We present the constraints on inflationary parameters in a flat ΛCDM universe obtained by WMAP three year data release, plus smaller scale CMB and two LSS data sets, 2dF and SDSS (treated separately). We use a Markov Chain Monte Carlo (MCMC) technique combined with an analytic description of the inflationary spectra in terms of the horizon flow functions (HFF). By imposing a consistency condition for the tensor-to-scalar ratio, we study the constraints both on single field standard inflation and on inflation with the violation of the null energy condition, which leads to a blue spectrum for gravitational waves. For standard inflation, the constraint on the tensor-to-scalar ratio we obtain from CMB data and 2dF05 is: r 0.01 < 0.26 at 2 σ cl. Without the consistency condition between the tensor-to-scalar ratio and the tensor slope, the constraints on the tensor amplitude is not significantly changed, but the constraints on the HFFs are significantly relaxed. We then show that when the third HFF ǫ 3 is allowed to be non-zero and to be of order unity, a large negative (at 2σ) value for the running of the scalar spectral index in standard inflation is found in any set of data we consider.
A flood warning system based on rainfall thresholds makes it possible to issue alarms via an off-line approach. This technique is useful for mitigating the effects of flooding in small-to-medium-sized basins characterized by an extremely rapid response to rainfall. Rainfall threshold values specify the amount of precipitation that occurs over a given period of time and are dependent on both the amount of soil moisture and the spatiotemporal distribution of the rainfall. The precipitation generates a critical discharge in a particular river cross section. Exceeding these values can produce a critical situation in river sites that make them susceptible to flooding. In this work, we present a comparison of methodologies for estimating rainfall thresholds. Critical precipitation amounts are evaluated using empirical data, hydrological simulations and probabilistic methods. The study focuses on three small-to-medium-sized basins located in central Italy. For each catchment, historical data are first used to theoretically evaluate the empirical rainfall thresholds. Next, we calibrate a semi-distributed hydrological model that is validated using rain gauge and weather radar data. Critical rainfall depths over 30 min and 1, 3, 6, 12 and 24 h durations are then evaluated using the hydrological simulation. In the probabilistic approach, rainfall threshold values result from a minimization of two different functions, one following the Bayesian decision theory and the other following the informative entropy concept. In order to implement both functions, it is necessary to evaluate the joint probability function. The joint probability function is built up as a bivariate distribution of rainfall depth for a given duration with the corresponding flow peak value. Finally, in order to assess the performance of each methodology, we construct contingency tables to highlight the system performance. © 2014 Springer Science+Business Media Dordrecht
Abstract. In small and medium-sized basins or in rivers characterized by intermittent discharges, with low or negligible/null observed values for long periods of the year, the correct representation of the discharge regime is important for issues related to water management and to define the amount and quality of water available for irrigation, domestic and recreational uses. In these cases, only one index as a statistical metric is often not enough; it is thus necessary to introduce Flow Duration Curves (FDC).The aim of this study is therefore to combine a stochastic index flow model capable of reproducing the FDC record period of a river, regardless of the persistence and seasonality of the series, with the theory of total probability in order to calculate how often a river is dry.The paper draws from preliminary analyses, including a study to estimate the correlation between discharge indicators Q 95 , Q 50 and Q 1 (discharges exceeding 95%, 50% or 1% of the time, respectively) and some fundamental characteristics of the basin, as well as to identify homogeneous regions in the target area through the study of several geomorphological features and climatic conditions. The stochastic model was then applied in one of the homogeneous regions that includes intermittent rivers. Finally, the model was regionalized by means of regression analysis in order to calculate the FDC for ungauged basins; the reliability of this method was tested using jackknife validation.
Flow duration curves are useful tools to estimate available surface water resources, at the basin scale. These represent the percentage of time during which discharge values are exceeded, irrespective of their temporal sequence. Annual flow duration curves are useful tools for evaluating all flow quantiles of a river and their confidence intervals, by removing the effects of variability from year to year. However, these tools fail to represent the hydrological regime of ephemeral rivers, since they cannot account for zero flows. In this work we propose a technique for calculating annual flow duration curves and their standard deviation in the case of intermittent rivers. In particular, we propose a generalization of the stochastic index method, in which we use the concept of total probability and order statistics. The method is proposed to determine the conditional distribution of positive flows, for given probability of dryness, and is implemented on three catchments in Italy and Greece, with low (<5%) and high (>40%) frequency of zero flows, respectively. Copyright (c) 2013 John Wiley & Sons, Ltd
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