Analyzing a response of catchments to rainfall inputs allows for deeper insights on the mechanisms of runoff generation at catchment scale. In this study an automated time series‐based event separation procedure consisting of available base flow separation, runoff event identification, and rainfall attribution methods and of a novel iterative procedure for the adjustment of thresholds used to identify single‐peak components of multiple‐peak events is proposed. Event runoff coefficient, time scale, rise time, and peak discharge of more than 220,000 identified rainfall‐runoff events are then used to analyze dynamics of event runoff response in 185 catchments at multiple temporal scales. In mountainous catchments with poor storage event runoff response is strongly controlled by the characteristics of rainfall and is generated by event‐fed saturation or infiltration excess. A distinct switch between saturated and unsaturated states occurs in these catchments. A weak relation between rainfall and runoff event properties is instead observed in lowland and hilly catchments with substantial storage, where a gradual transformation between functioning states occurs and the response is driven by preevent saturation. The seasonality of their event characteristics is governed by the contribution of snowmelt and the seasonality of the aridity index rather than of rainfall properties. Long‐term changes of total precipitation amount alone do not explain season‐specific long‐term changes of event characteristics that are rather consistent with changes of seasonal indicators of the wetness state. The effects of land use changes are detectable only in a few cases and display themselves mostly in the characteristic response time of catchments.

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. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods

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.

This study unveils regional patterns of rainfall‐runoff event characteristics in Germany and identifies their spatial controls. Characteristics describing mean value, variability, and seasonality of event runoff coefficient, time scale, rise time, and of the occurrence of multiple‐peak events are derived for a set of 196,073 rainfall‐runoff events observed in 401 mesoscale German catchments. Multiobjective performances of various variable selection methods are used to identify hydrologically relevant variables from a comprehensive set of 115 descriptors of climate, topography, geomorphology, soil, land use, hydrogeology, and geology for every catchment. Results show that although event characteristics have relatively clear regional patterns due to the dominance of climatic controls at regional scale, subsurface properties (i.e., catchment storage) play a considerable role for the prediction of event runoff response. Compared to other tested variable selection methods, the application of a backward elimination procedure allows for the most accurate prediction of spatial patterns and regionalized values of event characteristics identifying soil depth, hydraulic permeability and frequency, size, and seasonality of wet spells as hydrologically relevant catchment descriptors. Climatic and hydrogeological descriptors outperform other generic groups of catchment descriptors. The hydrological interpretation of the emergent regional pattern of event characteristics, their variability, and seasonality provides insight on archetypical catchment behaviors and their controls.

Glacierized high‐mountainous catchments are often the water towers for downstream region, and modeling these remote areas are often the only available tool for the assessment of water resources availability. Nevertheless, data scarcity affects different aspects of hydrological modeling in such mountainous glacierized basins. On the example of poorly gauged glacierized catchment in Central Asia, we examined the effects of input discretization, model complexity, and calibration strategy on model performance. The study was conducted with the GSM‐Socont model driven with climatic input from the corrected High Asia Reanalysis data set of two different discretizations. We analyze the effects of the use of long‐term glacier volume loss, snow cover images, and interior runoff as an additional calibration data. In glacierized catchments with winter accumulation type, where the transformation of precipitation into runoff is mainly controlled by snow and glacier melt processes, the spatial discretization of precipitation tends to have less impact on simulated runoff than a correct prediction of the integral precipitation volume. Increasing model complexity by using spatially distributed input or semidistributed parameters values does not increase model performance in the Gunt catchment, as the more complex model tends to be more sensitive to errors in the input data set. In our case, better model performance and quantification of the flow components can be achieved by additional calibration data, rather than by using a more distributed model parameters. However, a semidistributed model better predicts the spatial patterns of snow accumulation and provides more plausible runoff predictions at the interior sites.