Abstract
Abstract. Monitoring of flows in sewer systems is increasingly applied to calibrate urban drainage models used for long-term simulation. However, most often models are calibrated without considering the uncertainties. The generalized likelihood uncertainty estimation (GLUE) methodology is here applied to assess parameter and flow simulation uncertainty using a simplified lumped sewer model that accounts for three separate flow contributions: wastewater, fast runoff from paved areas, and slow infiltrating water from permeable areas. Recently GLUE methodology has been critisised for generating prediction limits without statistical coherence and consistency and for the subjectivity in the choice of a threshold value to distinguish "behavioural" from "non-behavioural" parameter sets. In this paper we examine how well the GLUE methodology performs when the behavioural parameter sets deduced from a calibration period are applied to generate prediction bounds in validation periods. By retaining an increasing number of parameter sets we aim at obtaining consistency between the GLUE generated 90% prediction limits and the actual containment ratio (CR) in calibration. Due to the large uncertainties related to spatio-temporal rain variability during heavy convective rain events, flow measurement errors, possible model deficiencies as well as epistemic uncertainties, it was not possible to obtain an overall CR of more than 80%. However, the GLUE generated prediction limits still proved rather consistent, since the overall CRs obtained in calibration corresponded well with the overall CRs obtained in validation periods for all proportions of retained parameter sets evaluated. When focusing on wet and dry weather periods separately, some inconsistencies were however found between calibration and validation and we address here some of the reasons why we should not expect the coverage of the prediction limits to be identical in calibration and validation periods in real-world applications. The large uncertainties result in wide posterior parameter limits, that cannot be used for interpretation of, for example, the relative size of paved area vs. the size of infiltrating area. We should therefore try to learn from the significant discrepancies between model and observations from this study, possibly by using some form of non-stationary error correction procedure, but it seems crucial to obtain more representative rain inputs and more accurate flow observations to reduce parameter and model simulation uncertainty.
Highlights
Simulation with deterministic urban drainage models is commonly used to assess the performance of sewer systems and to assess the efficacy of new upgrading or redesign proposals
In this paper we examine how well the generalized likelihood uncertainty estimation (GLUE) methodology performs when the behavioural parameter sets deduced from a calibration period are applied to generate prediction bounds in validation periods
The GLUE generated prediction limits still proved rather consistent, since the overall containment ratio (CR) obtained in calibration corresponded well with the overall CRs obtained in validation periods for all proportions of retained parameter sets evaluated
Summary
Simulation with deterministic urban drainage models is commonly used to assess the performance of sewer systems and to assess the efficacy of new upgrading or redesign proposals. In GLUE, modelling errors associated with each acceptable model are usually treated under the assumption that error series associated with a particular parameter set (such as over- or under-prediction of flow peaks) will be similar in prediction to those found in evaluation (Blazkova and Beven, 2009b) and GLUE is in many cases a welcomed alternative to traditional statistical inference that requires the error series to conform to a statistical known distribution often difficult to justify in real hydrological applications (Beven et al, 2008). 100 of the c0o.15mbined sewer s0y.4 stem was howe0.3ver found to be larger tha0.1n that of the se0p.3 arated area (see0.2 Table 1), probably bec0.a05use of infiltrati0o.2n inflow or un0i.n1 tended connecsize of contributing paved area versus the size of the area contributing with slow infiltration inflow After this brief introduction, we first present the case study area, the calibration and validation data, and the model in Sect. Impermeable fast runoff area Retention time, fast runoff Rain gauge weighting coefficient Impermeable slow-runoff area Retention time, infiltration runoff m h−1 m h−1 ha h – ha h
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
