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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 1
Hydrol. Earth Syst. Sci., 19, 485–506, 2015
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Precipitation: measurement and space time variability

Hydrol. Earth Syst. Sci., 19, 485–506, 2015
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Jan 2015

Research article | 26 Jan 2015

Precipitation variability within an urban monitoring network via microcanonical cascade generators

P. Licznar1, C. De Michele2, and W. Adamowski3 P. Licznar et al.
  • 1Faculty of Environmental Engineering, Wroclaw University of Technology, Wrocław, Poland
  • 2Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
  • 3Institute of Environmental Engineering, John Paul II Catholic University of Lublin, Stalowa Wola, Poland

Abstract. Understanding the variability of precipitation at small scales is fundamental in urban hydrology. Here we consider the case study of Warsaw, Poland, characterized by a precipitation-monitoring network of 25 gauges and microcanonical cascade models as the instrument of investigation.

We address the following issues partially investigated in literature: (1) the calibration of microcanonical cascade model generators in conditions of short time series (i.e., 2.5–5 years), (2) the identification of the probability distribution of breakdown coefficients (BDCs) through ranking criteria and (3) the variability among the gauges of the monitoring network of the empirical distribution of BDCs.

In particular, (1) we introduce an overlapping moving window algorithm to determine the histogram of BDCs and compare it with the classic non-overlapping moving window algorithm; (2) we compare the 2N–B distribution, a mixed distribution composed of two normal (N) and one beta (B), with the classic B distribution to represent the BDCs using the Akaike information criterion; and (3) we use the cluster analysis to identify patterns of BDC histograms among gauges and timescales.

The scarce representation of the BDCs at large timescales, due to the short period of observation (~ 2.5 years), is solved through the overlapping moving window algorithm. BDC histograms are described by a 2N–B distribution. A clear evolution of this distribution is observed, in all gauges, from 2N–B for small timescales, N–B for intermediate timescales and B distribution for large timescales.

The performance of the microcanonical cascades is evaluated for the considered gauges. Synthetic time series are analyzed with respect to the intermittency and the variability of intensity and compared to observed series. BDC histograms for each timescale are compared with the 25 gauges in Warsaw and with other gauges located in Poland and Germany.

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