All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling

Abstract

Simulation models are widely used in urban drainage engineering and research, but they are known to include errors and uncertainties that are not yet fully realised. Within the herein developed framework, we investigate model adequacy across multiple sites by comparing model results with measurements for three model objectives, namely surcharges (water level rises above defined critical levels related to basement flooding), overflows (water levels rise above a crest level), and everyday events (water levels stay below the top of pipes). We use multi-event hydrological signatures, i.e. metrics that extract specific characteristics of time series events in order to compare model results with the observations for the mentioned objectives through categorical and statistical data analyses. Furthermore, we assess the events with respect to sufficient or insufficient categorical performance and good, acceptable, or poor statistical performance. We also develop a method to reduce the weighting of individual events in the analyses, in order to acknowledge uncertainty in model and/or measurements in cases where the model is not expected to fully replicate the measurements. A case study including several years of water level measurements from 23 sites in two different areas shows that only few sites score a sufficient categorical performance in relation to the objective overflow and that sites do not necessarily obtain good performance scores for all the analysed objectives. The developed framework, however, highlights that it is possible to identify objectives and sites for which the model is reliable, and we also suggest methods for assessing where the model is less reliable and needs further improvement, which may be further refined in the future.