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  • English  (4)
  • 1
    Publication Date: 2021-10-14
    Description: This study proposes a new process-based framework to characterize and classify runoff events of various magnitudes occurring in a wide range of catchments. The framework uses dimensionless indicators that characterize space–time dynamics of precipitation events and their spatial interaction with antecedent catchment states, described as snow cover, distribution of frozen soils, and soil moisture content. A rigorous uncertainty analysis showed that the developed indicators are robust and regionally consistent. Relying on covariance- and ratio-based indicators leads to reduced classification uncertainty compared to commonly used (event-based) indicators based on absolute values of metrics such as duration, volume, and intensity of precipitation events. The event typology derived from the proposed framework is able to stratify events that exhibit distinct hydrograph dynamics even if streamflow is not directly used for classification. The derived typology is therefore able to capture first-order controls of event runoff response in a wide variety of catchments. Application of this typology to about 180,000 runoff events observed in 392 German catchments revealed six distinct regions with homogeneous event type frequency that match well regions with similar behavior in terms of runoff response identified in Germany. The detected seasonal pattern of event type occurrence is regionally consistent and agrees well with the seasonality of hydroclimatic conditions. The proposed framework can be a useful tool for comparative analyses of regional differences and similarities of runoff generation processes at catchment scale and their possible spatial and temporal evolution.
    Keywords: 551.48 ; event classification ; event type ; rainfall-runoff events ; event typology ; event characteristics ; runoff generation mechanisms
    Language: English
    Type: map
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  • 2
    Publication Date: 2020-02-12
    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.
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 3
    Publication Date: 2023-04-11
    Description: Statistical distributions of flood peak discharges often show heavy tail behavior, i.e., extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research towards testing the hypotheses and improving the prediction of heavy tails.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 4
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-06-27
    Description: The robust estimation of extreme flood magnitude in poorly observed or ungauged basins is of critical importance for designing mitigation measures, particularly in the presence of anthropogenic environmental change and accelerating climatic changes. Traditional methods for estimating flood extremes are strongly limited by the availability of sufficiently long timeseries as these are typically designed to use annual maxima or a few values above a high threshold. In the present work we use a recent statistical model, the Metastatistical Extreme Values (MEV) distribution, in combination with a conceptual model of flood generation processes, the Phisically-based Extreme Values (PhEV) distribution, to explore the possible estimation of high quantiles where few or no observations exist. The main novelty of the approach is the ability of extracting extreme streamflow values from "ordinary" streamflow peaks and to provide a characterization based on a limited and physically meaningful set of hydrological parameters. The proposed methodology aims to overcome limitations in data availability by exploiting the relatively large number of daily observations available even in short time series (as opposed to the low number of yearly maxima) and a few hydrological attributes of the catchment that may be "guessed" on the basis of limited information. A large-scale application on 178 catchments in Germany allows us to formulate a reliable calibration technique and to show its controllable estimation uncertainty: the median relative error computed on predicted extreme streamflow values is globally contained between -25% and +50%:.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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