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This study contributes to this understanding, specifically for small ( 100 years) rainfall return periods. The role of rainfall space-time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. The type-specific regionalisation approach offers a higher degree of freedom for regionalisation as it describes the relationships between catchment characteristics, meteorological causes of floods and response of watersheds. The results demonstrate how this consideration of deterministic aspects can improve the transferability of distribution parameters to ungauged catchments. By selection of most relevant features, depending on the flood type, the specifics of flood-generating processes and meteorological causes were considered. For regionalisation, we specified the parameters of each type-specific probability distribution separately with hierarchical clustering and regressions from catchment attributes. Their probability distributions are modelled by type-specific distribution functions which are combined into one statistical annual mixture model afterwards. The different flood types are classified according to their meteorological causes and hydrographs. Here, we apply flood types in regionalisation directly to consider the type-specific aspects of similarity. Similarity is usually defined by comparing catchment attributes or distances. It is often based on either the concept of hydrological similarity of catchments or spatial proximity. The regionalisation of flood frequencies is a precondition for the estimation of flood statistics for ungauged basins. Although the methodology proposed in this paper has been applied and tested in only one case study, it can be extended to other case studies due to its process-based orientation. Flood quantiles are then estimated by combining the maximum flows with the storm magnitude and ISMC in a trivariate probability distribution function through the application of Bayes' theorem and Lagrange's Mean Value theorem.
#Gridded response generator#
In order to incorporate the main flood-generating mechanisms, the integrated use of a multidimensional storm generator with distributed hydrological modelling is proposed. The semi-arid Mediterranean "Rambla del Poyo" catchment has been used as a representative case study where the influence of the spatio-temporal variability of the storms and the ISMC on floods can lead to differences of up to two orders of magnitude in quantiles when the most commonly used methods are applied. It combines the flood peak, storm magnitude, and initial soil moisture condition (ISMC) as the main flood-related statistical variables to be considered. This paper proposes a trivariate methodology for flood frequency estimation. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate. We find that the considered model parametric uncertainty is more influential than model structures and model priors. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies.
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There is a strong evidence of increasing flood hazards in many regions around the world. Fluvial floods drive severe risk to riverine communities.