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Uncertainty analysis of the influence of rainfall time resolution in the modelling of urban drainage systems
Author(s) -
Aronica Giuseppe,
Freni Gabriele,
Oliveri Elisa
Publication year - 2005
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.5645
Subject(s) - glue , surface runoff , environmental science , drainage basin , hydrology (agriculture) , temporal resolution , monte carlo method , runoff model , drainage , meteorology , statistics , mathematics , geology , geography , cartography , ecology , materials science , physics , geotechnical engineering , quantum mechanics , composite material , biology
In urban drainage modelling, rainfall temporal variability can be considered as one of the most critical knowledge elements when dealing with rainfall–runoff models input data. The rainfall data temporal resolution usually available for practical applications is often lower than that requested for the rainfall–runoff simulation in urban areas, greatly compromising model accuracy. The present paper evaluates the influence of rainfall temporal resolution on the uncertainty of the response of rainfall–runoff modelling in urban environments. Analyses have been carried out using historical rainfall–discharge data collected for about 10 years in Parco d'Orleans experimental catchment (Palermo, Italy). The historical rainfall data have been taken as a reference rainfall, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between ‘true’ and rescaled rainfall data has been carried out using a non‐dimensional performance index. Monte Carlo simulations have been carried out, applying two different rainfall–runoff models, using the recorded data and the resampled events. The results of the simulations were used to derive, for both models, the cumulative probabilities of peak discharges conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. Copyright © 2005 John Wiley & Sons, Ltd.