Comparison of different approaches to quantify the reliability of hydrological simulations

The focus of this study was to compare different uncertainty estimation approaches to evaluate their ability to predict the total amount of uncertainty in hydrological model predictions. Three different approaches have been compared. Two of them were based on Monte-Carlo sampling and the third approach was based on fitting a probability model to the error series of an optimized simulation. These approaches have been applied to a lumped and a semi-distributed model variant, to investigate the effects of changes in the model structure on the uncertainty assessment. The probability model was not able to predict the total amount of uncertainty when compared with the Monte-Carlo based approaches. The uncertainty related to the simulation of flood events was systematically underestimated

4 th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability PROBABILISTIC ANALYSIS OF HYDROLOGICAL LOADS TO OPTIMIZE THE DESIGN OF FLOOD CONTROL SYSTEMS

ABSTRACT: Recent severe flood events prompt for considering a broad range of different hydrological scenarios in the design of flood protection structures such as flood control reservoirs and polders, instead of using the traditional approach of considering one single design flood of a predefined return period only. In this paper a method to categorize generated hydrological loads is presented, which is then applied for the analysis, optimization and extension of the flood control system of the Unstrut watershed in Mid-East Germany. The spatial structure of the flood events is analyzed by the joint probability of the inflow peaks of two dams located downstream of the northern and southern main tributaries. For the design of flood detention structures it is important to consider the flood volume apart from the flood peak. Therefore the joint probability of corresponding flood peaks and volumes are used to categorize the flood events. Hereby the copula-method is used for the construction of the bivariate distribution function