Abstract
Flooding often has a negative impact on society. In particular, widespread flood events can cause a lot of damage. These events are often spatially and temporally heterogeneous and should be duly considered for an appropriate analysis of flooding. Therefore, a conditional multivariate approach is adapted and applied in order to (i) contribute to a better understanding of the spatial characteristics of fluvial floods and (ii) to deliver sets of synthetically generated flood events. The present paper focuses on a simulation procedure consisting of careful data preparation and selection and the application of a conditional multivariate approach. The conditional approach is adapted to account for the seasonality of runoff data. Model checks attuned to the model are presented to ensure the consistence of simulated and observed data. The Austrian Province Vorarlberg was chosen as the study area. A thorough data analysis of runoff time series showed that the hydrological behaviour is characterized by a strong seasonality that was considered within the applied modelling procedure. The analysis of the spatial dependence of high river flows identified regions where floods likely occur simultaneously and regions with low spatial dependence. The main result of the modelling procedure, a large set of widespread flood events, was successfully generated.
Highlights
Fluvial floods are a natural phenomenon that regularly cause significant losses to property and human life
The HT model has been applied successfully in hydrology and related fields; for example, in order to analyse the spatial dependence of runoff and precipitation in Great Britain (GB) [22], to simulate various ocean variables for extreme conditions [23,24,25], and in the context of flood risk analysis [21,26,27,28,29,30,31]
The present paper focuses on the generation of spatially heterogeneous flood events
Summary
Fluvial floods are a natural phenomenon that regularly cause significant losses to property and human life. The HT model has been applied successfully in hydrology and related fields; for example, in order to analyse the spatial dependence of runoff and precipitation in Great Britain (GB) [22], to simulate various ocean variables for extreme conditions [23,24,25], and in the context of flood risk analysis [21,26,27,28,29,30,31] These flood risk studies show that the HT model is an appropriate approach to generate widespread flood events and that it is well-suited to consider different physical sources of flooding such as high river flows and sea levels.
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