good performance reliability, resolution and sharpness. In general the single model forecasts of ECMWF proves to be the most skilful model and CMA the least skilful model in this study for 6 the Quzhou catchment area and the precipitation and hydrological forecasts. For short lead times of 1-2 days NCEP is least skilful. All evaluations of the grand ensemble hydrological forecasts show that they are beneficial. They show lower root mean squared errors (RMSE), continuous ranked probability scores (CRPS), reliability and resolution as compared to the single model EPSs. Also the sharpness is better than that of single model forecasts. The CRPS and RMSE graphs become smoother as a result of the different biases of the single forecasts that cancel out in the grand ensemble forecasts. Simple combination methods of the grand ensembles show similar skill as combinations of ensembles forecasts using weighted contributions based on skills. This is because EPSs with less skill than other EPSs still can add skill in a grand ensemble. A model with less skill might be able to add model structure errors that's missing in other EPSs with good skill and might have good performance on days when the other models show low performance. Generally it can be concluded that there is no significant difference between the different combination methods. Previous studies showed that increasing ensemble size leads to little improvement, however models with less members can be better than models with more members. Therefore it is best to use an approach where the models are weighted with the method of equal probability of selection so that the influence is not dependent on ensemble size.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/68844
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