Regional analysis of sub-hourly rainfall in Calabria by means of the Partial Duration Series approach

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1 FRIEND project MED group UNESCO IHP-VII ( ) 4 t International Worksop on Hydrological Extremes From prediction to prevention of ydrological risk in Mediterranean countries september 2011 University of Calabria, Aula Magna Press room Regional analysis of sub-ourly rainfall in Calabria by means of te Partial Duration Series approac Antonella Bodini 1, Stefano Luigi Gariano 2, Oreste Terranova 2 1 CNR-IMATI, Milano; 2 CNR-IRPI, Rende-Cosenza, terranova@irpi.cnr.it

2 Calabria large number of small drainage basins; very steep slopes and river beds; low permeability outcrops; very sort lag times. Hydrometric regime is strictly correlated wit rainfall. Intensity-Duration functions (IDf) are rarely known wit adequate accuracy at sub-ourly duration. Tey are commonly obtained by extrapolation from te >1 IDf.

3 4t International Worksop on Hydrological Extremes n.148 rain gauges wit time resolution of 5 minutes. Full years of events n.23 stations wit at least 10 years of observation were selected. Hydrological variables analyzed for eac rainfall event : maximum intensity in 5, 10, 15, 20, 30, 60 minutes.

4 Analysis of extreme events Generalized Pareto Distribution (GPD) z(p) = α {1-(1-p) k } / k k 0 z(p) = -α log (1-p) k = 0 Derivable from te generalized extreme value distribution (GEV); Models daily rainfall data exceeding a sufficiently-ig tresold; Estimates of return periods of a given event or quantile z T (return level) at a fixed time interval T; In case of limited lengt series, estimates of quantiles obtained wit GPD are better tan tose obtained wit GEV (as tey re computed based on a greater data set). Analysis of partial duration series (PDS) GPD is used for modeling erosive events assuming tat teir occurrence follows a omogeneous Poisson process wit constant intensity λ (average number of events per year).

5 Regional frequency analysis Opposed to at site analysis; Data from all te gauges of a given omogeneous region are employed; It makes up for te lack of data, typical of rare events; Except for te site specific scale factor (index flood), observations made at different locations are assumed as pertaining to a single common process; a common probability distribution is obtained from suc observations. Analysis conducted according to te Hosking & Wallis (1993) approac based on L-moments, wic produces robust and accurate estimates of te quantiles of a probability distribution.

6 Use of bot GPD and regional frequency analysis allows to analyze rare events, by improving te estimate of quantiles tanks to te ig number of data. Te greater number of data, if compared to te annual maximum series approac, derives from te time-scale (cf. GPD) and te spatial-scale (cf. regional approac).

7 RESULTS OF REGIONAL ANALYSIS Maximum rainfall intensity in 5 minutes Data from te 23 raingauges resulted eterogeneous, based on te Hosking & Wallis (1993) test. Two sub-regions resulted omogeneous (according to te Hosking & Wallis test): - positive sape parameter - negative sape parameter

8 RESULTS Maximum rainfall intensity in 5 minutes Te tresold was set based on visual inspection of te mean excess plot and, terefore, was adopted an initial tresold of 35 mm/. Estimated intensity for some relevant return time Raingauge T=10 T=20 T=50 T=

9 RESULTS Maximum rainfall intensity in 10 minutes Tresold: 35 mm/ One omogeneous region L_SKEW GENERALIZED PARETO DISTRIBUTION L_CV Estimated intensity for some relevant return time Raingauge T=10 T=20 T=50 T=

10 RESULTS Maximum rainfall intensity in 15 minutes Tresold lowered to 25 mm/. 23 stations form a omogeneous region Maximum rainfall intensity in 20 minutes Tresold: 35 mm/. 22 stations form a omogeneous region Maximum rainfall intensity in 30minutes Tresold: 35 mm/. 23 stations form a omogeneous region Maximum rainfall intensity in 60 minutes Tresold lowered to 20 mm/. 22 stations form a omogeneous region

11 IDF CURVES t, T = a t n

12 IDF CURVES t, T = a t n Raingauge T=10 T=100 ourly sub-ourly ourly sub-ourly a n a n a n a n

13 IDF CURVES

14 IDF CURVES

15 IDF CURVES ( d ) Lo Bosco ( 1987) = d Bell (1969) (60) ( d ) (60) = 0.54 d

16 IDF CURVES ( d ) Lo Bosco ( 1987) = d Bell (1969) (60) ( d ) (60) = 0.54 d

17 IDF CURVES a n b 1230 San Sosti Catanzaro Palermiti Serra San Bruno Fabrizia Caulonia Canolo Nuovo Ardore Superiore Cittanova Feroleto della Ciesa Mileto Tiriolo Lagonegro ( d ) (60) = a d n + b

18 IDF CURVES ( d ) (60) = a d n + b a 1230 San Sosti Catanzaro Palermiti Serra San Bruno Fabrizia Caulonia Canolo Nuovo Ardore Superiore Cittanova Feroleto della Ciesa Mileto Tiriolo Lagonegro n = a b = a 0.022

19 IDF CURVES ( d ) (60) = a d a a Only one parameter Preliminary results Few raingauges and few data (sort time series)

20 Tank you for your attention Antonella Bodini 1, Stefano Luigi Gariano 2, Oreste Terranova 2 1 CNR-IMATI, Milano; 2 CNR-IRPI, Rende-Cosenza, terranova@irpi.cnr.it