ALBERT

Description: A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods.

Description: A dynamical downscaling scheme is usually used to provide a short range flood forecasting system with high-resolved precipitation fields. Unfortunately, a single forecast of this scheme has a high uncertainty concerning intensity and location especially during extreme events. Alternatively, statistical downscaling techniques like the analogue method can be used which can supply a probabilistic forecasts. However, the performance of the analogue method is affected by the similarity criterion, which is used to identify similar weather situations. To investigate this issue in this work, three different similarity measures are tested: the euclidean distance (1), the Pearson correlation (2) and a combination of both measures (3). The predictor variables are geopotential height at 1000 and 700 hPa-level and specific humidity fluxes at 700 hPa-level derived from the NCEP/NCAR-reanalysis project. The study is performed for three mesoscale catchments located in the Rhine basin in Germany. It is validated by a jackknife method for a period of 44 years (1958–2001). The ranked probability skill score, the Brier Skill score, the Heidke skill score and the confidence interval of the Cramer association coefficient are calculated to evaluate the system for extreme events. The results show that the combined similarity measure yields the best results in predicting extreme events. However, the confidence interval of the Cramer coefficient indicates that this improvement is only significant compared to the Pearson correlation but not for the euclidean distance. Furthermore, the performance of the presented forecasting system is very low during the summer and new predictors have to be tested to overcome this problem.