ABSTRACT
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 ExtremesScience of Water > Hydrological ProcessesScience of Water > Methods.
ABSTRACT
Spatial configuration of initial errors strongly affects predictability of space-time chaotic systems. The predictability of numerical models can be adjusted by using prepared ensembles of initial conditions. We present a natural way of preparing ensembles based in using finite-amplitude perturbations with varying correlation. This allows one to take into account the underlying dynamics to generate initial perturbations with spatial correlations varying from fully correlated (bred vectors) to random fluctuations.
ABSTRACT
We study the spatiotemporal dynamics of random spatially distributed noninfinitesimal perturbations in one-dimensional chaotic extended systems. We find that an initial perturbation of finite size epsilon0 grows in time obeying the tangent space dynamic equations (Lyapunov vectors) up to a characteristic time tx(epsilon0) approximately b-(1/lambda(max))ln(epsilon0), where lambda(max) is the largest Lyapunov exponent and b is a constant. For times t
