Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

Abstract

Advances in mesoscale numerical weather pred-ication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however , that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself ; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

Tracking the uncertainty in flood alerts driven by grand ensemble weather predictions

“…The quantitative precipitation forecasts were reported as unreliable and the forecast discharge was negatively biased in both time and magnitude. Cluckie et al (2006) employed a distributed rainfall-runoff model (GBDM) (500 m grid resolution) driven by the ECMWF EPS which was first resolved to a 2 km grid resolution using a dynamical downscaling technique, then spatially corrected and finally temporally adjusted. The study ascertained the potential values of using EPS in flood forecasting.…”

Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation

“…In addition, a number of the uncertainty factors in the precipitation forecasting, which contribute to the uncertainty of the river flow forecast, should be taken in account, such as the uncertainty in the numerical weather prediction model (Cluckie et al 2006). Furthermore, the proposed risk model could be used to analyze accuracy and reliability of the predicted water level by integrating the relationship between the water stage and discharge, named the rating curve, so that the associated uncertainty could be taken into account, such as the uncertainty in the regarding parameters between the discharge and the water stage.…”

“…In addition, during a typhoon event, the weather forecast center provides precipitation forecast information using the real-time quantitative precipitation forecasting (QPF) (Collier and Kzyzysztofowicz 2000). Nevertheless, the forecasted precipitation is affected by the various uncertainties in observation and the meteorological model (Grecu and Krajewski 2000), thereby propagating the uncertainty within flood forecasting (Cluckie et al 2006;Gabellani et al 2007). Therefore, this study develops a risk analysis model primarily to explore the effect of the uncertainties in rainfall information and the parameters of the rainfall-runoff model on the estimation of the peak discharge as well as to quantify the risk of underestimating the predicted peak discharge for the rainfall predictions given.…”

A Data-Based Mechanistic Modelling Approach to Real-Time Flood Forecasting

“…The information upstream can include the observations of river water levels and/or rainfall measurements. In the situation where meteorological ensemble forecasts are available, they can be used to further extend the forecast lead times (Cluckie et al, 2006), as in the approach presented by Krzysztofowicz (1999Krzysztofowicz ( , 2002 and Pappenberger et al (2005), but such information was not necessary for the lead times required in the present study.…”