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Floods are among the most common and impactful natural events. The hazard of a flood event depends on its peak (Q), volume (V) and duration (D), which are interconnected to each other. Here, we used a worldwide dataset of daily discharge, two statistics (Kendall's tau and Spearman's rho) and a conceptual hydrological rainfall-runoff model as model-dependent realism, to investigate the factors controlling and the origin of the dependence between each couple of flood characteristics, with the focus to rainfall-driven events. From the statistical analysis of worldwide dataset, we found that the catchment area is ineffective in controlling the dependence between Q and V, while the dependencies between Q and D, and V and D show an increasing behavior with the catchment area. From the modeling activity, on the U.S. subdataset, we obtained that the conceptual hydrological model is able to represent the observed dependencies between each couple of variables for rainfall-driven flood events, and for such events, the pairwise dependence of each couple is not causal, is of spurious kind, coming from the "Principle of Common Cause". [ABSTRACT FROM AUTHOR]

Isolating the causes of extreme variations or changes in the hydroclimate is difficult due to the complexities of the driving mechanisms, but it is crucial for effective natural resource management. In Australia's Murray-Darling Basin (MDB), ocean-atmosphere processes causing hydroclimatic variations occur on time scales from days to centuries, all are important, and none are likely to act in isolation. Instead, interactions between all hydroclimatic drivers, on multiple time scales, are likely to have caused the variations observed in MDB instrumental records. A simplified framework is presented to assist natural resource managers in identifying the potential causes of hydroclimatic anomalies. The framework condenses an event into its fundamental elements, including its spatial and temporal signal and smallscale evolution. The climatic processes that are potentially responsible are then examined to determine possible causes. The framework was applied to a period of prolonged and severe dry conditions occurring in the southern MDB from 1997-2010, providing insights into possible causal mechanisms that are consistent with recent studies. The framework also assists in identifying uncertainties and gaps in our understanding that need to be addressed. [ABSTRACT FROM AUTHOR]

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts. [ABSTRACT FROM AUTHOR]

The use of copulas in hydrology is quite recent, and many important results still are to be discovered and/or derived. In this work we present some recent advances in hydrological modeling that exploit copulas. These include: (1) the calculation of conditional probabilities and their use in bivariate simulation; (2) the calculation of the level curves of joint distributions; (3) the calculation of the return periods of bivariate events, both in the conditional and unconditional cases; (4) the definition and calculation of the secondary return period; (5) a trivariate model for the temporal structure of the sequence of storms; (6) the statistical calculation of the storm depth; and (7) the calculation of the convolution variance. Several applications to hydrological data are shown. The models presented here may have important implications in many areas of water resources and hydrologic systems. [ABSTRACT FROM AUTHOR]

Abstract: The Generalized Pareto (GP) and Generalized Extreme Value (GEV) distributions have been widely applied in the frequency analysis of numerous meteorological and hydrological events. There are several techniques for the estimation of the parameters, which use the total sample as a source of information. In this paper, we show how valuable estimates are also possible considering only a proper subset of the sample, and we identify the portion of the sample containing the most relevant information for estimating a given parameter. In turn, this may prevent the use of anomalous values, which may adversely affect standard techniques. Here, we illustrate original techniques (based on linear combinations of ‘selected’ order statistics) to estimate the position parameter, the scale parameter, the quantiles, and the possible scaling behavior of the GP and GEV distributions with negative shape parameters. These estimators are generally unbiased and Mean-Square-Error-consistent. In addition, weakly consistent estimators of quantiles are introduced, the calculation of which does not require the knowledge of any parameter. Some case studies illustrate the applicability of the new techniques in hydrologic practice, and comparisons with standard methods are presented. The new estimators proposed may provide a reasonable alternative to standard methods, and may serve, at least, as a methodology to cross-check the estimates resulting from the application of other techniques. [Copyright &y& Elsevier]

The similarity of catchment responses is a fundamental issue for regionalization studies, and hydrograph attributes (i.e., Discharge Peak, Volume, and Duration) can reveal the signature and the synthesis of local scale processes. Here, we focus the attention on the “compatibility” between catchments, viz. on the possibility to transfer, from one catchment to another, the information about the dependence structures at play. In particular, we statistically investigate the possible relationships between the features of different Basin Scenarios (characterized via the Concentration Time T c and the Curve Number CN ) and the corresponding dependence structures ruling the joint statistics of Discharge, Volume, and Duration. Given a large set of synthetic runoff time series, generated via a rainfall-runoff model, recent non-parametric tests, based on empirical copulas, are used to compare the dependence structures associated with different soil uses and concentration times. The results indicate how the hydrological properties may affect the dependence structure. The outcomes of the investigation could be particularly effective in two practical applications: (1) for determining the degree of compatibility of the dependence structures associated with different basin scenarios, and (2) for enriching scanty data bases, in order to improve the estimation of multivariate copulas. [ABSTRACT FROM AUTHOR]