Uncertainties in rainfall-runoff modelling of green urban drainage systems : Measurements, data selection and model structure
Abstract: Green urban drainage systems are used to avoid flooding and damages to people and property, while limiting the downstream flooding and water quality problems caused by pipe-based drainage systems. Computer models are used to analyse and predict the performance of such systems for design and operation purposes. Such models are simplifications of reality and based on uncertain measured data, so uncertainties will be involved in the modelling process and its outcomes, which can affect the design and operation of these systems. These uncertainties have been investigated extensively for traditional pipe-based urban drainage systems, but not yet for green alternatives. Therefore, the overall objective of this thesis is to contribute to improved applicability and reliability of computer models of green urban drainage systems. Specifically, the thesis aims to (1) improve understanding of the uncertainties arising from (a) model structure and (b) calibration data selection, (2) evaluate two alternative calibration methods for green urban drainage models, (3) discuss desirable structural features in urban drainage models, and (4) evaluate several sensors for hydrometeorological measurements in urban catchments.The effects of model structure uncertainty were investigated using long-term simulations of synthetic catchments with varying soil types and depths for three different models. First, it was found that surface runoff could be a significant part of the annual water balance in all three models, depending on the soil type and depth considered. Second, differences were found in how sensitive the different models were to changes in soil type and depth. Third, the variation between different models was often large compared to the variation between different soil types. Fourth, the magnitude of inter-annual and inter-event variation varied between the models. Overall, the findings indicate that significant differences may occur in urban drainage modelling studies, depending on which model is used, and this may affect the design or operation of such systems.The uncertainty from calibration data selection was investigated primarily by calibrating both a low- and high-resolution stormwater model using different sets of events. These event sets used different rainfall-runoff statistics to rank all observed events before selecting the top six for use in calibration. In addition, they varied by either calibrating all parameters simultaneously, or by calibrating parameters for impervious and pervious surfaces separately. This last approach sped up the calibration process. In the validation period the high-resolution models performed better than their low-resolution counterparts and the two-stage calibrations matched runoff volume and peak flows better than single-stage calibrations. Overall, the way in which the calibration events are selected was shown to have a major impact on the performance of the calibrated model.Calibration data selection was also investigated by examining different ways of including soil water content (SWC) observations in the calibration process of a model of a swale. Some model parameters could be identified from SWC, but not from outflow observations. Including SWC in the model evaluation affected the precision of swale outflow predictions. Different ways of setting initial conditions in the model (observations or an equilibrium condition) affected both of these findings.The precipitation sensors used in this thesis showed generally satisfactory performance in field calibration checks. Different types of precipitation sensors were associated with different requirements for maintenance and data acquisition. Sensors for sewer pipe flow rates showed good agreement with a reference instrument in the laboratory, as long as installation conditions were good. Higher pipe slopes and upstream obstacles lead to larger measurement errors, but this last effect was reduced by increasing water levels in the pipe. Sensor fouling was a source of errors and gaps in field measurements, showing that regular maintenance is required. The findings show that the evaluated flow sensors can perform satisfactorily, if measurement sites are carefully selected.
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