Performance and limitations of ensemble river flow forecasts

High and low flows may cause several problems to society. Flood forecasting, low flow forecasting and hydrological forecasting in general are important to mitigate the negative consequences of extreme flow events and for economic use of a river. Ensemble prediction systems are increasingly used for hydrological forecasting. These systems provide an ensemble of forecasts for each forecast period instead of a single, deterministic forecast. There have been various studies on ensemble flow forecasting, but these studies have mainly focused on large river catchments and exclusively on flood forecasting or low flow forecasting. The objective of this study is to develop an ensemble flow forecasting system for the Biala Tarnowska catchment (~1000 km2) in Poland and to investigate the performance of this system for lead times from 1 to 10 days, for low, medium and high flows and for different hydrological circumstances. The ensemble flow forecasting system consists of a deterministic lumped hydrological (HBV) model with input data in the form of ensemble precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The meteorological ensemble forecast data from ECMWF consists of 1 control forecast and 50 perturbed ensembles. The deterministic calibration of the hydrological model has been based on observed precipitation, temperature and discharge data. It turned out that pre-processing of the precipitation forecasts with Quantile Mapping is best when it is applied separately for each lead time. For the temperature forecasts the best results are obtained if in addition seasonal distinction in a summer and winter season is applied. However, the best flow forecasts are obtained when no pre- or post-processing with Quantile Mapping is applied at all. Therefore no pre-processing of meteorological forecast and no postprocessing of flow forecasts has been applied. To improve the representation of the current situation in the catchment at the forecast day, the initial conditions in the hydrological model are updated based on discharge observations at one day before the forecast day. The performance of the flow forecasts deteriorates with lead time. The skill of the flow forecasts is determined with respect to the best reference forecast set, which are flow forecasts based on an ensemble of historical observations of precipitation and temperature on the same calendar day over the past 20 years. In general the skill of the flow forecasts is positive and maximum between lead times of 2 and 5 days, but this is very different for the low, medium and high flow forecasts. The low flow forecasts do not have skill until a lead of 2 days and after that they show a small positive skill. The medium flow forecasts do not provide skill for all lead times. The highest skills are obtained for the high flow forecasts. This has to do with the performance that historical observations of precipitation and temperature on the same calendar day provide for these flow categories and that the same initial conditions are used to generate the ensemble flow forecasts and the reference forecasts. Since in low flows initial conditions are more important, it is more difficult for the ensemble flow forecasting system to deviate from the reference forecasts and thus to be able to generate skilful flow forecasts. The forecast skill is also very different for different high flow and low flow producing processes. Regarding high flow forecasts the highest skill is obtained for the short-rain floods, but the skill decreases considerably for lead times larger than 5 days. Long-rain floods and snowmelt floods are more dependent on the initial conditions in the catchment, which leads to small forecast skills at short lead times. From a lead time of respectively 3 days and 2 days also long-rain floods and snowmelt floods are skilfully forecasted. The low skill of low flow forecasts is mainly caused by low rainfall/high evapotranspiration generated low flow forecasts, while the skill of snow accumulation generated low flows is relatively high. These results provide information about the system and in which situations it can be used to generate skilful flow forecasts. The performance of the flow forecasting system has also been researched on different properties of forecast quality. The sharpness of the forecasts is good, because forecast probabilities of low and high flows are most often close to 0 or 1, instead of forecast probabilities close to the mean probability. The resolution is also good, with high hit rates compared to false alarm rates for high and low flow forecasts. However, the reliability of the system is not good, particularly for small lead times. To improve the reliability of the ensemble flow forecasts it is recommended to also include hydrological model parameter and initial condition uncertainty, or to improve post-processing of the flow forecasts. In addition it is recommended to do further research to improve the reliability of the precipitation and temperature forecasts. The relative contribution of meteorological forecast errors and hydrological model errors (including initial conditions) has been researched to give recommendations about how the ensemble forecasting system can be improved effectively. In general the relative contribution of meteorological forecast errors increases with lead time and the relative contribution of hydrological model errors decreases with lead time. Regarding the different flow categories, when the objective of further research is to improve the high flow forecasts it is recommended to focus further research mainly on improving the meteorological forecasts, because in high flow forecasts errors from the meteorological forecasts are relatively more important. When the objective is to improve the low flow forecasts it is recommended to focus further research at first mainly on the hydrological model performance. The calibration was skewed to high discharges, so it is expected that an easy improvement of the forecasts can be achieved when the hydrological model would be calibrated on low flow situations. Besides improvement of the hydrological model, further research should be done to improve the meteorological forecasts. After all, it is recommended to extend the research to other catchments and (if possible) with a longer period of data, to be able to draw more general conclusions and to test more extreme high and low flow thresholds before the system is potentially applied operationally. In addition, it is recommended to incorporate statistical tests for the evaluation scores to increase confidence in the conclusions.