A Two-step Approach to Propagate Rating Curve Uncertainty in Elbe Decision Support System

The relationships between water level and discharge along the river channel are vital for decision support systems in river basin management. Normally the reliability of the so-called rating curves along the river channel depends greatly on the accuracy and duration of the measured discharge and water level data. In the Elbe Decision Support System (DSS), the rating curves are combined with the HEC-6 model to investigate the effects of river engineering measures on the Elbe river system. Under such situations, the uncertainty originated from the HEC-6 model is of great importance for the reliability of the rating curves. This paper presents a two-step approach to analyze the uncertainty in the rating curves and propagate it into the vegetation model used in the Elbe DSS. The first step is to identify the uncertainty sources. An analytical method is adopted to propagate the uncertainty sources into the final rating curves. The second step is to propagate the uncertainties in the rating curves into the model outputs of the vegetation model using Monte Carlo Analysis. By this two-step uncertainty analysis approach, the uncertainty in the rating curves is successfully propagated into the vegetation model in the Elbe DSS. The final Monte Carlo Simulation results show large uncertainties in the model outputs.