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- Montesarchio, Valeria, et al.
(författare)
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Comparison of methodologies for flood rainfall thresholds estimation
- 2015
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Ingår i: Natural Hazards. - : Springer Netherlands. - 0921-030X .- 1573-0840. ; 75:1, s. 909-934
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
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| 3. |
- Spina, Sandra, et al.
(författare)
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Data selection to assess bias in rainfall radar estimates: An entropy-based method
- 2013
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Ingår i: AIP Conference Proceedings. - : American Institute of Physics (AIP). - 0094-243X.
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Konferensbidrag (refereegranskat)abstract
- Miscalibration of radar determines a systematic error (i.e., bias) that is observed in radar estimates of rainfall. Although a rain gauge can provide a pointwise rainfall measurement, weather radar can cover an extended area. To compare the two measurements, it is necessary to individuate the weather radar measurements at the same location as the rain gauge. Bias is measured as the ratio between cumulative rain gauge measurements and the corresponding radar estimates. The rainfall is usually cumulated, taking into account all rainfall events registered in the target area. The contribution of this work is the determination of the optimal number of rainfall events that are necessary to calibrate rainfall radar. The proposed methodology is based on the entropy concept. In particular, the optimal number of events must fulfil two conditions, namely, maximisation of information content and minimisation of redundant information. To verify the methodology, the bias values are estimated with 1) a reduced number of events and 2) all available data. The proposed approach is tested on the Polar 55C weather radar located in the borough area of Rome (IT). The radar is calibrated against rainfall measurements of a couple of rain gauges placed in the Roman city centre. Analysing the information content of all data, it is found that it is possible to reduce the number of rainfall events without losing information in evaluating the bias.
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