Seamless Prediction
Improving operational runoff and water level forecasting by means of error correction processes
Since the beginning of 2012, the project has been dealing with considering and reducing the complex uncertainties inherent in operational runoff and water level prediction, at all scales relevant to water traffic management.
Our Centre is responsible for the sub-project “Model Output Statistics (MOS)” that develops processes for the statistical correction of prediction, intended to reduce the residual error of the forecasting model chain by means of measurement data.
More Information:
Ensemble techniques
Ascertainment of probabilistic runoff forecasts
Forecasting practice frequently uses ensemble techniques for the probabilistic quantification of forecasting uncertainties in runoff prediction. Meteorological ensemble forecasts serve as input for establishing probabilistic water level and runoff predictions.
Ascertainment of probabilistic runoff forecasts considering censored data
Hydrology and Water Resources Management (Hydrologie und Wasserbewirtschaftung, 58. Jahrgang, Heft 2, April 2014)
11 Pages, PDF-Document, 3 MB
in German language
Predictive Uncertainty
Estimation of the predictive uncertainty of hydrological model simulations and forecasts
This article presents a hitherto relatively rare method to estimate predictive uncertainty of hydrological model simulations and forecasts. The probability distribution of uncertainty is ascertained by means of a statistical analysis of the model performance in the past.
Estimation of the predictive uncertainty of hydrological ensemble forecasts
Koblenz, August 2015
164 Pages, PDF-Document, 21,3 MB
in German language
79 Pages, PDF-Document, 9,1 MB
in German language
Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction
Modelling of initial states
A concept for updating initial states of an operational hydrological model to improve discharge forecasts
Hydrological systems possess a memory for hydrological history that is emulated within models as a storage analogy. This memory includes the accumulated snow cover and the groundwater table as well as soil moisture, i.e. hydrological parameters that can be potentially effective over a long period of time.
Nachführung von Anfangszuständen des hydrologischen Modells HBV durch einen Ensemble Kalman Filter zur Verbesserung von Abflussvorhersagen
Koblenz, December 2014
80 Pages, PDF-Document, 13,3 MB
Report in German language
Communication of uncertainties
Probabilistic flow and water-level forecasts – communication strategies and potential uses for inland navigation
Hydrology and Water Resources Management (Hydrologie und Wasserbewirtschaftung, 58. Jahrgang, Heft 2, April 2014)
A central aspect many forecast centres are concerned with, is finding an adequate way of communicating the calculated uncertainties. This might be one of the main reasons for the prevailing reluctance to publish probabilistic forecasts. Only if we succeed in transforming the undisputed theoretical advantage of probabilistic forecasts into practical use, establishing probabilistical forecasts will go beyond being a purely academic exercise.
PDF DocumentSelection of models
Selection of optimum models and parameters to statistically correct runoff forecasts
Operational runoff and water level forecasting applies methods derived from time series analysis to statistically describe and correct model errors. There is a great degree of freedom when it comes to choosing the model, its order and the scheme for computing its parameters.
Korrektur von Abfluss-Vorhersagen mit Hilfe linearer und nichtlinearer Zeitreihenmodelle
Koblenz, December 2014
Report in German language
159 Pages, PDF-Document, 15,8 MB
Numerical integration
The numerical intergration of HBV96 and LARSIM-ME as a source of uncertainty
In recent years, the analysis of the impact of model structures on simulation uncertainty has gained increasing attention in hydrological research. The imperfect numerical integration of differential equation systems is an aspect of structural model uncertainty that continues to be neglected in many rainfall runoff and water balance models.
Parameter- und Modellstrukturunsicherheit von HBV96 in der operationellen Rheinvorhersage – Analysen und Verbesserungsvorschläge
Koblenz, January 2016
Report in German language
88 Pages, PDF-Document, 4,7 MB
