Abstract
Abstract. 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.
Highlights
Rainfall height variability makes data collection a relevant task for hydrological purposes
This paper develops an entropy-based approach to evaluate the maximum information content achievable by a rainfall network for different sampling time intervals
Krstanovic and Singh (1992a,b) assess the raingauge networks of Louisiana, USA, considering daily, two-day, weekly and monthly datasampling intervals, whereas Yoo et al (2008) evaluate the rainfall network of the Choongiu Dam Basin in Korea, using a mixed and a continuous distribution function applied to daily rainfall data
Summary
2009), flood forecasting (Lopez et al, 2005; Russo et al, 2006; Montesarchio et al, 2009) and sewer-system monitoring (Giulianelli et al, 2006) are strictly dependent on the space-time rainfall resolution; the design and evaluation of rainfall networks are, of great importance. Krstanovic and Singh (1992a,b) assess the raingauge networks of Louisiana, USA, considering daily, two-day, weekly and monthly datasampling intervals, whereas Yoo et al (2008) evaluate the rainfall network of the Choongiu Dam Basin in Korea, using a mixed and a continuous distribution function applied to daily rainfall data. This study develops an entropy-based approach for evaluating the maximum content of information reached by the network at different sampling time intervals in an urban area, where the response to intense rainfall events is generally rapid. Both seasons are combined for the annual data and compared
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