Abstract. An operative methodology for rainfall thresholds definition is illustrated, in order to provide at critical river section optimal flood warnings. Threshold overcoming could produce a critical situation in river sites exposed to alluvial risk and trigger the prevention and emergency system alert. The procedure for the definition of critical rainfall threshold values is based both on the quantitative precipitation observed and the hydrological response of the basin. Thresholds values specify the precipitation amount for a given duration that generates a critical discharge in a given cross section and are estimated by hydrological modelling for several scenarios (e.g.: modifying the soil moisture conditions). Some preliminary results, in terms of reliability analysis (presence of false alarms and missed alarms, evaluated using indicators like hit rate and false alarm rate) for the case study of Mignone River are presented.

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. Flash flood events are floods characterised by a very rapid response of basins to storms, often resulting in loss of life and property damage. Due to the specific spacetime scale of this type of flood, the lead time available for triggering civil protection measures is typically short. Rainfall threshold values specify the amount of precipitation for a given duration that generates a critical discharge in a given river cross section. If the threshold values are exceeded, it can produce a critical situation in river sites exposed to alluvial risk. It is therefore possible to directly compare the observed or forecasted precipitation with critical reference values, without running online real-time forecasting systems. The focus of this study is the Mignone River basin, located in Central Italy. The critical rainfall threshold values are evaluated by minimising a utility function based on the informative entropy concept and by using a simulation approach based on radar data. The study concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed alarms.

Hydrological models are the basis of operational flood-forecasting systems. The accuracy of these models is strongly dependent on the quality and quantity of the input information represented by rainfall height. Finer space-time rainfall resolution results in more accurate hazard forecasting. In this framework, an optimum raingauge network is essential in predicting flood events. This paper develops an entropy-based approach to evaluate the maximum information content achievable by a rainfall network for different sampling time intervals. The procedure is based on the determination of the coefficients of transferred and nontransferred information and on the relative isoinformation contours. The nontransferred information value achieved by the whole network is strictly dependent on the sampling time intervals considered. An empirical curve is defined, to assess the objective of the research: the nontransferred information value is plotted versus the associated sampling time on a semi-log scale. The curve has a linear trend. In this paper, the methodology is applied to the high-density raingauge network of the urban area of Rome

Floods cause severe damage worldwide in terms of human injury and have severe economic consequences, causing landslides, road accidents and inundation of agricultural areas. Non-structural flood protection strategies offer the possibility to protect the land, preventing flood event effects. In particular, early warning systems can inform of flood occurrences. These systems can be based on the evaluation of a rainfall threshold value that is the rainfall amount that determines a critical discharge in a given river cross-section. The alert is issued if the observed or forecast rainfall equals the threshold value without the support of online real-time rainfall-runoff forecasting systems. The critical rainfall threshold values are evaluated by a probabilistic methodology, considering the joint cumulative distribution of cumulated rainfall and the corresponding peak discharge, for different durations. To estimate the joint distributions two approaches are considered: the meta-Gaussian and the copula functions. The rainfall threshold values are estimated for the Mignone River basin (Italy). Results show that both joint distribution functions perform well in estimating the rainfall threshold values, as found from reliability analysis. In particular, the copula-based approach leads to issuing the alert in advance. Copyright (c) 2013 John Wiley & Sons, Ltd