Abstract
This work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.
Highlights
The quantitative measurement and forecasting of precipitation is crucial for predicting and mitigating the effects of flood-producing storms
The results clearly indicate that the perturbations are able to reproduce the covariance of the residual errors
The RR error model takes into account the error covariance matrix computed with radar rainfall and raingauge measurements for the year 2007 for the study area shown in Fig. 1 in the UK
Summary
The quantitative measurement and forecasting of precipitation is crucial for predicting and mitigating the effects of flood-producing storms. Real-time management of urban drainage systems requires measurements and forecasts of precipitation with high spatial and temporal resolutions (Verworn, 2002; Einfalt et al, 2004). For instance a typical urban catchment of about 10 km requires spatial and temporal resolutions of about 3 km and 5 min respectively (Berne et al, 2004). Early studies highlighted the fact that for urban hydrology it is desirable to have rainfall data with spatial and temporal resolutions of 1 km and 1 min respectively (Schilling, 1991). There are well-documented cases where rainfall with finer spatial resolution (e.g. 5 ha) is required in environments dominated by convective storms (Faures et al, 1995). Foundation for Water Research described how fine resolution rainfall data
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