Agnethe Nedergaard Pedersen

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.

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: ‘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 as “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.

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: ‘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 as “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.

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: ‘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 as “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.

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: ‘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 as “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.

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: ‘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 as “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.

Digital twins of urban drainage systems require simulation models that can adequately replicate the physical system. All models have their limitations, and it is important to investigate when and where simulation results are acceptable and to communicate the level of performance transparently to end users. This paper first defines a classification of four possible ‘locations of uncertainty’ in integrated urban drainage models. It then develops a structured framework for identifying and diagnosing various types of errors. This framework compares model outputs with in-sewer water level observations based on hydrologic and hydraulic signatures. The approach is applied on a real case study in Odense, Denmark, with examples from three different system sites: a typical manhole, a small flushing chamber, and an internal overflow structure. This allows diagnosing different model errors ranging from issues in the underlying asset database and missing hydrologic processes to limitations in the model software implementation. Structured use of signatures is promising for continuous, iterative improvements of integrated urban drainage models. It also provides a transparent way to communicate the level of model adequacy to end users.

This paper describes a comprehensive and unique open-access data set for research within hydrological and hydraulic modelling of urban drainage systems. The data come from a mainly combined urban drainage system covering a 1.7 km² area in the town of Bellinge, a suburb of the city of Odense, Denmark. The data set consists of up to 10 years of observations (2010–2020) from 13 level meters, 1 flow meter, 1 position sensor and 4 power sensors in the system, along with rainfall data from three rain gauges and two weather radars (X- and C-band), and meteorological data from a nearby weather station. The system characteristics of the urban drainage system (information about manholes, pipes, etc.) can be found in the data set along with characteristics of the surface area (contour lines, surface description, etc.). Two detailed hydrodynamic, distributed urban drainage models of the system are provided in the software systems MIKE URBAN and EPA Storm Water Management Model (SWMM). The two simulation models generally show similar responses, but systematic differences are present since the models have not been calibrated. With this data set we provide a useful case that will enable independent testing and replication of results from future scientific developments and innovation within urban hydrology and urban drainage systems research. The data set can be downloaded from https://doi.org/10.11583/DTU.c.5029124 (Pedersen et al., 2021a).

This paper describes a comprehensive and unique open-access data set for research within hydrological and hydraulic modelling of urban drainage systems. The data comes from a mainly combined urban drainage system covering a 1.7 km² area in the town of Bellinge, a suburb to the city of Odense, Denmark. The data set consists of up to 10 years of observations (2010–2020) from 13 level meters, one flow meter, one position-sensor and four power sensors in the system, along with rainfall data from three rain gauges and two weather radars (X- and C-band), and meteorological data from a nearby weather station. The system characteristics of the urban drainage system (information about manholes, pipes etc.) can be found in the data set along with characteristics of the surface area (contour lines etc.). Two detailed hydrodynamic, distributed urban drainage models of the system are provided in the software systems Mike Urban and EPA SWMM. The two simulation models generally show similar responses, but systematic differences are present since the models have not been calibrated. With this data set we provide a useful case that enables independent testing and replication of results from future scientific developments and innovation within urban hydrology and urban drainage system research. The data set can be downloaded from https://doi.org/10.11583/DTU.c.5029124, (Pedersen et al., 2021a).