Quantifying radar-rainfall uncertainties in urban drainage flow modelling

Abstract

a b s t r a c tThis 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.

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

“…Rainfall is an important driver for many hydrological processes and represents one of the main sources of uncertainty in studying hydrological response (Niemczynowicz, 1988;Einfalt et al, 2004;Thorndahl et al, 2017;Rico-Ramirez et al, 2015). Urban areas affect the local hydrological system, not only by increasing the imperviousness degree of the soil but also by changing rainfall generation and intensity patterns.…”

“…High-resolution topographical datasets have promoted development of more detailed and more complex numerical models for predicting flows (Gironás et al, 2010;Smith et al, 2013). However, model complexity and resolution need to be balanced with the availability and quality of rainfall input data and datasets for catchment representation (Morin et al, 2001;Rafieeinasab et al, 2015;Rico-Ramirez et al, 2015;Rafieeinasab et al, 2015;Pina et al, 2016). This is particularly critical in small catchments, where flows are sensitive to variations at small space and timescales as a result of the fast hydrological response and the high catchment variability (Fabry et al, 1994;Singh, 1997).…”

Weather radar rainfall data in urban hydrology

“…Ciach et al, 2007;Gires et al, 2012;Pegram et al, 2011;Villarini et al, 2014;Rico-Ramirez et al, 2015). It is expected that these uncertainty-based methods and development of rainfall ensembles for hydrological applications will gain more impact in future applications, concurrently with development in probabilistic/ensemble models for urban hydrology.…”

“…attenuation, clutter removal, and reflectivity-rainfall conversion. These fundamentals are indeed crucial for the quality of rainfall estimation and should definitely not be disregarded by users of radar rainfall, but they are omitted from the paper since they have been discussed in depth in primers such as Doviak and Zrnić (1993), Collier (1996), Bringi and Chandrasekar (2001), Meischner (2004), Michaelides (2008), and Rinehart (2010). Furthermore, there are pioneering and significant journal papers such as Marshall and Palmer (1945), , Wilson and Brandes (1979), Smith and Krajewski (1991), Krajewski and Smith (2002), Einfalt et al (2004), Delrieu et al (2009), Krajewski et al (2010), , and Berne and Krajewski (2013) which also provide general information on specifications and applications of radar rainfall.…”

Radar and rain gauge rainfall discrepancies driven by changes in atmospheric conditions

“…Since TBR measurements are prone to errors such as blockage, atmospheric effects as well as sampling errors, thus, it is crucial to minimize errors in gauge measurements if used as the ground truth. Therefore, the rain gauge data were quality controlled by removing those gauges with significant deviation with the nearest neighbors in this analysis [ Rico‐Ramirez et al ., ]. Both rainfall measurements are accumulated to generate time series with a temporal resolution of 1 h and covering a period from 2007 to 2010; only year 2008 is selected as it contains relatively complete data after a quality check.…”