Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system

Abstract

[1] Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood forecasting with deterministic weather forecasts. The Bayesian revision yields a posterior density, conditional on all information available to the forecaster at the onset of a forecast run. This conditional density objectively quantifies the uncertainty. Here the Bayesian approach is generalized for use with ensemble weather predictions. An end-to-end application of a Bayesian postprocessor for ensemble streamflow forecasts in the river Rhine forecasting system is presented. A verification of the postprocessor shows good performance when compared in terms of the ranked probability skill score to non-Bayesian uncertainty assessment, such as ranking threshold exceedance probabilities for members of a streamflow ensemble prediction. In this context it is also addressed how the proposed Bayesian processor can serve in supporting rational decision making for flood warning under conditions of uncertainty.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

“…It has a total area of 336 km 2 , which is considerably smaller than most catchments, and even sub-catchments, referred to in the current literature on ensemble streamflow forecasting (e.g. Dietrich et al, 2008;Jaun and Ahrens, 2009;Thielen et al, 2009b;Reggiani et al, 2009). Earlier studies have shown that forecast skill depends on temporal and spatial scales.…”

“…No calibration (e.g. Reggiani et al, 2009) or correction for underdispersion or bias was applied to the output of the atmospheric forecasts before their use in PREVAH. Similarly, no bias correction or statistical post-processing of the hydrologic and hydraulic ensemble forecasts was done.…”

“…Laio and Tamea (2007) proposed new tools for economic evaluation of probability discharge forecasts, but emphasize that the choice of the therefore necessary costloss functions is subjective and may be disputed. Reggiani et al (2009) suggested a stirring approach consisting of the combination of calibrated probabilistic forecasts to cost-loss functions to estimate economic risk.…”

Technical Note: The normal quantile transformation and its application in a flood forecasting system

“…This most convenient property has been incorporated into the HUP (Krzysztofowicz and Kelly, 2000;Krzysztofowicz and Herr, 2001;Krzysztofowicz and Maranzano, 2004), which is now part of several operational forecasting systems (e.g. Reggiani et al, 2009;Bogner and Pappenberger, 2011) in order to estimate the predictive uncertainty of the hydrological forecasts. In Montanari and Brath (2004) and Montanari and Grossi (2008) some problems of the NQT are discussed regarding its limited possibility of making the probability distribution of bivariate random variables multivariate Gaussian.…”

An information-theoretical perspective on weighted ensemble forecasts

“…yearly sets of daily time series of various hydrological variables on various locations (Faber and Stedinger, 2001). Other examples of ensembles are the ensemble weather forecasts of European Centre for Medium range Weather Forecasts (ECMWF), realtime hydrological forecasts based on these ensemble weather forecasts (Reggiani et al, 2009;Pagano et al, 2013), and the extended streamflow predictions (ESP) that are used throughout the USA as an input for reservoir optimization models or decision making about flood protection.…”